{"meta":{"query_hash":"309772ca6058","filters":{"topic":"Industrial Vision Systems and Defect Detection"},"cohort_total":743,"direct_labels_cover":0,"predictions_cover":743,"exported":743,"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/309772ca6058","api":"https://metacan.xera.ac/api/v1/cohort?topic=Industrial+Vision+Systems+and+Defect+Detection"},"results":[{"id":"W112404579","doi":"10.1007/978-3-642-23860-4_50","title":"A System for Providing Visual Feedback of Machine Faults","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Visual feedback; Computer science; Artificial intelligence","score_opus":0.029808372697460887,"score_gpt":0.2310896513184278,"score_spread":0.2012812786209669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W112404579","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030315973,0.00035213624,0.027552303,0.0000011178576,0.0023633903,0.0014160977,0.000117376934,0.0005857264,0.9673087],"genre_scores_gemma":[0.73558843,0.000011750609,0.00021059693,0.0000026315215,0.00069652183,0.000052226955,0.000029299164,0.00016775032,0.2632408],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99889904,0.000005975215,0.00056084193,0.0002030249,0.00016479346,0.00016633523],"domain_scores_gemma":[0.9994348,0.000049975097,0.00015878984,0.00019361574,0.00010458329,0.00005823144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020031005,0.0002853606,0.0005426588,0.00019602697,0.000038082926,0.000012422544,0.00008640868,0.0005494234,0.00011371688],"category_scores_gemma":[0.000009339927,0.00024406322,0.00024200324,0.000023474639,0.000011551033,0.000049714145,0.000021453347,0.0001874344,0.000078212666],"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":[0.0018263017,0.000097764205,0.000027211492,0.03892274,0.0036896102,0.000051426912,0.0013306729,0.0019855257,0.025051417,0.55054957,0.037218437,0.33924934],"study_design_scores_gemma":[0.0048259725,0.0018903086,0.0000075760677,0.006901163,0.000567612,0.0001499184,0.00022410745,0.040316314,0.09083119,0.0011384066,0.85055405,0.0025933606],"about_ca_topic_score_codex":0.00007240612,"about_ca_topic_score_gemma":0.0000151524055,"teacher_disagreement_score":0.81333566,"about_ca_system_score_codex":0.000110388646,"about_ca_system_score_gemma":0.000019446295,"threshold_uncertainty_score":0.99526095},"labels":[],"label_agreement":null},{"id":"W112479126","doi":"10.5006/c2001-01074","title":"Understanding the Size Effect in Nace TM0177 Method D (DCB) Testing and Implications for Users","year":2001,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Shell (Canada)","funders":"","keywords":"Materials science; Computer science","score_opus":0.1361418806612068,"score_gpt":0.32490404546307694,"score_spread":0.18876216480187014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W112479126","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":[0.21861556,0.000063375075,0.7712517,0.00025606246,0.00025960596,0.00084870437,0.000002703237,0.00023257181,0.008469715],"genre_scores_gemma":[0.9965524,0.0000047311432,0.0031536047,0.00002479922,0.000079584985,0.000075004566,2.7861321e-7,0.000015702628,0.000093903705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950725,0.000048115322,0.0001472078,0.00010715695,0.000040746767,0.00014953277],"domain_scores_gemma":[0.99684507,0.0029846898,0.0000196412,0.00010884025,0.000014011812,0.000027721437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073154224,0.000080418846,0.0001111558,0.000050839986,0.00013495162,0.00005268683,0.0000411677,0.00006969092,0.000004493167],"category_scores_gemma":[0.0004665221,0.00005553901,0.00002787779,0.00030749716,0.000008220131,0.00006579773,0.000009644813,0.000075567026,0.0000016626403],"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":[0.00026678154,0.00005652079,0.12417864,0.00048529988,0.00027675563,0.000007198092,0.0020727566,0.1533769,0.19045559,0.019707656,0.010611979,0.49850392],"study_design_scores_gemma":[0.010025945,0.0010055884,0.13278115,0.0004807716,0.00017518952,0.000320952,0.004575275,0.7830219,0.01760911,0.027698165,0.020700479,0.0016054356],"about_ca_topic_score_codex":0.000092538576,"about_ca_topic_score_gemma":0.000066046916,"teacher_disagreement_score":0.7779368,"about_ca_system_score_codex":0.00010167277,"about_ca_system_score_gemma":0.000005136986,"threshold_uncertainty_score":0.22648151},"labels":[],"label_agreement":null},{"id":"W1153875826","doi":"","title":"Contributions of Local Orientational and Positional Contour Features to Shape Discrimination","year":2003,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Computer vision; Psychology; Computer science","score_opus":0.025636410171891687,"score_gpt":0.32173753901847163,"score_spread":0.29610112884657996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1153875826","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":[0.9964021,0.000046285182,0.0013084488,0.00006472739,0.00039530217,0.00020512895,0.00003857229,0.00002729371,0.001512171],"genre_scores_gemma":[0.99936455,4.987978e-7,0.0004914562,0.000037978607,0.000037676567,0.000027067112,0.0000065987306,0.0000057016027,0.00002846283],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990002,0.000085380285,0.00021437439,0.00021842802,0.00028061058,0.00020103152],"domain_scores_gemma":[0.99928844,0.00012228277,0.000050166127,0.00006150281,0.00030066035,0.00017697473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047752378,0.00010633082,0.00014994598,0.00018254791,0.00021336976,0.000028484972,0.00007476338,0.0000851109,0.00005885262],"category_scores_gemma":[0.00042705282,0.00009835922,0.000023737914,0.00060562254,0.00096465455,0.0002323619,0.000022280581,0.00011700512,0.000009674266],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013987938,0.000026318017,0.001402745,0.000009681567,0.000012304067,0.000007994998,0.00048625597,0.0013341893,0.97651047,0.019584104,0.000119680604,0.00049226615],"study_design_scores_gemma":[0.00039836674,0.00041996056,0.15670432,0.00004428079,0.000010920014,0.00030232462,0.00079203665,0.0020150905,0.83455765,0.0045121377,0.00007114027,0.0001717819],"about_ca_topic_score_codex":0.000038569597,"about_ca_topic_score_gemma":0.0000022138825,"teacher_disagreement_score":0.15530159,"about_ca_system_score_codex":0.00011286325,"about_ca_system_score_gemma":0.00011856694,"threshold_uncertainty_score":0.40109724},"labels":[],"label_agreement":null},{"id":"W1429519608","doi":"","title":"Differential Age Effects on Sequential versus Simultaneous Spatial Interval (SI) Discrimination","year":2006,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Interval (graph theory); Differential (mechanical device); Mathematics; Differential effects; Statistics; Medicine; Internal medicine; Combinatorics; Physics; Thermodynamics","score_opus":0.03619082312039918,"score_gpt":0.3085679418179267,"score_spread":0.2723771186975275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1429519608","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":[0.990168,0.000009926297,0.0005566761,0.000020692662,0.005729288,0.00037098283,0.000008259301,0.00021146065,0.002924712],"genre_scores_gemma":[0.9988261,4.4657963e-7,0.00007410818,0.000013207881,0.0009018408,0.000037248257,0.00001799536,0.000028031087,0.000101059944],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99788904,0.00018091917,0.00035705577,0.0005077257,0.0005500999,0.0005151457],"domain_scores_gemma":[0.99903184,0.0004068801,0.00009988292,0.00019131448,0.000102641665,0.00016745046],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026698576,0.00030049225,0.00030225888,0.00029175266,0.0003444495,0.00013794686,0.00027953464,0.00021330075,0.00007100953],"category_scores_gemma":[0.00035152386,0.00027085972,0.00009253141,0.0005606889,0.00094276795,0.00026014767,0.00006747731,0.00036133995,0.00011783418],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001493928,0.00005709412,0.00016554931,0.000026971522,0.000017301802,0.00041537313,0.0002298582,0.004401233,0.9905225,0.00028342108,0.000053347296,0.0036779267],"study_design_scores_gemma":[0.0012525815,0.0017826022,0.008020374,0.00009209047,0.000028088376,0.00007317978,0.000049213286,0.03553036,0.95148706,0.0012341838,0.000062943516,0.0003873068],"about_ca_topic_score_codex":0.0005706889,"about_ca_topic_score_gemma":0.0000438357,"teacher_disagreement_score":0.039035454,"about_ca_system_score_codex":0.00031138083,"about_ca_system_score_gemma":0.00007095886,"threshold_uncertainty_score":0.99997437},"labels":[],"label_agreement":null},{"id":"W1444672150","doi":"10.5281/zenodo.1329723","title":"Application Of Particle Swarm Optimization Technique For An Optical Fiber Alignment System","year":2012,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Computer science; Particle (ecology); Optical fiber; Fiber; Materials science; Optics; Physics; Algorithm; Composite material; Biology","score_opus":0.033683868089490566,"score_gpt":0.24589534953066614,"score_spread":0.21221148144117558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1444672150","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":[0.011660375,0.000032663815,0.9724631,0.000013917065,0.00013064913,0.0012223595,0.000060863204,0.0008445744,0.013571517],"genre_scores_gemma":[0.9965554,0.000003426966,0.002430012,0.000004013736,0.0002099358,0.0000014906666,0.00026083246,0.0004809622,0.00005394014],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.999039,0.000098057986,0.0002803964,0.00015105202,0.00019177009,0.00023970984],"domain_scores_gemma":[0.9992836,0.000016926308,0.000063966494,0.00026515505,0.0002392826,0.00013108141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007920927,0.00009294262,0.00012353098,0.00008895346,0.00040242195,0.00010189334,0.00019047882,0.00009490808,0.00022186081],"category_scores_gemma":[0.00007173869,0.00009675014,0.000038935672,0.0002566281,0.00002781862,0.00026049576,0.00007882878,0.00008283751,0.00038238594],"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":[0.00024122588,0.0005099883,0.00001243547,0.0010345458,0.00013891899,8.5755704e-7,0.0017180258,0.2753248,0.41681248,0.029082838,0.020026391,0.25509748],"study_design_scores_gemma":[0.00089875504,0.00048752036,0.000106263804,0.00007130915,0.00004414927,0.00008340429,0.00063796906,0.21752079,0.28181556,0.000016442867,0.49799377,0.00032405413],"about_ca_topic_score_codex":0.0000056410345,"about_ca_topic_score_gemma":3.1997207e-8,"teacher_disagreement_score":0.984895,"about_ca_system_score_codex":0.00016418638,"about_ca_system_score_gemma":0.0000010897866,"threshold_uncertainty_score":0.4914925},"labels":[],"label_agreement":null},{"id":"W1501598340","doi":"10.1109/icce-tw.2015.7216922","title":"Application of blast furnace falling materials trajectories measurement using laser scan technology","year":2015,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Blast furnace; Falling (accident); Scanner; Mechanical engineering; Computer science; Materials science; Engineering; Artificial intelligence; Metallurgy","score_opus":0.05627067375904148,"score_gpt":0.24754923811004637,"score_spread":0.19127856435100488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501598340","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":[0.87161493,0.00007092427,0.1261335,0.000007737644,0.00070005556,0.0002286652,0.000003722403,0.00026592702,0.00097453746],"genre_scores_gemma":[0.99891555,0.0000016618104,0.00087705994,0.000001473481,0.00015428654,0.000014360556,9.1027124e-7,0.000016827078,0.00001784926],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926126,0.000017900977,0.00028571454,0.00009622178,0.00022046475,0.00011841857],"domain_scores_gemma":[0.9995659,0.0000053857175,0.000057652323,0.00014624321,0.00018697238,0.000037820937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039190784,0.000089344976,0.0001751219,0.000121490106,0.000031840504,0.000021930933,0.000056276298,0.00013717201,0.000012022008],"category_scores_gemma":[0.000033089927,0.000080424266,0.000020696554,0.00025878995,0.000015783524,0.00006939218,0.0000116901165,0.000054767483,0.000010239462],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016566359,0.000008903222,0.00014454959,0.000034975055,0.000025016323,3.067714e-7,0.00008395541,0.017765982,0.9777388,0.00011367851,0.00016336357,0.0039038814],"study_design_scores_gemma":[0.00027963918,0.000038429564,0.000028559412,0.000032881206,0.000010831432,0.0000067790156,0.0004690317,0.0052027465,0.9921795,0.00009391391,0.0015621962,0.000095456286],"about_ca_topic_score_codex":0.00037863612,"about_ca_topic_score_gemma":0.00005516487,"teacher_disagreement_score":0.12730065,"about_ca_system_score_codex":0.00015113561,"about_ca_system_score_gemma":0.0000191393,"threshold_uncertainty_score":0.32796064},"labels":[],"label_agreement":null},{"id":"W1509558959","doi":"10.1109/ccv.1988.590005","title":"Unsupervised Bayesian Estimation For Segmenting Textured Images","year":2005,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Bayesian probability; Pattern recognition (psychology); Market segmentation; Segmentation; Image segmentation; Computer vision","score_opus":0.010970205930204662,"score_gpt":0.2321860079059386,"score_spread":0.22121580197573393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1509558959","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":[0.025773866,0.000060462728,0.958758,0.00007943984,0.0004121675,0.00039087085,0.0000054780735,0.0005916669,0.013928061],"genre_scores_gemma":[0.97883976,0.0000014030527,0.019900247,0.00002595919,0.00040336364,0.000037039368,0.0000083093455,0.00002031177,0.0007636166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950874,0.0000078922985,0.00018050213,0.00008955993,0.00007416888,0.0001391248],"domain_scores_gemma":[0.99980026,0.0000331866,0.000016396896,0.00008905405,0.000027692886,0.000033415352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001494301,0.000081855396,0.00009290152,0.00006403762,0.00007065585,0.00004985171,0.00003433786,0.00007768455,0.00010200631],"category_scores_gemma":[0.000024287869,0.00007338797,0.000051497213,0.0000859447,0.000004436892,0.00016722259,0.0000044086355,0.000050567593,0.00004717537],"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":[0.000018693849,0.000016228252,0.000039962113,0.00009084755,0.00004037003,4.5418795e-7,0.0002547412,0.14761357,0.080765225,0.00035658228,0.020949584,0.74985373],"study_design_scores_gemma":[0.0005656885,0.000022899596,0.00007850734,0.000017792663,0.000008622901,0.0000032804453,0.00008152875,0.87674797,0.10526618,0.000045883182,0.017036159,0.00012550536],"about_ca_topic_score_codex":0.000008569586,"about_ca_topic_score_gemma":0.000006034525,"teacher_disagreement_score":0.9530659,"about_ca_system_score_codex":0.000042023366,"about_ca_system_score_gemma":0.000004245277,"threshold_uncertainty_score":0.29926747},"labels":[],"label_agreement":null},{"id":"W1519098991","doi":"10.1109/mim.2006.1634956","title":"Smart laser vision sensors simplify inspection - Enhancing competitiveness by improving productivity in the tire and rubber industry","year":2006,"lang":"en","type":"article","venue":"IEEE Instrumentation & Measurement Magazine","topic":"Industrial Vision Systems and Defect Detection","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 Windsor","funders":"","keywords":"Scrap; Reliability (semiconductor); Process (computing); Automotive engineering; Productivity; Quality (philosophy); Noise (video); Production (economics); Engineering; Computer science; Reliability engineering; Artificial intelligence; Mechanical engineering","score_opus":0.013537806588589767,"score_gpt":0.22502715774545023,"score_spread":0.21148935115686046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519098991","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":[0.99579436,0.00006116456,0.0010220372,0.00015080128,0.0009780433,0.0006701475,0.00001120778,0.00018322893,0.0011289844],"genre_scores_gemma":[0.99941844,0.000005047667,0.000028362048,0.000030088018,0.0003325993,0.00006691711,0.00001941788,0.000028640028,0.000070502145],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981386,0.00020984409,0.00047039046,0.000310481,0.0006189845,0.00025167246],"domain_scores_gemma":[0.99951667,0.000038880178,0.000115393326,0.00018157357,0.000108024375,0.000039488274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012282445,0.00022604543,0.00020317882,0.00016923177,0.00019712563,0.0001476157,0.00006545275,0.00020433607,0.000019441262],"category_scores_gemma":[0.000036216683,0.0001951293,0.000038059352,0.00040368686,0.000038235736,0.00045995292,0.000011748107,0.00046286176,0.00002129993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008324974,0.00015742537,0.02427964,0.00017543683,0.000025721016,0.0000068202753,0.00044549035,0.006342627,0.9246349,0.000028553393,0.0027058641,0.04111427],"study_design_scores_gemma":[0.0044759526,0.00035914627,0.2532896,0.00046019253,0.00007689429,0.00008440401,0.0017485314,0.014630027,0.7100068,0.00006673188,0.013889096,0.00091264845],"about_ca_topic_score_codex":0.0005256867,"about_ca_topic_score_gemma":0.0010747861,"teacher_disagreement_score":0.22900997,"about_ca_system_score_codex":0.00036982883,"about_ca_system_score_gemma":0.00002264203,"threshold_uncertainty_score":0.7957142},"labels":[],"label_agreement":null},{"id":"W1527336293","doi":"10.4271/2015-01-0023","title":"Automotive ADAS Camera System Configuration Using Multi-Core Microcontroller","year":2015,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Infineon Technologies (Canada)","funders":"","keywords":"Automotive industry; Microcontroller; Computer science; Core (optical fiber); Embedded system; Automotive engineering; Advanced driver assistance systems; Engineering; Artificial intelligence; Telecommunications; Aerospace engineering","score_opus":0.040079379386781265,"score_gpt":0.26659438289830006,"score_spread":0.2265150035115188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527336293","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":[0.94424355,0.00066724536,0.00014993548,0.0003452672,0.002613187,0.0031859844,0.0001194513,0.0097413715,0.038934026],"genre_scores_gemma":[0.99625075,0.000023663253,0.0021246332,0.00027619512,0.0005229081,0.00026533086,0.000034058678,0.00018329058,0.00031914286],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9957118,0.00020483352,0.0013853777,0.000909916,0.00090333243,0.0008847586],"domain_scores_gemma":[0.99760675,0.00023276551,0.00026464346,0.0009894789,0.0003684522,0.00053789315],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009401751,0.00084143947,0.0011975353,0.0003253134,0.00036108936,0.00018811639,0.00047766298,0.0012152097,0.00009120458],"category_scores_gemma":[0.00052329566,0.0007334339,0.00045736303,0.000803268,0.00032089057,0.0005625313,0.00013460661,0.0013106768,0.00027703348],"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":[0.00029441275,0.000103244354,0.000048385973,0.000073483134,0.0000830047,0.00004192107,0.00008351376,0.0015973459,0.992689,0.0010266715,0.002399671,0.0015593239],"study_design_scores_gemma":[0.011067882,0.0033832216,0.8547484,0.0021013983,0.00048690467,0.0012332476,0.0026105312,0.00015510997,0.0044757253,0.00029697316,0.115740344,0.0037002377],"about_ca_topic_score_codex":0.00023973963,"about_ca_topic_score_gemma":0.014969375,"teacher_disagreement_score":0.9882133,"about_ca_system_score_codex":0.0012732955,"about_ca_system_score_gemma":0.00014563937,"threshold_uncertainty_score":0.99951166},"labels":[],"label_agreement":null},{"id":"W1539274144","doi":"10.4271/2004-01-2819","title":"Vision Based In-Process Inspection for Robotic Automated Riveting","year":2004,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"Ministry of Industry and Information Technology of the People's Republic of China","keywords":"Rivet; Process (computing); Computer science; Computer vision; Engineering drawing; Artificial intelligence; Robot; Engineering; Mechanical engineering; Programming language","score_opus":0.013716981354612025,"score_gpt":0.26855101170533935,"score_spread":0.2548340303507273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539274144","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":[0.95796037,0.00022780255,0.00018530643,0.0010413645,0.0015233932,0.0029685996,0.000030915402,0.021905815,0.014156464],"genre_scores_gemma":[0.9967598,0.000029476163,0.0019647367,0.00026560886,0.00024439016,0.00051173917,0.000036297766,0.00016244262,0.0000255093],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9963834,0.000086554755,0.0011891421,0.00086233544,0.0006481344,0.0008304085],"domain_scores_gemma":[0.9984289,0.00033396645,0.00016942053,0.00070200657,0.00012923012,0.00023650157],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00077923335,0.00064157485,0.00084055966,0.0004921377,0.00037648156,0.00013016212,0.0004289277,0.0010972844,0.000054667253],"category_scores_gemma":[0.0006983549,0.0005925694,0.0003717338,0.0013422058,0.00022931083,0.0005043508,0.00006868335,0.0010653213,0.00007046919],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028879946,0.00018681209,0.00004490543,0.00014672599,0.00001844352,0.000020411406,0.000025625846,0.06598831,0.9298045,0.0010272452,0.00051799667,0.0019302396],"study_design_scores_gemma":[0.004430736,0.0027822854,0.979709,0.0014097551,0.0000740659,0.00008959963,0.00018364888,0.00015165137,0.0030699396,0.001998519,0.004839477,0.0012613015],"about_ca_topic_score_codex":0.000068128684,"about_ca_topic_score_gemma":0.024075165,"teacher_disagreement_score":0.9796641,"about_ca_system_score_codex":0.0008506188,"about_ca_system_score_gemma":0.00009101138,"threshold_uncertainty_score":0.99965256},"labels":[],"label_agreement":null},{"id":"W1540530916","doi":"10.1007/978-3-642-01044-6_69","title":"Nonnegative Matrix Factorization for Binary Data to Extract Elementary Failure Maps from Wafer Test Images","year":2009,"lang":"en","type":"book-chapter","venue":"Studies in classification, data analysis, and knowledge organization","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Non-negative matrix factorization; Binary number; Logical matrix; Probabilistic logic; Wafer; Computer science; Matrix (chemical analysis); Superposition principle; Binary data; Matrix decomposition; Pattern recognition (psychology); Algorithm; Artificial intelligence; Mathematics; Engineering; Arithmetic; Materials science","score_opus":0.11265509268367462,"score_gpt":0.3503634868738844,"score_spread":0.23770839419020978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1540530916","genre_codex":"methods","genre_gemma":"dataset","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":[0.0015995486,0.034863345,0.79161197,0.0016292697,0.0063119796,0.009799698,0.13802795,0.0017256871,0.014430552],"genre_scores_gemma":[0.45811695,0.02581525,0.010241041,0.00007388401,0.005559356,0.00012332661,0.46490797,0.0005026315,0.03465957],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973311,0.000054005526,0.0009949072,0.001135395,0.00025503183,0.00022960249],"domain_scores_gemma":[0.99669117,0.0004613977,0.0003166874,0.0016625464,0.0007797803,0.00008839922],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053541636,0.00049116096,0.00081490516,0.00088773423,0.00030174424,0.00014471897,0.0005957188,0.00040287903,0.0000877033],"category_scores_gemma":[0.0008189276,0.00048836984,0.000052822776,0.0011853946,0.000065505716,0.000670072,0.0003836784,0.00026502836,0.00005017098],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012576468,0.00042596328,0.02328695,0.0013436426,0.012482354,0.000011991949,0.006447724,0.0011480639,0.011808707,0.0030635474,0.8356167,0.104238555],"study_design_scores_gemma":[0.002057945,0.00028618803,0.027983524,0.0010877239,0.00878065,0.000004171436,0.0034590047,0.03397983,0.0009950817,0.002316324,0.91628766,0.0027618809],"about_ca_topic_score_codex":0.00006236236,"about_ca_topic_score_gemma":0.0024688917,"teacher_disagreement_score":0.78137094,"about_ca_system_score_codex":0.00033734582,"about_ca_system_score_gemma":0.000080309335,"threshold_uncertainty_score":0.9997568},"labels":[],"label_agreement":null},{"id":"W1541679039","doi":"10.2172/948550","title":"SQA(TM): Surface Quality Assured Steel Bar Program","year":2009,"lang":"en","type":"report","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Product (mathematics); Charter; Process (computing); Manufacturing engineering; Steel bar; Bar (unit); Control (management); Engineering; Computer science; Structural engineering; Artificial intelligence","score_opus":0.07389321338175597,"score_gpt":0.3485743620735772,"score_spread":0.27468114869182125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1541679039","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014890879,0.0017552166,0.0013593682,0.00003464136,0.010191147,0.0023114309,0.00008167343,0.004993997,0.96438164],"genre_scores_gemma":[0.8297756,0.0008295803,0.0018409721,0.000045533794,0.005344882,0.00014682728,0.00029863787,0.00036214737,0.16135582],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.996614,0.00014077648,0.0010849236,0.00045605513,0.0012324573,0.00047183438],"domain_scores_gemma":[0.99846506,0.00007610397,0.00021208916,0.0006734941,0.00040225039,0.00017097362],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0020329682,0.00050690986,0.0009633695,0.00013017019,0.00008562067,0.0001764184,0.00018149438,0.0014433434,0.00039623628],"category_scores_gemma":[0.00016246882,0.00044530578,0.0003862283,0.0003209291,0.000015983835,0.00010558878,0.000027356575,0.0008911409,0.00033003764],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030482488,0.00013420425,0.00013055408,0.00087632146,0.00043729207,0.000056557135,0.000055575616,0.006898577,0.0009778588,0.000047290436,0.6483919,0.34196338],"study_design_scores_gemma":[0.00032913158,0.00015874779,0.0009994506,0.0001295211,0.00005412623,0.000046255234,0.00003618971,0.0004983058,0.0007877912,0.000011770051,0.9963304,0.00061832624],"about_ca_topic_score_codex":0.0011053237,"about_ca_topic_score_gemma":0.00011225543,"teacher_disagreement_score":0.8148847,"about_ca_system_score_codex":0.0004894683,"about_ca_system_score_gemma":0.00027775852,"threshold_uncertainty_score":0.999853},"labels":[],"label_agreement":null},{"id":"W1543727189","doi":"10.1002/opph.201500021","title":"Seeing Beyond the Visible","year":2015,"lang":"en","type":"article","venue":"Optik & Photonik","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Allied Vision Technologies (Canada)","funders":"","keywords":"Computer science; Image sensor; Artificial intelligence; Computer vision; CMOS; Machine vision; Optics; Remote sensing; Materials science; Optoelectronics; Physics; Geology","score_opus":0.024622857710338366,"score_gpt":0.22406522565401574,"score_spread":0.19944236794367737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543727189","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":[0.6984163,0.0008022499,0.0015468547,0.00007908389,0.0036966014,0.00039093042,0.0000056774925,0.00053329626,0.294529],"genre_scores_gemma":[0.9983515,0.00000898234,0.00016078234,0.00006182873,0.00033050549,0.000025859687,0.0000018767827,0.000026703805,0.0010319739],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99932647,0.000026133619,0.00015830254,0.00010761409,0.00018984948,0.00019164676],"domain_scores_gemma":[0.9995685,0.000030459132,0.000022667431,0.0002514575,0.000042565138,0.00008430818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038260166,0.0001054262,0.00011784227,0.000047405043,0.000088672416,0.00008470208,0.000110443056,0.00009229583,0.000054819702],"category_scores_gemma":[0.000023672314,0.00007440961,0.000052548792,0.00020979381,0.000014415407,0.000105626386,0.000023012703,0.00018196226,0.0003644923],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019947467,0.000106539235,0.0012040328,0.00014317104,0.00040210743,0.00010878505,0.009727941,0.22365656,0.16014475,0.0048167016,0.4971356,0.10235432],"study_design_scores_gemma":[0.0008630716,0.00011787667,0.0001301702,0.00003322631,0.00002091213,0.0000649851,0.001069166,0.045782436,0.08216764,0.0004146754,0.8690096,0.0003262589],"about_ca_topic_score_codex":0.00009984434,"about_ca_topic_score_gemma":0.000011435327,"teacher_disagreement_score":0.37187397,"about_ca_system_score_codex":0.000072305294,"about_ca_system_score_gemma":0.000022807308,"threshold_uncertainty_score":0.46849325},"labels":[],"label_agreement":null},{"id":"W1543839472","doi":"","title":"Proceedings, 2000 International Workshops on Parallel Processing, 21-24 August 2000, Toronto, Canada","year":2000,"lang":"en","type":"book","venue":"IEEE Computer Society Press eBooks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Library science; Regional science; Media studies; Political science; Telecommunications; Operations research; Geography; Computer science; Sociology; Engineering","score_opus":0.019065648257233312,"score_gpt":0.2137805627951117,"score_spread":0.1947149145378784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543839472","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013940565,0.0013805319,0.0036644554,0.000012448747,0.012051172,0.00092275924,0.00013570407,0.0008254331,0.9808681],"genre_scores_gemma":[0.00175951,0.00019429869,0.0008442078,0.00039779238,0.010726409,0.00016469034,0.00007863302,0.0002548313,0.9855796],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99710214,0.00001811876,0.0007213997,0.000680553,0.00090791663,0.00056986645],"domain_scores_gemma":[0.99898016,0.000044398283,0.00023549152,0.00032534267,0.00019763298,0.00021697229],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001526262,0.0007457606,0.0006515668,0.000042672953,0.00019891893,0.0004356695,0.000624656,0.0010138523,0.00010084187],"category_scores_gemma":[0.000001922918,0.0007619003,0.00038737216,0.000024423283,0.000049618022,0.00018676001,0.00006220417,0.0011243174,0.000020357773],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003339204,0.000013282452,5.233846e-7,0.00019838497,0.00028245684,0.000010964294,0.00071130024,0.011524721,0.0000044730223,0.00007689594,0.915368,0.0717756],"study_design_scores_gemma":[0.00063904305,0.000059157177,0.0000057816274,0.000921296,0.00004857455,0.000024200193,0.000016207146,0.03993683,0.00007640671,0.000019761179,0.9575055,0.00074722414],"about_ca_topic_score_codex":0.020718338,"about_ca_topic_score_gemma":0.0072495514,"teacher_disagreement_score":0.071028374,"about_ca_system_score_codex":0.0034225192,"about_ca_system_score_gemma":0.001144357,"threshold_uncertainty_score":0.9994832},"labels":[],"label_agreement":null},{"id":"W1545779207","doi":"10.1063/1.1291301","title":"Optical absorption depth profiling of photodegraded poly(vinylchloride) (PVC) films by quantitative photothermal deflection technique","year":2000,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Photothermal therapy; Materials science; Optics; Deconvolution; Repeatability; Deflection (physics); Attenuation coefficient; Photothermal spectroscopy; Impulse response; Impulse (physics); Analytical Chemistry (journal); Chemistry; Nanotechnology; Chromatography","score_opus":0.026632975947018925,"score_gpt":0.257725275761794,"score_spread":0.23109229981477508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1545779207","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":[0.9337902,0.00010372761,0.042504463,0.000013529298,0.00021039523,0.0008527538,0.000012404868,0.00048612864,0.0220264],"genre_scores_gemma":[0.9961304,0.000057353664,0.0032148068,0.000012447988,0.00006749805,0.00022543436,0.000009249163,0.00004445981,0.00023834268],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985908,0.000012693807,0.00048719754,0.00029884707,0.00029049543,0.00031993797],"domain_scores_gemma":[0.9994489,0.00003722125,0.00010672366,0.00009612662,0.00021982369,0.00009123053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031388155,0.00025029457,0.00032759286,0.00016010973,0.00008926533,0.00008197425,0.00013457285,0.00031456567,0.00033071105],"category_scores_gemma":[0.000050410075,0.0002448424,0.000091254806,0.00042022357,0.00006311395,0.0003883975,0.000012127426,0.00036379328,0.000049382732],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013377257,0.000030004428,0.00046785065,0.00008782979,0.000027019885,5.2377806e-7,0.00045164168,0.000021438349,0.9909515,0.0005512678,0.00038024332,0.006896951],"study_design_scores_gemma":[0.00042007247,0.00033548567,0.00027421856,0.00017151557,0.000021908594,0.000026764483,0.0005127608,0.033740535,0.96349806,0.00016339096,0.0005534454,0.00028181978],"about_ca_topic_score_codex":0.0001752104,"about_ca_topic_score_gemma":0.00000654037,"teacher_disagreement_score":0.062340207,"about_ca_system_score_codex":0.000084607476,"about_ca_system_score_gemma":0.000037781294,"threshold_uncertainty_score":0.99843836},"labels":[],"label_agreement":null},{"id":"W1548421713","doi":"10.22456/2175-2745.7033","title":"Inspeção Automática de Defeitos em Madeiras de Pinus usando Visão Computacional","year":2008,"lang":"en","type":"article","venue":"Revista de Informática Teórica e Aplicada","topic":"Industrial Vision Systems and Defect Detection","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":"École de Technologie Supérieure","funders":"","keywords":"Artificial intelligence; Grayscale; Pattern recognition (psychology); Feature selection; Computer science; Feature (linguistics); Set (abstract data type); Image (mathematics); Histogram","score_opus":0.016792915936086984,"score_gpt":0.22580455474852137,"score_spread":0.2090116388124344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548421713","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":[0.8805757,0.00083211093,0.10297573,0.00009352639,0.00032443146,0.0007779309,0.000044463835,0.0015423427,0.012833756],"genre_scores_gemma":[0.9950524,0.0001829744,0.0033902326,0.00033859463,0.0006359304,0.0000887815,0.000013229706,0.000081760234,0.00021611498],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99721843,0.000088459274,0.0008660917,0.00032314297,0.00049892976,0.0010049591],"domain_scores_gemma":[0.9983649,0.0003087963,0.00013579013,0.0005542557,0.00010689958,0.00052932056],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005930211,0.00041920194,0.00058498647,0.00026110644,0.00036233442,0.00020569586,0.00039038848,0.00045981942,0.00016146447],"category_scores_gemma":[0.00026975403,0.00040790567,0.00028843587,0.00076637574,0.000087719,0.00027119098,0.00009451338,0.0007615893,0.00038811073],"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":[0.0011940283,0.0009253902,0.03250772,0.0030464544,0.0019665693,0.0020645023,0.012766849,0.09698835,0.057166513,0.0404458,0.36385012,0.3870777],"study_design_scores_gemma":[0.0021687748,0.00030239014,0.012454288,0.00032337368,0.00011282568,0.0036157756,0.00025976056,0.19259842,0.004662978,0.000261612,0.7819256,0.0013141894],"about_ca_topic_score_codex":0.000075827294,"about_ca_topic_score_gemma":0.000003884147,"teacher_disagreement_score":0.4180755,"about_ca_system_score_codex":0.00083870295,"about_ca_system_score_gemma":0.00031982272,"threshold_uncertainty_score":0.9998373},"labels":[],"label_agreement":null},{"id":"W1559513838","doi":"10.1109/pacrim.1995.519580","title":"Industrial inspection employing a three dimensional vision system and a neural network classifier","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"University of Victoria","funders":"","keywords":"Artificial neural network; Artificial intelligence; Classifier (UML); Computer science; Machine vision; Computer vision; Pattern recognition (psychology); Software; Data set; Data mining","score_opus":0.046200275431905,"score_gpt":0.2166908371217342,"score_spread":0.1704905616898292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1559513838","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":[0.98128796,0.00035454874,0.0033021637,0.000039455128,0.0046291556,0.00041816558,0.000002525075,0.0016004813,0.008365527],"genre_scores_gemma":[0.99791634,0.0000032467055,0.0000762988,0.000015949356,0.0017704789,0.000017742494,0.0000015941282,0.000032380616,0.00016598556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988665,0.000048806764,0.00034309653,0.00022658655,0.00024022092,0.0002747967],"domain_scores_gemma":[0.9995959,0.00006422135,0.000044731893,0.00015494811,0.000033225027,0.00010700464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023082776,0.00018147907,0.00022899697,0.00011176631,0.0002510539,0.000111711204,0.000043966764,0.00029372328,0.0000814998],"category_scores_gemma":[0.000015668109,0.00015351005,0.00006312854,0.00031768388,0.00002099784,0.00022324715,0.000032459186,0.00032839327,0.00005897769],"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":[0.0003621758,0.00011523177,0.019535147,0.00027476205,0.00037054686,0.00012373015,0.00055397884,0.35952333,0.0099939415,0.0025820334,0.2781383,0.3284268],"study_design_scores_gemma":[0.001102426,0.0001619219,0.0011958613,0.00013582932,0.000019306182,0.00013784497,0.00006239859,0.99249995,0.00016689251,0.00001584879,0.0042523234,0.0002493782],"about_ca_topic_score_codex":0.000078566634,"about_ca_topic_score_gemma":0.000044468863,"teacher_disagreement_score":0.63297665,"about_ca_system_score_codex":0.00010987996,"about_ca_system_score_gemma":0.0000034766606,"threshold_uncertainty_score":0.6259958},"labels":[],"label_agreement":null},{"id":"W1571492063","doi":"10.1109/iscas.2004.1328901","title":"In-camera detection of fabric defects","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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 Calgary","funders":"CMC Microsystems","keywords":"Preprocessor; Pixel; Computer science; Field-programmable gate array; Computer vision; Artificial intelligence; Computer graphics (images); Data pre-processing; Computer hardware","score_opus":0.010193644564497407,"score_gpt":0.21033145345568513,"score_spread":0.20013780889118774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1571492063","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":[0.9678624,0.000057387857,0.02398344,0.0000034707584,0.00065295363,0.00010314156,3.506933e-7,0.00009923144,0.007237583],"genre_scores_gemma":[0.9998031,0.0000054528286,0.00004868377,0.000007717919,0.00007030778,0.0000063360512,1.479886e-7,0.000009512516,0.000048703998],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99958897,0.000008357697,0.00016987647,0.000063128115,0.000076495584,0.00009318093],"domain_scores_gemma":[0.9998571,0.000011958439,0.000016166692,0.00007910168,0.000015021964,0.000020648244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008518564,0.000057830257,0.00010243768,0.00015081896,0.000011505167,0.000005824947,0.000024621604,0.00007965389,0.000026980684],"category_scores_gemma":[0.000012855103,0.000053565076,0.000037044818,0.00031004022,0.000005226316,0.00009037422,0.0000042552647,0.00008237565,0.00005891255],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016852788,0.0000242156,0.00025969784,0.00005822082,0.000013215229,0.0000059109825,0.000190998,0.094534084,0.8860471,0.0004959312,0.00007354534,0.018280195],"study_design_scores_gemma":[0.0007706599,0.00008620692,0.0021849733,0.000030619354,0.0000032197133,0.00001293504,0.00006834681,0.0009218132,0.99406713,0.000351547,0.0013989173,0.000103615435],"about_ca_topic_score_codex":0.0003091356,"about_ca_topic_score_gemma":0.0001147899,"teacher_disagreement_score":0.108020015,"about_ca_system_score_codex":0.00007084368,"about_ca_system_score_gemma":0.0000060837842,"threshold_uncertainty_score":0.21843204},"labels":[],"label_agreement":null},{"id":"W1575292894","doi":"10.1007/3-540-48222-9","title":"Computer vision systems : second International Workshop, ICVS 2001, Vancouver, Canada, July 7-8, 2001 : proceedings","year":2001,"lang":"en","type":"book","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Machine vision; Stereopsis; Obstacle avoidance; Video tracking; Mobile robot; Robot; Video processing","score_opus":0.012289797057172704,"score_gpt":0.2079875446853266,"score_spread":0.1956977476281539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1575292894","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015293021,0.00029330218,0.004692586,0.000020540165,0.051782455,0.00083530194,0.00012714666,0.0005716258,0.9415241],"genre_scores_gemma":[0.002549514,0.00006952909,0.000121640885,0.00009275358,0.007294715,0.00004388346,0.00009519268,0.00016090367,0.98957187],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969493,0.000015947588,0.0009323462,0.0005582102,0.00091848377,0.0006256712],"domain_scores_gemma":[0.99870336,0.00009234209,0.00024610068,0.00027241002,0.00039728562,0.00028848014],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003782096,0.000630337,0.00073684246,0.0003930844,0.000120656056,0.00031420207,0.0004034272,0.00097047526,0.0019138998],"category_scores_gemma":[0.000017177195,0.00059956237,0.00017607743,0.00020022591,0.000020159501,0.0002970686,0.000092573464,0.00096779526,0.00019765683],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001938015,0.000009091444,0.000004070803,0.0001689635,0.00015945731,0.00004760234,0.000017183032,0.004190119,0.000020685928,0.00004484007,0.9924164,0.0029022223],"study_design_scores_gemma":[0.00050393405,0.000057029956,0.0000066811617,0.00072789047,0.000027709633,0.00010074675,0.000041993037,0.04094092,0.000022333237,0.000017435164,0.95692134,0.0006319762],"about_ca_topic_score_codex":0.023291266,"about_ca_topic_score_gemma":0.29204535,"teacher_disagreement_score":0.2687541,"about_ca_system_score_codex":0.0023694383,"about_ca_system_score_gemma":0.0005419741,"threshold_uncertainty_score":0.9996456},"labels":[],"label_agreement":null},{"id":"W1586125175","doi":"10.1007/978-3-540-30125-7_50","title":"Contour Description Through Set Operations on Dynamic Reference Shapes","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Feature (linguistics); Set (abstract data type); Pattern recognition (psychology); Artificial intelligence; Simple (philosophy); Simplicity; Data mining","score_opus":0.04254482368783489,"score_gpt":0.2626204485023634,"score_spread":0.2200756248145285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1586125175","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":[0.0019133668,0.00017675893,0.9879903,0.000048443722,0.0023433533,0.00040270897,0.000023962542,0.0002532882,0.0068477755],"genre_scores_gemma":[0.9916381,0.000045984027,0.0074226013,0.00019409155,0.0003980357,0.000011749735,0.000015380028,0.000039032053,0.00023498839],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982956,0.000015849553,0.00037506074,0.0005534632,0.00045346422,0.00030653283],"domain_scores_gemma":[0.9993417,0.000079435005,0.000054592554,0.0003517847,0.00011164901,0.00006082901],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025790997,0.0003469366,0.0003311907,0.00033476666,0.00020035158,0.0002871855,0.00036321324,0.00043573452,0.0000654501],"category_scores_gemma":[0.00003443053,0.00031184359,0.000069382826,0.00022881049,0.00013027473,0.0003232506,0.0000635283,0.00076011533,0.00011160642],"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":[0.0000058488718,0.000005076963,0.0000011914517,0.000024120984,0.000007614429,0.000012147883,0.00032701428,0.9313415,0.0012074783,0.0034246396,0.000018561717,0.06362478],"study_design_scores_gemma":[0.0007517644,0.0005030016,0.0001157102,0.001845895,0.000019813751,0.000070607035,0.0000016833748,0.9418615,0.002840736,0.046466675,0.0044040577,0.0011185445],"about_ca_topic_score_codex":0.00009818087,"about_ca_topic_score_gemma":0.00034770972,"teacher_disagreement_score":0.98972476,"about_ca_system_score_codex":0.00069405825,"about_ca_system_score_gemma":0.00014402081,"threshold_uncertainty_score":0.99993336},"labels":[],"label_agreement":null},{"id":"W1591601192","doi":"10.5772/21546","title":"Machine Vision for Inspection: A Case Study","year":2011,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Queen's University; Western University","funders":"Queen's University","keywords":"Automated optical inspection; Machine vision; Visual inspection; Electronics; Artificial intelligence; Engineering; Automotive industry; Automated X-ray inspection; Image processing; Tearing; Production line; Engineering drawing; Computer science; Manufacturing engineering; Automotive engineering; Mechanical engineering; Electrical engineering; Image (mathematics)","score_opus":0.04499725912952503,"score_gpt":0.2680176071996574,"score_spread":0.22302034807013238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1591601192","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018648616,0.00026272456,0.02069103,0.0000018346932,0.004318011,0.0029237429,0.000077751814,0.0017853915,0.9680747],"genre_scores_gemma":[0.83367646,0.000003624615,0.000048528287,0.0000076127444,0.0007602888,0.00015157438,0.0000050831413,0.00015463443,0.16519219],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987839,0.000012134521,0.00051084196,0.00032944648,0.0001759956,0.00018768155],"domain_scores_gemma":[0.999212,0.000047373276,0.00010789663,0.00042883653,0.0001235129,0.00008038337],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025587936,0.00038432912,0.00043990123,0.0003355903,0.00015473862,0.000054978074,0.00010684536,0.0005546769,0.00009730779],"category_scores_gemma":[0.000011339394,0.00035703194,0.00022941075,0.000016128457,0.000027075228,0.000038329315,0.000044438053,0.0005641251,0.0001106454],"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":[0.0008068008,0.0002199277,0.000013364191,0.0008852496,0.0022324778,0.0098629305,0.0065603,0.00003188767,0.010153863,0.01173047,0.014438439,0.9430643],"study_design_scores_gemma":[0.002195248,0.0032491165,0.0000011433826,0.00054528966,0.00028711028,0.0056922627,0.00043701235,0.0005530887,0.009798825,0.006084404,0.9699277,0.0012287814],"about_ca_topic_score_codex":0.00029672834,"about_ca_topic_score_gemma":0.00037359778,"teacher_disagreement_score":0.9554893,"about_ca_system_score_codex":0.00015652392,"about_ca_system_score_gemma":0.000019770881,"threshold_uncertainty_score":0.9998882},"labels":[],"label_agreement":null},{"id":"W159875822","doi":"10.1007/978-3-540-79224-6_16","title":"Industrial Applications of the Stiction Quantification Algorithm","year":2008,"lang":"en","type":"book-chapter","venue":"Advances in industrial control","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Stiction; Computer science; Algorithm; Materials science; Nanotechnology; Microelectromechanical systems","score_opus":0.028973864648808335,"score_gpt":0.23637620345136814,"score_spread":0.20740233880255982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W159875822","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062232756,0.02725352,0.20875153,0.00024323487,0.08027122,0.025719263,0.0032107367,0.0014780815,0.6524501],"genre_scores_gemma":[0.9032488,0.0062856576,0.00020825097,0.000052179326,0.020023828,0.0014526843,0.00022419351,0.00046299782,0.06804141],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976692,0.00007870278,0.0011557827,0.00034960744,0.00049650343,0.00025020487],"domain_scores_gemma":[0.9983841,0.00028714375,0.00054339576,0.0005820344,0.00014229324,0.0000610173],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00037697563,0.00037369062,0.00066904747,0.0003291701,0.00013325812,0.000024621513,0.00031887606,0.0013950982,0.00007200137],"category_scores_gemma":[0.00011783611,0.00032056015,0.00024607877,0.0002754136,0.0001363292,0.00022987185,0.00002677629,0.0014242488,0.00003536293],"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":[0.0001310086,0.000030179408,0.00008552197,0.000032152315,0.00011353152,0.0000027567519,0.000042256626,0.019011755,0.00017194923,0.0031739941,0.004154533,0.97305036],"study_design_scores_gemma":[0.0033749167,0.00009008751,0.000012039679,0.00034970802,0.00009546875,0.000012849427,0.00002028186,0.0025660435,0.0004901121,0.00058704894,0.99201953,0.000381909],"about_ca_topic_score_codex":0.0000438637,"about_ca_topic_score_gemma":0.000035240413,"teacher_disagreement_score":0.98786503,"about_ca_system_score_codex":0.0003003183,"about_ca_system_score_gemma":0.00013647808,"threshold_uncertainty_score":0.99992466},"labels":[],"label_agreement":null},{"id":"W1598966747","doi":"10.1007/s00107-007-0216-2","title":"Investigation of Urea-Melamine-Formaldehyde (UMF) resin penetration in Medium-Density Fiberboard (MDF) by High Resolution Confocal Laser Scanning Microscopy","year":2007,"lang":"de","type":"article","venue":"European Journal of Wood and Wood Products","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"FPInnovations; Université Laval","funders":"FPInnovations","keywords":"Materials science; Penetration (warfare); Composite material; Urea-formaldehyde; Fiberboard; Confocal laser scanning microscopy; Phenol formaldehyde resin; Porosity; Penetration depth; Microporous material; Microscope; Polystyrene; Formaldehyde; Polymer; Adhesive; Optics; Biomedical engineering; Chemistry","score_opus":0.01548130558998727,"score_gpt":0.21882348962730566,"score_spread":0.20334218403731838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1598966747","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":[0.9899691,0.0044248374,0.0011276273,0.0004488194,0.0033156108,0.00032732615,0.000018983252,0.000027254231,0.00034047637],"genre_scores_gemma":[0.995823,0.00029023475,0.0011578248,0.00003898055,0.0024766189,4.630896e-7,0.000029720542,0.000058458972,0.00012465661],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9961926,0.000701776,0.0017904766,0.00033359017,0.0005585075,0.00042308494],"domain_scores_gemma":[0.9979218,0.00010035633,0.0010305865,0.00025056518,0.00046657052,0.00023008088],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.006290724,0.00033360958,0.0005815331,0.0005309687,0.00014289941,0.00010905195,0.00015833646,0.00019306842,0.000009445704],"category_scores_gemma":[0.00044545162,0.00032299905,0.00008772428,0.0006669328,0.00012000046,0.0005539568,0.00005013639,0.0008721887,0.000013463944],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011324375,0.00007279244,0.0011044248,0.00044431034,0.00018865366,0.00018609386,0.003605528,0.0014157585,0.96998864,0.000037249567,0.014948701,0.0068753976],"study_design_scores_gemma":[0.003380776,0.0015234117,0.029370269,0.001677922,0.00016993454,0.00013155874,0.00042253066,0.0001914148,0.95645994,0.000031091575,0.0062301434,0.00041100287],"about_ca_topic_score_codex":0.00010771898,"about_ca_topic_score_gemma":0.000029625822,"teacher_disagreement_score":0.028265845,"about_ca_system_score_codex":0.00014870649,"about_ca_system_score_gemma":0.00013820278,"threshold_uncertainty_score":0.9999222},"labels":[],"label_agreement":null},{"id":"W1610960555","doi":"10.1109/ccece.1995.526672","title":"Extraction of characteristics from an image by analysis with multiple spatial resolutions","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sketch; Artificial intelligence; Computer science; Computer vision; Pyramid (geometry); Pattern recognition (psychology); Vigilance (psychology); Feature extraction; Detector; Gaussian; Mathematics; Algorithm; Telecommunications","score_opus":0.013202280770676696,"score_gpt":0.2207852418093454,"score_spread":0.2075829610386687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1610960555","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":[0.69936526,0.000016666223,0.29878747,0.0000055538653,0.00014262427,0.00007601447,0.00014400171,0.0001323648,0.0013300462],"genre_scores_gemma":[0.99889654,0.00000765078,0.0007119162,0.0000029279054,0.00012513589,0.0000061023607,0.00007591387,0.000012524564,0.00016130615],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942034,0.000026029147,0.00022194177,0.00011168571,0.00012621559,0.00009381232],"domain_scores_gemma":[0.9996238,0.0000403015,0.000053741325,0.00018304786,0.000050324314,0.00004881685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005430322,0.00008222272,0.00017574654,0.00011528536,0.0000454002,0.000025730384,0.00003551432,0.000085261665,0.0007106487],"category_scores_gemma":[0.000016012935,0.00006983093,0.000051482326,0.00029961366,0.0000128078855,0.00016616053,0.0000035702672,0.000090150585,0.000025427693],"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":[0.00014308449,0.00034114157,0.025532465,0.000027894444,0.0012274602,0.000010132546,0.00066920934,0.009887848,0.9082299,0.000017049178,0.010144418,0.043769423],"study_design_scores_gemma":[0.0005766795,0.00014079266,0.047364995,0.0000108527665,0.0002746227,0.0000024167582,0.00012932248,0.91162467,0.034839787,0.0000019500003,0.004827614,0.0002062709],"about_ca_topic_score_codex":0.003058308,"about_ca_topic_score_gemma":0.0005303822,"teacher_disagreement_score":0.90173686,"about_ca_system_score_codex":0.000029098075,"about_ca_system_score_gemma":0.0000017169428,"threshold_uncertainty_score":0.7781105},"labels":[],"label_agreement":null},{"id":"W1612175259","doi":"10.1007/978-3-319-11755-3","title":"Image Analysis and Recognition","year":2014,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":607,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.012605590466771965,"score_gpt":0.22285932852126172,"score_spread":0.21025373805448977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1612175259","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":[0.0018349886,0.00010093069,0.9940734,0.000008049618,0.0010825683,0.00013405971,0.000005568932,0.000120148216,0.002640306],"genre_scores_gemma":[0.8644028,0.00007581742,0.12949708,0.00035446277,0.004510163,0.000029754248,0.000077751036,0.00011420182,0.0009379842],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988879,0.000024570763,0.00023474771,0.0003848715,0.00026039523,0.00020753639],"domain_scores_gemma":[0.9993984,0.00015878728,0.000056367408,0.0002561174,0.00006939441,0.000060899783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005159317,0.00018959295,0.00033248085,0.0009146159,0.000071271796,0.00018835698,0.00016373426,0.00025907162,0.000016201682],"category_scores_gemma":[0.000040854047,0.00017369144,0.00007369167,0.00093453756,0.00011510918,0.000122897,0.000057482794,0.00036957642,0.00002755056],"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":[0.000002339686,0.000002400147,0.000029204448,0.000059114383,0.000039332444,0.0000065271,0.00013458043,0.024536788,0.00048291273,0.0000019052321,0.00017144009,0.97453344],"study_design_scores_gemma":[0.0002007085,0.00007487999,0.000278019,0.00019952036,0.00009432115,0.000021511381,1.1639199e-7,0.98862153,0.0024752729,0.0054241773,0.0021618865,0.00044806523],"about_ca_topic_score_codex":0.00002107137,"about_ca_topic_score_gemma":0.0000760391,"teacher_disagreement_score":0.9740854,"about_ca_system_score_codex":0.00015445727,"about_ca_system_score_gemma":0.00004796134,"threshold_uncertainty_score":0.70829314},"labels":[],"label_agreement":null},{"id":"W1632754434","doi":"10.1167/15.12.1031","title":"Rejecting probability summation for RF patterns, not so Quick!","year":2015,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Industrial Vision Systems and Defect Detection","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":"","keywords":"Summation; Radio frequency; Pooling; Detection theory; Modulation (music); Mathematics; Pattern recognition (psychology); Computer science; Physics; Acoustics; Artificial intelligence; Telecommunications; Detector","score_opus":0.0557823954913072,"score_gpt":0.297580382591577,"score_spread":0.24179798710026978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1632754434","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":[0.9480718,0.000091389826,0.048885837,0.00006884103,0.0024403834,0.00024082586,0.0000039679608,0.000048123322,0.0001488261],"genre_scores_gemma":[0.9980821,0.000006808159,0.0009186747,0.000013509284,0.000937897,0.0000033074623,0.0000013913648,0.00001675972,0.0000195412],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99889797,0.000045890727,0.00056252343,0.00007553814,0.0002919672,0.00012609184],"domain_scores_gemma":[0.99914086,0.000089598965,0.00022660279,0.00010617687,0.00034137908,0.00009536472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018079262,0.00008901586,0.00020491693,0.00012043488,0.00006436208,0.00007992026,0.00006794638,0.00011808247,0.0000056871913],"category_scores_gemma":[0.0003620896,0.00007076113,0.000115136805,0.000100055564,0.000006135811,0.00032038148,0.000010758185,0.0001817592,0.000007238121],"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":[0.0019763266,0.0002518171,0.008544287,0.0010563383,0.00018764018,0.000023863531,0.004106028,0.10370493,0.17610227,0.00018921324,0.045677673,0.65817964],"study_design_scores_gemma":[0.023410875,0.013154143,0.048727494,0.004961266,0.00030372592,0.0010060462,0.0049366057,0.25244033,0.3017913,0.00654218,0.3406231,0.0021029043],"about_ca_topic_score_codex":0.000018295837,"about_ca_topic_score_gemma":0.000007974553,"teacher_disagreement_score":0.6560767,"about_ca_system_score_codex":0.0001889166,"about_ca_system_score_gemma":0.000043197128,"threshold_uncertainty_score":0.28855553},"labels":[],"label_agreement":null},{"id":"W1666138844","doi":"10.1063/1.2060467","title":"Clean Fabrication and Cleanliness Monitoring in SNO","year":2005,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Industrial Vision Systems and Defect Detection","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":"Laurentian University","funders":"","keywords":"Fabrication; Materials science; Computer science; Nanotechnology","score_opus":0.028955104790746086,"score_gpt":0.251050596913445,"score_spread":0.2220954921226989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1666138844","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":[0.9943999,0.00006358331,0.0017931082,0.00006724179,0.00024820765,0.00013732874,5.86175e-7,0.00018850561,0.00310151],"genre_scores_gemma":[0.999078,0.00006358056,0.00021940282,0.000007787223,0.0005314697,0.000022150827,5.2091127e-7,0.00001492458,0.000062162835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938244,0.0000030453698,0.00020323723,0.00014898616,0.00010170614,0.00016058548],"domain_scores_gemma":[0.999762,0.000012781792,0.00003093901,0.000044264227,0.000100992416,0.000049035127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017214731,0.000101750746,0.00012266662,0.00012208488,0.00003919088,0.00008842793,0.00005954333,0.00010322292,0.000010169096],"category_scores_gemma":[0.00004177049,0.00010381715,0.000013933372,0.0002003238,0.000012553309,0.00034781548,0.000016599211,0.00016776421,0.000023022869],"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":[0.000026090822,0.000021354092,0.12742922,0.000107989916,0.000010753361,9.32125e-7,0.0033766793,0.0001415279,0.056994308,0.0007189761,0.00033002967,0.81084216],"study_design_scores_gemma":[0.0030491676,0.00024182476,0.39170498,0.0008722732,0.000029662859,0.000059106096,0.007143847,0.4569034,0.096751966,0.00087762834,0.041083686,0.0012824385],"about_ca_topic_score_codex":0.00003511394,"about_ca_topic_score_gemma":0.000009175361,"teacher_disagreement_score":0.8095597,"about_ca_system_score_codex":0.00006627232,"about_ca_system_score_gemma":0.000007827184,"threshold_uncertainty_score":0.42335406},"labels":[],"label_agreement":null},{"id":"W1670064250","doi":"10.1109/dmca.1997.603541","title":"Multivariable identification of paper machine cross-direction process using an automated tool","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Honeywell (Canada)","funders":"","keywords":"Identification (biology); Process (computing); Actuator; Computer science; Multivariable calculus; Process control; Control engineering; Machine tool; System identification; Data modeling; Data mining; Artificial intelligence; Engineering; Mechanical engineering","score_opus":0.03307159756332176,"score_gpt":0.3016055678662318,"score_spread":0.26853397030291004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1670064250","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":[0.9866495,0.000038410642,0.008890124,0.0000014299736,0.00068271934,0.00021618942,0.00000956277,0.0012737035,0.0022383356],"genre_scores_gemma":[0.9993697,0.0000028052266,0.00019404083,0.0000034208028,0.00009339781,0.000009937639,0.0000055554297,0.00002245513,0.00029870993],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991656,0.000027914753,0.00040704376,0.00013584815,0.00014511833,0.000118500015],"domain_scores_gemma":[0.9996022,0.000013234777,0.00007375648,0.00016898639,0.000112332855,0.000029471268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002396154,0.00009579901,0.00013073452,0.00010271155,0.000082000646,0.00006444083,0.00004882956,0.00013303936,0.00040141854],"category_scores_gemma":[0.00003934762,0.00008819812,0.000032572538,0.00031929885,0.000010726248,0.0005782398,0.0000046909577,0.00007862979,0.00006462087],"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":[0.000010251676,0.00006364991,0.0008873296,0.0000862093,0.000021857011,5.2383194e-7,0.00020761615,0.29630244,0.6964282,0.000015346397,0.00019810032,0.0057784817],"study_design_scores_gemma":[0.0002571525,0.000019207127,0.0015367874,0.00001526942,0.000007048491,0.000007914204,0.000028489914,0.8178047,0.17993636,0.000008008664,0.0002862834,0.00009277399],"about_ca_topic_score_codex":0.00018388305,"about_ca_topic_score_gemma":0.0000073663196,"teacher_disagreement_score":0.52150226,"about_ca_system_score_codex":0.00005265443,"about_ca_system_score_gemma":0.000003823153,"threshold_uncertainty_score":0.43952513},"labels":[],"label_agreement":null},{"id":"W174584624","doi":"10.22260/isarc2013/0124","title":"Improving Site and Materials Handling Operations through Autonomous Vehicle-Related Technologies","year":2013,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Automation; Computer science; Download; Suite; Automotive industry; Manufacturing engineering; Systems engineering; Engineering; Transport engineering; Operating system","score_opus":0.008494468632457884,"score_gpt":0.19022831420067574,"score_spread":0.18173384556821784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W174584624","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":[0.99717236,0.00016805709,0.000096832126,0.00015281617,0.0003903005,0.0004221673,0.000004881489,0.00045317193,0.0011394084],"genre_scores_gemma":[0.9991599,0.000018651412,0.00055165164,0.0000068285335,0.000049712427,0.000047229092,4.571127e-7,0.000021155227,0.00014442223],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99929535,0.0000042627225,0.00030163195,0.00013532951,0.00009926107,0.00016417757],"domain_scores_gemma":[0.99970967,0.00001958885,0.00006641972,0.00008287448,0.00010586253,0.00001559512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018700643,0.00011964046,0.00018104906,0.00005875034,0.00018528376,0.00016375312,0.00012740078,0.00016725723,0.00001740754],"category_scores_gemma":[0.00012811061,0.00008501384,0.00003533471,0.00018903235,0.00005416611,0.00042506613,0.00009987731,0.00017540922,0.000019732874],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021280557,0.000003860876,0.00017237061,0.00007633081,0.000017950206,4.1994983e-8,0.0005306837,0.0002187647,0.99477804,0.0005555145,0.00023558612,0.0034087454],"study_design_scores_gemma":[0.00031768042,0.000042132826,0.0005373933,0.0001311688,0.00002200253,0.000016190193,0.0012716305,0.012904262,0.9833475,0.0009135464,0.00033472528,0.00016178378],"about_ca_topic_score_codex":0.00035517424,"about_ca_topic_score_gemma":0.0000023028483,"teacher_disagreement_score":0.012685497,"about_ca_system_score_codex":0.000034719167,"about_ca_system_score_gemma":0.0000064531687,"threshold_uncertainty_score":0.34667638},"labels":[],"label_agreement":null},{"id":"W1859906963","doi":"10.1167/15.12.522","title":"Psychophysical evaluation of planar shape representations for object recognition","year":2015,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Industrial Vision Systems and Defect Detection","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":"York University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Mathematics; Fourier transform; Representation (politics); Ellipse; Boundary (topology); Shape analysis (program analysis); Fourier series; Cognitive neuroscience of visual object recognition; Wavelet; Computer science; Computer vision; Object (grammar); Algorithm; Geometry; Mathematical analysis","score_opus":0.12492959003185336,"score_gpt":0.37441204697900404,"score_spread":0.24948245694715068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1859906963","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":[0.9832232,0.00015956219,0.012970306,0.000042500862,0.0019754488,0.0003106536,0.000012316633,0.000020324129,0.0012856909],"genre_scores_gemma":[0.9981185,0.000010168988,0.0011910795,0.000004428957,0.0006434049,0.000006371591,0.000006877757,0.000011774552,0.000007382088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988135,0.00008282733,0.00046097845,0.00005955118,0.0005137088,0.00006940305],"domain_scores_gemma":[0.99864537,0.000091279726,0.00022208053,0.000082405175,0.0008950827,0.000063801235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015509345,0.00006210689,0.0001702806,0.00017609971,0.000025112451,0.000017398219,0.00004681261,0.00008050133,0.000017113009],"category_scores_gemma":[0.00033219653,0.000052206706,0.000115552924,0.00016292128,0.000007592085,0.00023360571,0.00000281448,0.00010271077,0.000008635653],"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":[0.0010371773,0.00021888556,0.00013645634,0.00009150274,0.0001735403,0.000002810955,0.0011109607,0.03610849,0.16129562,0.000050033814,0.069848806,0.7299257],"study_design_scores_gemma":[0.023303991,0.0077032363,0.016975792,0.0014760173,0.00089669664,0.00028984415,0.0029188048,0.82362825,0.08156653,0.02796405,0.012586691,0.0006900733],"about_ca_topic_score_codex":0.000004733529,"about_ca_topic_score_gemma":0.0000013963314,"teacher_disagreement_score":0.7875198,"about_ca_system_score_codex":0.00008388954,"about_ca_system_score_gemma":0.00005150084,"threshold_uncertainty_score":0.21289277},"labels":[],"label_agreement":null},{"id":"W1864812830","doi":"10.1109/icsmc.2000.884432","title":"Trinocular data registration using a three-dimensional self-organizing feature map","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Western University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Feature (linguistics); Feature vector; Curse of dimensionality; Feature extraction; Identification (biology); Object (grammar); Computer vision","score_opus":0.0704330219151767,"score_gpt":0.23974142681547572,"score_spread":0.16930840490029903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1864812830","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":[0.81685007,0.0042823968,0.14250109,0.0005716822,0.008042782,0.0016526948,0.000075407625,0.0052790944,0.0207448],"genre_scores_gemma":[0.9915432,0.000004834378,0.0070614945,0.000029658237,0.0006974031,0.0000020917983,0.000026350588,0.000038468297,0.00059649907],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991448,0.000020976298,0.00020076272,0.00022479577,0.00023388292,0.000174814],"domain_scores_gemma":[0.99935365,0.000021292097,0.000038351227,0.00049096486,0.0000400095,0.000055755896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020331325,0.00013379697,0.00014160198,0.00007547365,0.00011839694,0.000081186685,0.00013162833,0.00018810474,0.00026038615],"category_scores_gemma":[0.000019343137,0.00012116291,0.000035189965,0.00022542116,0.000005985176,0.00031885202,0.000045348625,0.00019939279,0.00014176362],"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":[0.000053138247,0.00022367062,0.0016174004,0.00045983196,0.0006874508,0.00022285547,0.0005098924,0.08315904,0.2468977,0.0011288783,0.6300859,0.034954254],"study_design_scores_gemma":[0.00044728932,0.00002361562,0.00008740656,0.000043480104,0.000030029416,0.00010084311,0.00002260071,0.94744694,0.0041958285,0.000017340864,0.04735989,0.00022475967],"about_ca_topic_score_codex":0.000076211836,"about_ca_topic_score_gemma":0.000042526986,"teacher_disagreement_score":0.86428785,"about_ca_system_score_codex":0.00007580529,"about_ca_system_score_gemma":0.000008463822,"threshold_uncertainty_score":0.49408802},"labels":[],"label_agreement":null},{"id":"W1888553712","doi":"10.1109/iscas.2002.1010791","title":"On the use of hash functions for defect detection in textures for in-camera web inspection systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":19,"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 Calgary; University of Windsor","funders":"","keywords":"Hash function; Computer science; SHA-2; Cryptographic hash function; Cryptography; Feature hashing; Computer vision; Pattern recognition (psychology); Artificial intelligence; Theoretical computer science; Double hashing; Algorithm; Computer security","score_opus":0.057627081415696384,"score_gpt":0.2387804373876951,"score_spread":0.18115335597199872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1888553712","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":[0.84447205,0.000078498284,0.14875686,0.000012023536,0.0027666336,0.0023781857,0.00002268945,0.00018849157,0.0013245704],"genre_scores_gemma":[0.9990088,0.000005020965,0.000048885475,0.000010833126,0.00008999311,0.00057805097,0.0000020547095,0.000025122967,0.00023126113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991358,0.00008234778,0.00036499763,0.0001495122,0.0000976881,0.00016966731],"domain_scores_gemma":[0.999043,0.00065588683,0.000056172514,0.00016236726,0.000062055224,0.000020517835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048816344,0.00012227747,0.00019097976,0.0003195797,0.00008915177,0.000048360132,0.000032397784,0.00016668247,0.000008358222],"category_scores_gemma":[0.00039762395,0.000089546586,0.00012423834,0.0004111557,0.000011073912,0.00011871559,0.000002499068,0.00014648223,0.0000056668673],"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":[0.0005287673,0.00016803134,0.00096983666,0.0003633682,0.00013420265,7.0453433e-7,0.00028515415,0.8636509,0.089296974,0.020119553,0.013967952,0.0105145695],"study_design_scores_gemma":[0.0063121254,0.0017893227,0.0021146885,0.0004904277,0.00007668661,0.000032086726,0.0019265478,0.69412553,0.123486966,0.0007128564,0.16815403,0.0007787152],"about_ca_topic_score_codex":0.00029583895,"about_ca_topic_score_gemma":0.0014472789,"teacher_disagreement_score":0.16952534,"about_ca_system_score_codex":0.00015621871,"about_ca_system_score_gemma":0.000016105641,"threshold_uncertainty_score":0.36516038},"labels":[],"label_agreement":null},{"id":"W1907871985","doi":"10.1109/ccece.2001.933598","title":"Morphological skeleton algorithm for PDP production line inspection","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Skeletonization; Computer science; Computer vision; Artificial intelligence; Process (computing); Image processing; Pixel; Line (geometry); Field (mathematics); Mathematical morphology; Production line; Medial axis; Image (mathematics); Algorithm; Pattern recognition (psychology); Engineering; Mathematics","score_opus":0.041635665217769305,"score_gpt":0.2355293081581911,"score_spread":0.19389364294042177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1907871985","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":[0.14872901,0.00020759639,0.8282462,0.00019460346,0.0074572824,0.0010794556,0.000009061176,0.0029303224,0.011146508],"genre_scores_gemma":[0.99018013,0.000034649358,0.004105953,0.00001918734,0.0022033602,0.00008861086,0.000006477537,0.000024141993,0.0033374573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936855,0.0000134397715,0.00019837209,0.00016828818,0.00009833675,0.00015304443],"domain_scores_gemma":[0.9997523,0.000017351686,0.000021538113,0.000117640746,0.00005249559,0.000038661936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001469027,0.00010034449,0.00012157515,0.00008563041,0.00009224129,0.000025877935,0.000031683132,0.00015432159,0.00019689827],"category_scores_gemma":[0.00003871985,0.000083475774,0.0000653129,0.00018973713,0.0000099735935,0.00010872343,0.0000050861395,0.00011454316,0.00014346812],"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":[0.000010018158,0.00006212233,0.000014376738,0.000024742832,0.000027644897,0.0000033176098,0.00006491539,0.006683323,0.025294147,0.000085444975,0.06352003,0.9042099],"study_design_scores_gemma":[0.000697035,0.00040485265,0.0000955453,0.000015575568,0.000014460957,0.0001189959,0.00007009813,0.7836297,0.08173906,0.00010477775,0.13284178,0.0002680822],"about_ca_topic_score_codex":0.000015157286,"about_ca_topic_score_gemma":0.000003762284,"teacher_disagreement_score":0.9039418,"about_ca_system_score_codex":0.00006980939,"about_ca_system_score_gemma":0.0000012275494,"threshold_uncertainty_score":0.34040433},"labels":[],"label_agreement":null},{"id":"W1924687241","doi":"10.1109/cca.2003.1223258","title":"Identifying strength of boards using mechanical modeling and a Weibull-based feature","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"University of British Columbia","funders":"","keywords":"Weibull distribution; Finite element method; Grading (engineering); Feature (linguistics); Structural engineering; Stress (linguistics); Nondestructive testing; Computer science; Engineering; Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics; Physics","score_opus":0.03420115373527515,"score_gpt":0.26067562758278806,"score_spread":0.2264744738475129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1924687241","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":[0.61279017,0.000079940866,0.3865846,0.0000074804075,0.00021975911,0.00007001011,0.000002288227,0.00009775663,0.00014799387],"genre_scores_gemma":[0.99410963,0.0000036877288,0.0057685548,0.0000068506733,0.00008242569,0.00000175388,8.985137e-7,0.000017329285,0.000008863181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940217,0.000011624665,0.00020429277,0.00011229708,0.00014507482,0.00012452208],"domain_scores_gemma":[0.999778,0.0000130340395,0.000025196496,0.000102805374,0.00003375508,0.00004725525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016235429,0.00009599397,0.00016369364,0.000096150696,0.00004630639,0.000030968942,0.000033672055,0.0001508244,0.000008894246],"category_scores_gemma":[0.000017372135,0.00008613984,0.000050181316,0.00013894633,0.0000062235295,0.000078013436,0.000012588169,0.00014440753,0.0000013315577],"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":[0.000013928019,0.000009146027,0.000011018717,0.000075973716,0.000020806508,0.00000287068,0.000058088634,0.88121045,0.116563514,0.0004931261,0.0000130173685,0.0015280718],"study_design_scores_gemma":[0.00068308023,0.000029259538,0.0000056838694,0.00011558939,0.000015803525,0.000009657896,0.0001335034,0.89964896,0.09903,0.00019357857,0.000031132124,0.00010375615],"about_ca_topic_score_codex":0.00016048069,"about_ca_topic_score_gemma":0.000015807314,"teacher_disagreement_score":0.3813195,"about_ca_system_score_codex":0.000055070086,"about_ca_system_score_gemma":0.0000204864,"threshold_uncertainty_score":0.35126805},"labels":[],"label_agreement":null},{"id":"W1932323509","doi":"","title":"Spatiotemporal volume video event detection for fault monitoring in assembly automation","year":2012,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Queen's University","funders":"","keywords":"Downtime; Computer science; Event (particle physics); Fault detection and isolation; Real-time computing; Artificial intelligence; Set (abstract data type); Automation; Testbed; Data mining; Computer vision; Fault (geology); Similarity (geometry); Image (mathematics); Engineering","score_opus":0.024036103599849576,"score_gpt":0.2693934329889873,"score_spread":0.24535732938913773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1932323509","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":[0.7349952,0.00004857504,0.2609891,0.0000068882973,0.002812867,0.0003490481,0.0000012553882,0.00031099736,0.00048605687],"genre_scores_gemma":[0.9981823,0.0000021085004,0.00052295154,0.0000017608359,0.00094051386,0.0001200207,0.0000028944662,0.000023057313,0.00020440713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992262,0.00002527969,0.0002962949,0.00009074188,0.00012735264,0.00023413231],"domain_scores_gemma":[0.9997438,0.00003205316,0.000039405008,0.000095447744,0.00003561962,0.000053682652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045786242,0.00010158132,0.00012304906,0.00016057413,0.0000515123,0.000033235672,0.000031162042,0.00015881605,0.000013133878],"category_scores_gemma":[0.000048367026,0.00010114255,0.000056559405,0.00018303662,0.0000023830776,0.00043592002,0.0000062495483,0.00009868781,0.000053204745],"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":[0.00007655876,0.00008940243,0.03599519,0.00023909449,0.000052569798,7.985695e-7,0.001114873,0.091033086,0.20309585,0.00010177569,0.001563384,0.6666374],"study_design_scores_gemma":[0.00096295984,0.00011264188,0.06640196,0.00007761302,0.000011827403,0.000007318894,0.00033789975,0.5503476,0.36688787,0.000031056843,0.014491756,0.00032948388],"about_ca_topic_score_codex":0.000209614,"about_ca_topic_score_gemma":0.000044894878,"teacher_disagreement_score":0.6663079,"about_ca_system_score_codex":0.00023366779,"about_ca_system_score_gemma":0.0000057123502,"threshold_uncertainty_score":0.41244733},"labels":[],"label_agreement":null},{"id":"W194158148","doi":"10.1016/s0020-0255(00)00031-1","title":"Intelligent Manufacturing and Fault Diagnosis","year":2000,"lang":"en","type":"article","venue":"Information Sciences","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Fault (geology); Artificial intelligence; Data mining; Geology; Seismology","score_opus":0.022549827253893966,"score_gpt":0.24324087990782475,"score_spread":0.2206910526539308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W194158148","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":[0.95795256,0.000044937442,0.001368525,0.00002898085,0.00028629196,0.00008403204,0.0000020693465,0.00012707051,0.040105563],"genre_scores_gemma":[0.9996896,0.00011268946,0.00006222941,0.000044619082,0.000036383746,0.000011735862,6.358863e-7,0.0000012645571,0.00004087452],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995699,0.00000552405,0.00016306488,0.000037743022,0.00013776819,0.0000860079],"domain_scores_gemma":[0.9998819,0.0000243069,0.000015301099,0.000039292074,0.000008795356,0.000030397463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019987984,0.00004617767,0.00004756709,0.00008754275,0.000118618154,0.00016065307,0.000047163012,0.00003321227,0.00031353184],"category_scores_gemma":[0.00000877201,0.000036604142,0.000012855677,0.00011290158,0.000028225688,0.0009613541,0.0000051574084,0.00004168126,0.00027522352],"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":[0.0000010339573,7.46246e-7,0.00012703163,0.00000764138,0.000001674491,8.538233e-8,0.0005178715,0.019354086,0.000009379294,0.000037991118,0.0006979249,0.97924453],"study_design_scores_gemma":[0.00019955014,0.000076926255,0.003954212,0.00004897793,0.000003575938,0.000023596078,0.0010249452,0.07438095,0.070774935,0.00019432753,0.8490889,0.000229108],"about_ca_topic_score_codex":0.000045335804,"about_ca_topic_score_gemma":0.0000031042155,"teacher_disagreement_score":0.9790154,"about_ca_system_score_codex":0.000013420727,"about_ca_system_score_gemma":0.000003665809,"threshold_uncertainty_score":0.35375333},"labels":[],"label_agreement":null},{"id":"W1955939834","doi":"10.1109/ccece.1993.332407","title":"Glass fragments detector for a jar filling process","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Université Laval","funders":"","keywords":"Detector; Process (computing); Computer science; Property (philosophy); Artificial intelligence; Computer graphics (images); Optics; Computer vision; Physics","score_opus":0.03207396023445986,"score_gpt":0.23816870342146934,"score_spread":0.2060947431870095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1955939834","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":[0.8345275,0.00022473553,0.14118981,0.000022322602,0.0021702314,0.0009115547,0.000016522608,0.0010338628,0.01990349],"genre_scores_gemma":[0.9982372,0.0000038657145,0.0003125148,0.000016780818,0.00030382047,0.00008853939,0.0000010062046,0.000024202745,0.001012051],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994493,0.0000053086333,0.0001650058,0.000107340966,0.00010079957,0.0001722404],"domain_scores_gemma":[0.999776,0.000032827455,0.000017212735,0.000091255366,0.000036835267,0.00004589122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000075438504,0.000090843176,0.00011018557,0.00005709305,0.00006466036,0.00003463867,0.000049167156,0.000099937606,0.00030779294],"category_scores_gemma":[0.000026704589,0.00007852253,0.00005388207,0.00012061982,0.000003806492,0.00008518926,0.0000037362756,0.000067548994,0.00011834722],"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":[0.000183222,0.00026042885,0.0012100714,0.0021799041,0.0006206388,0.000021866163,0.0037725263,0.15650038,0.121132165,0.00057718426,0.12557115,0.5879705],"study_design_scores_gemma":[0.0013934505,0.00016212711,0.00004311418,0.00007308044,0.000016454942,0.000010168317,0.0001826908,0.8029863,0.115275085,0.000099645666,0.07938936,0.00036846596],"about_ca_topic_score_codex":0.0000063151087,"about_ca_topic_score_gemma":0.0000035640298,"teacher_disagreement_score":0.646486,"about_ca_system_score_codex":0.000032659405,"about_ca_system_score_gemma":0.0000017125186,"threshold_uncertainty_score":0.3370117},"labels":[],"label_agreement":null},{"id":"W1963580462","doi":"10.1167/14.10.1157","title":"Perceptual learning of detection of band-limited noise patterns","year":2014,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Stimulus (psychology); Perception; Noise (video); Artificial intelligence; Pattern recognition (psychology); Perceptual learning; Noise reduction; Contrast (vision); False alarm; Observer (physics); Computer science; Psychology; Audiology; Mathematics; Speech recognition; Communication; Cognitive psychology; Medicine; Physics","score_opus":0.011038922827175241,"score_gpt":0.2332367860980314,"score_spread":0.22219786327085614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963580462","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":[0.95244634,0.00005442119,0.04636129,0.000004118079,0.0007979978,0.00004460747,9.535391e-7,0.000018460614,0.00027180376],"genre_scores_gemma":[0.99955696,0.00003449728,0.000057499783,0.0000020933344,0.00031459407,3.0721304e-7,4.4185765e-7,0.000015666603,0.000017934703],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99886256,0.000101326405,0.00059957494,0.000055003784,0.00029114573,0.00009038985],"domain_scores_gemma":[0.99923575,0.00008959317,0.00034445044,0.000085564556,0.0001947162,0.00004990749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006651715,0.00008463263,0.0002793405,0.00026828834,0.000031948315,0.000011121971,0.000062746985,0.00012562088,0.00003875679],"category_scores_gemma":[0.00014947364,0.00006882864,0.00014717062,0.00015518905,0.000011130329,0.0001443699,0.000008741919,0.00028656016,0.0000042442057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009107609,0.000020394591,0.0013573816,0.00006463356,0.000022605893,0.0000010227863,0.0003026417,0.020656282,0.90514475,0.0000023449807,0.000059434355,0.072277404],"study_design_scores_gemma":[0.0027702723,0.004381102,0.13668272,0.0011777978,0.00008956,0.00014982732,0.0009835701,0.07282462,0.77471894,0.000025980888,0.005927736,0.0002678808],"about_ca_topic_score_codex":0.000020037392,"about_ca_topic_score_gemma":0.0000024465623,"teacher_disagreement_score":0.13532534,"about_ca_system_score_codex":0.000037110174,"about_ca_system_score_gemma":0.0000071537443,"threshold_uncertainty_score":0.28067502},"labels":[],"label_agreement":null},{"id":"W1964995476","doi":"10.1515/hf.2008.041","title":"A new image analysis algorithm for calculating percentage wood failure","year":2008,"lang":"en","type":"article","venue":"Holzforschung","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Image (mathematics); Mathematics; Computer science; Artificial intelligence","score_opus":0.015958352647448222,"score_gpt":0.23820738849802528,"score_spread":0.22224903585057706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964995476","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":[0.15899563,0.00012093104,0.8386921,0.000011781331,0.0004074519,0.00034470184,0.000028999564,0.00038822673,0.001010137],"genre_scores_gemma":[0.9267837,0.00001089194,0.07105245,0.000017081804,0.0009322886,0.000038250088,0.000042052423,0.00005625976,0.0010670476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998953,0.00001834699,0.0003109557,0.00021418514,0.00019148678,0.00031205264],"domain_scores_gemma":[0.9995079,0.000045971363,0.000046476292,0.00021789856,0.00004656826,0.00013520668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015453341,0.00017717147,0.0003132253,0.000250302,0.00019778423,0.00004812195,0.000084160696,0.00016158282,0.00012582508],"category_scores_gemma":[0.000022580949,0.00017221028,0.00033505698,0.0005956998,0.000010622618,0.00021096363,0.000016401405,0.00016923028,0.000046156398],"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":[0.00006409903,0.00008372623,0.0035249568,0.00024029594,0.0037263855,0.00018896909,0.005018159,0.05710028,0.10294152,0.00019950933,0.0894062,0.7375059],"study_design_scores_gemma":[0.0027004152,0.0001451425,0.0051613813,0.000059235295,0.00062699633,0.00013770937,0.0008342591,0.92622155,0.019282194,0.000037043785,0.043858025,0.00093602634],"about_ca_topic_score_codex":0.00016740107,"about_ca_topic_score_gemma":0.000035086792,"teacher_disagreement_score":0.8691213,"about_ca_system_score_codex":0.00007925584,"about_ca_system_score_gemma":0.000021233747,"threshold_uncertainty_score":0.70225316},"labels":[],"label_agreement":null},{"id":"W1966322892","doi":"10.1057/palgrave.jors.2601363","title":"An automated surface surveillance system","year":2002,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Department of National Defence; University of Ottawa","funders":"","keywords":"Computer science; Information system; Operations research; Engineering","score_opus":0.0663606705683462,"score_gpt":0.3445808029151294,"score_spread":0.27822013234678317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966322892","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":[0.9953806,0.00041889097,0.00058952096,0.0004018825,0.0012634313,0.00019618384,0.0000116717265,0.00013512177,0.0016026925],"genre_scores_gemma":[0.998744,0.000044464807,0.00026644696,0.000013804856,0.00057731906,0.0000016000217,5.0508544e-7,0.000013713826,0.00033816256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981072,0.0003539939,0.00032611287,0.000067984474,0.000959109,0.00018561468],"domain_scores_gemma":[0.9988676,0.00015337636,0.000055509965,0.0001704089,0.00066506723,0.000088077155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029616365,0.000069662914,0.00013595576,0.000029292954,0.00035019775,0.00016252103,0.0002581319,0.00010274125,0.00007027744],"category_scores_gemma":[0.000110637484,0.00004379426,0.00015858456,0.00039286722,0.000039063878,0.0002551313,0.000020010608,0.00053794775,0.000035595447],"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":[0.000017226948,0.00005507898,0.001069808,0.00007171847,0.00013970505,0.0000062979184,0.000909005,0.5769297,0.08295012,0.00035217925,0.33690885,0.0005903132],"study_design_scores_gemma":[0.00036660206,0.000088878944,0.002102743,0.000069312824,0.0000023785185,0.00010311903,0.00055795634,0.98485196,0.0029400038,0.00000580624,0.008840101,0.00007115698],"about_ca_topic_score_codex":0.000015630196,"about_ca_topic_score_gemma":0.000002678287,"teacher_disagreement_score":0.40792224,"about_ca_system_score_codex":0.00034267106,"about_ca_system_score_gemma":0.000051021423,"threshold_uncertainty_score":0.26934734},"labels":[],"label_agreement":null},{"id":"W1969427705","doi":"10.1117/12.484104","title":"Preprocessing of Edge of Light images: towards a quantitative evaluation","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Industrial Vision Systems and Defect Detection","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":"National Research Council Canada","funders":"","keywords":"Rivet; Enhanced Data Rates for GSM Evolution; Brightness; Fuselage; Edge detection; Feature extraction; Computer science; Preprocessor; Corrosion; Artificial intelligence; Computer vision; Structural engineering; Engineering; Image processing; Materials science; Image (mathematics); Optics; Physics","score_opus":0.026031094043279777,"score_gpt":0.27045351110912036,"score_spread":0.24442241706584059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969427705","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":[0.9893532,0.0003530727,0.00036153558,0.00010136777,0.00045371772,0.0006841168,0.000034929533,0.000073339,0.008584765],"genre_scores_gemma":[0.9674825,0.000048513277,0.032083575,0.0000064461447,0.00015177767,0.00011311446,0.000003331393,0.000055654793,0.00005509663],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99749357,6.498739e-8,0.0010305035,0.00028845036,0.00091023906,0.00027720036],"domain_scores_gemma":[0.9961928,0.00012398526,0.0005228323,0.00006854585,0.0030163927,0.00007540579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016342126,0.00028083142,0.00054068124,0.00017904727,0.000052226103,0.00005102302,0.00041042137,0.00024268447,0.000018359096],"category_scores_gemma":[0.0014316982,0.00024060499,0.00054731604,0.0005414708,0.00012879421,0.000527205,0.000048191243,0.00025746363,9.104461e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007356871,0.00007947978,0.00012021855,0.0012302886,0.0004050847,2.7039938e-8,0.00060732337,0.001497485,0.8909316,0.10319133,0.0010499229,0.00081363594],"study_design_scores_gemma":[0.0011316738,0.0003363188,0.00031070138,0.0006833517,0.00019231098,0.000008860361,0.002331986,0.037522074,0.95513844,0.000990948,0.0010915765,0.00026172434],"about_ca_topic_score_codex":0.000011911367,"about_ca_topic_score_gemma":1.1832886e-7,"teacher_disagreement_score":0.10220038,"about_ca_system_score_codex":0.00016913787,"about_ca_system_score_gemma":0.000065123764,"threshold_uncertainty_score":0.9811587},"labels":[],"label_agreement":null},{"id":"W1976749526","doi":"10.1016/j.imavis.2004.04.001","title":"Latest research in computer vision—a special issue on VI 2002","year":2004,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; University of Alberta","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images); Data science","score_opus":0.03230777123516048,"score_gpt":0.3444990287897892,"score_spread":0.31219125755462873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976749526","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":[0.9623266,0.00015139474,0.013067605,0.00031468942,0.0030646522,0.00051000423,0.000004052805,0.0002998301,0.0202612],"genre_scores_gemma":[0.991099,0.000019896619,0.0012834143,0.000047917765,0.0074537573,0.0000018216235,0.0000020381349,0.000030609142,0.000061518615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985131,0.00009570809,0.0003628289,0.00031238902,0.00033598708,0.0003799685],"domain_scores_gemma":[0.9993995,0.00020811107,0.000030322724,0.0001894946,0.000074744676,0.000097855285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095161324,0.00016348112,0.00023235506,0.00040420317,0.00022478684,0.00023309224,0.00009795726,0.00015622492,0.00008754195],"category_scores_gemma":[0.00002865958,0.00015005645,0.00004466475,0.0005121238,0.000042317253,0.00017570707,0.00009442215,0.00056864234,0.0005608116],"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":[0.0000824233,0.0001283372,0.00011411348,0.00009430425,0.000012620851,0.00025229465,0.00079140015,0.093892924,0.0053694053,0.00014922438,0.031050418,0.86806256],"study_design_scores_gemma":[0.008041924,0.0030198488,0.016232744,0.0039150952,0.000009671733,0.00019536837,0.00039401077,0.69287384,0.013805238,0.0008535394,0.25939828,0.0012604535],"about_ca_topic_score_codex":0.00007550832,"about_ca_topic_score_gemma":0.0000125913575,"teacher_disagreement_score":0.8668021,"about_ca_system_score_codex":0.000105596584,"about_ca_system_score_gemma":0.000013370153,"threshold_uncertainty_score":0.7208286},"labels":[],"label_agreement":null},{"id":"W1980603096","doi":"10.1167/iovs.13-13195","title":"The TFOS International Workshop on Contact Lens Discomfort: Introduction","year":2013,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Eye Institute; Allergan; Santen; CooperVision","keywords":"Contact lens; Optometry; Medicine; Ophthalmology","score_opus":0.03840049760712839,"score_gpt":0.30626490761026054,"score_spread":0.26786441000313216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980603096","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":[0.9853746,0.00002009711,0.000036063644,0.00093769334,0.005005977,0.00033492243,0.0000020425675,0.000102549915,0.008186],"genre_scores_gemma":[0.99842954,0.0000050460776,0.000040742303,0.00006164158,0.00085830607,0.00009187114,0.0000025068628,0.000013168549,0.0004971714],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99857605,0.00007940884,0.00027804798,0.00032746402,0.00038076914,0.00035823692],"domain_scores_gemma":[0.99926436,0.00020897313,0.000080896585,0.00021372944,0.00011407217,0.00011798362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060187967,0.00016338065,0.00014038592,0.00014410808,0.00053573615,0.00022165004,0.00036514914,0.000103078826,0.00017230755],"category_scores_gemma":[0.00044350087,0.00011033888,0.000047272697,0.0005559415,0.00090668857,0.0006426939,0.00006654067,0.00035021457,0.00028296304],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029038092,0.00004169046,0.0022007737,0.0000044346316,0.00005050559,0.000014882519,0.00052441115,0.0028644342,0.9749292,0.00277384,0.0063339383,0.010232839],"study_design_scores_gemma":[0.0011252075,0.0013727996,0.10975054,0.00016952644,0.000022049775,0.0006304281,0.0026797033,0.046937913,0.8016831,0.009294856,0.02525441,0.0010794171],"about_ca_topic_score_codex":0.00007109117,"about_ca_topic_score_gemma":0.000002389374,"teacher_disagreement_score":0.17324607,"about_ca_system_score_codex":0.00020827884,"about_ca_system_score_gemma":0.000052950727,"threshold_uncertainty_score":0.44994888},"labels":[],"label_agreement":null},{"id":"W1985351004","doi":"10.1515/jisys.2011.015","title":"Automatic Detection of Defects on Periodically Patterned Textures","year":2011,"lang":"en","type":"article","venue":"Journal of Intelligent Systems","topic":"Industrial Vision Systems and Defect Detection","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":"Horizon College and Seminary","funders":"University of Hong Kong","keywords":"Histogram; Block (permutation group theory); Pattern recognition (psychology); Block size; Artificial intelligence; Matrix (chemical analysis); Distortion (music); Cluster analysis; Mathematics; Similarity (geometry); Hierarchical clustering; Luminance; Computer science; Measure (data warehouse); Image quality; Contrast (vision); Image (mathematics); Computer vision; Key (lock); Data mining; Combinatorics","score_opus":0.03334697812977805,"score_gpt":0.22533168496682857,"score_spread":0.19198470683705052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985351004","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":[0.96402365,0.0004992461,0.029670676,0.0000013293434,0.00424146,0.00021293794,0.0000019985018,0.000058808608,0.0012899159],"genre_scores_gemma":[0.9995041,0.000027975568,0.000029481564,0.0000059036993,0.00037012954,0.0000050155813,1.8282834e-7,0.0000247496,0.000032480923],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982539,0.000113571026,0.0010120346,0.00008000321,0.00039116121,0.00014935873],"domain_scores_gemma":[0.99902046,0.00007587091,0.00045632347,0.00016193755,0.0001879713,0.00009744776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063155714,0.0001523713,0.00040632728,0.00034798274,0.000036500514,0.00003300251,0.00013536999,0.00016913854,0.00006839696],"category_scores_gemma":[0.00009676034,0.000112985545,0.0002202354,0.00016527598,0.000018305485,0.00009552431,0.000007672217,0.00027299966,0.000049126425],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011554057,0.0007558278,0.0047313306,0.0036159926,0.0018897606,0.00032354423,0.012945375,0.042604417,0.64262253,0.0006244175,0.004100876,0.2846305],"study_design_scores_gemma":[0.00080406555,0.0029664743,0.0042512827,0.0022372112,0.00010703428,0.00074861944,0.0017141829,0.024142083,0.9592406,0.000056298948,0.00334961,0.00038252666],"about_ca_topic_score_codex":0.00006234999,"about_ca_topic_score_gemma":0.0000068707327,"teacher_disagreement_score":0.31661808,"about_ca_system_score_codex":0.000117513824,"about_ca_system_score_gemma":0.000021130798,"threshold_uncertainty_score":0.46074167},"labels":[],"label_agreement":null},{"id":"W1985607758","doi":"10.1021/ie0492101","title":"Modeling and Optimization of Product Appearance:  Application to Injection-Molded Plastic Panels","year":2005,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"McMaster University","funders":"","keywords":"Computer science; Process (computing); Product (mathematics); Quality (philosophy); Artificial intelligence; Obstacle; Nondeterministic algorithm; Computer vision; Process engineering; Mathematics; Algorithm; Engineering","score_opus":0.04936412612002783,"score_gpt":0.2953485235923174,"score_spread":0.24598439747228956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985607758","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":[0.9123145,0.00017782721,0.085904576,0.000044202236,0.0003499134,0.00066785945,0.000010576061,0.00029431097,0.00023627366],"genre_scores_gemma":[0.99802595,0.000013573138,0.00020226135,8.0913503e-7,0.0015545575,0.00010150368,0.00000692985,0.00004317816,0.000051219406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985247,0.000023724278,0.00040196296,0.00029196293,0.0004203934,0.00033722122],"domain_scores_gemma":[0.9992894,0.00008397263,0.000025663236,0.00025635108,0.00019430729,0.0001503377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072939263,0.00016302457,0.00022525527,0.00020520715,0.00007841866,0.00005544111,0.0001268311,0.00025866088,0.000015016151],"category_scores_gemma":[0.0005315091,0.00018178725,0.000034156834,0.00080470357,0.000019365338,0.00013334863,0.00004459957,0.00059528503,0.000009642746],"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":[0.000022925671,0.000007794836,0.000010078681,0.0000723957,0.000011655504,2.6070924e-7,0.000053733267,0.66946983,0.32603264,0.0000028747302,0.00004662232,0.004269181],"study_design_scores_gemma":[0.00034919058,0.000028725326,0.0000055109385,0.00012703953,0.000004681371,0.000009808945,0.00003866217,0.7342299,0.2645012,0.0000012428011,0.00057761004,0.00012639674],"about_ca_topic_score_codex":0.00005970312,"about_ca_topic_score_gemma":7.303144e-7,"teacher_disagreement_score":0.08571151,"about_ca_system_score_codex":0.00018490011,"about_ca_system_score_gemma":0.000044766242,"threshold_uncertainty_score":0.7413069},"labels":[],"label_agreement":null},{"id":"W1986976726","doi":"10.1016/s0007-8506(07)60613-1","title":"Computer-Aided Planning of Laser Scanning of Complex Geometries","year":2003,"lang":"en","type":"article","venue":"CIRP Annals","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Laser scanning; CAD; Visibility; Plan (archaeology); Scanner; Computer Aided Design; Computer science; Motion planning; Computer-aided; Artificial intelligence; Computer vision; Engineering drawing; Laser; Engineering; Optics; Robot","score_opus":0.0936967195489951,"score_gpt":0.29428390584758646,"score_spread":0.20058718629859135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986976726","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":[0.97270316,0.00038173882,0.019025978,0.0000045750353,0.00054193224,0.000108105596,0.000013090556,0.00008377318,0.0071376455],"genre_scores_gemma":[0.9994047,0.0000051185743,0.0004109668,0.00001430505,0.00008937374,0.0000023836265,0.0000030665733,0.00001538121,0.000054647633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991864,0.000036743233,0.00037077893,0.000092822724,0.00015507077,0.00015820617],"domain_scores_gemma":[0.9995169,0.00008800549,0.00009839439,0.00015055333,0.00010165208,0.000044509514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003637705,0.00009762338,0.00029791569,0.00023622077,0.00003839765,0.0000147869,0.000061782666,0.000085082305,0.00006565477],"category_scores_gemma":[0.00007321191,0.00009750507,0.0000793959,0.0003976015,0.000027430751,0.00007354294,0.000010267152,0.000086217064,0.000008129453],"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":[0.000098995435,0.00011767688,0.016821127,0.0009990997,0.0005166999,0.00002785031,0.002668883,0.687869,0.1910461,0.0014648333,0.05861708,0.039752636],"study_design_scores_gemma":[0.0013152646,0.00038460337,0.02149266,0.00064835715,0.000027354934,0.000030732677,0.0005972536,0.01556732,0.8958877,0.00023933339,0.063337505,0.0004719564],"about_ca_topic_score_codex":0.000017009741,"about_ca_topic_score_gemma":4.1740228e-7,"teacher_disagreement_score":0.70484155,"about_ca_system_score_codex":0.00000953001,"about_ca_system_score_gemma":0.000011072531,"threshold_uncertainty_score":0.39761415},"labels":[],"label_agreement":null},{"id":"W1987294882","doi":"10.1063/1.3553365","title":"Publisher's Note: “Optical detection system for probing cantilever deflections parallel to a sample surface” [Rev. Sci. Instrum. 82, 013701 (2011)]","year":2011,"lang":"en","type":"article","venue":"Review of Scientific Instruments","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Cantilever; Sample (material); Surface (topology); Optics; Computer science; Physics; Materials science; Mathematics; Geometry; Thermodynamics; Composite material","score_opus":0.04858615366653617,"score_gpt":0.26182564285953963,"score_spread":0.21323948919300345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987294882","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":[0.83634484,0.0018891012,0.09984277,0.0000535793,0.04021005,0.009268117,0.00023704492,0.0009980531,0.01115644],"genre_scores_gemma":[0.9884446,0.0001138144,0.010761395,0.000022080152,0.00011522844,0.00025967,0.000024748788,0.000051209463,0.00020720923],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973053,0.00007391916,0.0010086461,0.00053400215,0.0005330425,0.00054509455],"domain_scores_gemma":[0.998527,0.000049454833,0.00021386716,0.00055729004,0.00041164295,0.00024076912],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00163839,0.00028905857,0.0005777273,0.0003429016,0.00043094394,0.00019682184,0.00029440134,0.000196691,0.00010002762],"category_scores_gemma":[0.0002224814,0.00026566596,0.0002562004,0.001049205,0.00007340745,0.0005763454,0.000071341616,0.00020919656,0.00016147156],"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":[0.0002847903,0.00033038,0.000819502,0.03694483,0.00046211595,0.000004226564,0.0011349891,0.00071923446,0.06217592,0.0012196673,0.023404328,0.8725],"study_design_scores_gemma":[0.008758505,0.0026768108,0.002780983,0.06303664,0.001490993,0.00044950045,0.0013941963,0.039324906,0.4414427,0.00080228737,0.43300566,0.0048368326],"about_ca_topic_score_codex":0.00033203242,"about_ca_topic_score_gemma":0.000110734814,"teacher_disagreement_score":0.8676632,"about_ca_system_score_codex":0.00041895866,"about_ca_system_score_gemma":0.000114236005,"threshold_uncertainty_score":0.99997956},"labels":[],"label_agreement":null},{"id":"W1988545127","doi":"10.1016/j.optlaseng.2013.10.009","title":"Object shape-based optical sensing methodology and system for condition monitoring of contaminated engine lubricants","year":2013,"lang":"en","type":"article","venue":"Optics and Lasers in Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lubricant; Materials science; Computer science; Object (grammar); Computer vision; Optics; Artificial intelligence; Composite material; Physics","score_opus":0.024423446799911576,"score_gpt":0.24617567537267335,"score_spread":0.22175222857276178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988545127","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":[0.87855667,0.00010868405,0.12037024,0.0000045416496,0.00050866586,0.00030892395,0.0000075390903,0.000091806265,0.000042915603],"genre_scores_gemma":[0.97738624,0.000014144543,0.022467606,0.000001188043,0.00007243506,0.000023523571,0.0000040930195,0.000028335726,0.0000024088918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992736,0.000020172283,0.00029594364,0.00013522842,0.00007021807,0.00020481097],"domain_scores_gemma":[0.99941754,0.00034888875,0.000035262823,0.000077500226,0.00005515428,0.00006564566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003159835,0.00013967999,0.0003073589,0.00020257992,0.000026383575,0.000028991903,0.000026130376,0.00015035484,0.0000012524577],"category_scores_gemma":[0.0000873517,0.00014460812,0.000030294532,0.00013569977,0.00001696739,0.00007216362,0.000010157663,0.00012979747,4.0702574e-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":[0.000042606895,0.000015994161,0.0012810945,0.0022741032,0.000112130554,0.00001683249,0.00027355988,0.57034576,0.38513947,0.0005414057,0.000014368215,0.039942663],"study_design_scores_gemma":[0.00083252665,0.000075693824,0.0021383697,0.0003514192,0.000018274426,0.000020333779,0.00033352757,0.90426594,0.09179918,0.0000049435794,0.000014176593,0.00014559773],"about_ca_topic_score_codex":0.000038129758,"about_ca_topic_score_gemma":0.000001533706,"teacher_disagreement_score":0.3339202,"about_ca_system_score_codex":0.00004867925,"about_ca_system_score_gemma":0.0000065887616,"threshold_uncertainty_score":0.58969474},"labels":[],"label_agreement":null},{"id":"W1990790849","doi":"10.1145/1186954.1187070","title":"Automatic selection of level-of-detail based on just-noticeable-difference (JND)","year":2005,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Selection (genetic algorithm); Just-noticeable difference; Computer science; Significant difference; Artificial intelligence; Statistics; Mathematics","score_opus":0.0783939956080789,"score_gpt":0.2622025935603041,"score_spread":0.1838085979522252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990790849","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":[0.9293553,0.000015725587,0.057089664,0.000022762137,0.00045803748,0.0002494748,0.0000071005747,0.00033488977,0.01246703],"genre_scores_gemma":[0.9975109,0.0000011419119,0.0017265519,0.000014173032,0.00010516655,0.000007602709,0.0000012283857,0.000016796932,0.00061642454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991241,0.000036085847,0.00037644675,0.00010553685,0.00022037566,0.00013746302],"domain_scores_gemma":[0.99956006,0.0001026555,0.00007201051,0.00015644393,0.00007067295,0.00003813899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020427584,0.000115401075,0.00022269766,0.00018214218,0.000029090159,0.000010762812,0.000061004914,0.00012836326,0.000618768],"category_scores_gemma":[0.00005049201,0.000099040524,0.00006848322,0.00031002832,0.000010734976,0.00006402334,0.000005022887,0.00011941716,0.00005385927],"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":[0.0000714272,0.00017915084,0.0010022789,0.0006156999,0.00007683213,8.866989e-7,0.00021645048,0.58539337,0.13540643,0.0005394576,0.003416519,0.2730815],"study_design_scores_gemma":[0.00038648327,0.00014415383,0.0021602265,0.000085862244,0.000014434095,0.0000016731527,0.000028857277,0.7769383,0.21967152,0.0000061696296,0.0004678656,0.000094462135],"about_ca_topic_score_codex":0.00007862342,"about_ca_topic_score_gemma":0.000047006808,"teacher_disagreement_score":0.27298704,"about_ca_system_score_codex":0.00006724167,"about_ca_system_score_gemma":0.00002548696,"threshold_uncertainty_score":0.6775075},"labels":[],"label_agreement":null},{"id":"W1992215913","doi":"10.1007/s00170-008-1516-3","title":"A machine vision system for the detection of missing fasteners on steel stampings","year":2008,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Machine vision; Automotive industry; Artificial intelligence; Computer vision; Fuzzy logic; Graphical user interface; Engineering; Line (geometry); Production line; Computer science; Mechanical engineering; Mathematics","score_opus":0.012826815763507277,"score_gpt":0.24415705512488362,"score_spread":0.23133023936137634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992215913","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":[0.87349635,0.00049744255,0.12204899,0.00055243983,0.002928811,0.0002426932,0.0000070291485,0.00011971129,0.0001065399],"genre_scores_gemma":[0.9990703,0.00010505746,0.000528017,0.00001545444,0.00022454155,0.000009201201,4.0422708e-7,0.00002062331,0.000026412232],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897075,0.000018751123,0.0004770061,0.00008560553,0.0003210903,0.00012677806],"domain_scores_gemma":[0.9989865,0.0002530684,0.00037715785,0.00017586927,0.00018753798,0.000019874828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003173979,0.00012071316,0.00020911616,0.00036249828,0.00014529088,0.000016571046,0.0004416964,0.000107512606,0.000001965506],"category_scores_gemma":[0.000088993744,0.000070726725,0.0001289792,0.00009768922,0.000062511965,0.00010988226,0.000036344638,0.00032967454,0.000001485105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000687161,0.0000207094,0.0000074595355,0.000040909268,0.0002571652,0.00002279226,0.00017848513,0.18062407,0.45667607,0.00026356924,0.0000687139,0.3611529],"study_design_scores_gemma":[0.00093011995,0.00031751097,0.00013821933,0.00022332052,0.00001973952,0.0008932346,0.0005046021,0.0049309153,0.98328936,0.00034493383,0.008331895,0.00007615626],"about_ca_topic_score_codex":0.000009079231,"about_ca_topic_score_gemma":0.000003676322,"teacher_disagreement_score":0.5266133,"about_ca_system_score_codex":0.00020166658,"about_ca_system_score_gemma":0.000013133711,"threshold_uncertainty_score":0.2884152},"labels":[],"label_agreement":null},{"id":"W1995004567","doi":"10.1007/s10086-014-1428-9","title":"Yield analysis of Hem-Fir (N) lamina for Japanese visual and machine grade standards","year":2014,"lang":"en","type":"article","venue":"Journal of Wood Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lamina; Young's modulus; Universal testing machine; Ultimate tensile strength; Structural engineering; Composite material; Materials science; Grading (engineering); Mathematics; Engineering; Botany; Biology","score_opus":0.020280310324945615,"score_gpt":0.28550506408045123,"score_spread":0.26522475375550564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995004567","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":[0.97809917,0.00012286135,0.021083212,0.00002412212,0.00039168532,0.000052727915,0.000010738507,0.000008286214,0.00020722605],"genre_scores_gemma":[0.99966043,0.000012791522,0.00018405923,0.0000062424183,0.00011382159,6.646165e-7,1.3831612e-7,0.0000045606057,0.000017316694],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989836,0.000015925922,0.0003345803,0.000077285884,0.00046461582,0.0001239656],"domain_scores_gemma":[0.99931014,0.0001233236,0.00015738499,0.00007876243,0.000247335,0.000083040824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022040254,0.00006547217,0.0002768911,0.00048304486,0.000072378294,0.0000434817,0.00010947347,0.000042649546,0.000007935694],"category_scores_gemma":[0.00040793532,0.000048816397,0.000102114995,0.00087204913,0.00006154607,0.00023787336,0.000013279796,0.00009795758,1.5120484e-7],"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":[0.00026597842,0.000117269024,0.0031309472,0.00021951261,0.000648282,0.000006743266,0.002900727,0.051554166,0.88605475,0.00029091048,0.0011507885,0.05365991],"study_design_scores_gemma":[0.0027071252,0.003789689,0.01508678,0.0003255843,0.0010325202,0.00018028481,0.0015766263,0.6650608,0.3009504,0.00016041966,0.008599392,0.00053036533],"about_ca_topic_score_codex":0.00002351233,"about_ca_topic_score_gemma":0.000014917057,"teacher_disagreement_score":0.6135066,"about_ca_system_score_codex":0.000047572634,"about_ca_system_score_gemma":0.000037296177,"threshold_uncertainty_score":0.19906749},"labels":[],"label_agreement":null},{"id":"W1995677940","doi":"10.1117/12.452659","title":"<title>Recognition of free-form shapes using spherical SOFMs</title>","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Cognitive neuroscience of visual object recognition; Orientation (vector space); Coordinate system; Node (physics); Object (grammar); Feature (linguistics); Pattern recognition (psychology); Geometry; Mathematics; Physics","score_opus":0.021416610308936726,"score_gpt":0.23069522060248882,"score_spread":0.2092786102935521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995677940","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":[0.96555674,0.00011985411,0.00028159062,0.00006610999,0.00047372404,0.00022271698,0.000032143995,0.00010523926,0.033141907],"genre_scores_gemma":[0.9554794,0.00018784746,0.04207576,0.000030096417,0.0014164138,0.000049775535,0.000009708942,0.00010989523,0.00064110366],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987961,1.6176754e-8,0.00044254432,0.00016115676,0.00038887796,0.00021128562],"domain_scores_gemma":[0.99918246,0.000045962366,0.00014691899,0.000039008377,0.0005235135,0.00006210673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025794527,0.00017682757,0.0002655502,0.00007587522,0.000037495116,0.0000411077,0.0002975673,0.0002007411,0.00017390122],"category_scores_gemma":[0.00017374434,0.0001543596,0.00033498887,0.00025959846,0.00006457477,0.00021691929,0.000054508946,0.0001845197,0.000017347274],"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":[0.00005774555,0.0000659243,0.00008954143,0.00042863257,0.00033886827,2.3043476e-7,0.000097399214,0.00046937255,0.9210466,0.056398697,0.015000692,0.0060062786],"study_design_scores_gemma":[0.0033037732,0.0006873464,0.00086825626,0.0014891144,0.00047580933,0.00014537973,0.00092575065,0.41413802,0.37372348,0.005617894,0.19719356,0.0014315925],"about_ca_topic_score_codex":0.0000049823707,"about_ca_topic_score_gemma":1.2710139e-7,"teacher_disagreement_score":0.5473231,"about_ca_system_score_codex":0.00009656204,"about_ca_system_score_gemma":0.000015115121,"threshold_uncertainty_score":0.62946016},"labels":[],"label_agreement":null},{"id":"W1997147881","doi":"10.1007/s11661-012-1364-5","title":"Determination of the Minimum Scan Size to Obtain Representative Textures by Electron Backscatter Diffraction","year":2012,"lang":"en","type":"article","venue":"Metallurgical and Materials Transactions A","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Grain size; Electron backscatter diffraction; Texture (cosmology); Backscatter (email); Materials science; Diffraction; Range (aeronautics); Cluster analysis; Optics; Computer science; Physics; Artificial intelligence; Image (mathematics); Composite material","score_opus":0.009087804078356325,"score_gpt":0.23924128789696825,"score_spread":0.23015348381861192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997147881","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":[0.91883427,0.00007924945,0.07950555,0.00008836768,0.0008554183,0.00033987814,0.00003693724,0.00005183613,0.00020849716],"genre_scores_gemma":[0.9989651,0.000021106927,0.00015137754,0.000024798195,0.000113969865,0.000066366665,0.0000027798262,0.0000147886985,0.0006397113],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991868,0.000112381094,0.00026877777,0.00011554313,0.00013474726,0.00018175163],"domain_scores_gemma":[0.99966466,0.0000768905,0.000048391285,0.000110951405,0.000025499381,0.00007362574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023169458,0.00011194795,0.00019461343,0.000038491016,0.00009848246,0.000045203473,0.000046430403,0.00010388443,0.00036073074],"category_scores_gemma":[0.0000181893,0.000081224294,0.000063388885,0.00015193054,0.00002702737,0.00017976596,0.000005094632,0.0000846908,0.0000105305],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054491833,0.00004858281,0.000005831871,0.000034785447,0.00003241518,1.9612224e-7,0.0002674438,0.00015433208,0.99547577,0.000017021111,0.0004740164,0.0034351335],"study_design_scores_gemma":[0.00046875476,0.00008832771,0.005369521,0.000035237455,0.00009177312,0.00004705155,0.00016006612,0.00034579393,0.9742893,0.000041166557,0.018859377,0.00020361427],"about_ca_topic_score_codex":0.00014875931,"about_ca_topic_score_gemma":0.000016548775,"teacher_disagreement_score":0.08013083,"about_ca_system_score_codex":0.00003279509,"about_ca_system_score_gemma":0.000003906051,"threshold_uncertainty_score":0.39497486},"labels":[],"label_agreement":null},{"id":"W1998146576","doi":"10.2316/journal.206.2011.3.206-3418","title":"MULTI-VIEW RECONSTRUCTION OF ANNULAR OUTDOOR SCENES FROM BINOCULAR VIDEO USING GLOBAL RELAXATION ITERATION","year":2011,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Relaxation (psychology); Binocular disparity; Binocular vision; Computer graphics (images); Medicine; Internal medicine","score_opus":0.039543704338097764,"score_gpt":0.2619011962873237,"score_spread":0.22235749194922597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998146576","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":[0.6557211,0.0003311631,0.34162444,0.000013726504,0.0021719474,0.00006647072,0.000013245384,0.000022709077,0.000035162448],"genre_scores_gemma":[0.9557443,0.000115140494,0.04386249,0.0000060947677,0.0002525473,6.2636036e-7,0.000008023155,0.0000086828695,0.0000020857738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989027,0.000047137495,0.0006408229,0.00007821442,0.0002679246,0.00006321592],"domain_scores_gemma":[0.9989109,0.000018128738,0.00047560968,0.00006070114,0.0004967775,0.00003785405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025220914,0.00009590489,0.00017748658,0.00016699737,0.000033837932,0.000054334232,0.00007474825,0.00011064897,0.000018234892],"category_scores_gemma":[0.00006744382,0.00008970677,0.00007390003,0.00010751038,0.000018661643,0.00056448823,0.000011815802,0.00008481052,0.000001952271],"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":[0.00021588587,0.00017204486,0.038309466,0.00012207795,0.0008478647,0.00003131867,0.0020390048,0.4539408,0.1318805,0.0011821777,0.00008248237,0.37117636],"study_design_scores_gemma":[0.0012341556,0.000104800114,0.048564214,0.0006361774,0.00009090853,0.00026477454,0.00018117257,0.9295478,0.0181445,0.00095772767,0.00010648602,0.00016731709],"about_ca_topic_score_codex":0.00010189146,"about_ca_topic_score_gemma":0.000006595134,"teacher_disagreement_score":0.47560695,"about_ca_system_score_codex":0.000121456316,"about_ca_system_score_gemma":0.000024012916,"threshold_uncertainty_score":0.36581358},"labels":[],"label_agreement":null},{"id":"W1999236506","doi":"10.1115/msec2011-50178","title":"A Quantitative Study of Illumination Techniques for Machine Vision Based Inspection","year":2011,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Queen's University","funders":"","keywords":"Backlight; Computer science; Computer vision; Task (project management); Machine vision; Sample (material); Artificial intelligence; Set (abstract data type); Field (mathematics); Engineering; Mathematics; Liquid-crystal display","score_opus":0.05175895301781711,"score_gpt":0.2935615058805196,"score_spread":0.2418025528627025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999236506","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":[0.59165126,0.000010753229,0.40037656,0.0000018465006,0.0002983275,0.0010581239,0.000005456468,0.00056658225,0.006031116],"genre_scores_gemma":[0.9953936,7.2764294e-7,0.004441803,0.0000025326294,0.000025314677,0.000090634574,0.0000029254913,0.000015447382,0.000027059385],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994738,0.000028810562,0.00023709801,0.00009621185,0.00009818821,0.00006588715],"domain_scores_gemma":[0.99967587,0.00004237041,0.00005149136,0.00010015716,0.000113674934,0.000016446449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002720002,0.0000764813,0.00012812411,0.00020493445,0.000044013155,0.0000064087326,0.000033323176,0.00006856665,0.00002669563],"category_scores_gemma":[0.000033569726,0.00006461326,0.000039659873,0.0001858864,0.000008216983,0.000110842935,0.000004563315,0.000049329115,0.000002645922],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042511644,0.005629272,0.0062464434,0.001011634,0.0005268265,0.0000076300275,0.028602637,0.004687962,0.5564262,0.01321751,0.00662082,0.3727719],"study_design_scores_gemma":[0.0020409392,0.010317289,0.0052326266,0.00007227172,0.000042910433,0.0000017623994,0.0041890237,0.32505688,0.65178806,0.00019857356,0.00077997835,0.00027967623],"about_ca_topic_score_codex":0.00036516372,"about_ca_topic_score_gemma":0.00015422798,"teacher_disagreement_score":0.4037423,"about_ca_system_score_codex":0.000035348105,"about_ca_system_score_gemma":0.0000042335423,"threshold_uncertainty_score":0.26348522},"labels":[],"label_agreement":null},{"id":"W1999311375","doi":"10.1139/l05-077","title":"Effectiveness of neuro-fuzzy recognition approach in evaluating steel bridge paint conditions","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bridge (graph theory); Artificial neural network; Artificial intelligence; Computer science; Engineering; Fuzzy logic; Pattern recognition (psychology)","score_opus":0.02525933442355141,"score_gpt":0.22702875188192131,"score_spread":0.2017694174583699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999311375","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":[0.97608423,0.0003191968,0.019979663,0.0000028165175,0.000925432,0.00020754985,0.00002712929,0.00002774658,0.002426241],"genre_scores_gemma":[0.99960697,0.0000023919447,0.00013196858,0.0000016747273,0.00020494492,0.000009127862,0.000009353112,0.000031123214,0.0000024443489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99890465,0.000094281684,0.0005261654,0.000084699896,0.00015578908,0.00023443528],"domain_scores_gemma":[0.9993356,0.00019861103,0.00010088905,0.000088345696,0.00013526523,0.0001412928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001148825,0.00012897915,0.00028542412,0.0006923568,0.00003289012,0.000029732646,0.00007703068,0.00010676724,0.00001994147],"category_scores_gemma":[0.00018348786,0.00014284336,0.00009774262,0.00040225676,0.000012721661,0.0001684241,0.0000030667327,0.00031868962,0.000002016502],"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":[0.0000075390344,0.0000075082066,0.0008654296,0.00026499986,0.00002102618,0.00002875987,0.000060982657,0.9696879,0.028095338,0.00009457827,0.00016303224,0.0007029168],"study_design_scores_gemma":[0.00466367,0.0005804733,0.56750864,0.0042751865,0.000104210056,0.0009452647,0.00021545339,0.39814204,0.020700132,0.0010230961,0.00083085586,0.0010109523],"about_ca_topic_score_codex":0.0019835145,"about_ca_topic_score_gemma":0.0051182853,"teacher_disagreement_score":0.57154584,"about_ca_system_score_codex":0.00026766027,"about_ca_system_score_gemma":0.0001334459,"threshold_uncertainty_score":0.5824983},"labels":[],"label_agreement":null},{"id":"W2000001678","doi":"10.5740/jaoacint.govval02","title":"3M™ Petrifilm™ Environmental Listeria Plate","year":2013,"lang":"en","type":"article","venue":"Journal of AOAC International","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Listeria; Listeria monocytogenes; Geology; Bacteria; Paleontology","score_opus":0.0069795236628903985,"score_gpt":0.18886712068088402,"score_spread":0.18188759701799362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000001678","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":[0.9846882,0.0000732662,0.0018908055,0.000080974976,0.0058109267,0.0000654538,0.00000994288,0.0000235543,0.007356856],"genre_scores_gemma":[0.99780166,0.000020409492,0.0002700385,0.000025768792,0.0011693144,0.0000021760295,0.0000026146563,0.000013096223,0.0006949222],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99918354,0.000014065938,0.0003792734,0.000048501865,0.00028596158,0.00008868801],"domain_scores_gemma":[0.9996856,0.00002493242,0.00012684178,0.000055062068,0.000047778933,0.000059801285],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013941996,0.00007845624,0.0001164322,0.00015855464,0.000023604342,0.00007862573,0.00012596401,0.0000686543,0.0019324169],"category_scores_gemma":[0.000019306646,0.00006616109,0.000099249584,0.000037287304,0.0000106704065,0.00034944373,0.000013901706,0.0001861307,0.00029618948],"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":[0.00023581255,0.0002209925,0.0060582547,0.000037543276,0.0010870752,0.00015741975,0.0010065441,0.019630166,0.64418966,0.00028733286,0.20546956,0.12161963],"study_design_scores_gemma":[0.0065804054,0.0009105618,0.07891566,0.00034134596,0.00007596296,0.0039429837,0.0010650634,0.047472306,0.17990346,0.0010078023,0.6788061,0.0009783386],"about_ca_topic_score_codex":0.000009039836,"about_ca_topic_score_gemma":3.6946014e-7,"teacher_disagreement_score":0.47333655,"about_ca_system_score_codex":0.00012688882,"about_ca_system_score_gemma":0.000008040969,"threshold_uncertainty_score":0.9989799},"labels":[],"label_agreement":null},{"id":"W2003660891","doi":"10.1109/tim.2010.2069850","title":"Fabric Texture Analysis Using Computer Vision Techniques","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":121,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Fast Fourier transform; Fractal; Woven fabric; Artificial intelligence; Surface finish; Computer vision; Computer science; Surface roughness; Fractal dimension; Surface (topology); Algorithm; Computer graphics (images); Mathematics; Geometry; Engineering; Mathematical analysis; Materials science; Mechanical engineering","score_opus":0.025319502339369047,"score_gpt":0.2572308427386,"score_spread":0.23191134039923092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003660891","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":[0.30971953,0.000006182791,0.68852544,0.000011447079,0.0011355842,0.00020730209,0.0000063462735,0.00018792505,0.00020024372],"genre_scores_gemma":[0.9967659,0.000012714846,0.0030282584,0.00003966662,0.0000988054,0.000023642806,0.0000019005629,0.000015311907,0.000013782325],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99907637,0.00003083232,0.00024792855,0.00017373549,0.00034645767,0.00012466386],"domain_scores_gemma":[0.99965626,0.000009157538,0.0000394664,0.00013724004,0.000082127415,0.00007575857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026836118,0.00014181156,0.00015665063,0.00041113893,0.00018109754,0.00008225071,0.00003630102,0.00013011116,0.00008186211],"category_scores_gemma":[8.884805e-7,0.00013225774,0.000095694246,0.0004358779,0.000019166788,0.00015615273,4.90929e-7,0.00025765848,0.000007542182],"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":[0.000029944627,0.000057355493,0.00004616753,0.000025064148,0.0002814014,8.283889e-7,0.00020010004,0.02311905,0.4082723,0.0000140749335,0.00008526479,0.5678685],"study_design_scores_gemma":[0.0012327593,0.0004000444,0.0015816712,0.00009180192,0.00066191214,0.000040612085,0.00024641823,0.2744021,0.7151458,0.000023687637,0.0056089344,0.00056421704],"about_ca_topic_score_codex":0.000056391764,"about_ca_topic_score_gemma":0.00013473665,"teacher_disagreement_score":0.6870464,"about_ca_system_score_codex":0.00010115068,"about_ca_system_score_gemma":0.000012171406,"threshold_uncertainty_score":0.53933144},"labels":[],"label_agreement":null},{"id":"W2006694727","doi":"10.1109/tim.2008.924931","title":"Estimating the Strength of Boards Using Mixed Signals of MOE and X-Ray Images","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"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":"Oulun Yliopisto; University of British Columbia","keywords":"Feature (linguistics); Grading (engineering); Mathematics; Artificial intelligence; Computer science; Algorithm; Pattern recognition (psychology); Engineering","score_opus":0.05862270261933572,"score_gpt":0.254170579688593,"score_spread":0.1955478770692573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006694727","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":[0.77934086,0.000052652143,0.21988012,0.000007678535,0.00042416112,0.00018074259,0.00001172812,0.000023360146,0.000078668985],"genre_scores_gemma":[0.9983522,0.000037813923,0.0015561443,0.0000050239523,0.000020810403,0.0000119193355,3.5052759e-7,0.0000092461505,0.000006444756],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99918497,0.0000484982,0.0003022436,0.00008755262,0.0002993537,0.00007738866],"domain_scores_gemma":[0.99970263,0.000027893386,0.000076592536,0.00007891578,0.000081886865,0.000032059754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027951293,0.00009198542,0.00014012007,0.00009728169,0.00014869831,0.000010903554,0.000024303486,0.000042506057,0.0000132032765],"category_scores_gemma":[0.00000498865,0.0000730916,0.000037665486,0.00011504776,0.000052775806,0.00009159651,4.800187e-7,0.00008228467,3.5514347e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058973856,0.000069194946,0.0001351256,0.00016048519,0.00015379504,5.949263e-7,0.0015112328,0.28333604,0.59805983,0.0000071514273,0.000048968515,0.116458625],"study_design_scores_gemma":[0.0010598243,0.00016558879,0.0014567006,0.00016565362,0.00006646672,0.000016665957,0.00085068267,0.07693786,0.9191214,0.000009301661,0.000035102137,0.00011478099],"about_ca_topic_score_codex":0.0001079168,"about_ca_topic_score_gemma":0.000009667833,"teacher_disagreement_score":0.32106155,"about_ca_system_score_codex":0.000044230295,"about_ca_system_score_gemma":0.000019516805,"threshold_uncertainty_score":0.2980589},"labels":[],"label_agreement":null},{"id":"W2007538239","doi":"10.1167/14.10.1427","title":"Spatial integration of orientation-defined texture","year":2014,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Industrial Vision Systems and Defect Detection","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":"McGill University","funders":"","keywords":"Curvature; Orientation (vector space); Texture (cosmology); Summation; Mathematics; Spatial frequency; Noise (video); SIGNAL (programming language); Physics; Geometry; Mathematical analysis; Optics; Artificial intelligence; Computer science; Psychology","score_opus":0.010190620998082328,"score_gpt":0.2518040812504025,"score_spread":0.24161346025232017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007538239","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":[0.75634015,0.000055272572,0.23996764,0.000029193241,0.002191282,0.00006211488,0.0000013219487,0.000019798557,0.0013332556],"genre_scores_gemma":[0.99891156,0.000009814979,0.0005073332,0.0000063452967,0.00053012423,3.6888804e-7,0.0000012883136,0.000009085146,0.000024084202],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991778,0.00004367515,0.0004443184,0.00004102829,0.00023558982,0.000057563357],"domain_scores_gemma":[0.9994245,0.000059941034,0.00021666703,0.00007468704,0.00018801316,0.000036186888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042812608,0.00006428529,0.00017091523,0.00015655324,0.000023866316,0.00001754467,0.00004963513,0.000092932736,0.00003827008],"category_scores_gemma":[0.00014559171,0.000047130034,0.00008310649,0.00013391787,0.0000071972545,0.00015382869,0.0000047049775,0.00015359197,0.000008102299],"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":[0.00017678157,0.000043864045,0.00039578034,0.00005052148,0.000035068755,0.0000023185607,0.0005229141,0.009498468,0.4852674,0.000578529,0.006093322,0.49733502],"study_design_scores_gemma":[0.009470889,0.00798563,0.13706185,0.002316024,0.00020854978,0.00034822433,0.0010704199,0.3052197,0.44616407,0.0036189451,0.08570278,0.00083292375],"about_ca_topic_score_codex":0.00001750592,"about_ca_topic_score_gemma":0.000006196838,"teacher_disagreement_score":0.4965021,"about_ca_system_score_codex":0.000033211843,"about_ca_system_score_gemma":0.00001067111,"threshold_uncertainty_score":0.19219069},"labels":[],"label_agreement":null},{"id":"W2007547152","doi":"10.4028/www.scientific.net/kem.326-328.513","title":"The Measurement of Residual Deformation in PBGA Package after Reflow Process Using A Newly Developed Laser Profiler","year":2006,"lang":"en","type":"article","venue":"Key engineering materials","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Materials science; Scanner; Deformation (meteorology); Laser; Residual; Laser scanning; Process (computing); Displacement (psychology); Optics; Computer science; Composite material","score_opus":0.019140017330043373,"score_gpt":0.22011484442388377,"score_spread":0.2009748270938404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007547152","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":[0.99629843,0.0000646928,0.0019481329,0.00000523904,0.0009775509,0.00040356198,0.00001879548,0.00012992324,0.00015369414],"genre_scores_gemma":[0.99910814,0.0000032706519,0.00046673993,0.0000016739635,0.0002593126,0.0001056219,0.000005997033,0.0000394801,0.000009753972],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866056,0.000034528563,0.0006189473,0.000113542876,0.00031340434,0.00025902083],"domain_scores_gemma":[0.9996112,0.000020369207,0.000076016666,0.00016596208,0.0001022582,0.000024214878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088197127,0.00016969474,0.00022874848,0.00013784913,0.000040619332,0.00008172952,0.000080138445,0.00012519165,0.00001618333],"category_scores_gemma":[0.000057451754,0.000130973,0.000027355803,0.00023438448,0.000008317903,0.00018614628,0.000015504625,0.00008310333,0.000019073328],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099455094,0.0000104976725,0.000141205,0.00037305514,0.000021054304,0.0000045080365,0.00026338245,0.2062708,0.79236853,0.000024056631,0.0001536624,0.00026980997],"study_design_scores_gemma":[0.00048919406,0.000019230994,0.005461988,0.00034281248,0.000011066728,0.000006364352,0.000048793947,0.0056703966,0.9869166,0.000027280556,0.00079264387,0.00021361402],"about_ca_topic_score_codex":0.000085570275,"about_ca_topic_score_gemma":0.00003260272,"teacher_disagreement_score":0.2006004,"about_ca_system_score_codex":0.00017238552,"about_ca_system_score_gemma":0.000030943407,"threshold_uncertainty_score":0.53409237},"labels":[],"label_agreement":null},{"id":"W2007854896","doi":"10.1007/s00138-005-0009-8","title":"Estimation and monitoring of product aesthetics: application to manufacturing of “engineered stone” countertops","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Subspace topology; Computer science; Artificial intelligence; Projection (relational algebra); Product (mathematics); Computer vision; Obstacle; Quality (philosophy); Feature (linguistics); Pattern recognition (psychology); Mathematics; Algorithm","score_opus":0.005407939820326883,"score_gpt":0.2391212930975568,"score_spread":0.23371335327722992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007854896","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":[0.7631431,0.00031440676,0.23555918,0.00003500241,0.00004919582,0.0005640666,0.000017818502,0.00008649704,0.00023070842],"genre_scores_gemma":[0.99781054,0.000026174837,0.0019278789,0.0000017131513,0.000077775454,0.00010381701,0.000012303336,0.000015507749,0.000024293957],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999349,0.000008858376,0.00030028282,0.00015082813,0.00011218957,0.00007883716],"domain_scores_gemma":[0.9996524,0.000024280163,0.000060669216,0.00018530463,0.000037777056,0.00003956448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012915728,0.00010167456,0.00015511806,0.00014606393,0.000054611537,0.000016962984,0.000044040782,0.00004649953,0.0000018047662],"category_scores_gemma":[0.000004690492,0.00009377321,0.000020324036,0.00015737947,0.000017736958,0.000056674733,0.000018347202,0.000051205247,0.0000028672991],"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":[0.000017139391,0.000056189012,0.0013691342,0.00026718565,0.0000130489325,1.2477226e-7,0.000119859964,0.082638726,0.2488188,0.0013041286,0.00009350126,0.66530216],"study_design_scores_gemma":[0.0007269381,0.0001352681,0.046327397,0.00016183895,0.00004338827,0.00002327684,0.00008995725,0.249581,0.68700075,0.00064852106,0.014918027,0.0003436568],"about_ca_topic_score_codex":0.00011730678,"about_ca_topic_score_gemma":0.000004309882,"teacher_disagreement_score":0.66495854,"about_ca_system_score_codex":0.00001678428,"about_ca_system_score_gemma":0.0000030211008,"threshold_uncertainty_score":0.38239607},"labels":[],"label_agreement":null},{"id":"W2008143997","doi":"10.5539/jas.v4n10p169","title":"The Effect of Camera and Light Source Characteristics on Image Quality in Machine Vision Application in Food Industry","year":2012,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"RGB color model; Artificial intelligence; Computer vision; Computer science; Machine vision; Software; Image processing; Camera resectioning; MATLAB; Digital camera; Smart camera; Toolbox; Image (mathematics)","score_opus":0.006816608224101176,"score_gpt":0.2540883446774035,"score_spread":0.2472717364533023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008143997","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":[0.9992486,0.00012699787,0.00006614667,0.00007347101,0.0002624262,0.00011831408,0.0000010643723,0.000004790506,0.000098141994],"genre_scores_gemma":[0.9998285,0.000013883217,0.000010885581,0.0000025150764,0.00013504195,0.0000019525983,1.6739943e-7,0.000002154182,0.000004893029],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99905705,0.00008095497,0.00038114353,0.000056954268,0.0002779691,0.0001459306],"domain_scores_gemma":[0.99948317,0.00013643062,0.00020437462,0.000061134495,0.000051308205,0.00006359182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021336335,0.00007242321,0.00016222938,0.00008394389,0.00006494789,0.000042147527,0.00010600983,0.00008188737,6.192114e-7],"category_scores_gemma":[0.0001738498,0.000032990403,0.000027640803,0.00046282087,0.000041775624,0.00033497627,0.000018320408,0.00039305442,9.731987e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008276,0.0000313516,0.06643089,0.000037717648,0.000005619754,5.357025e-7,0.000534307,0.0006097637,0.8814369,0.000039840957,0.00007026192,0.05072006],"study_design_scores_gemma":[0.00029490184,0.00034805702,0.9423881,0.00008184483,0.0000023965868,0.000027227823,0.00011913255,0.00023979502,0.056157783,0.0000017516641,0.00028344948,0.000055539862],"about_ca_topic_score_codex":0.000032522123,"about_ca_topic_score_gemma":0.000012755349,"teacher_disagreement_score":0.87595725,"about_ca_system_score_codex":0.00010044611,"about_ca_system_score_gemma":0.0000062415966,"threshold_uncertainty_score":0.17076477},"labels":[],"label_agreement":null},{"id":"W2010145024","doi":"10.1007/s10032-007-0038-0","title":"Special issue on graphics recognition","year":2007,"lang":"en","type":"article","venue":"International Journal on Document Analysis and Recognition (IJDAR)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Graphics; Computer science; Computer graphics (images)","score_opus":0.018557932251957097,"score_gpt":0.27138354599134834,"score_spread":0.2528256137393912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010145024","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":[0.9267631,0.000068678826,0.008092875,0.00046590998,0.013799365,0.0002641204,0.000073981995,0.00016781171,0.05030416],"genre_scores_gemma":[0.97118276,0.00085010275,0.0001518025,0.00046947773,0.026607852,0.000006705503,0.00014627658,0.000029083658,0.0005559228],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980364,0.0000654013,0.0006635678,0.00022186793,0.0007897799,0.00022302789],"domain_scores_gemma":[0.9990464,0.00013039746,0.00020815269,0.00009274877,0.00034807576,0.00017422954],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010554458,0.00021037165,0.0002666026,0.0016646618,0.00017437372,0.00037867308,0.00011866925,0.00017433662,0.0024627591],"category_scores_gemma":[0.00006110995,0.00018124921,0.00033525468,0.0005593708,0.000025344636,0.0002802883,0.000014253898,0.0004775551,0.00038773462],"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":[0.0004686086,0.0001168698,0.00053419993,0.000007150918,0.002363487,0.00012793289,0.0001790961,0.0007968772,0.00027902078,0.000121061326,0.014012249,0.98099345],"study_design_scores_gemma":[0.007831357,0.0019895176,0.023413394,0.0008915353,0.0021951264,0.0006648671,0.0011502076,0.0034186968,0.04086235,0.015084127,0.9003491,0.0021497658],"about_ca_topic_score_codex":0.00002578617,"about_ca_topic_score_gemma":0.000052052994,"teacher_disagreement_score":0.9788437,"about_ca_system_score_codex":0.00019129508,"about_ca_system_score_gemma":0.000010157847,"threshold_uncertainty_score":0.99844915},"labels":[],"label_agreement":null},{"id":"W2010332406","doi":"10.1016/j.patrec.2004.10.019","title":"Analysis of errors of handwritten digits made by a multitude of classifiers","year":2004,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Reliability (semiconductor); Artificial intelligence; Random subspace method; Pattern recognition (psychology); Multitude; Machine learning; Quality (philosophy); Natural language processing; Data mining; Classifier (UML)","score_opus":0.020244580201227682,"score_gpt":0.22229108770463382,"score_spread":0.20204650750340614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010332406","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":[0.9469957,0.000025498011,0.052139387,0.000060433304,0.00020947617,0.0001437844,0.00024510786,0.000043393284,0.00013725497],"genre_scores_gemma":[0.99966514,0.000010806019,0.000090558555,0.00007951545,0.00003099593,0.000012105482,0.000089529334,0.000017171695,0.0000041585017],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895215,0.000031487976,0.00052158977,0.00013571806,0.00022228078,0.00013676762],"domain_scores_gemma":[0.999521,0.0000417484,0.00019161691,0.00013637017,0.000065682514,0.000043547992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012899302,0.000121144476,0.00037721454,0.00044151442,0.000016301969,0.000007684021,0.000070480775,0.00010468345,0.00005831748],"category_scores_gemma":[0.000021088817,0.00012213264,0.00022060036,0.00055524247,0.000042792824,0.00010024739,0.000009136873,0.00010232851,0.000008766202],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004360572,0.00009030466,0.013908603,0.00027090413,0.001615841,0.0000036020472,0.0007462985,0.028982807,0.8936977,7.607215e-7,0.0007273228,0.059912276],"study_design_scores_gemma":[0.0030768353,0.00016983155,0.027473198,0.00050181116,0.0010581963,0.0000046289965,0.00052236306,0.0061860485,0.96022105,0.000016185488,0.00028950072,0.00048034053],"about_ca_topic_score_codex":0.00037252074,"about_ca_topic_score_gemma":0.000031086016,"teacher_disagreement_score":0.06652338,"about_ca_system_score_codex":0.00004840511,"about_ca_system_score_gemma":0.0000059994945,"threshold_uncertainty_score":0.49804243},"labels":[],"label_agreement":null},{"id":"W2013142057","doi":"10.1007/s10086-011-1237-3","title":"Comparison analysis of nonlinear segmentation methods for selected wood features in sugar maple","year":2012,"lang":"en","type":"article","venue":"Journal of Wood Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"FPInnovations","keywords":"Artificial neural network; Maple; Segmentation; Pixel; Artificial intelligence; Pattern recognition (psychology); Fuzzy logic; Computer science; Sugar; Identification (biology); Process (computing); Generalization; Mathematics; Botany; Biology","score_opus":0.04066821952025114,"score_gpt":0.3904947322514963,"score_spread":0.34982651273124515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013142057","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":[0.91284895,0.00031068068,0.08600582,0.000008760711,0.00063276355,0.00009903424,0.0000032602732,0.000008379429,0.000082348706],"genre_scores_gemma":[0.95192194,0.000006332515,0.04793117,0.000003108437,0.00012266774,0.00000172242,8.8189864e-7,0.0000046878326,0.000007460284],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896675,0.00006193676,0.0004745786,0.0000608116,0.00025730123,0.00017861981],"domain_scores_gemma":[0.99922335,0.00012811885,0.00024057477,0.00007496886,0.00026327424,0.00006973095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024173965,0.000064248154,0.00031158846,0.0008616801,0.000045078697,0.00002791107,0.00012243475,0.000056108518,0.0000075332455],"category_scores_gemma":[0.00025531463,0.000052092684,0.00009434519,0.0028612935,0.000024434896,0.00040345965,0.000008498157,0.00014183726,4.021575e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059267746,0.000086356915,0.012185746,0.000028782351,0.00017163754,2.8820753e-7,0.0018774346,0.052802138,0.9129568,0.00002320885,0.00026151107,0.01954686],"study_design_scores_gemma":[0.0007949048,0.00028298394,0.048245437,0.00004813148,0.00027768966,0.000015344527,0.0011177008,0.12836857,0.81990516,0.000010289814,0.00079506234,0.00013871209],"about_ca_topic_score_codex":0.00001501409,"about_ca_topic_score_gemma":0.000012848684,"teacher_disagreement_score":0.0930516,"about_ca_system_score_codex":0.00010605593,"about_ca_system_score_gemma":0.00004658689,"threshold_uncertainty_score":0.21242781},"labels":[],"label_agreement":null},{"id":"W2014302897","doi":"10.5539/cis.v2n3p137","title":"An Optimised Method-Based an Improved Neural Network Classifier","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Classifier (UML); Artificial neural network; Artificial intelligence; Nonlinear system; Machine learning; Pattern recognition (psychology); Quadratic programming; Quadratic equation; Data mining; Mathematical optimization; Mathematics","score_opus":0.017814433991824034,"score_gpt":0.26996532502370435,"score_spread":0.2521508910318803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014302897","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":[0.12447667,0.000006966961,0.8731397,0.000020391657,0.00081284135,0.00014218486,0.0000011113805,0.00022772554,0.0011724111],"genre_scores_gemma":[0.96876323,0.000001313459,0.030420654,0.00056473765,0.00023898981,0.0000024888548,0.000004711887,0.0000024415401,0.0000014547346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992995,0.000024335866,0.00022712671,0.00009990081,0.0001631543,0.00018597412],"domain_scores_gemma":[0.9995384,0.000015075112,0.000040639414,0.00017531916,0.000092067676,0.00013845415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065729255,0.00008535549,0.000094278555,0.00014231518,0.00020552814,0.0004275266,0.00013861699,0.000055028366,0.000005228234],"category_scores_gemma":[0.000006088157,0.000072402574,0.000018519542,0.00044023577,0.000031653573,0.0050495695,0.000008720483,0.00009152541,0.00000499515],"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":[0.0000075149683,0.0000031913014,0.00001344243,0.0000034285956,5.9112267e-7,9.7695086e-8,0.0001825799,0.6141505,0.0010636541,0.00015041101,0.00016057643,0.384264],"study_design_scores_gemma":[0.00027778881,0.00023834269,0.004729579,0.0000060571256,0.0000013929963,0.0000054037137,0.00001682237,0.9911245,0.0007796756,0.000018435217,0.0027003665,0.00010162794],"about_ca_topic_score_codex":0.0000035668365,"about_ca_topic_score_gemma":2.4957478e-7,"teacher_disagreement_score":0.84428656,"about_ca_system_score_codex":0.000023971288,"about_ca_system_score_gemma":0.000025716867,"threshold_uncertainty_score":0.41226488},"labels":[],"label_agreement":null},{"id":"W2015247407","doi":"10.1109/ipfa.2012.6306253","title":"Capacitor dendrite failure analysis for lidless CPU testing","year":2012,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"U.S. Department of Energy","keywords":"Capacitor; Root cause; Computer science; Reliability engineering; Product (mathematics); Root cause analysis; Materials science; Electronic engineering; Embedded system; Automotive engineering; Electrical engineering; Engineering; Voltage","score_opus":0.04964036684512513,"score_gpt":0.24362295426676597,"score_spread":0.19398258742164085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015247407","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":[0.6003007,0.00013016711,0.38982144,0.000012903671,0.0020004876,0.00033318042,0.000018660536,0.00063879427,0.0067436127],"genre_scores_gemma":[0.9947582,3.567476e-7,0.0035305994,0.000008401804,0.001213227,0.000036776793,0.00000435956,0.000019082445,0.00042899197],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936485,0.000013134094,0.00018806111,0.00008521622,0.00009376633,0.00025496745],"domain_scores_gemma":[0.9995744,0.00012535705,0.000024982037,0.00013027091,0.000058456717,0.00008649659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024109737,0.00009839877,0.00017770228,0.00016868673,0.00007675041,0.000042072654,0.000043720636,0.00011898927,0.000060308164],"category_scores_gemma":[0.00007367963,0.000084505504,0.00012261725,0.0005832654,0.0000046680543,0.00016064737,0.0000068446784,0.0000811821,0.000046009904],"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":[0.000071876035,0.00018189133,0.3623717,0.00073149445,0.0054258206,0.0000054816423,0.0036265135,0.114650674,0.25875363,0.003281351,0.06924339,0.18165617],"study_design_scores_gemma":[0.002561699,0.0002512216,0.029171344,0.00012894916,0.0018684854,0.00006125556,0.0034311253,0.4321983,0.14259356,0.00008252252,0.38550588,0.0021456724],"about_ca_topic_score_codex":0.00006641668,"about_ca_topic_score_gemma":0.00004090039,"teacher_disagreement_score":0.39445746,"about_ca_system_score_codex":0.000041173582,"about_ca_system_score_gemma":0.0000035210383,"threshold_uncertainty_score":0.34460345},"labels":[],"label_agreement":null},{"id":"W2019614771","doi":"10.1007/s00170-012-4552-y","title":"Fuzzy automated visual broken edge detection","year":2012,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Edge detection; Enhanced Data Rates for GSM Evolution; Fuzzy logic; Visual inspection; Computer vision; Cluster analysis; Machine vision; Engineering; Computer science; Image processing; Pattern recognition (psychology); Data mining; Image (mathematics)","score_opus":0.008949664788005872,"score_gpt":0.2587402559911666,"score_spread":0.24979059120316074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019614771","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":[0.9874756,0.00031428353,0.004295578,0.00019158098,0.006356232,0.00008838927,0.0000015086741,0.0006214654,0.00065539667],"genre_scores_gemma":[0.9986257,0.00006520854,0.00037441033,0.000023853912,0.0008576135,0.0000055041623,5.685831e-7,0.00002256987,0.000024575535],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990135,0.00002100526,0.0003995973,0.00006410764,0.00028449856,0.00021731587],"domain_scores_gemma":[0.9994401,0.000060663875,0.00020980001,0.00012128376,0.00012269733,0.000045486624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029575313,0.00012581931,0.00016791002,0.000459916,0.000065129316,0.000030196476,0.00037907957,0.00015957352,0.000015273123],"category_scores_gemma":[0.000078099634,0.00009125038,0.00008571076,0.00012308026,0.00003604293,0.00026895,0.00006061211,0.0004388496,0.0000367527],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020038414,0.000049078273,0.00017141088,0.000013395027,0.0003815458,0.000023738354,0.00017448132,0.035862427,0.5286349,0.00036604944,0.00076609704,0.43335646],"study_design_scores_gemma":[0.00053684233,0.000092644106,0.0010582809,0.000039651888,0.000015538068,0.0007959159,0.00018965539,0.0007722535,0.97830063,0.0010979244,0.01698987,0.0001107878],"about_ca_topic_score_codex":0.000004740048,"about_ca_topic_score_gemma":0.000003424752,"teacher_disagreement_score":0.44966573,"about_ca_system_score_codex":0.00022293844,"about_ca_system_score_gemma":0.00000838386,"threshold_uncertainty_score":0.37210825},"labels":[],"label_agreement":null},{"id":"W2019883056","doi":"10.1007/s00170-015-6832-9","title":"Adapting the coherent point drift algorithm to the fixtureless dimensional inspection of compliant parts","year":2015,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Point (geometry); Algorithm; Computer science; Computer vision; Mathematics; Geometry","score_opus":0.01959209698091076,"score_gpt":0.25053765917204995,"score_spread":0.2309455621911392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019883056","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":[0.9465561,0.00035256086,0.040857986,0.0064307493,0.005209561,0.0002903759,0.000008346238,0.00012617283,0.00016814983],"genre_scores_gemma":[0.9980243,0.000020119627,0.0013522757,0.00008354114,0.00046780144,0.000010608141,7.698077e-7,0.000015828851,0.000024792342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869525,0.000043642027,0.000505529,0.000091614595,0.00051319774,0.00015074461],"domain_scores_gemma":[0.99896353,0.00013650066,0.00031410521,0.00022578919,0.00031736374,0.000042691023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000619241,0.00012814753,0.00019680605,0.00020510153,0.00010284473,0.0000311637,0.0006655585,0.00008638743,0.000007257094],"category_scores_gemma":[0.00012226593,0.00006397459,0.00008869243,0.0001353207,0.00006793928,0.00009911085,0.00013872515,0.0005242717,0.000009274908],"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":[0.00019642606,0.000035467412,0.000023222829,0.0000054019465,0.0002733299,0.00003207448,0.000561586,0.7849997,0.014300712,0.0007669282,0.004855619,0.19394949],"study_design_scores_gemma":[0.0016135408,0.0005046155,0.00069389655,0.00034404921,0.000047269677,0.0020516077,0.00331441,0.016425306,0.8816522,0.0069194026,0.08618004,0.00025370595],"about_ca_topic_score_codex":0.00002908638,"about_ca_topic_score_gemma":0.000028138165,"teacher_disagreement_score":0.8673515,"about_ca_system_score_codex":0.00017528595,"about_ca_system_score_gemma":0.000025138279,"threshold_uncertainty_score":0.26088083},"labels":[],"label_agreement":null},{"id":"W2020408370","doi":"10.1167/8.6.271","title":"Getting the most out of classification images","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Industrial Vision Systems and Defect Detection","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":"","keywords":"Bonferroni correction; Thresholding; Pattern recognition (psychology); Artificial intelligence; Computer science; Pixel; Mathematics; Contextual image classification; Observer (physics); Statistical hypothesis testing; Image processing; Image (mathematics); Statistics","score_opus":0.019816165652010144,"score_gpt":0.27699277666046795,"score_spread":0.2571766110084578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020408370","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":[0.98904735,0.00010941906,0.0045136036,0.00017645472,0.003693885,0.00007397802,0.0000015355614,0.000019777994,0.002364003],"genre_scores_gemma":[0.9990264,0.000018355719,0.00027388474,0.0000070349,0.0006276442,4.2905643e-7,2.1604005e-7,0.000009261897,0.000036728474],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925613,0.000028483404,0.00040246474,0.000036016798,0.0002109079,0.00006596568],"domain_scores_gemma":[0.9993572,0.00008791179,0.00024273283,0.00011923683,0.0001642817,0.000028596705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078671484,0.00005417607,0.000118870696,0.000084671774,0.000049879258,0.000030413037,0.00009598832,0.000082512954,0.000021676555],"category_scores_gemma":[0.00014711652,0.000032377517,0.00007074767,0.000102992686,0.000016676508,0.00014628409,0.000008639161,0.00036409032,0.000008571307],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011009028,0.000007856244,0.00012726746,0.000009388334,0.000009161402,9.0363164e-7,0.00012152423,0.00037692607,0.94170725,0.000031640113,0.0037727489,0.053824298],"study_design_scores_gemma":[0.0020943498,0.0008551354,0.17252587,0.0005988354,0.0001052989,0.00029776778,0.001611976,0.0407645,0.645197,0.00058986316,0.13495187,0.0004075751],"about_ca_topic_score_codex":0.0000036109357,"about_ca_topic_score_gemma":0.0000024950016,"teacher_disagreement_score":0.29651034,"about_ca_system_score_codex":0.000014641985,"about_ca_system_score_gemma":0.00001359192,"threshold_uncertainty_score":0.15818115},"labels":[],"label_agreement":null},{"id":"W2020513908","doi":"10.4028/www.scientific.net/msf.539-543.339","title":"Methodology for Automatic Control of Automotive Al-Si Cast Components","year":2007,"lang":"en","type":"article","venue":"Materials science forum","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Magna International (Canada); University of Windsor","funders":"","keywords":"Artificial neural network; Automotive industry; Materials science; Casting; Identification (biology); Software; Automotive engine; Digital image; Quality (philosophy); Image (mathematics); Computer science; Engineering drawing; Automotive engineering; Image processing; Artificial intelligence; Metallurgy; Engineering","score_opus":0.051404507132638666,"score_gpt":0.3088902746685654,"score_spread":0.2574857675359267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020513908","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":[0.8042146,0.0000115967005,0.19135234,0.00003511593,0.003564272,0.00046066171,0.00005070676,0.00012678487,0.00018395417],"genre_scores_gemma":[0.9968039,4.2394464e-7,0.0030069277,0.000056812063,0.000078499856,0.000020414109,0.0000021331145,0.000013911413,0.000017022781],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865764,0.00005408544,0.00046489315,0.00016867838,0.00021993772,0.00043476681],"domain_scores_gemma":[0.9992822,0.00022844311,0.000115431256,0.00016348614,0.0001418322,0.000068606525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038850391,0.00011216682,0.00034015533,0.00022705337,0.00012900382,0.000043482232,0.00017683358,0.00008494925,0.000048457587],"category_scores_gemma":[0.00028528608,0.000096141775,0.000048162143,0.00026387165,0.00014131752,0.0001590167,0.000026731954,0.00003865729,0.000018853307],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031126838,0.00000807765,0.00004666919,0.00003438865,0.000008803178,7.733993e-7,0.00010403348,0.00039299077,0.99677277,0.00027619879,0.00020044865,0.0021237514],"study_design_scores_gemma":[0.0007237608,0.00016644555,0.0022991458,0.000037203175,0.000010010111,0.000016605403,0.00020316624,0.007889916,0.98703617,0.00019428068,0.0013074607,0.00011580857],"about_ca_topic_score_codex":0.000070769354,"about_ca_topic_score_gemma":0.0000072430153,"teacher_disagreement_score":0.19258928,"about_ca_system_score_codex":0.00007965463,"about_ca_system_score_gemma":0.000026148637,"threshold_uncertainty_score":0.39205477},"labels":[],"label_agreement":null},{"id":"W2020577241","doi":"10.1007/s00170-011-3524-y","title":"An adaptable automated visual inspection scheme through online learning","year":2011,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Calgary; Alberta Innovates","funders":"","keywords":"Scheme (mathematics); Visual inspection; Computer science; Artificial intelligence; Computer vision; Engineering drawing; Engineering; Mathematics","score_opus":0.02067494128146723,"score_gpt":0.27993242611457975,"score_spread":0.2592574848331125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020577241","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":[0.98466694,0.00010519926,0.011419744,0.00007015852,0.001961563,0.00007183812,0.0000016415736,0.0012404039,0.00046248976],"genre_scores_gemma":[0.99384815,0.00010290621,0.005644375,0.000018247356,0.000329338,0.000003147292,0.000002281036,0.000026724989,0.000024817487],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990216,0.000026581045,0.0004239855,0.00010327415,0.00025872572,0.00016585259],"domain_scores_gemma":[0.99938333,0.000026028078,0.0002509823,0.00013298457,0.00017713006,0.000029532855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018537181,0.00013264015,0.00018172902,0.00032876758,0.000094939285,0.000028236673,0.00046041547,0.00015483251,0.000039620467],"category_scores_gemma":[0.000058610924,0.00010039404,0.00006517831,0.00013417669,0.000044401982,0.00039970383,0.000047184716,0.00064839824,0.000011467411],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085304683,0.0003014094,0.0006122001,0.00002372477,0.00080008944,0.00021148675,0.0019556042,0.33549905,0.48482713,0.001737576,0.0006309705,0.17254771],"study_design_scores_gemma":[0.00091637904,0.00056570006,0.0013122236,0.00009285509,0.000017212311,0.0007197684,0.0020811188,0.031746402,0.95309633,0.0029010978,0.0063678403,0.00018308483],"about_ca_topic_score_codex":0.0000298945,"about_ca_topic_score_gemma":0.000015376723,"teacher_disagreement_score":0.4682692,"about_ca_system_score_codex":0.00016059977,"about_ca_system_score_gemma":0.000012953314,"threshold_uncertainty_score":0.409395},"labels":[],"label_agreement":null},{"id":"W2023739648","doi":"10.1109/icices.2014.7033989","title":"Fabric quality testing using image processing","year":2014,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alpha Technologies (Canada)","funders":"","keywords":"Weaving; Textile; Artificial intelligence; Process (computing); Computer vision; Computer science; Woven fabric; Clothing; Texture (cosmology); Gabor filter; Visual inspection; Image processing; Feature extraction; Image (mathematics); Engineering; Materials science; Mechanical engineering; Composite material","score_opus":0.0761009398184522,"score_gpt":0.2995944848890914,"score_spread":0.2234935450706392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023739648","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":[0.57611424,0.000018117405,0.37172437,0.0000026240643,0.00032605912,0.00007582747,4.1042588e-7,0.0006138286,0.051124547],"genre_scores_gemma":[0.98950416,1.11641484e-7,0.010043279,0.00001264001,0.0003391768,0.0000021750957,2.6475414e-7,0.000019420278,0.00007878456],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993459,0.000039079543,0.00023401306,0.000106630556,0.000115896604,0.00015847544],"domain_scores_gemma":[0.9996784,0.0000790009,0.000034726898,0.0001063911,0.000059674883,0.000041822863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048749082,0.00008698194,0.00012828124,0.000058233138,0.00009869023,0.00008524923,0.000040245628,0.0000692828,0.000023402412],"category_scores_gemma":[0.00024100192,0.0000769066,0.000028496297,0.00026520612,0.000008498477,0.00016068266,0.000011069716,0.00010219894,0.00004051348],"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":[0.000004736383,0.000010360109,0.0014664703,0.00023675265,0.000010016661,0.0000013295434,0.00012999149,0.017114863,0.6242754,0.00012659286,0.00032489735,0.35629863],"study_design_scores_gemma":[0.00028707224,0.000023279943,0.001817247,0.00007711972,0.0000073980036,0.000019172008,0.00010523584,0.9654957,0.029888,0.00014251882,0.0018821582,0.00025506775],"about_ca_topic_score_codex":0.00016456255,"about_ca_topic_score_gemma":0.0000035665444,"teacher_disagreement_score":0.9483809,"about_ca_system_score_codex":0.000044006018,"about_ca_system_score_gemma":0.000008759696,"threshold_uncertainty_score":0.313616},"labels":[],"label_agreement":null},{"id":"W2025859658","doi":"10.1167/7.9.359","title":"Classification image analysis of oriented texture detection","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Observer (physics); Orientation (vector space); Dipole; Pattern recognition (psychology); Computer science; Mathematics; Physics; Geometry","score_opus":0.009491368289493885,"score_gpt":0.26378169206821683,"score_spread":0.25429032377872296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025859658","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":[0.95386875,0.000031313037,0.043835487,0.000013092903,0.0016869268,0.000054987726,0.000003178514,0.000025891886,0.00048039653],"genre_scores_gemma":[0.99931854,0.0000135365435,0.00037082436,0.000002281562,0.00026534408,5.952273e-7,0.0000016960674,0.000010165889,0.00001699272],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9990213,0.000032643366,0.00053089217,0.000063924126,0.0002745937,0.00007665184],"domain_scores_gemma":[0.99910325,0.000044866672,0.00032187346,0.00014757113,0.0003280506,0.000054407166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005309444,0.00007702819,0.00025189406,0.0006851013,0.000037543596,0.000022666853,0.00006608104,0.00016924653,0.00006774352],"category_scores_gemma":[0.00010765814,0.000060222435,0.00021704749,0.0009517354,0.000014665743,0.00022893859,0.0000060464863,0.00037811135,0.0000057413386],"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":[0.00004078481,0.000019875399,0.00030557273,0.000009170838,0.00013712546,0.0000014361137,0.00006248836,0.0010702813,0.9640144,0.000016386044,0.00023350077,0.034088958],"study_design_scores_gemma":[0.0013351855,0.0007114221,0.3690998,0.00010451676,0.0010675895,0.00009001909,0.00038502025,0.27620998,0.33104855,0.000059861235,0.019618953,0.00026908837],"about_ca_topic_score_codex":0.000008170976,"about_ca_topic_score_gemma":0.000022985347,"teacher_disagreement_score":0.63296586,"about_ca_system_score_codex":0.00003685511,"about_ca_system_score_gemma":0.000011101995,"threshold_uncertainty_score":0.24557996},"labels":[],"label_agreement":null},{"id":"W2028559162","doi":"10.1889/1.3069850","title":"65.3: Next Generation of Frame‐Rate Conversion Algorithm","year":2008,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Industrial Vision Systems and Defect Detection","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":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Frame (networking); Frame rate; Quality (philosophy); Algorithm; Computer engineering; Real-time computing; Telecommunications; Artificial intelligence","score_opus":0.03383514993130998,"score_gpt":0.22307115351056125,"score_spread":0.18923600357925127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028559162","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":[0.8836436,0.00027505078,0.00047788938,0.00009103583,0.0017195954,0.0005723489,0.000032037788,0.00053033815,0.11265806],"genre_scores_gemma":[0.9992039,0.00023548874,0.00017670622,0.000018177347,0.00020196011,0.000012486652,0.000013092418,0.000027226479,0.00011091277],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883604,0.000043756067,0.00049073267,0.00018303203,0.00026490798,0.00018155498],"domain_scores_gemma":[0.9993921,0.00006840314,0.00010937653,0.00027287437,0.0000804383,0.00007684219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020054194,0.00015943314,0.00032291983,0.00011678045,0.00007237437,0.000009123596,0.00012107066,0.00032023696,0.000044209686],"category_scores_gemma":[0.00003962548,0.0001488678,0.00015163208,0.00026281163,0.000096531476,0.0001413841,0.000028048218,0.00021281987,0.000019493464],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001248636,0.000025941474,0.00011009705,0.000032677668,0.00001676093,0.000004437643,0.000037131584,0.002881736,0.9959157,0.000041142648,0.00063478004,0.0002871044],"study_design_scores_gemma":[0.00061762036,0.0003324782,0.0014730669,0.00007301203,0.00002470937,0.000043815988,0.00004074229,0.0018485119,0.9890227,0.0000042660263,0.006275415,0.00024367416],"about_ca_topic_score_codex":0.00006462525,"about_ca_topic_score_gemma":0.000002851496,"teacher_disagreement_score":0.11556031,"about_ca_system_score_codex":0.000080520855,"about_ca_system_score_gemma":0.000027568778,"threshold_uncertainty_score":0.60706526},"labels":[],"label_agreement":null},{"id":"W2032654709","doi":"10.1167/14.10.561","title":"Visual masking with faces: Interruption of a trailing mask at critical SOA does not reduce masking.","year":2014,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Stimulus onset asynchrony; Backward masking; Masking (illustration); Stimulus (psychology); Percept; Computer science; Audiology; Psychology; Perception; Medicine; Neuroscience","score_opus":0.023450037928366762,"score_gpt":0.3096193335478013,"score_spread":0.28616929561943455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032654709","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":[0.9114775,0.000063383115,0.086431384,0.00006915354,0.0013414987,0.00008470064,0.0000021361657,0.000040229625,0.0004900007],"genre_scores_gemma":[0.998139,0.000015910775,0.001108245,0.000012482921,0.0006458039,0.0000011970543,6.275893e-7,0.000031814754,0.000044873934],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983284,0.00010078814,0.00074490084,0.00013099237,0.0004953095,0.0001996154],"domain_scores_gemma":[0.99911475,0.00020825832,0.00026833452,0.000120528806,0.0001851443,0.00010300001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009492512,0.00016193149,0.0003904885,0.00027370668,0.00008204187,0.000058824466,0.00010274766,0.00016835639,0.000060418668],"category_scores_gemma":[0.00019958518,0.000104563806,0.0001472626,0.00015764023,0.000039649312,0.00032520707,0.00002741552,0.00034667164,0.000007658913],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010243307,0.00008569272,0.00028934624,0.00031023787,0.000085444146,0.000025613554,0.0014076091,0.023449527,0.9098489,0.00013137556,0.0002055032,0.06313639],"study_design_scores_gemma":[0.004448991,0.005882688,0.0046841307,0.0064329333,0.00022159202,0.0011239488,0.0024738023,0.2187065,0.7462592,0.00022666363,0.008771829,0.0007677269],"about_ca_topic_score_codex":0.000007946082,"about_ca_topic_score_gemma":0.0000069137977,"teacher_disagreement_score":0.19525698,"about_ca_system_score_codex":0.0001047743,"about_ca_system_score_gemma":0.00001737804,"threshold_uncertainty_score":0.4263988},"labels":[],"label_agreement":null},{"id":"W2034780874","doi":"10.1541/ieejias.126.25","title":"Textile Surface Inspection by Using Translation Invariant Wavelet Shrinkage","year":2006,"lang":"en","type":"article","venue":"IEEJ Transactions on Industry Applications","topic":"Industrial Vision Systems and Defect Detection","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":"Adidas (Canada)","funders":"","keywords":"Wavelet; Shrinkage; Wavelet transform; Invariant (physics); Spline (mechanical); Computer vision; Translation (biology); Artificial intelligence; Mathematics; Computer science; Algorithm; Engineering; Structural engineering; Statistics","score_opus":0.019358433286055226,"score_gpt":0.23462505187481109,"score_spread":0.21526661858875587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034780874","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":[0.09157567,0.00008858758,0.89851534,0.000103389815,0.00046117895,0.000952714,0.00023199615,0.0011631709,0.0069079287],"genre_scores_gemma":[0.99783313,0.000012423945,0.0009192067,0.000020771062,0.00031806255,0.00023190636,0.00006168108,0.00006682385,0.00053598854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850374,0.00005509715,0.00051784713,0.00034748702,0.0002696978,0.00030615192],"domain_scores_gemma":[0.99930245,0.00007025509,0.00007772387,0.00038073905,0.00006608073,0.000102748854],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017412372,0.00026895848,0.0002169058,0.0001852014,0.00055628736,0.00010144455,0.000120506964,0.000743373,0.00019020888],"category_scores_gemma":[0.0000015012839,0.00030458928,0.00011699587,0.00091217796,0.00004793216,0.00029011647,0.0000012885906,0.00096508703,0.00010927159],"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":[0.00002215005,0.00027748116,0.00003066361,0.00003829809,0.00006247942,0.000001562999,0.000078961304,0.7998933,0.16489218,0.00038752123,0.003941101,0.0303743],"study_design_scores_gemma":[0.002515745,0.00017836959,0.0003916382,0.00013822195,0.00023152078,0.0001425082,0.00054343184,0.38190508,0.3265278,0.0005025639,0.2853252,0.0015979065],"about_ca_topic_score_codex":0.00042680543,"about_ca_topic_score_gemma":0.000054624346,"teacher_disagreement_score":0.90625745,"about_ca_system_score_codex":0.0002859746,"about_ca_system_score_gemma":0.000038284783,"threshold_uncertainty_score":0.99994063},"labels":[],"label_agreement":null},{"id":"W2034891599","doi":"10.1007/s00138-013-0590-1","title":"Defect identification on specular machined surfaces","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; McMaster University","funders":"","keywords":"Specular reflection; Reflection (computer programming); Optics; Specular highlight; Surface (topology); Machining; Materials science; Light-emitting diode; Computer science; Geometry; Physics","score_opus":0.006455038286421097,"score_gpt":0.22847957325490986,"score_spread":0.22202453496848876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034891599","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":[0.9040504,0.0009070841,0.07475587,0.0007727498,0.00044424625,0.0023146407,0.00005005429,0.0011072272,0.015597718],"genre_scores_gemma":[0.9989286,0.0000657293,0.00011189094,0.00010616767,0.00008896705,0.00027681037,0.00003267564,0.000024611438,0.0003645577],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992168,0.000031144693,0.00025542045,0.00021932618,0.00014730552,0.00013002241],"domain_scores_gemma":[0.9994973,0.000052309784,0.000041895135,0.00028422754,0.00003615018,0.00008812157],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016592602,0.00014397319,0.00013392439,0.0001258529,0.00018836765,0.00014444282,0.00007758173,0.00007478507,0.00017793084],"category_scores_gemma":[0.000011661566,0.00011642452,0.000063552456,0.00024913097,0.00002010186,0.00011164193,0.000017646918,0.00014844665,0.00086408947],"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":[0.000019028377,0.000178162,0.0012890095,0.00009961715,0.00009172611,0.0000011155516,0.00012594718,0.010577412,0.26071748,0.0105714565,0.039713167,0.6766159],"study_design_scores_gemma":[0.001240974,0.00022337699,0.05030837,0.00006545691,0.000045618286,0.00002353186,0.00010506606,0.33069116,0.016143868,0.0028344712,0.5975606,0.0007575115],"about_ca_topic_score_codex":0.00009707662,"about_ca_topic_score_gemma":0.000006703801,"teacher_disagreement_score":0.6758584,"about_ca_system_score_codex":0.000024763365,"about_ca_system_score_gemma":0.000003027513,"threshold_uncertainty_score":0.9999139},"labels":[],"label_agreement":null},{"id":"W2040990256","doi":"10.2316/journal.206.2013.3.206-3917","title":"A SIMULATED ANNEALING APPROACH FOR INTEGRATING CELL FORMATION WITH MACHINE LAYOUT AND CELL LAYOUT","year":2013,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Simulated annealing; Computer science; Standard cell; Page layout; Integrated circuit; Operating system; Machine learning","score_opus":0.009288013179657531,"score_gpt":0.2117691719021308,"score_spread":0.20248115872247327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040990256","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":[0.45048973,0.00013743866,0.54857874,0.000044535474,0.00023393013,0.00018603362,0.000005477768,0.000030636344,0.00029346862],"genre_scores_gemma":[0.980153,0.000019085222,0.019598704,0.000015190143,0.00015926691,0.0000034788984,0.000018840003,0.0000139770755,0.000018448332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992817,0.000016958393,0.0003709887,0.00006246786,0.00018601665,0.00008187326],"domain_scores_gemma":[0.99927175,0.00005662638,0.00022873843,0.00003469806,0.00036255547,0.000045631026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020781223,0.00009755247,0.00013659341,0.00016715251,0.00005324989,0.00019355661,0.000054361324,0.00006903606,0.0000034253244],"category_scores_gemma":[0.00002095868,0.00007329014,0.00003270814,0.000056032863,0.000008945156,0.0004989905,0.000009775577,0.00012103818,8.304178e-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":[0.00003106833,0.00002049536,0.00032060203,0.00010163573,0.00004043152,0.0000012106527,0.00065651454,0.98005223,0.008266397,0.00007766645,0.00013906333,0.010292704],"study_design_scores_gemma":[0.001034523,0.000118929114,0.00035061224,0.000086499254,0.000018221057,0.000058302146,0.00035456158,0.99474263,0.0029895748,0.00007517895,0.000084397296,0.00008658702],"about_ca_topic_score_codex":0.000022697997,"about_ca_topic_score_gemma":0.0000015660399,"teacher_disagreement_score":0.52966326,"about_ca_system_score_codex":0.000044347278,"about_ca_system_score_gemma":0.000009503933,"threshold_uncertainty_score":0.2988685},"labels":[],"label_agreement":null},{"id":"W2043813414","doi":"10.4028/www.scientific.net/amr.816-817.806","title":"An Inspection Approach for Nonrigid Mechanical Parts","year":2013,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"École de Technologie Supérieure","funders":"","keywords":"Flexibility (engineering); CAD; Curvature; Process (computing); Engineering; Sheet metal; Mechanical engineering; Structural engineering; Engineering drawing; Computer science; Mathematics; Geometry","score_opus":0.07113133962757778,"score_gpt":0.35546338854642395,"score_spread":0.28433204891884617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043813414","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":[0.9183511,0.000035531455,0.075304784,0.000017044229,0.0014379598,0.0021818248,0.000026610458,0.00068163197,0.0019634718],"genre_scores_gemma":[0.9935208,0.00001897387,0.0042240517,0.000005412661,0.00066354085,0.0013627696,0.000038968494,0.000049195856,0.000116282565],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985727,0.00014473364,0.00028432172,0.00026398906,0.00029251978,0.00044170558],"domain_scores_gemma":[0.9992363,0.00007733229,0.00002284829,0.00030649488,0.0002356744,0.0001213889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009869764,0.00011985463,0.00020905345,0.0001541171,0.00020544564,0.00020592207,0.00013337692,0.0001778636,0.00016444232],"category_scores_gemma":[0.000091068185,0.00010648862,0.00003372365,0.00021146543,0.000026276994,0.0004640135,0.000022439994,0.00015707708,0.00015969365],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055427412,0.00002809836,9.907383e-7,0.00006618564,0.000008388356,3.857869e-7,0.00003235543,0.0028033776,0.9869153,0.00063264417,0.0013592002,0.0080976365],"study_design_scores_gemma":[0.0006059185,0.00037317426,0.00004665795,0.000017596865,0.0000023583664,0.000006656948,0.00019903286,0.014325842,0.97598743,0.0012873341,0.0069780108,0.00016999844],"about_ca_topic_score_codex":0.00009487746,"about_ca_topic_score_gemma":0.0000034861828,"teacher_disagreement_score":0.07516966,"about_ca_system_score_codex":0.00010099944,"about_ca_system_score_gemma":0.000013804489,"threshold_uncertainty_score":0.43424797},"labels":[],"label_agreement":null},{"id":"W2047009932","doi":"10.1109/i2mtc.2012.6229334","title":"Automated tuning of a vision-based inspection system for industrial food manufacturing","year":2012,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Machine vision; Computer science; Visual inspection; Process (computing); Artificial intelligence; Feature extraction; Product (mathematics); Quality (philosophy); Machine learning; Computer vision","score_opus":0.031953785645439686,"score_gpt":0.24507854481568872,"score_spread":0.21312475917024903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047009932","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":[0.9320427,0.000043970485,0.05604797,0.0000041512067,0.0038778887,0.0006702992,0.000018462337,0.004862928,0.0024315931],"genre_scores_gemma":[0.99887085,2.0425563e-7,0.0003506566,0.0000024579517,0.0006739139,0.000048269158,0.000007115171,0.000034281828,0.0000122386],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903744,0.000034551147,0.0004008095,0.00010392842,0.00016392382,0.0002593501],"domain_scores_gemma":[0.99952716,0.00011223376,0.00008525136,0.00014976114,0.000044148823,0.00008146105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043583664,0.00013926355,0.00023961725,0.00022606621,0.00009208678,0.000026079095,0.00005298365,0.00025258234,0.000009945773],"category_scores_gemma":[0.000033430028,0.00012408358,0.000106130734,0.00016653213,0.000008806907,0.00018694565,0.000008825793,0.00012154621,0.000010672144],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014562234,0.0004960035,0.0057706465,0.0050186752,0.001223652,0.0000036776858,0.0017336487,0.35234708,0.49102035,0.0038209928,0.050261356,0.0868477],"study_design_scores_gemma":[0.0019762854,0.00031021176,0.0005223005,0.00023264742,0.00003107683,0.000007720492,0.00040563062,0.4098378,0.5834627,0.0000015291344,0.0029969641,0.00021512774],"about_ca_topic_score_codex":0.0000448609,"about_ca_topic_score_gemma":0.000004097202,"teacher_disagreement_score":0.09244234,"about_ca_system_score_codex":0.00017323137,"about_ca_system_score_gemma":0.000016002867,"threshold_uncertainty_score":0.5059982},"labels":[],"label_agreement":null},{"id":"W2049877863","doi":"10.4028/www.scientific.net/amm.685.405","title":"Automatic Inspection System for Surface Defect of Precision Steel Ball","year":2014,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MD Precision (Canada)","funders":"","keywords":"Ball (mathematics); Robustness (evolution); System of measurement; Engineering; Computer science; Mechanical engineering; Structural engineering; Mathematics; Physics; Geometry","score_opus":0.010568304074239717,"score_gpt":0.20294850165041642,"score_spread":0.1923801975761767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049877863","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":[0.74434155,0.000028709619,0.2520637,0.000001176465,0.0016194566,0.00076111924,0.000027497535,0.0004702535,0.00068653625],"genre_scores_gemma":[0.99765944,0.000010034308,0.0020874478,0.0000026240282,0.0001370062,0.000058785503,0.0000071677596,0.000031214757,0.000006283245],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920464,0.000029801307,0.0003719918,0.00014982907,0.00010722133,0.00013653425],"domain_scores_gemma":[0.9995777,0.000084114465,0.000104071296,0.00015813932,0.00003692321,0.00003902795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008174414,0.00012802637,0.00033565238,0.000054188622,0.00006559981,0.00004335875,0.00004650008,0.00015875603,0.0000057935463],"category_scores_gemma":[0.000017569893,0.000113435475,0.000043334203,0.00006821416,0.0000031960822,0.000038258957,0.000018563273,0.00003185707,0.000008108462],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044242614,0.0000070105325,2.50756e-7,0.00085530564,0.000030930238,7.0661194e-8,0.00006128958,0.00035167765,0.8779472,0.11309286,0.00018304553,0.0074260747],"study_design_scores_gemma":[0.0012833835,0.00023289616,0.000013896334,0.00019885691,0.000067881534,0.0000090480435,0.00018662143,0.064877085,0.9276569,0.0030367426,0.0021968065,0.00023990276],"about_ca_topic_score_codex":0.000013772652,"about_ca_topic_score_gemma":0.0000017360136,"teacher_disagreement_score":0.25331786,"about_ca_system_score_codex":0.00003629911,"about_ca_system_score_gemma":0.000005090592,"threshold_uncertainty_score":0.46257645},"labels":[],"label_agreement":null},{"id":"W2052047602","doi":"10.1117/12.702955","title":"Bad pixel location algorithm for cell phone cameras","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Advanced Micro Devices (Canada)","funders":"","keywords":"Computer science; Pixel; Block (permutation group theory); Calibration; Image sensor; Phone; Computer vision; Cluster analysis; CMOS; Artificial intelligence; Algorithm; Real-time computing; Computer hardware; Electronic engineering; Mathematics","score_opus":0.011087557702301095,"score_gpt":0.22619227754654192,"score_spread":0.21510471984424082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052047602","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":[0.9829516,0.00017375308,0.01200297,0.00016769204,0.0010588159,0.00091601926,0.000050827435,0.00020864092,0.0024696842],"genre_scores_gemma":[0.8147246,0.0000799547,0.18199146,0.00006734592,0.0021629012,0.000290741,0.00002113315,0.00016637595,0.0004954752],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99776036,1.0025884e-8,0.0008573878,0.00032373893,0.0005833848,0.00047510886],"domain_scores_gemma":[0.9975593,0.00018777823,0.0002766916,0.000060539543,0.001777749,0.00013795846],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011344044,0.0003324468,0.0004172467,0.00016535257,0.00010453851,0.000100215264,0.0004934742,0.00033323557,0.000008186662],"category_scores_gemma":[0.00022308364,0.00029888924,0.00055835466,0.0004753566,0.00009009861,0.00043203775,0.000055170814,0.0003264534,0.0000039605657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098531855,0.00011041795,0.000060333503,0.00072455395,0.00033817437,7.235978e-8,0.00025547805,0.0007338265,0.94396275,0.036252208,0.008636422,0.008827258],"study_design_scores_gemma":[0.0024981336,0.00047009246,0.00035833637,0.00033222872,0.00017361503,0.000020158204,0.0017329665,0.20140857,0.76665074,0.0004747497,0.025257222,0.00062321185],"about_ca_topic_score_codex":0.000014799856,"about_ca_topic_score_gemma":2.8436284e-7,"teacher_disagreement_score":0.20067474,"about_ca_system_score_codex":0.00026221565,"about_ca_system_score_gemma":0.000025123873,"threshold_uncertainty_score":0.9999463},"labels":[],"label_agreement":null},{"id":"W2052345280","doi":"10.1097/00001888-200304000-00006","title":"On the Equivalence of Classic ROC Analysis and the Loss-function Model to Set Cut Points in Sequential Testing","year":2003,"lang":"en","type":"article","venue":"Academic Medicine","topic":"Industrial Vision Systems and Defect Detection","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 Toronto; University Health Network","funders":"","keywords":"Equivalence (formal languages); Cut-point; Set (abstract data type); Statistics; Mathematics; Function (biology); Receiver operating characteristic; Computer science; Artificial intelligence; Discrete mathematics; Biology","score_opus":0.07520335253080809,"score_gpt":0.29788162915969185,"score_spread":0.22267827662888376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052345280","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":[0.94373244,0.00014951643,0.053705215,0.0004352173,0.00025865773,0.00027257064,0.0000020009911,0.000030752173,0.0014136329],"genre_scores_gemma":[0.99946475,0.0000337451,0.000014287789,0.00025181504,0.00011323022,0.00001739629,8.677673e-7,0.000008779661,0.00009512104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989395,0.00013847866,0.00038095418,0.00013839724,0.00025825296,0.00014441884],"domain_scores_gemma":[0.99905545,0.0006243544,0.000073939955,0.00016698828,0.000035439785,0.000043807097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001929829,0.000102512684,0.00025450875,0.00021216863,0.00005024917,0.0000040601867,0.00008456322,0.00013122475,0.00002707158],"category_scores_gemma":[0.0014072662,0.00005407551,0.000030117282,0.0010233519,0.00007065599,0.000042386062,0.000014425135,0.00047136724,0.000004880526],"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":[0.0006009792,0.000009082444,0.010353942,0.00016252774,0.0005502861,0.0000068634613,0.011027348,0.87102365,0.057558756,0.03383849,0.006760423,0.008107628],"study_design_scores_gemma":[0.0033351947,0.00026076948,0.0017668813,0.00070359494,0.00051611225,0.000023829334,0.0015248463,0.97246015,0.0064134216,0.012316063,0.0004286482,0.0002504643],"about_ca_topic_score_codex":0.00009550393,"about_ca_topic_score_gemma":0.000012743487,"teacher_disagreement_score":0.101436496,"about_ca_system_score_codex":0.00004373635,"about_ca_system_score_gemma":0.000012495084,"threshold_uncertainty_score":0.22051352},"labels":[],"label_agreement":null},{"id":"W2053996691","doi":"10.1109/tmm.2014.2298832","title":"Texture Modeling Using Contourlets and Finite Mixtures of Generalized Gaussian Distributions and Applications","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Contourlet; Histogram; Computer science; Gaussian; Texture (cosmology); Artificial intelligence; Range (aeronautics); Probability density function; Pattern recognition (psychology); Probability distribution; Image texture; Mixture model; Computer vision; Image (mathematics); Image processing; Mathematics; Statistics; Wavelet transform; Wavelet","score_opus":0.018695182438690176,"score_gpt":0.24052769148454506,"score_spread":0.2218325090458549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053996691","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":[0.091999054,0.00013536586,0.90704703,0.000011925868,0.0002496668,0.00025900913,0.000108165856,0.000097533586,0.00009226114],"genre_scores_gemma":[0.9973387,0.0000472649,0.00238261,0.000008438581,0.00013093633,0.000046679997,0.0000067469114,0.000020092308,0.000018522549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930936,0.000040222123,0.00025106047,0.00015961159,0.00010239688,0.00013732564],"domain_scores_gemma":[0.9995529,0.00011712506,0.000036741152,0.00015204106,0.00004575176,0.00009544363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113760405,0.00013804813,0.00020219343,0.00012883304,0.00016727613,0.000026524858,0.000036795715,0.00016635461,0.000013209361],"category_scores_gemma":[0.000007706822,0.0001302927,0.000051571944,0.00014539775,0.000043264885,0.00007665941,7.3636284e-7,0.00019290911,0.0000027986673],"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":[0.00003362366,0.000045293273,0.000014751851,0.00008485747,0.00007861186,4.904889e-7,0.00030358342,0.8151796,0.087572515,0.00008827986,0.000033381395,0.096565016],"study_design_scores_gemma":[0.00066663395,0.000027836548,0.000024032915,0.000043131888,0.00005016168,0.000008369019,0.00003129994,0.97880054,0.019477068,0.000064185544,0.00067167083,0.00013506095],"about_ca_topic_score_codex":0.00009879376,"about_ca_topic_score_gemma":0.00004031553,"teacher_disagreement_score":0.90533966,"about_ca_system_score_codex":0.000025052173,"about_ca_system_score_gemma":0.000008358545,"threshold_uncertainty_score":0.5313182},"labels":[],"label_agreement":null},{"id":"W2054232931","doi":"10.1364/ao.52.004072","title":"Ultrafast web inspection with hybrid dispersion laser scanner","year":2013,"lang":"en","type":"article","venue":"Applied Optics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":32,"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 Key Research and Development Program of China; Program for New Century Excellent Talents in University; Natural Sciences and Engineering Research Council of Canada; Burroughs Wellcome Fund","keywords":"Scanner; Optics; Ultrashort pulse; Laser; Dispersion (optics); Wafer; Laser scanning; Materials science; Pixel; Frame rate; Computer science; Optoelectronics; Physics","score_opus":0.005168067574940878,"score_gpt":0.16318089405186975,"score_spread":0.15801282647692888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054232931","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":[0.9599766,0.0000068303916,0.0037166155,0.000016910242,0.00045121484,0.00034915734,0.0000039229135,0.000547656,0.034931082],"genre_scores_gemma":[0.99904484,0.000012341393,0.00026198706,0.000022535898,0.00033920674,0.00005634103,0.000009021897,0.000040209674,0.00021350903],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993244,0.000006077366,0.00016111974,0.00014514994,0.00016809422,0.00019515294],"domain_scores_gemma":[0.999655,0.000017984208,0.000028032957,0.00017897262,0.000043419277,0.000076555814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059855363,0.00014143459,0.00013334493,0.00007728858,0.000095401345,0.00007634718,0.000053458305,0.00008491392,0.00007737637],"category_scores_gemma":[0.0000033562935,0.00011204734,0.000029178642,0.0001801236,0.00002226748,0.000116587435,0.000009651432,0.00017909825,0.0006526252],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024316691,0.00023402969,0.0018022975,0.00027630012,0.00038031663,0.000028689221,0.0007999044,0.33539093,0.5382933,0.004444483,0.059331764,0.05877482],"study_design_scores_gemma":[0.0041564554,0.00045469488,0.0034962827,0.0001458782,0.00010488121,0.0001442931,0.001714284,0.22634847,0.7260342,0.00021695333,0.035654515,0.0015290477],"about_ca_topic_score_codex":0.000031372732,"about_ca_topic_score_gemma":0.000009139817,"teacher_disagreement_score":0.18774092,"about_ca_system_score_codex":0.00006678149,"about_ca_system_score_gemma":0.000009390758,"threshold_uncertainty_score":0.8388395},"labels":[],"label_agreement":null},{"id":"W2057892417","doi":"10.1109/iecon.2012.6388510","title":"New concept for corrosion inspection of urban pipeline networks by digital image processing","year":2012,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Pipeline (software); Computer vision; Artificial intelligence; Pipeline transport; Computer science; Image processing; Digital image processing; Mobile robot; Machine vision; Robot; Digital camera; Digital image; Computer graphics (images); Engineering; Image (mathematics); Mechanical engineering","score_opus":0.010727027523869002,"score_gpt":0.22723036887547585,"score_spread":0.21650334135160684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057892417","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":[0.008505679,0.000641329,0.98599005,0.0000042469237,0.0010479717,0.00025297585,0.000011250522,0.00031883948,0.0032276786],"genre_scores_gemma":[0.9972779,0.0000028014106,0.0005007034,0.0000057138095,0.0010578028,0.0000071805475,0.000026579342,0.000024694995,0.0010965904],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939585,0.00000524147,0.00025092185,0.00007804038,0.00008832227,0.00018161406],"domain_scores_gemma":[0.99971473,0.000026037203,0.000054556236,0.00007236131,0.000052893836,0.00007939854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009340086,0.00009734131,0.00014452929,0.000040345694,0.00004935343,0.000049325336,0.00003275995,0.00011850771,0.000026611095],"category_scores_gemma":[0.000022334933,0.000084319836,0.000053718504,0.00014083942,0.000010267073,0.00050642545,0.000007710106,0.000078270714,0.0000055464375],"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":[0.00008484556,0.000048527792,0.00081501267,0.00008284245,0.000010238456,1.0577661e-7,0.00046244898,0.00458189,0.061898634,0.0000989679,0.7114791,0.22043738],"study_design_scores_gemma":[0.0027168833,0.00031952406,0.00018078915,0.00018510502,0.000043691398,0.00001762678,0.0006563273,0.5703983,0.29337385,0.000027176946,0.13148746,0.00059328304],"about_ca_topic_score_codex":0.000021127813,"about_ca_topic_score_gemma":9.886063e-7,"teacher_disagreement_score":0.9887723,"about_ca_system_score_codex":0.00003995896,"about_ca_system_score_gemma":0.000007998077,"threshold_uncertainty_score":0.34384632},"labels":[],"label_agreement":null},{"id":"W2058955624","doi":"10.1117/12.585918","title":"Stereovision-based close-up dimensional inspection","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Industrial Vision Systems and Defect Detection","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":"McMaster University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images)","score_opus":0.012656079569859825,"score_gpt":0.22733888617987516,"score_spread":0.21468280661001535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058955624","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":[0.9946864,0.000101600664,0.00013817822,0.0006779653,0.0011050786,0.0005315423,0.000041055097,0.000371338,0.0023468083],"genre_scores_gemma":[0.9773189,0.000019622006,0.020888247,0.000080341044,0.0012936004,0.00011282618,0.000009131844,0.00008672997,0.00019061014],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99760807,1.864495e-8,0.00083600415,0.000349309,0.00081994385,0.00038666266],"domain_scores_gemma":[0.99826044,0.00014713049,0.0002514839,0.00007153962,0.0011339061,0.0001354734],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006376909,0.0003509523,0.00042204588,0.00018431971,0.00012316133,0.0001189114,0.0004951349,0.00031517938,0.000026937785],"category_scores_gemma":[0.0002655457,0.00029827183,0.00066067005,0.0004064174,0.0001123544,0.0005545557,0.0000685122,0.00041980972,0.000009369468],"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":[0.00021717511,0.00013117072,0.0002110362,0.00049338304,0.0004494187,1.08164215e-7,0.00017991087,0.00953611,0.8555431,0.10927955,0.020804789,0.0031542943],"study_design_scores_gemma":[0.0030276093,0.0004997195,0.0007753878,0.0005896411,0.00016629194,0.00002840351,0.0006638084,0.4885871,0.47623083,0.00044159914,0.028255966,0.00073360937],"about_ca_topic_score_codex":0.000010368478,"about_ca_topic_score_gemma":3.629218e-7,"teacher_disagreement_score":0.47905102,"about_ca_system_score_codex":0.0003189753,"about_ca_system_score_gemma":0.00003098744,"threshold_uncertainty_score":0.99994695},"labels":[],"label_agreement":null},{"id":"W2059221709","doi":"10.1111/j.1468-0394.2012.00641.x","title":"Fuzzy machine vision based clip detection","year":2012,"lang":"en","type":"article","venue":"Expert Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University; Concordia University","funders":"","keywords":"CLIPS; Computer science; Truck; Fuzzy logic; Artificial intelligence; Robustness (evolution); Machine vision; Automotive industry; Computer vision; Machine learning; Data mining; Automotive engineering","score_opus":0.017996910852626575,"score_gpt":0.2514250073058561,"score_spread":0.2334280964532295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059221709","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":[0.5760243,0.027437603,0.22310084,0.00006180295,0.09116655,0.0026708215,0.000039792078,0.0054208147,0.07407751],"genre_scores_gemma":[0.9969969,0.000010536541,0.00004009097,0.000023235058,0.0025441393,0.00009372047,0.00000680577,0.000055065633,0.00022952975],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987224,0.000115670904,0.0003746675,0.00015129992,0.00027967917,0.00035629026],"domain_scores_gemma":[0.99939823,0.000056011853,0.000056021563,0.00029398914,0.000037732887,0.00015799247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054154726,0.00019582557,0.00024436796,0.00016916421,0.0001325005,0.0000737234,0.00007430351,0.00024001996,0.00003685656],"category_scores_gemma":[0.000026440406,0.00016859084,0.000099018005,0.00024163915,0.000009568856,0.00028452452,0.000011466244,0.00017626748,0.00043621915],"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":[0.0002575237,0.00028376907,0.0035049256,0.0005837736,0.0002041706,0.000015398587,0.0025546772,0.034086138,0.7811438,0.00050025457,0.054590795,0.12227481],"study_design_scores_gemma":[0.0015816339,0.00024371692,0.0012239951,0.00025856847,0.000015937516,0.000102867874,0.000477484,0.18188156,0.088232465,0.0000054072048,0.72519547,0.0007809013],"about_ca_topic_score_codex":0.00048281736,"about_ca_topic_score_gemma":0.000011657556,"teacher_disagreement_score":0.6929113,"about_ca_system_score_codex":0.00017344428,"about_ca_system_score_gemma":0.0000061629266,"threshold_uncertainty_score":0.68749344},"labels":[],"label_agreement":null},{"id":"W2061823850","doi":"10.1177/1099636209105377","title":"Nondestructive Evaluation of Sandwich Structures: A Review of Some Inspection Techniques","year":2009,"lang":"en","type":"review","venue":"Journal of Sandwich Structures & Materials","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":40,"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":"Indigenous and Northern Affairs Canada; Federal Aviation Administration; Iowa State University; National Science Foundation","keywords":"Nondestructive testing; Engineering; Aviation; Field (mathematics); Construction engineering; Computer science; Systems engineering; Forensic engineering; Aerospace engineering","score_opus":0.04873381811638525,"score_gpt":0.3584684925822633,"score_spread":0.3097346744658781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061823850","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":[0.00370317,0.990456,0.000073416086,0.0000032850128,0.0035265698,0.0017220175,0.0002091966,0.00010343552,0.00020294661],"genre_scores_gemma":[0.017413585,0.9794966,0.0007180702,0.000011887562,0.0021992682,0.000028142882,0.00004203489,0.00008734208,0.0000030975289],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99292207,0.00091095094,0.004062329,0.00029742642,0.001507293,0.00029991163],"domain_scores_gemma":[0.99346894,0.00013008754,0.004442375,0.0004957547,0.0013525543,0.00011031234],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031764142,0.00071787386,0.004571883,0.001008168,0.00007124462,0.00007160713,0.00043632314,0.00090723153,0.00027080582],"category_scores_gemma":[0.000497693,0.00051248306,0.0008466339,0.00060865766,0.00007246266,0.00040279873,0.000042466167,0.00063783885,0.0000016919857],"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":[0.00008715289,0.000021699552,5.8228056e-7,0.09757057,0.00081838405,0.000007459045,0.000088225584,0.000036024292,0.010078903,0.00015374026,0.0028090754,0.8883282],"study_design_scores_gemma":[0.003107533,0.0020931396,0.00024632827,0.54515344,0.0161993,0.003945357,0.00013042684,0.0000166093,0.17830868,0.012804724,0.23570377,0.0022906729],"about_ca_topic_score_codex":0.000020502766,"about_ca_topic_score_gemma":0.0000011246639,"teacher_disagreement_score":0.8860375,"about_ca_system_score_codex":0.00048496536,"about_ca_system_score_gemma":0.0004746762,"threshold_uncertainty_score":0.9997327},"labels":[],"label_agreement":null},{"id":"W2062123859","doi":"10.1366/0003702053945985","title":"Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation","year":2005,"lang":"en","type":"article","venue":"Applied Spectroscopy","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":320,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Baseline (sea); Computer science; Environmental science; Political science","score_opus":0.01079070597794205,"score_gpt":0.27807822057516185,"score_spread":0.2672875145972198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062123859","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":[0.96107054,0.00003940142,0.03340947,0.000056794575,0.00013710285,0.0012296067,0.000039300037,0.002096556,0.001921207],"genre_scores_gemma":[0.9724367,0.000009238084,0.026864378,0.000030856634,0.00032338445,0.00015207795,0.00012908712,0.000032821044,0.000021428708],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991475,0.000016934591,0.00037405035,0.00014500177,0.00013192942,0.00018463547],"domain_scores_gemma":[0.9996203,0.000045051285,0.00008970916,0.00012250416,0.000079425496,0.000042998377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028192584,0.0001291809,0.00018435618,0.00014302682,0.00005749808,0.000021952334,0.000052930773,0.0001095625,0.000056850542],"category_scores_gemma":[0.000015644464,0.00013123683,0.000033860662,0.00034867346,0.000014512406,0.00007912277,0.0000059564445,0.000078842604,0.000017794115],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047456273,0.0000035085193,0.000046107056,0.000027720373,0.000020278854,1.5196093e-7,0.00008551179,0.00020484717,0.9876098,0.00047147824,0.0075539383,0.003929207],"study_design_scores_gemma":[0.00051344145,0.00010822688,0.00012124159,0.000013510798,0.000018014647,0.000005296142,0.000055017365,0.018581912,0.9749594,0.00021908169,0.0052804747,0.00012442331],"about_ca_topic_score_codex":0.00007892557,"about_ca_topic_score_gemma":0.00006355607,"teacher_disagreement_score":0.018377064,"about_ca_system_score_codex":0.00013039442,"about_ca_system_score_gemma":0.000040283554,"threshold_uncertainty_score":0.53516823},"labels":[],"label_agreement":null},{"id":"W2063680016","doi":"10.1115/detc2011-48493","title":"Vision Based Fault Detection of Automated Assembly Equipment","year":2011,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Queen's University","funders":"","keywords":"Machine vision; Computer science; Fault detection and isolation; Component (thermodynamics); Fault (geology); Artificial intelligence; Embedded system; Real-time computing; Actuator","score_opus":0.027816396347260137,"score_gpt":0.2450611964211961,"score_spread":0.21724480007393596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063680016","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":[0.8555849,0.000021668957,0.11325203,0.000001946285,0.0012091127,0.0002532566,0.0000022795907,0.001956821,0.027717944],"genre_scores_gemma":[0.99941754,0.0000011423157,0.00044531422,0.0000061262763,0.000037314006,0.000010788982,0.0000013023861,0.00001726135,0.00006321205],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993105,0.000027964114,0.0002785123,0.000099743695,0.00015684847,0.00012643954],"domain_scores_gemma":[0.99968714,0.000019254689,0.00004251087,0.00015118954,0.000056369012,0.00004355582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020374142,0.000096306954,0.00013648233,0.00014329427,0.00003121979,0.0000100918405,0.000046946567,0.00013566988,0.0001422599],"category_scores_gemma":[0.000014115764,0.00008181941,0.000061185274,0.00021310998,0.0000073763263,0.00009516967,0.0000084888325,0.00007179021,0.000069065885],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000432373,0.00003908216,0.00004136851,0.000048816368,0.00002278041,0.0000018971804,0.000095998075,0.0038689217,0.96947795,0.00003217491,0.0006007791,0.02572698],"study_design_scores_gemma":[0.00030462226,0.00017592909,0.0012310218,0.000025188425,0.0000059450877,0.0000018104353,0.000032107964,0.30859733,0.68880343,0.000005577177,0.0007375192,0.00007951541],"about_ca_topic_score_codex":0.00017617848,"about_ca_topic_score_gemma":0.00001990182,"teacher_disagreement_score":0.3047284,"about_ca_system_score_codex":0.000050621173,"about_ca_system_score_gemma":0.000007924212,"threshold_uncertainty_score":0.33364987},"labels":[],"label_agreement":null},{"id":"W2066127769","doi":"10.1109/crv.2014.48","title":"Robust Detection of Paint Defects in Moulded Plastic Parts","year":2014,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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 Guelph","funders":"","keywords":"Computer vision; Artificial intelligence; Contrast (vision); Computer science; Image (mathematics); Materials science","score_opus":0.02106778178877506,"score_gpt":0.19815074716801875,"score_spread":0.1770829653792437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066127769","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":[0.8206842,0.000013908687,0.17461734,0.0000012147076,0.00081246,0.00011434087,4.288188e-7,0.00014581397,0.0036103248],"genre_scores_gemma":[0.9997832,0.0000016230457,0.00004351341,0.0000035332498,0.0001037517,0.000013079005,5.610287e-7,0.0000137975185,0.000036939913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993703,0.000051555107,0.00024567737,0.00009610953,0.00010224712,0.00013411487],"domain_scores_gemma":[0.9996628,0.0001399333,0.00002814027,0.000112485344,0.000021498643,0.0000351089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030593568,0.000084148305,0.00015622895,0.00015228748,0.0000173032,0.000010230046,0.000031600164,0.00012158759,0.000027650236],"category_scores_gemma":[0.00014682821,0.00007549541,0.000041377694,0.0002086963,0.0000072820553,0.00005381608,0.000007123415,0.00011157779,0.00002890303],"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":[0.00003901589,0.000025969488,0.0014633638,0.00012746565,0.000012997847,0.0000017706735,0.00012647024,0.7695413,0.19738008,0.00029971526,0.00021995671,0.030761847],"study_design_scores_gemma":[0.0012046149,0.0002672702,0.009589269,0.000112393085,0.000011642707,0.000008537156,0.00011458548,0.54281914,0.4424791,0.00012451533,0.0030002503,0.0002686737],"about_ca_topic_score_codex":0.00011465813,"about_ca_topic_score_gemma":0.00027424583,"teacher_disagreement_score":0.24509904,"about_ca_system_score_codex":0.00004637532,"about_ca_system_score_gemma":0.0000036332042,"threshold_uncertainty_score":0.30786133},"labels":[],"label_agreement":null},{"id":"W2067008501","doi":"10.1109/ccece.2012.6334997","title":"Optimal spatial resolution in vision-based inspetion of pipes using Catadioptric sensors","year":2012,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Catadioptric system; Computer vision; Image resolution; Artificial intelligence; Computer science; Resolution (logic); Focal length; Projection (relational algebra); Perspective (graphical); Optics; Image sensor; Automated X-ray inspection; Image processing; Image (mathematics); Physics; Lens (geology); Algorithm","score_opus":0.02215268744030457,"score_gpt":0.25173581174878024,"score_spread":0.22958312430847566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067008501","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":[0.92358977,0.00007890183,0.074688196,0.0000028456764,0.0006306117,0.00014633598,0.0000025866161,0.00008930336,0.0007714371],"genre_scores_gemma":[0.99842775,0.0000021737353,0.0012963165,0.0000024225737,0.00023589426,0.0000032295784,0.000004550826,0.000015034444,0.000012650147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918514,0.00005525305,0.00031472516,0.00008287326,0.00016259364,0.00019939766],"domain_scores_gemma":[0.9997132,0.000042661093,0.000047065805,0.00011551968,0.000033112232,0.000048474623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037638628,0.000094255905,0.00015615251,0.00033893716,0.000029429273,0.000009984323,0.00003179676,0.00014269023,0.000045637258],"category_scores_gemma":[0.000037381087,0.00008876852,0.00004727629,0.00037549107,0.000012156684,0.00018364665,0.000007576445,0.000109686145,0.000014841714],"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":[0.0000500834,0.00003874926,0.003850962,0.00004144657,0.000005584553,8.230114e-7,0.00012822484,0.9391712,0.05338942,0.000025989353,0.00008549335,0.0032120307],"study_design_scores_gemma":[0.00060413737,0.00006166516,0.012174862,0.00007120261,0.000008571062,0.0000056718754,0.000080884,0.88443667,0.101948395,0.0000011710792,0.0004643327,0.00014246201],"about_ca_topic_score_codex":0.0013940008,"about_ca_topic_score_gemma":0.000042906602,"teacher_disagreement_score":0.07483795,"about_ca_system_score_codex":0.0001455694,"about_ca_system_score_gemma":0.000015040615,"threshold_uncertainty_score":0.3619875},"labels":[],"label_agreement":null},{"id":"W2070415340","doi":"10.1007/s00138-010-0277-9","title":"Integrated imaging and vision techniques for industrial inspection: a special issue on machine vision and applications","year":2010,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Microstructural Sciences","funders":"","keywords":"Machine vision; Computer science; Context (archaeology); Aerospace; Automotive industry; Process (computing); Systems engineering; Artificial intelligence; Data science; Manufacturing engineering; Engineering","score_opus":0.00927958701302217,"score_gpt":0.28352399327063327,"score_spread":0.2742444062576111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070415340","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":[0.18398935,0.0026698413,0.71147037,0.0066902065,0.0048771915,0.028022576,0.0020934334,0.008309697,0.051877342],"genre_scores_gemma":[0.9871603,0.00030279145,0.0023287367,0.00014359737,0.00791023,0.0015312101,0.0002146158,0.00009896053,0.00030955762],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99856204,0.000036813984,0.0004649169,0.0005238244,0.00018534658,0.00022707555],"domain_scores_gemma":[0.99908364,0.00016481233,0.000105346466,0.00035297574,0.00009987299,0.00019334073],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041574327,0.00032776417,0.00032271363,0.0003769445,0.0006962183,0.0002743774,0.00010796669,0.00029061182,0.0000674543],"category_scores_gemma":[0.00003979256,0.0002703841,0.00006373899,0.00045748684,0.000118646734,0.0002101751,0.000072253446,0.0006449209,0.000017841943],"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":[0.00007836027,0.00008501895,0.00022656299,0.000039933133,0.000013371496,3.984772e-7,0.00004936958,0.000015287467,0.022542376,0.0030669726,0.008266539,0.9656158],"study_design_scores_gemma":[0.0011539788,0.00026986594,0.0006311925,0.000065621105,0.000032567896,0.000044066517,0.00007310334,0.030622028,0.003917867,0.0007595032,0.9621036,0.00032661713],"about_ca_topic_score_codex":0.00010581239,"about_ca_topic_score_gemma":0.000095892174,"teacher_disagreement_score":0.9652892,"about_ca_system_score_codex":0.000041173334,"about_ca_system_score_gemma":0.000016002406,"threshold_uncertainty_score":0.99997485},"labels":[],"label_agreement":null},{"id":"W2072910132","doi":"10.1149/05201.0765ecst","title":"Yellowing Mechanism and Solution for Chip Card Module","year":2013,"lang":"en","type":"article","venue":"ECS Transactions","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Curing (chemistry); Fourier transform infrared spectroscopy; Nitrogen; Mold; Chip; Chemistry; Materials science; Chemical engineering; Polymer chemistry; Composite material; Computer science; Engineering; Organic chemistry; Telecommunications","score_opus":0.015917711013762412,"score_gpt":0.20407280656137575,"score_spread":0.18815509554761334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072910132","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":[0.15011404,0.00005071659,0.847287,0.000042318126,0.0010777648,0.00038999348,0.00001045794,0.00022851731,0.00079916575],"genre_scores_gemma":[0.9981098,0.000011807216,0.0011425046,0.0000068222917,0.00014251845,0.00015937795,0.000002102214,0.000020205234,0.00040482642],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995735,0.000009196364,0.00012317028,0.00009742499,0.000057704943,0.00013898638],"domain_scores_gemma":[0.99981076,0.000025054409,0.000009757737,0.00007825086,0.000025649266,0.000050505743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000075026684,0.000078138124,0.00009514042,0.00006613669,0.00016532521,0.000042116706,0.000021492797,0.00010085326,0.000046702695],"category_scores_gemma":[0.0000033770427,0.00007958421,0.000062971994,0.000073973075,0.0000067695855,0.0001780549,8.558959e-7,0.000091832975,0.000033629247],"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":[0.000023462682,0.000057054007,0.000019039075,0.00026525732,0.00029302773,0.0000011428446,0.0015941227,0.059127614,0.74612105,0.0019275161,0.004192371,0.18637832],"study_design_scores_gemma":[0.0014464906,0.00016091591,0.0003997742,0.00006208859,0.000094334224,0.000031195417,0.00035158734,0.83680546,0.1323808,0.0038007372,0.023985494,0.00048110395],"about_ca_topic_score_codex":0.0001068346,"about_ca_topic_score_gemma":0.000016205786,"teacher_disagreement_score":0.8479958,"about_ca_system_score_codex":0.000032394895,"about_ca_system_score_gemma":0.0000044530148,"threshold_uncertainty_score":0.324535},"labels":[],"label_agreement":null},{"id":"W2073836218","doi":"10.1007/s001380050134","title":"High performance computing for industrial visual inspection","year":2000,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Visual inspection; Computer science; Artificial intelligence; Computer vision; Computer graphics (images)","score_opus":0.01216406481162484,"score_gpt":0.2566917617105863,"score_spread":0.24452769689896142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073836218","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":[0.9481128,0.00006886689,0.04888113,0.00004170707,0.00024045004,0.000695784,0.000020288027,0.0005211094,0.001417858],"genre_scores_gemma":[0.9986499,0.000045835866,0.00019439097,0.00002168677,0.00075377466,0.00009881606,0.000034553734,0.000019627514,0.00018140493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993937,0.000011947944,0.00022599014,0.00015884243,0.00008199767,0.00012750288],"domain_scores_gemma":[0.9997531,0.000042247906,0.000026368518,0.00009983821,0.000022899636,0.000055538185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001495032,0.00010979991,0.00012959033,0.00007913107,0.0003321689,0.000056591332,0.00004245638,0.00011054982,0.00007030724],"category_scores_gemma":[0.0000042101337,0.00009816017,0.000032482505,0.00022128664,0.000016893699,0.00008207624,0.000008715668,0.00014050676,0.00004426634],"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":[0.000025589741,0.000023283259,0.00025166894,0.00001814473,0.000008285579,4.852587e-8,0.00002399374,0.0073723188,0.0011982035,0.00032018722,0.0010762882,0.98968196],"study_design_scores_gemma":[0.001511331,0.00023418445,0.0038071652,0.00003531475,0.000017087481,0.000012598952,0.000025313384,0.7385528,0.0026033393,0.000078605386,0.2528833,0.00023895933],"about_ca_topic_score_codex":0.000036813817,"about_ca_topic_score_gemma":0.000003100855,"teacher_disagreement_score":0.989443,"about_ca_system_score_codex":0.000026581081,"about_ca_system_score_gemma":0.000005199953,"threshold_uncertainty_score":0.40028557},"labels":[],"label_agreement":null},{"id":"W2083572527","doi":"10.1007/s00170-013-5540-6","title":"The inspection of deformable bodies using curvature estimation and Thompson-Biweight test","year":2014,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Dimensioning; Gaussian curvature; Curvature; Flexibility (engineering); Process (computing); Rigidity (electromagnetism); Computer science; Residual; Structural engineering; Artificial intelligence; Engineering; Engineering drawing; Mechanical engineering; Mathematics; Algorithm; Statistics; Geometry","score_opus":0.006291678076146997,"score_gpt":0.22738742988689245,"score_spread":0.22109575181074545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083572527","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":[0.9743575,0.00041485133,0.022790868,0.00057065714,0.0014849101,0.00007310118,0.00000161377,0.00007979359,0.00022669966],"genre_scores_gemma":[0.9975268,0.0002160874,0.0020401946,0.000008256421,0.00018106095,0.0000017074809,2.9396438e-7,0.000010157229,0.000015420172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992939,0.000014909357,0.0003409256,0.00005452685,0.00019768106,0.000098045464],"domain_scores_gemma":[0.9992465,0.00019652881,0.00028936,0.00011373882,0.00013953111,0.000014368269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003302604,0.000086753644,0.00013563708,0.00020068832,0.00013777096,0.000036200923,0.00024898158,0.000091007816,0.0000015299736],"category_scores_gemma":[0.00022059737,0.000050554016,0.00004068276,0.00006122942,0.00007810582,0.00019653908,0.00004770577,0.00030296846,7.329378e-7],"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":[0.00016182195,0.000021342123,0.00037479374,0.000035845573,0.00023708395,0.0000045179336,0.0002742993,0.44346482,0.13696848,0.003162198,0.00022025015,0.41507453],"study_design_scores_gemma":[0.0005823172,0.00014888965,0.00062786276,0.00016331654,0.000026275096,0.0005485761,0.00033197794,0.11250608,0.8617854,0.0134898145,0.009689663,0.00009982512],"about_ca_topic_score_codex":0.000006016173,"about_ca_topic_score_gemma":0.000009151243,"teacher_disagreement_score":0.7248169,"about_ca_system_score_codex":0.00008458326,"about_ca_system_score_gemma":0.000007668224,"threshold_uncertainty_score":0.20615329},"labels":[],"label_agreement":null},{"id":"W2085896549","doi":"10.1057/palgrave.jors.2601885","title":"Two optimization models of the optimum inspection problem","year":2005,"lang":"en","type":"article","venue":"Journal of the Operational Research Society","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"University of Toronto","funders":"","keywords":"Computer science; Operations research; Project management; Scheduling (production processes); Purchasing; Management science; Mathematical optimization; Operations management; Engineering; Systems engineering; Mathematics","score_opus":0.06112229944376461,"score_gpt":0.3283507921021784,"score_spread":0.26722849265841375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085896549","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":[0.6833814,0.0013774424,0.27127427,0.011866495,0.004396972,0.0025454687,0.000049799404,0.00012342757,0.024984753],"genre_scores_gemma":[0.99283713,0.00008616443,0.0058101835,0.000031259762,0.00082669844,0.0000059449135,5.0059265e-7,0.00001251843,0.00038959263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823487,0.00018468355,0.0004040313,0.000060073104,0.0009817703,0.00013455702],"domain_scores_gemma":[0.99879223,0.00008637769,0.00010986619,0.00014409953,0.0008290088,0.00003842042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020893107,0.00006566408,0.0001112923,0.00004738187,0.0003616868,0.00007236076,0.0002441143,0.000082703424,0.000037395683],"category_scores_gemma":[0.00009876596,0.00003596209,0.00024129223,0.00045970443,0.00006459632,0.00042950382,0.000053887157,0.0005973709,0.0000022547576],"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":[0.000010970881,0.000018843728,0.000024579307,0.000008523002,0.00003549503,4.5760608e-8,0.00036001668,0.97915184,0.0067175203,0.0006965396,0.012566157,0.00040946362],"study_design_scores_gemma":[0.00050483155,0.00004538089,0.00010479174,0.00008271913,0.00000755159,0.000025793175,0.00025604566,0.9848465,0.010810825,0.00035733427,0.002911489,0.00004677205],"about_ca_topic_score_codex":0.000017155193,"about_ca_topic_score_gemma":0.0000096409885,"teacher_disagreement_score":0.30945578,"about_ca_system_score_codex":0.00034030864,"about_ca_system_score_gemma":0.00018615155,"threshold_uncertainty_score":0.2781839},"labels":[],"label_agreement":null},{"id":"W2087975376","doi":"10.1109/icecs.2011.6122385","title":"Image processing technique for segmenting microstructural porosity of laser-welded thermoplastics","year":2011,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"University of Windsor","funders":"","keywords":"Welding; Materials science; Pixel; Porosity; Thermoplastic; Image segmentation; Process (computing); Image processing; Noise (video); Segmentation; Laser; Composite material; Microstructure; Computer science; Artificial intelligence; Image (mathematics); Optics","score_opus":0.02281152118790705,"score_gpt":0.22905016227954078,"score_spread":0.20623864109163373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087975376","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":[0.62958014,0.000009363751,0.36555088,8.464946e-7,0.00028744104,0.00045376978,0.000012736852,0.00019840254,0.003906398],"genre_scores_gemma":[0.97136605,5.4367666e-7,0.0284638,0.0000028226068,0.00006216446,0.000023880119,0.0000023230036,0.000019328418,0.00005907817],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994763,0.000008024694,0.00022855384,0.00008722589,0.000059981936,0.0001399194],"domain_scores_gemma":[0.99973845,0.000018372642,0.000057426943,0.00008416882,0.00007666043,0.000024936773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014785373,0.00009211366,0.00013695154,0.000051910632,0.000057295947,0.00001573168,0.000053796783,0.00010129743,0.000039106708],"category_scores_gemma":[0.00002007851,0.000076844844,0.000054157787,0.00008704347,0.000015758957,0.0001023855,0.000013180894,0.00007246089,0.0000019789359],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022322878,0.0000061052215,0.00014156765,0.0001474645,0.000009149239,4.5220128e-7,0.00018966598,0.00001537223,0.99614483,0.000014814395,0.00013942436,0.003168848],"study_design_scores_gemma":[0.00024884637,0.000047750385,0.0004230156,0.00004439475,0.000010626219,0.00000925516,0.00012427264,0.002615485,0.9962489,0.000078561716,0.000051990788,0.00009691327],"about_ca_topic_score_codex":0.000029263332,"about_ca_topic_score_gemma":0.0000047186836,"teacher_disagreement_score":0.3417859,"about_ca_system_score_codex":0.000020821588,"about_ca_system_score_gemma":0.000008983179,"threshold_uncertainty_score":0.31336418},"labels":[],"label_agreement":null},{"id":"W2088627316","doi":"10.1109/eit.2011.5978613","title":"Capsule image segmentation in pharmaceutical applications using edge-based techniques","year":2011,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Thresholding; Image segmentation; Binary image; Image processing; Digital image processing; Enhanced Data Rates for GSM Evolution; Feature detection (computer vision); Image quality; Image texture; Digital image; Segmentation; Image (mathematics)","score_opus":0.07202910124931085,"score_gpt":0.3122672450635391,"score_spread":0.24023814381422826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088627316","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":[0.114851385,0.000030130039,0.85731643,0.0000043892933,0.00017107047,0.00070675445,0.000003829946,0.00060803356,0.026307944],"genre_scores_gemma":[0.9785631,0.0000022512043,0.021169065,0.000021085609,0.000080951686,0.00012330091,0.0000030635554,0.000016129208,0.00002107367],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952215,0.000020119214,0.00018377496,0.00009248677,0.00006808394,0.0001134137],"domain_scores_gemma":[0.99982405,0.000008358146,0.000015385584,0.00009303061,0.000023889901,0.00003527443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001400022,0.00007195896,0.00007796403,0.00013335363,0.000030565752,0.000015512827,0.000037760685,0.00007591924,0.00017993503],"category_scores_gemma":[0.0000032473133,0.000070102455,0.000025844329,0.00021570941,0.000012017107,0.000107913074,0.000005554037,0.000098103454,0.000038258244],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001037482,0.000049152382,0.00025039428,0.000043543314,0.0000072276425,0.0000027622596,0.000119537275,0.00042672647,0.957991,0.0001837814,0.00025680507,0.040658668],"study_design_scores_gemma":[0.00023886531,0.000013050443,0.00011289415,0.000012951412,0.0000058001287,0.000002789942,0.000075256874,0.044929728,0.9529029,0.000040861192,0.0015672061,0.00009770042],"about_ca_topic_score_codex":0.00014433249,"about_ca_topic_score_gemma":0.000008642453,"teacher_disagreement_score":0.8637117,"about_ca_system_score_codex":0.00009159082,"about_ca_system_score_gemma":0.000011167112,"threshold_uncertainty_score":0.28586954},"labels":[],"label_agreement":null},{"id":"W2089410926","doi":"10.1007/s00170-008-1801-1","title":"A tolerance method for industrial image-based inspection","year":2008,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Sample (material); Computer vision; Automated X-ray inspection; Image (mathematics); Pixel; Artificial neural network; Computer science; Noise (video); Machining; Taguchi methods; Range (aeronautics); Tolerance analysis; Engineering; Image processing; Engineering drawing; Machine learning; Mechanical engineering","score_opus":0.022212679791775762,"score_gpt":0.27343058372984036,"score_spread":0.2512179039380646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089410926","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":[0.662828,0.00008301912,0.33187026,0.00092302833,0.003740513,0.00019258213,0.000005927837,0.00023280461,0.00012381126],"genre_scores_gemma":[0.9748843,0.000038388825,0.024077196,0.00004969202,0.0008799975,0.000020623685,0.0000010327692,0.000022058011,0.000026760972],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906695,0.00001987907,0.00042307345,0.00009492091,0.00025016497,0.00014504221],"domain_scores_gemma":[0.99921685,0.00015054058,0.0002404424,0.00013902794,0.00022719266,0.00002591466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028445382,0.00011924856,0.0002041946,0.00041578477,0.000111355235,0.000021225253,0.00041981196,0.00016230052,0.000009530781],"category_scores_gemma":[0.00015930833,0.00008937012,0.00012559016,0.000115472416,0.000052658626,0.00016941318,0.000024062294,0.00043455517,0.000003994984],"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":[0.0012722675,0.00006228986,0.00007107199,0.000017918706,0.00032157777,0.00008943575,0.00015271874,0.4704937,0.29130152,0.00047799235,0.0062946165,0.22944488],"study_design_scores_gemma":[0.002053636,0.00017882955,0.000075139964,0.000055902674,0.000012381093,0.00066296884,0.000076346376,0.005865756,0.95344853,0.0016315951,0.035832666,0.00010623883],"about_ca_topic_score_codex":0.0000059485355,"about_ca_topic_score_gemma":0.0000030946478,"teacher_disagreement_score":0.66214705,"about_ca_system_score_codex":0.00019101397,"about_ca_system_score_gemma":0.000033907305,"threshold_uncertainty_score":0.3644408},"labels":[],"label_agreement":null},{"id":"W2089605793","doi":"10.4028/www.scientific.net/amm.229-231.1706","title":"Reverse Design and Application of Irregular Planar Part Based on Image Processing","year":2012,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Image processing; Computer vision; Software; MATLAB; CAD; Digital image processing; Computer Aided Design; Process (computing); Edge detection; Artificial intelligence; Binary image; Planar; Digital image; Vectorization (mathematics); Computer graphics (images); Image (mathematics); Engineering drawing; Engineering","score_opus":0.01498008453651703,"score_gpt":0.21173425173388025,"score_spread":0.19675416719736322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089605793","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":[0.047503576,0.000070052156,0.95119154,0.0000065329823,0.0003020433,0.00057566294,0.000016118798,0.00008759046,0.0002468502],"genre_scores_gemma":[0.9932408,0.000015721384,0.0064974744,0.000021332578,0.00012529119,0.000069435155,0.0000073177393,0.000019414469,0.0000032661003],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994936,0.000019180445,0.00018396933,0.00009848054,0.000080283615,0.00012450526],"domain_scores_gemma":[0.999754,0.000021211421,0.000061816434,0.00010063057,0.000013771241,0.00004855467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005324468,0.000096360636,0.00016990096,0.00004809493,0.000045492292,0.000025981519,0.000022526126,0.00009888842,0.000008599785],"category_scores_gemma":[0.0000051641155,0.000086730746,0.000008942535,0.00005029971,0.0000054378597,0.000053319967,0.000006541096,0.00003142839,0.0000050756616],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000623339,0.000012187107,4.2660307e-7,0.00017982283,0.0000059485637,1.595583e-7,0.000058390477,0.00009885213,0.9786321,0.01250656,0.00022605748,0.00821717],"study_design_scores_gemma":[0.00058297144,0.00006211632,0.000013104448,0.00006724987,0.000032065305,0.0000038098485,0.00007670715,0.011767264,0.980152,0.001754921,0.0053004613,0.00018731956],"about_ca_topic_score_codex":0.0000051380603,"about_ca_topic_score_gemma":8.466085e-8,"teacher_disagreement_score":0.9457372,"about_ca_system_score_codex":0.000010892412,"about_ca_system_score_gemma":0.0000045220822,"threshold_uncertainty_score":0.35367772},"labels":[],"label_agreement":null},{"id":"W2090657190","doi":"10.1109/tcsvt.2015.2409632","title":"Monitoring Thermal Stress in Wafer-Scale Integrated Circuits by the Attentive Vision Method Using an Infrared Camera","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","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":"Polytechnique Montréal; Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Trois-Rivières; Lviv Polytechnic National University; Université du Québec en Outaouais; CMC Microsystems","keywords":"Wafer; Infrared; Integrated circuit; Thermal infrared; Scale (ratio); Computer vision; Computer science; Electronic circuit; Artificial intelligence; Stress (linguistics); Materials science; Optoelectronics; Optics; Engineering; Electrical engineering; Physics","score_opus":0.04720733862339554,"score_gpt":0.29541423998578265,"score_spread":0.2482069013623871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090657190","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":[0.5849722,0.00058735086,0.41123095,0.000015269157,0.0019377461,0.00079675956,0.00009714935,0.0002978199,0.000064734195],"genre_scores_gemma":[0.9993958,0.000022227308,0.00006688292,0.0000053562117,0.00011288117,0.00026133604,0.0000034453144,0.000058080812,0.00007402713],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982841,0.00018915397,0.00054133317,0.00038205076,0.00021727371,0.00038608926],"domain_scores_gemma":[0.99912274,0.00013512294,0.00010118946,0.00035562008,0.00017404486,0.00011126624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006529618,0.00028029355,0.00044466503,0.000430719,0.0002503476,0.00012216602,0.0001918138,0.00050173246,0.000002612699],"category_scores_gemma":[0.000015781035,0.00022149338,0.00007479895,0.00064760377,0.00006402493,0.0002976765,0.0000023892846,0.0005594393,0.000003987567],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007708928,0.00024775098,0.00076401624,0.00019679192,0.00023505368,0.000012991651,0.0025205372,0.25738972,0.38239378,0.000048378537,0.0002027928,0.35591108],"study_design_scores_gemma":[0.0052895863,0.0016420308,0.00017601893,0.001347386,0.00017592625,0.00033345015,0.024763461,0.46243802,0.497414,0.00016993615,0.005046263,0.0012039541],"about_ca_topic_score_codex":0.0006791683,"about_ca_topic_score_gemma":0.00007209624,"teacher_disagreement_score":0.41442356,"about_ca_system_score_codex":0.00027827173,"about_ca_system_score_gemma":0.000036811238,"threshold_uncertainty_score":0.9032238},"labels":[],"label_agreement":null},{"id":"W2094234524","doi":"10.1007/s00170-011-3332-4","title":"On-line 3-D system for the inspection of deformable parts","year":2011,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; University of Alberta","funders":"","keywords":"Finite element method; Automation; Line (geometry); Function (biology); Computer science; Deformation (meteorology); Standard deviation; CAD; Engineering; Mechanical engineering; Engineering drawing; Algorithm; Structural engineering; Mathematics; Geometry","score_opus":0.02643672915090693,"score_gpt":0.2460162433494369,"score_spread":0.21957951419852997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094234524","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":[0.91824174,0.00018041203,0.07530817,0.00021531094,0.004943877,0.00023702664,0.0000061348615,0.00016726245,0.00070004404],"genre_scores_gemma":[0.9991848,0.000048838014,0.0005056214,0.000010572824,0.00020356088,0.000013670303,2.7740623e-7,0.000013049805,0.000019610325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992549,0.000008615247,0.00039416616,0.000053900992,0.00018593534,0.000102516635],"domain_scores_gemma":[0.99925256,0.00012773208,0.00028486724,0.00015526442,0.00016613572,0.000013429358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028817434,0.000084545456,0.00014788915,0.00023703036,0.00006918751,0.000009543429,0.0004229667,0.00008148222,0.0000062530967],"category_scores_gemma":[0.00006202336,0.000046829464,0.0000909921,0.00006612121,0.000041400075,0.00009762933,0.000029148656,0.00023447239,0.0000031773293],"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":[0.001548086,0.00006756695,0.00003007866,0.000077577766,0.00083612476,0.000019876092,0.0004973446,0.7981624,0.029254012,0.023157248,0.0009173818,0.1454323],"study_design_scores_gemma":[0.0006661126,0.0003078515,0.00006514542,0.000120775025,0.000021203228,0.00025138157,0.00056496955,0.0034190929,0.9866858,0.004354572,0.0034873942,0.000055699456],"about_ca_topic_score_codex":0.0000093856825,"about_ca_topic_score_gemma":0.000005322573,"teacher_disagreement_score":0.9574318,"about_ca_system_score_codex":0.00012269728,"about_ca_system_score_gemma":0.00000903336,"threshold_uncertainty_score":0.19096501},"labels":[],"label_agreement":null},{"id":"W2094406164","doi":"10.1109/cjece.2005.1532600","title":"Call for papers - Canadian Journal of Electrical and Computer Engineering Special issue on computer vision","year":2005,"lang":"en","type":"paratext","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Inclusion (mineral); Computer science; Sociology; Social science","score_opus":0.006145667690625641,"score_gpt":0.1893520575874258,"score_spread":0.18320638989680016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094406164","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":[0.059009213,0.024225933,0.8284166,0.00058450224,0.0820019,0.0023521818,0.00031839,0.00015822747,0.0029330791],"genre_scores_gemma":[0.69338596,0.001182559,0.019425288,0.0006964028,0.28294832,0.000021412923,0.00006669807,0.0007026189,0.0015707486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99712574,0.000046481597,0.001210507,0.00032169136,0.00032800742,0.0009675669],"domain_scores_gemma":[0.9970407,0.00034931407,0.00027838306,0.00016364321,0.00029777607,0.0018701869],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041302864,0.00063779595,0.0012155476,0.0020366132,0.00017280158,0.00042545804,0.0003373494,0.00075568917,0.00009963899],"category_scores_gemma":[0.00004865977,0.0006025016,0.000352357,0.00048212265,0.000033496304,0.00019334743,0.000017205415,0.00174756,0.000017956723],"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":[0.000061733415,0.00001616556,0.000021455558,0.00021492084,0.0004223194,0.00033384826,0.00020114158,0.4870807,0.00009913365,0.000120703764,0.33620572,0.17522219],"study_design_scores_gemma":[0.0009209281,0.0016490507,0.00032617679,0.00056169654,0.000069433394,0.0012737311,0.0000012843119,0.38158572,0.00008889831,0.000002316145,0.6130045,0.0005162805],"about_ca_topic_score_codex":0.0011333076,"about_ca_topic_score_gemma":0.0019025574,"teacher_disagreement_score":0.80899125,"about_ca_system_score_codex":0.0007707624,"about_ca_system_score_gemma":0.00077078503,"threshold_uncertainty_score":0.9996426},"labels":[],"label_agreement":null},{"id":"W2102543972","doi":"10.1109/pacrim.2003.1235804","title":"Implementation of a mechanics based system for estimating the strength of a board using mixed signals of MOE and x-ray images","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"University of British Columbia","funders":"","keywords":"Image processing; Computer science; Mathematics; Artificial intelligence; Image (mathematics)","score_opus":0.02675553812348004,"score_gpt":0.2788055854982488,"score_spread":0.25205004737476877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102543972","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":[0.5121451,0.00001926348,0.48738188,0.000002251351,0.000103993494,0.00029058385,0.000023504877,0.00002017739,0.00001327021],"genre_scores_gemma":[0.96953094,3.384927e-7,0.030415472,9.615336e-7,0.00002651688,0.000012509438,0.0000014266601,0.000011138951,7.183999e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992992,0.000025551006,0.0004121812,0.000066714085,0.00011647713,0.0000798313],"domain_scores_gemma":[0.99951905,0.00011444163,0.00016911537,0.000089415946,0.00009304571,0.000014903572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004063311,0.00007251302,0.0002014503,0.00007110545,0.00003302573,0.000009158956,0.00003362749,0.000045683122,0.0000040775712],"category_scores_gemma":[0.000024987012,0.000052620584,0.000054281663,0.000114582006,0.000007597827,0.00005221692,0.0000074428235,0.000029423394,5.802539e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018049734,0.0000074649147,0.000028616781,0.00081516424,0.00004588342,1.1552188e-7,0.0002062922,0.40658164,0.5874802,0.00040605245,0.000010160217,0.0044003436],"study_design_scores_gemma":[0.0007448414,0.00008841453,0.000028120518,0.00016474687,0.000032020136,8.305908e-7,0.002093467,0.4064374,0.5903408,0.000029328177,0.0000015753004,0.000038443533],"about_ca_topic_score_codex":0.00066458044,"about_ca_topic_score_gemma":0.000023400344,"teacher_disagreement_score":0.45738584,"about_ca_system_score_codex":0.00003492455,"about_ca_system_score_gemma":0.0000248979,"threshold_uncertainty_score":0.21458052},"labels":[],"label_agreement":null},{"id":"W2104499695","doi":"10.1504/ijqet.2012.049684","title":"Correlation studies between visual perception and waviness measurements for coated surfaces","year":2012,"lang":"en","type":"article","venue":"International Journal of Quality Engineering and Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"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 Windsor","funders":"University of Windsor","keywords":"Waviness; Optics; Mathematics; Materials science; Statistics; Composite material; Physics","score_opus":0.09510555585079303,"score_gpt":0.3647391269460029,"score_spread":0.2696335710952099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104499695","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":[0.9673266,0.0009931517,0.029856928,0.00013827153,0.0015303426,0.000070201575,0.000005226108,0.00007432734,0.000004951506],"genre_scores_gemma":[0.9991147,0.000076207674,0.00042657033,0.0000026993102,0.0003574365,0.0000040675363,0.0000027143785,0.000009967212,0.000005649318],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99931824,0.000015665162,0.00035363628,0.00005239899,0.00015750079,0.00010258045],"domain_scores_gemma":[0.99940497,0.00010706249,0.00010210399,0.000030066612,0.00032028338,0.000035514193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006437189,0.00008442481,0.00019714658,0.00029177833,0.000028359698,0.000018619356,0.000041374653,0.00013109106,0.0000011660796],"category_scores_gemma":[0.00028030976,0.000076096476,0.000026560368,0.000086034765,0.000020662146,0.00018619564,0.000016155178,0.00013237863,5.9442857e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023035667,0.0001257917,0.33405116,0.0005648686,0.003396863,0.000005502954,0.0026479408,0.03205068,0.41360497,0.0020102519,0.00063792465,0.21067367],"study_design_scores_gemma":[0.01565685,0.001754019,0.61979765,0.0023322394,0.00076316256,0.0015587825,0.009696555,0.1704241,0.14651087,0.002156519,0.026872523,0.0024767523],"about_ca_topic_score_codex":0.000003818584,"about_ca_topic_score_gemma":6.4838156e-7,"teacher_disagreement_score":0.28574646,"about_ca_system_score_codex":0.0000743229,"about_ca_system_score_gemma":0.0000037539317,"threshold_uncertainty_score":0.31031242},"labels":[],"label_agreement":null},{"id":"W2106288709","doi":"10.1109/ispa.2003.1296426","title":"Identifying the strength of boards using mixed signals of MOE and X-ray image","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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 British Columbia","funders":"Ministry of Education, India; Ministry of Earth Sciences","keywords":"Finite element method; Feature (linguistics); Grading (engineering); Computer science; Image processing; Nondestructive testing; Stress (linguistics); Pattern recognition (psychology); Structural engineering; Artificial intelligence; Engineering; Image (mathematics); Physics","score_opus":0.029695502522182737,"score_gpt":0.2589857366992714,"score_spread":0.22929023417708866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106288709","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":[0.96921206,0.00014686865,0.02915898,0.0000039232723,0.00024638447,0.00010676689,0.0000040340533,0.000037361046,0.0010836425],"genre_scores_gemma":[0.99876827,0.000008911334,0.0011359457,0.0000018239074,0.000051116953,0.0000012495802,2.8845466e-7,0.0000099695435,0.000022420822],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945474,0.000020555377,0.0002486228,0.000064439475,0.00012727825,0.00008434705],"domain_scores_gemma":[0.9997375,0.000039748313,0.000050840856,0.00011045609,0.000041336098,0.000020104417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002515511,0.000067335466,0.00014720224,0.00006289305,0.000035305187,0.000020250405,0.000041866744,0.000056318302,0.000025960991],"category_scores_gemma":[0.000023263357,0.000046168334,0.00004487021,0.00013972542,0.000025649755,0.00009079681,0.000016002794,0.000067971145,0.0000013897863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069982584,0.000009020619,0.0000593323,0.00008732813,0.000041245978,0.0000010318778,0.00036408548,0.025871437,0.9705153,0.00021362615,0.00005996133,0.0027706386],"study_design_scores_gemma":[0.00069696526,0.000049869057,0.001146479,0.00015037716,0.000030304149,0.000007361742,0.0012822351,0.011282056,0.98489153,0.0002208932,0.00012968227,0.000112241854],"about_ca_topic_score_codex":0.0003693363,"about_ca_topic_score_gemma":0.000011663057,"teacher_disagreement_score":0.029556233,"about_ca_system_score_codex":0.000018665944,"about_ca_system_score_gemma":0.000010019283,"threshold_uncertainty_score":0.188269},"labels":[],"label_agreement":null},{"id":"W2107957549","doi":"10.3233/xst-2009-0228","title":"Fuzzy intelligent quality monitoring model for X-ray image processing","year":2009,"lang":"en","type":"article","venue":"Journal of X-Ray Science and Technology","topic":"Industrial Vision Systems and Defect Detection","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":"Toronto Metropolitan University; University of Ottawa","funders":"","keywords":"Fuzzy logic; Reliability (semiconductor); Computer science; Image quality; Quality (philosophy); Data mining; Bayesian probability; Quality management; Image processing; Artificial intelligence; Reliability engineering; Computer vision; Machine learning; Image (mathematics); Management system; Engineering; Operations management","score_opus":0.03812967427405922,"score_gpt":0.3186520228206577,"score_spread":0.28052234854659847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107957549","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":[0.8450116,0.0005475811,0.15308298,0.00032357397,0.000559513,0.00013317444,9.77613e-7,0.00008713782,0.000253453],"genre_scores_gemma":[0.9920189,0.000046024943,0.007731521,0.000008279513,0.00016740241,0.000002281123,2.7674558e-8,0.000005331815,0.000020228861],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99898773,0.000006188755,0.00040468105,0.00011807028,0.00026220034,0.00022111624],"domain_scores_gemma":[0.9991825,0.000020295232,0.00014966207,0.000100796926,0.00048283112,0.00006390007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012656967,0.00008832766,0.00020812057,0.00053599314,0.00017657955,0.00008721552,0.00019569561,0.00012566175,3.720363e-7],"category_scores_gemma":[0.00027386192,0.000069816255,0.000037414386,0.00067550835,0.00012126344,0.0004483263,0.000015114108,0.00024346294,8.433525e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022413007,0.000023254179,0.00015308223,0.000029177514,0.0000071340205,0.0000043749847,0.000276465,0.011298252,0.64649564,0.0004964069,0.000098557226,0.34109524],"study_design_scores_gemma":[0.001752505,0.001682752,0.001728933,0.000535049,0.000056974608,0.00030986316,0.0036530707,0.376877,0.58058447,0.029328266,0.0028142082,0.00067689765],"about_ca_topic_score_codex":9.634713e-7,"about_ca_topic_score_gemma":2.9438337e-7,"teacher_disagreement_score":0.36557877,"about_ca_system_score_codex":0.00009145545,"about_ca_system_score_gemma":0.0000772221,"threshold_uncertainty_score":0.28470242},"labels":[],"label_agreement":null},{"id":"W2108673330","doi":"10.1109/asmc.2012.6212882","title":"Development of CMP pad using an unpatterned surface inspection system","year":2012,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dow Chemical (Canada)","funders":"","keywords":"Chemical-mechanical planarization; Blanket; Wafer; Copper interconnect; Haze; Materials science; Characterization (materials science); Process (computing); Lithography; Semiconductor device fabrication; Etching (microfabrication); Computer science; Nanotechnology; Optoelectronics; Layer (electronics); Chemistry; Composite material","score_opus":0.06267991958895137,"score_gpt":0.25969878920413775,"score_spread":0.1970188696151864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108673330","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":[0.9221158,0.000027505877,0.07434711,2.9462274e-7,0.0012424937,0.000109690634,8.493949e-7,0.0005500465,0.0016061871],"genre_scores_gemma":[0.9962125,3.1155716e-7,0.0035638094,0.000001205136,0.00017917402,0.0000019439067,0.000001584191,0.0000180894,0.00002139178],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999312,0.000028700331,0.00029316358,0.00007094709,0.00013558587,0.00015961993],"domain_scores_gemma":[0.9997345,0.00000810656,0.00004394902,0.00011423437,0.000033883625,0.00006531231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032937992,0.000089324465,0.00013897615,0.000063217296,0.00006275222,0.000013463643,0.00003368006,0.00009377327,0.000015943495],"category_scores_gemma":[0.000002671418,0.00007955164,0.000025484629,0.0001466886,0.0000043692235,0.00022925714,0.00000928331,0.000060578303,0.00002923949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030014953,0.00010209929,0.013883126,0.0005035557,0.00012022445,0.000001133948,0.0068895207,0.050549697,0.9176319,0.00042748798,0.00019486855,0.009666349],"study_design_scores_gemma":[0.0005217325,0.000037345628,0.0057062847,0.00016151291,0.000018613517,0.000035588568,0.0042904494,0.1474668,0.83841145,5.751688e-7,0.003003972,0.00034567682],"about_ca_topic_score_codex":0.00011902831,"about_ca_topic_score_gemma":0.000012721836,"teacher_disagreement_score":0.09691709,"about_ca_system_score_codex":0.00019391415,"about_ca_system_score_gemma":0.0000120827735,"threshold_uncertainty_score":0.32440218},"labels":[],"label_agreement":null},{"id":"W2109073894","doi":"10.1109/robot.2002.1013616","title":"Automated inspection system using range data","year":2003,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Point cloud; Computer science; Range (aeronautics); Process (computing); Noise (video); Set (abstract data type); Artificial intelligence; Dispersion (optics); Computer vision; Automated X-ray inspection; CAD; Engineering drawing; Engineering; Image processing; Optics","score_opus":0.07293630210380629,"score_gpt":0.2725051493526376,"score_spread":0.19956884724883128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109073894","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":[0.6699901,0.00029487477,0.1480888,0.0000029226965,0.008500336,0.0006013599,0.000026214082,0.04568737,0.126808],"genre_scores_gemma":[0.9992277,0.0000014339652,0.000560861,0.0000026354705,0.00014191303,0.000002474898,0.0000050781855,0.000020939768,0.000036960744],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938905,0.00004499715,0.00019304162,0.00013598226,0.000108927146,0.00012799453],"domain_scores_gemma":[0.9995438,0.000012708985,0.00002086845,0.0003608179,0.000023918406,0.00003787542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029442197,0.00008564237,0.00011717822,0.00008663838,0.00008465606,0.0000463245,0.000069605034,0.00010419865,0.000016368678],"category_scores_gemma":[0.000023537568,0.00007673163,0.000020101324,0.0002586905,0.0000046191367,0.00020039844,0.000012037959,0.00007701883,0.00010492689],"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":[0.00009227108,0.00014560058,0.0047927555,0.0018225249,0.0007762271,0.00014863316,0.0007725014,0.33485797,0.22434889,0.02345844,0.39836544,0.010418772],"study_design_scores_gemma":[0.0002886314,0.000008964616,0.00007447258,0.00003512229,0.0000107993665,0.00008810678,0.00022116197,0.9858249,0.0037114066,7.801995e-7,0.009625942,0.00010973125],"about_ca_topic_score_codex":0.00014753435,"about_ca_topic_score_gemma":0.00001210577,"teacher_disagreement_score":0.65096694,"about_ca_system_score_codex":0.00013117405,"about_ca_system_score_gemma":0.0000107066935,"threshold_uncertainty_score":0.3129025},"labels":[],"label_agreement":null},{"id":"W211567330","doi":"10.1007/s00170-015-7214-z","title":"Further development of adaptable automated visual inspection—part II: implementation and evaluation","year":2015,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Support vector machine; Construct (python library); Visual inspection; Implementation; Scheme (mathematics); Feature (linguistics); Component (thermodynamics); Machine vision; Computer vision; Software engineering","score_opus":0.028096175482042213,"score_gpt":0.3189896687191317,"score_spread":0.29089349323708946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W211567330","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":[0.9954255,0.00018660104,0.0023920103,0.00018037178,0.001378243,0.0001332153,0.0000013368193,0.00017421605,0.00012848734],"genre_scores_gemma":[0.9981711,0.00002558705,0.0016526219,0.0000053912145,0.0001166905,0.000008628773,0.0000019964514,0.000010590904,0.0000073967713],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998958,0.000021335953,0.00046343866,0.00006634638,0.000401418,0.00008947967],"domain_scores_gemma":[0.9992637,0.000020304515,0.0002700062,0.00006820699,0.00035113102,0.00002662743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005855859,0.0000836473,0.00013338574,0.0003414023,0.000057585425,0.000015309275,0.00015454917,0.00007095135,0.000018039014],"category_scores_gemma":[0.000043424312,0.000063859756,0.000024423462,0.00009012897,0.00002709104,0.00015650872,0.000057369605,0.0001470457,0.0000020900186],"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":[0.00025398782,0.00004822231,0.00022771445,0.000014375918,0.00046684864,0.0000059504478,0.00247643,0.1264659,0.08410477,0.00026246885,0.0010916152,0.7845817],"study_design_scores_gemma":[0.0016963143,0.00020514257,0.0006987912,0.000063550964,0.000024044512,0.00016760801,0.004589616,0.0090220645,0.9721485,0.001451034,0.009834365,0.0000989635],"about_ca_topic_score_codex":0.000006054074,"about_ca_topic_score_gemma":0.000025769968,"teacher_disagreement_score":0.88804376,"about_ca_system_score_codex":0.00024337578,"about_ca_system_score_gemma":0.00005900537,"threshold_uncertainty_score":0.2604125},"labels":[],"label_agreement":null},{"id":"W2115980065","doi":"10.1889/1.3499951","title":"P‐38: Unique Electrical Measurement Technology for Compensation, Inspection, and Process Diagnostics of AMOLED HDTV","year":2010,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IGNIS Innovation (Canada)","funders":"","keywords":"AMOLED; Backplane; Compensation (psychology); OLED; Thin-film transistor; Mura; Process (computing); High-definition television; Computer science; Factory (object-oriented programming); Materials science; Electrical engineering; Computer hardware; Liquid-crystal display; Engineering; Optoelectronics; Telecommunications; Active matrix; Nanotechnology","score_opus":0.008361492299219864,"score_gpt":0.22723625265336272,"score_spread":0.21887476035414286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115980065","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":[0.9659483,0.00022758238,0.00072135596,0.00043280548,0.00091012596,0.0017956458,0.000025030798,0.0010481577,0.028890986],"genre_scores_gemma":[0.99955976,0.00006990198,0.00012257787,0.0000057878715,0.0000790136,0.00012721689,0.0000030725728,0.000028046326,0.000004611877],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988159,0.000016892916,0.0005099839,0.00019968885,0.0002662915,0.00019124843],"domain_scores_gemma":[0.99905264,0.00017542066,0.0001279152,0.0002246442,0.0003537839,0.00006556688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036832836,0.0001568256,0.0003564034,0.00022858012,0.00006982538,0.00001089743,0.00012755312,0.00043011233,0.0000041655603],"category_scores_gemma":[0.00060304836,0.00014608436,0.000070581715,0.00043920512,0.00014173014,0.000045864028,0.000019584397,0.00034337008,6.943442e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035657635,0.00005679042,0.0017817364,0.00011273818,0.000024306892,3.9279468e-7,0.000032270513,0.00031690803,0.99425834,0.0030641283,0.00011631908,0.0002004027],"study_design_scores_gemma":[0.0010312516,0.0006202158,0.0062003806,0.00012664012,0.000057943736,0.0000312912,0.00007534779,0.00021842684,0.98755413,0.0005000332,0.0032973594,0.000287004],"about_ca_topic_score_codex":0.000009734612,"about_ca_topic_score_gemma":0.00007283773,"teacher_disagreement_score":0.03361146,"about_ca_system_score_codex":0.00006931253,"about_ca_system_score_gemma":0.000051388208,"threshold_uncertainty_score":0.59571475},"labels":[],"label_agreement":null},{"id":"W2116854923","doi":"10.1109/icassp.1984.1172635","title":"Multi-level thresholding and its application to feature extraction in machine parts","year":2005,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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 Windsor","funders":"","keywords":"Thresholding; Balanced histogram thresholding; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature extraction; Feature (linguistics); Image (mathematics); Extension (predicate logic); Computer vision; Image processing","score_opus":0.03700295155508456,"score_gpt":0.2832243503705262,"score_spread":0.2462213988154416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116854923","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":[0.8852537,0.00052773353,0.110392764,0.00033820438,0.0003722665,0.0007380718,0.000007552294,0.00033457877,0.0020350935],"genre_scores_gemma":[0.9977721,0.000015086383,0.0014299904,0.00004085784,0.00018299176,0.000041876163,0.0000027034941,0.000013143639,0.0005012432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995545,0.000008841232,0.00012849833,0.00012662928,0.00007102192,0.00011049086],"domain_scores_gemma":[0.99983275,0.00001392197,0.000013617361,0.00007413105,0.000014980862,0.000050585357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014785465,0.000080688595,0.000087289525,0.00011511819,0.000033638727,0.000023544111,0.0000238198,0.00011164736,0.000016629423],"category_scores_gemma":[0.000012718507,0.00007421022,0.000013567728,0.00016076281,0.00000130893,0.00015248914,0.000008573251,0.00013465347,0.000056708384],"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":[0.00003515232,0.000039541697,0.0012396491,0.0000417059,0.000011918983,0.0000022096567,0.0003764269,0.14768292,0.3896963,0.0002300407,0.0023700688,0.45827407],"study_design_scores_gemma":[0.0007360854,0.000025120386,0.023586113,0.00003930303,0.000004172101,0.000023338833,0.000061478124,0.8533983,0.059442487,0.0000053539766,0.062453367,0.00022485272],"about_ca_topic_score_codex":0.000044024677,"about_ca_topic_score_gemma":0.00044700765,"teacher_disagreement_score":0.7057154,"about_ca_system_score_codex":0.00006269943,"about_ca_system_score_gemma":0.0000020934808,"threshold_uncertainty_score":0.3026205},"labels":[],"label_agreement":null},{"id":"W2122010129","doi":"10.1109/robot.1994.351336","title":"Crack detection using contact sensing","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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 Alberta","funders":"University of Alberta","keywords":"Visibility; Computer science; Robot; Computer vision; Artificial intelligence; Optics; Physics","score_opus":0.045301850748844456,"score_gpt":0.21938925457333572,"score_spread":0.17408740382449125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122010129","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":[0.8682442,0.000060050264,0.10732331,0.0000031952266,0.0014629664,0.00010370588,5.0648185e-7,0.00053359615,0.022268478],"genre_scores_gemma":[0.99915457,0.0000035349358,0.0002455179,0.000012272746,0.0003194614,4.462578e-7,1.5084306e-7,0.000018900306,0.00024513245],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995231,0.000017250972,0.00015271295,0.00008388091,0.00008826785,0.00013477619],"domain_scores_gemma":[0.99980503,0.000018892802,0.000016532425,0.0000974651,0.000023650562,0.000038411334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007688327,0.00008122548,0.000097386255,0.00008046409,0.0000770006,0.000044822158,0.00001750108,0.00009830625,0.00024467194],"category_scores_gemma":[0.000013319292,0.00007635342,0.00004463173,0.00015568959,0.0000033278607,0.000119168944,0.000004462454,0.00010489323,0.00019533755],"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":[0.000007255133,0.000008306555,0.000068769135,0.000027209713,0.000040099432,0.000013860545,0.00018691846,0.0204258,0.7287907,0.00002836136,0.0010284861,0.24937426],"study_design_scores_gemma":[0.00022159833,0.000025955913,0.000075752636,0.000017176035,0.0000069719376,0.000073685114,0.00005823671,0.8891272,0.10202911,0.000008616739,0.008226688,0.00012899817],"about_ca_topic_score_codex":0.00010996075,"about_ca_topic_score_gemma":0.000019853385,"teacher_disagreement_score":0.8687014,"about_ca_system_score_codex":0.000079559584,"about_ca_system_score_gemma":0.0000010271661,"threshold_uncertainty_score":0.3113602},"labels":[],"label_agreement":null},{"id":"W2123476364","doi":"10.5006/c2004-04156","title":"Smart Pigs and Defect Assessment Codes: Completing the Circle","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Calgary Laboratory Services","funders":"","keywords":"Computer science; Materials science","score_opus":0.01734805279677507,"score_gpt":0.2436932446743795,"score_spread":0.22634519187760443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123476364","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":[0.90774083,0.00011970262,0.05474038,0.00008163499,0.0005790103,0.00027733712,0.0000027392289,0.00036375,0.036094606],"genre_scores_gemma":[0.9994915,0.000006529693,0.0002768994,0.000050616138,0.00010755837,0.000011815682,0.0000010924949,0.000012122877,0.000041865016],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99951047,0.000023474,0.00014319336,0.00008942546,0.00010587744,0.00012755542],"domain_scores_gemma":[0.99975306,0.00006432607,0.000015808508,0.00011712322,0.000015795466,0.000033885302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003173432,0.000071848175,0.00009790112,0.000027939986,0.00013837242,0.00007022515,0.000040129682,0.000048862094,0.000033414482],"category_scores_gemma":[0.000007815395,0.000051591127,0.000043061314,0.00009195683,0.000014741358,0.000046542486,0.000016298041,0.00012873774,0.000030752115],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009441292,0.00024509177,0.030714722,0.0007849876,0.001202476,0.000078315505,0.0052932776,0.3575811,0.1650196,0.054179896,0.01650913,0.36829698],"study_design_scores_gemma":[0.017353036,0.0016027994,0.49343023,0.0009860257,0.00032566936,0.0009586233,0.010582293,0.13274823,0.02977088,0.0074692196,0.30115613,0.0036168534],"about_ca_topic_score_codex":0.00026753882,"about_ca_topic_score_gemma":0.00006622184,"teacher_disagreement_score":0.4627155,"about_ca_system_score_codex":0.000054411845,"about_ca_system_score_gemma":0.000009318773,"threshold_uncertainty_score":0.2103825},"labels":[],"label_agreement":null},{"id":"W2125597755","doi":"10.1007/s10479-009-0669-2","title":"Applications and extensions of cost curves to marine container inspection","year":2009,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bentley (Canada); Government of Canada","funders":"","keywords":"Container (type theory); Theory of computation; Computer science; Marine engineering; Reliability engineering; Operations research; Engineering drawing; Mathematics; Mechanical engineering; Engineering; Algorithm","score_opus":0.1612641464641404,"score_gpt":0.43289161872632004,"score_spread":0.27162747226217965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125597755","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":[0.9309621,0.0018112926,0.03409859,0.0061836727,0.00017765457,0.0042557493,0.00009593203,0.00024095124,0.022174088],"genre_scores_gemma":[0.99863523,0.0006699888,0.00020137454,0.00006410746,0.00006822825,0.00010431053,0.000010636754,0.000006825963,0.00023931456],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992209,0.000056735393,0.00025763866,0.000106167776,0.00022463009,0.00013389462],"domain_scores_gemma":[0.9989023,0.000059456583,0.000009867055,0.00019720191,0.0007524749,0.00007869738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006026039,0.000055931065,0.00013750717,0.00032309696,0.00014175239,0.00002236443,0.00005870667,0.000052514566,0.00003585029],"category_scores_gemma":[0.00016192035,0.000051892865,0.000025972422,0.0006693823,0.000035825546,0.00010544862,0.000024551138,0.00013964795,0.000013694354],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012108697,0.00040267306,0.0006460486,0.00033326293,0.00010352469,0.0000029818373,0.0007662978,0.043335408,0.44530007,0.029617729,0.08907844,0.39029247],"study_design_scores_gemma":[0.0020761169,0.003809104,0.119927935,0.0014034377,0.000042070267,0.000069701884,0.0019840512,0.0814502,0.561611,0.0017420037,0.22490665,0.0009777487],"about_ca_topic_score_codex":0.00025241714,"about_ca_topic_score_gemma":0.00011710727,"teacher_disagreement_score":0.38931474,"about_ca_system_score_codex":0.000011911262,"about_ca_system_score_gemma":0.000025012438,"threshold_uncertainty_score":0.21161295},"labels":[],"label_agreement":null},{"id":"W2126420722","doi":"10.1109/icsmc.2007.4413687","title":"Framework for color-texture classification in machine vision inspection of industrial products","year":2007,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; RGB color model; Color space; Texture (cosmology); Computer science; Computer vision; Pattern recognition (psychology); Image texture; Color model; Color analysis; Contextual image classification; Color image; Visual inspection; Image (mathematics); Image processing","score_opus":0.05050824720696039,"score_gpt":0.29973339938759214,"score_spread":0.24922515218063174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126420722","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":[0.6900516,0.000083319435,0.30313188,0.00007921388,0.0026112215,0.001215165,0.000009469696,0.00032909797,0.002489006],"genre_scores_gemma":[0.9979604,0.0000055572477,0.0012629721,0.0000065408217,0.0006488712,0.000021383354,0.000011366556,0.000020509946,0.00006241607],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990456,0.0000210419,0.00045113033,0.00016583836,0.0001497868,0.00016657227],"domain_scores_gemma":[0.99950445,0.00013094275,0.000081203325,0.0001609244,0.000089188936,0.000033285993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081438496,0.000107352935,0.00018428735,0.00026655456,0.000040224822,0.000015397345,0.000053380965,0.00043745563,0.000014452086],"category_scores_gemma":[0.00026920548,0.00009608646,0.00004251062,0.00063264597,0.000013747031,0.000116334646,0.0000072077564,0.00027654727,0.000005849997],"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":[0.0021135788,0.00036298434,0.011801072,0.00034295776,0.00008026414,0.0000036896129,0.0009338019,0.010798084,0.37876785,0.043293186,0.010228117,0.5412744],"study_design_scores_gemma":[0.008717028,0.0021981508,0.052840557,0.0008689057,0.000060602848,0.000025931564,0.0017796092,0.23560388,0.6372565,0.0044608777,0.05499996,0.0011880016],"about_ca_topic_score_codex":0.000062084386,"about_ca_topic_score_gemma":0.00008537427,"teacher_disagreement_score":0.5400864,"about_ca_system_score_codex":0.00013057898,"about_ca_system_score_gemma":0.00001619122,"threshold_uncertainty_score":0.39182922},"labels":[],"label_agreement":null},{"id":"W2127010609","doi":"10.1016/j.cviu.2013.08.004","title":"Special section on Advances in Machine Vision Beyond the Visible Spectrum (BVS)","year":2013,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Artificial intelligence; Computer science; RGB color model; Categorization; Pattern recognition (psychology); Computer vision; Infrared; Face (sociological concept); Section (typography); Spectrum (functional analysis); Optics; Physics","score_opus":0.013891554116379075,"score_gpt":0.23616834691025493,"score_spread":0.22227679279387585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127010609","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":[0.16562617,0.0010170144,0.746713,0.001196209,0.0212943,0.0016582982,0.000009239054,0.00061206985,0.061873708],"genre_scores_gemma":[0.9948119,0.00026866217,0.00030102453,0.00012134047,0.0043759965,0.000008922617,0.0000042118027,0.000031368396,0.00007659801],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988241,0.000075905125,0.00031619193,0.0002768198,0.00023499016,0.00027195027],"domain_scores_gemma":[0.99953634,0.0001301067,0.000051990828,0.00018868282,0.000017736631,0.000075128504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030378898,0.0002151465,0.0002211893,0.0003031745,0.0002689172,0.000415748,0.00009522866,0.000112803726,0.00018158075],"category_scores_gemma":[0.000008350352,0.00014902701,0.0000628137,0.00035182928,0.00004668283,0.000788782,0.00005457211,0.00036239435,0.00007524966],"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":[0.000530125,0.00029247595,0.00085475616,0.00037628188,0.00009621515,0.000085260304,0.0028151476,0.02270807,0.026659882,0.018741792,0.2743064,0.6525336],"study_design_scores_gemma":[0.004659394,0.0021412256,0.0056953686,0.0007400278,0.000020806965,0.00013332473,0.001542305,0.8569123,0.0035117466,0.015698703,0.10784019,0.0011045949],"about_ca_topic_score_codex":0.000032074964,"about_ca_topic_score_gemma":0.000049228336,"teacher_disagreement_score":0.83420426,"about_ca_system_score_codex":0.00025263627,"about_ca_system_score_gemma":0.000005710221,"threshold_uncertainty_score":0.6077145},"labels":[],"label_agreement":null},{"id":"W2128962390","doi":"10.1007/s00170-015-7213-0","title":"Further development of adaptable automated visual inspection—part I: concept and scheme","year":2015,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Adaptability; Scheme (mathematics); Block (permutation group theory); Focus (optics); Key (lock); Computer science; Quality (philosophy); Feature (linguistics); Perspective (graphical); Engineering; Artificial intelligence; Control engineering; Engineering drawing; Industrial engineering; Systems engineering; Operating system","score_opus":0.01771230953196497,"score_gpt":0.2624336050503203,"score_spread":0.24472129551835534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128962390","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":[0.992783,0.00033760737,0.0046194894,0.00017034271,0.0015314253,0.000067623856,0.0000012996375,0.00027461772,0.00021456604],"genre_scores_gemma":[0.9960569,0.00002745152,0.0037269057,0.0000084939,0.00013842285,0.0000029500109,5.7244125e-7,0.000012482958,0.000025802397],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991473,0.000011569133,0.00041950148,0.00006583155,0.00025482953,0.00010097673],"domain_scores_gemma":[0.9994414,0.000027347643,0.000216478,0.00007659233,0.00020313947,0.00003504053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022235412,0.000092027985,0.00016971027,0.00027396038,0.000034600434,0.000015835703,0.00021420793,0.000093992996,0.0000109557595],"category_scores_gemma":[0.00006114142,0.000067692294,0.000027856895,0.000081637256,0.00005515223,0.000118000964,0.00006155003,0.00021869727,0.000003775104],"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":[0.0007626219,0.00013857831,0.00042209943,0.00004178665,0.0015420272,0.00007765462,0.0053111264,0.19753812,0.32072586,0.0013486876,0.0047329487,0.46735847],"study_design_scores_gemma":[0.0010837392,0.00012382361,0.00024874596,0.00009261173,0.000007746985,0.00029097608,0.0015438686,0.003231287,0.9647439,0.00070321804,0.027830904,0.000099146055],"about_ca_topic_score_codex":0.0000036209806,"about_ca_topic_score_gemma":0.000005510289,"teacher_disagreement_score":0.64401805,"about_ca_system_score_codex":0.00013432433,"about_ca_system_score_gemma":0.00003799962,"threshold_uncertainty_score":0.27604115},"labels":[],"label_agreement":null},{"id":"W2129403787","doi":"10.5539/cis.v3n2p163","title":"Solder Joints Detection Method Based on Surface Recovery","year":2010,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Soldering; Computer science; Artificial intelligence; Computer vision; Process (computing); Materials science; Composite material","score_opus":0.01065551560463672,"score_gpt":0.23599980324239195,"score_spread":0.22534428763775521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129403787","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":[0.24376614,9.991261e-7,0.748618,0.00001268355,0.002788329,0.000099964716,0.0000015259286,0.00013014088,0.004582204],"genre_scores_gemma":[0.99174166,0.0000014820292,0.007975189,0.0001656826,0.000103532315,0.0000022571785,8.984384e-7,0.0000029890755,0.000006303835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938256,0.000011618766,0.00018116018,0.00008396913,0.00021932778,0.00012136719],"domain_scores_gemma":[0.9996294,0.000046052082,0.00003599377,0.00013582295,0.00008800836,0.00006473387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007689519,0.000071651004,0.000071497016,0.0002049487,0.00017417592,0.00024486682,0.00007407157,0.00006368338,0.000014286954],"category_scores_gemma":[0.000031913645,0.00006147189,0.000021009353,0.00037812296,0.000033197648,0.0022831277,0.000017145201,0.00016394777,0.00006434015],"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":[0.000013649725,0.0000050439944,0.000045819022,0.000024540082,0.0000023356895,1.6946744e-7,0.0002438225,0.23396772,0.026424345,0.00016195605,0.00051579304,0.73859483],"study_design_scores_gemma":[0.00018349885,0.0000620168,0.002325502,0.000009895835,9.402392e-7,0.000008245982,0.000008797073,0.9502108,0.036830492,0.000016007692,0.010261553,0.00008222988],"about_ca_topic_score_codex":0.0000070612255,"about_ca_topic_score_gemma":0.0000012166757,"teacher_disagreement_score":0.7479755,"about_ca_system_score_codex":0.000023584687,"about_ca_system_score_gemma":0.000021404618,"threshold_uncertainty_score":0.25067508},"labels":[],"label_agreement":null},{"id":"W2133026324","doi":"10.1017/s0952523804213402","title":"Repeatability indices for the Farnsworth D-15 test","year":2004,"lang":"en","type":"article","venue":"Visual Neuroscience","topic":"Industrial Vision Systems and Defect Detection","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":"University of Waterloo","funders":"Transport Canada","keywords":"Repeatability; Test (biology); Psychology; Biology; Mathematics; Statistics; Ecology","score_opus":0.02993439388718255,"score_gpt":0.2863441584862159,"score_spread":0.25640976459903336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133026324","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":[0.96172273,0.00006724531,0.034130592,0.00009116786,0.0027353116,0.0005706083,0.000017178485,0.00032198417,0.00034317776],"genre_scores_gemma":[0.99958116,0.0000076266733,0.00002984459,0.00009507903,0.0001835386,0.000039752234,3.779644e-7,0.000010653278,0.00005197034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992243,0.000011113884,0.0001688597,0.00020622156,0.00018924178,0.000200244],"domain_scores_gemma":[0.99948794,0.00021162776,0.000031912696,0.0001933144,0.000026560052,0.0000486555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034214254,0.00008896555,0.00008381206,0.000041551855,0.0002716235,0.000095394964,0.00017041976,0.000044973436,0.0000032571647],"category_scores_gemma":[0.0004986999,0.000059394853,0.000049931754,0.00039689997,0.00007273964,0.00015113763,0.000023762197,0.00011767149,0.000011634608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008648535,0.00040611732,0.022886844,0.00023084514,0.000013645902,0.00001975772,0.0013033367,0.25363216,0.6126272,0.0015952281,0.0014821079,0.10571628],"study_design_scores_gemma":[0.003195552,0.0030181226,0.24933268,0.00013110897,0.00005362681,0.00014084074,0.00061220746,0.16471703,0.32597545,0.00087117555,0.250679,0.0012732141],"about_ca_topic_score_codex":0.0000389544,"about_ca_topic_score_gemma":0.0000158371,"teacher_disagreement_score":0.28665176,"about_ca_system_score_codex":0.00003338947,"about_ca_system_score_gemma":0.000024658113,"threshold_uncertainty_score":0.24220519},"labels":[],"label_agreement":null},{"id":"W2138220814","doi":"10.1109/wescan.1995.493947","title":"A link reversal marking garbage collector integrated with the system swapper in a virtual memory system","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Garbage collection; Garbage; Computer science; Link (geometry); Virtual memory; Computer hardware; Memory management; Embedded system; Semiconductor memory; Computer network","score_opus":0.010208947531183087,"score_gpt":0.1618168823146168,"score_spread":0.1516079347834337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138220814","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":[0.8059544,0.00043938175,0.0173651,0.00012152756,0.0028211002,0.0021613243,0.000017124768,0.0025567964,0.16856326],"genre_scores_gemma":[0.99597704,0.0000021841222,0.0000466455,0.00001624025,0.00031911457,0.000084785286,0.0000012299608,0.000046389112,0.003506378],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986993,0.00015233632,0.00036786788,0.00021744074,0.00026774505,0.00029533045],"domain_scores_gemma":[0.99943537,0.00011140651,0.000053643325,0.00028210544,0.000054220403,0.000063235195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048110055,0.00022148482,0.0003161164,0.00019772921,0.00013104154,0.00010743627,0.00013266825,0.00020197922,0.000103943916],"category_scores_gemma":[0.000017038807,0.00013056985,0.0000648793,0.0008234727,0.000020022364,0.000101229205,0.000018087645,0.00039524626,0.00015916897],"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":[0.0025877496,0.000308162,0.0024685904,0.0061663548,0.0024721203,0.0030141599,0.030464914,0.37419355,0.062281843,0.007886221,0.41127303,0.096883304],"study_design_scores_gemma":[0.0027832985,0.0003275748,0.00016249549,0.001758544,0.000048438294,0.00038189607,0.027088597,0.9443951,0.002640235,1.67207e-7,0.019816281,0.0005973319],"about_ca_topic_score_codex":0.00039080917,"about_ca_topic_score_gemma":0.00024890297,"teacher_disagreement_score":0.5702016,"about_ca_system_score_codex":0.0005411881,"about_ca_system_score_gemma":0.000016623657,"threshold_uncertainty_score":0.5324484},"labels":[],"label_agreement":null},{"id":"W2138357589","doi":"10.1115/esda2014-20102","title":"Image-Based Versus Signal-Based Sensors for Machine Fault Detection and Isolation","year":2014,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Fault detection and isolation; Computer science; Fault (geology); Image sensor; SIGNAL (programming language); Real-time computing; Machine vision; Artificial intelligence; Grayscale; Isolation (microbiology); Embedded system; Image (mathematics)","score_opus":0.01446148563228016,"score_gpt":0.22968089667073888,"score_spread":0.2152194110384587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138357589","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":[0.1835461,0.000010961793,0.81410086,0.000023664636,0.0006217317,0.00029886197,0.000006431667,0.0003310743,0.0010603021],"genre_scores_gemma":[0.99713707,3.8605037e-7,0.0025269347,0.00002004828,0.0002068444,0.000031849417,0.000009214227,0.00002604285,0.00004161102],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994183,0.000035720066,0.00017527373,0.00013958057,0.000095381525,0.00013575425],"domain_scores_gemma":[0.9995168,0.00023787605,0.000031739557,0.00010155686,0.00005940062,0.000052616273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002560675,0.00011774745,0.00012487092,0.00011834744,0.00009787334,0.000051159404,0.000021846781,0.00012638756,0.000036050405],"category_scores_gemma":[0.000069137146,0.000107860404,0.000054883218,0.00010882355,0.000010031413,0.000080737685,0.000002549729,0.00008171204,0.000014690528],"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":[0.0013969236,0.000025805115,0.00011018234,0.00024942035,0.00005023607,6.993412e-7,0.000043119035,0.26042852,0.59834987,0.000073988784,0.0007410827,0.13853015],"study_design_scores_gemma":[0.002340801,0.00027116304,0.000082770915,0.000009233177,0.000014466913,6.5698214e-7,0.000011283083,0.8784586,0.1136215,0.0000122984975,0.005054406,0.00012280435],"about_ca_topic_score_codex":0.00008689972,"about_ca_topic_score_gemma":0.0000757157,"teacher_disagreement_score":0.81359094,"about_ca_system_score_codex":0.000048050922,"about_ca_system_score_gemma":0.000006205796,"threshold_uncertainty_score":0.43984196},"labels":[],"label_agreement":null},{"id":"W2142154582","doi":"10.1109/crv.2009.13","title":"A Support Vector Machine Based Online Learning Approach for Automated Visual Inspection","year":2009,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Scheme (mathematics); Support vector machine; Computer science; Retraining; Artificial intelligence; Matching (statistics); Visual inspection; Feature extraction; Feature (linguistics); Machine learning; Computer vision; Pattern recognition (psychology)","score_opus":0.0173398813863376,"score_gpt":0.27000673813197956,"score_spread":0.252666856745642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142154582","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":[0.22035375,0.000029561037,0.7591426,0.000038645954,0.00076732493,0.0008340606,0.000021051826,0.011596558,0.007216482],"genre_scores_gemma":[0.9960085,8.8568567e-7,0.0030950718,0.000042533335,0.00033675221,0.000020367199,0.00019623573,0.00002776609,0.00027186063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921626,0.000026002373,0.00025852254,0.00016581948,0.00012988634,0.00020352691],"domain_scores_gemma":[0.9997444,0.000025718178,0.000035631463,0.00008412734,0.00004895488,0.00006116432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020245498,0.00014401475,0.00018798452,0.0001596264,0.000110917565,0.000043233562,0.00004076445,0.0001532313,0.000053752017],"category_scores_gemma":[0.000040682724,0.00012870682,0.0000891877,0.00025801867,0.0000050170142,0.00007124791,0.0000033222768,0.0001827701,0.000013841909],"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":[0.00029867265,0.0003413982,0.00036727582,0.00013949184,0.000075935845,0.0000050189174,0.00015485761,0.84774965,0.07265344,0.00018834442,0.010335059,0.06769085],"study_design_scores_gemma":[0.0009588866,0.0005811306,0.0013990644,0.000007664851,0.000010943872,0.000008324951,0.000041289055,0.9885578,0.004787129,0.0000025553768,0.0034856596,0.0001595151],"about_ca_topic_score_codex":0.000029554903,"about_ca_topic_score_gemma":0.0000051119296,"teacher_disagreement_score":0.7756548,"about_ca_system_score_codex":0.00008671087,"about_ca_system_score_gemma":0.000014236203,"threshold_uncertainty_score":0.52485114},"labels":[],"label_agreement":null},{"id":"W2143108759","doi":"10.1109/ccece.1993.332255","title":"Applying digital image technology to pulp and paper","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Newsprint Company (Canada)","funders":"","keywords":"Reel; Paper machine; Trimming; Mill; Computer science; Calipers; Engineering drawing; Impression; Engineering; Mechanical engineering","score_opus":0.010823189605783197,"score_gpt":0.2083756136998941,"score_spread":0.19755242409411092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143108759","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":[0.6645814,0.0003257283,0.027084112,0.00033508943,0.0007498942,0.0008974505,0.000007268856,0.0020604013,0.30395862],"genre_scores_gemma":[0.99858564,0.0000040916857,0.00024594492,0.000030237185,0.000072043236,0.000048925453,2.0636642e-7,0.000011921642,0.0010009883],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99965614,0.0000016923904,0.00009391255,0.00008603193,0.00004817808,0.00011403292],"domain_scores_gemma":[0.9998468,0.000008957763,0.000004860947,0.00008743679,0.00001090266,0.000041033116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000026730062,0.000061785504,0.00007669739,0.000110131055,0.000031985113,0.00006822288,0.000027258102,0.00008424057,0.00018852374],"category_scores_gemma":[0.000018419822,0.000053439635,0.000013362683,0.00018314472,0.0000071958507,0.00014772144,0.000017985089,0.00007139557,0.00038900328],"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":[0.000003128504,0.0000094032775,0.0003540628,0.000019953075,0.00001919749,0.000011162106,0.00010541867,0.00017857073,0.09037645,0.00053264067,0.017676467,0.8907136],"study_design_scores_gemma":[0.00056210253,0.00013133601,0.00016001242,0.00003326844,0.000005951242,0.00012155774,0.0005534586,0.010658787,0.026063852,0.0002319288,0.9610675,0.0004102345],"about_ca_topic_score_codex":0.0000039727565,"about_ca_topic_score_gemma":0.0000015992107,"teacher_disagreement_score":0.943391,"about_ca_system_score_codex":0.000013193931,"about_ca_system_score_gemma":4.3075053e-7,"threshold_uncertainty_score":0.499998},"labels":[],"label_agreement":null},{"id":"W2143804471","doi":"10.5539/mas.v6n4p77","title":"Partial Erosion-Based Feature Extraction Approach for Plastic Bottle Shape Classification","year":2012,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Structuring element; Silhouette; Erosion; Artificial intelligence; Histogram; Computer science; Pixel; Pattern recognition (psychology); Normalization (sociology); Plastic bottle; Feature (linguistics); Mathematical morphology; Bin; Image processing; Computer vision; Bottle; Image (mathematics); Algorithm; Geology; Materials science","score_opus":0.045430823915907866,"score_gpt":0.26674627107214655,"score_spread":0.2213154471562387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143804471","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":[0.06806365,0.000028935863,0.9280884,0.000008388045,0.0010631063,0.00046147034,0.0000068118666,0.00023572374,0.0020434654],"genre_scores_gemma":[0.9956513,5.159239e-7,0.003551725,0.000014075704,0.00047022334,0.00024712086,0.0000121254125,0.00002026429,0.000032629057],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988852,0.000008565351,0.0001584403,0.00024402191,0.00033337495,0.00037037508],"domain_scores_gemma":[0.9995,0.000078503916,0.00005163202,0.00019867788,0.000044755638,0.00012642586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005972011,0.00012493717,0.00011584324,0.00012350641,0.00030960483,0.000093682356,0.00013012261,0.00014248755,0.000009222394],"category_scores_gemma":[0.000045606623,0.000112589165,0.000040703268,0.00035708025,0.00006299119,0.0002628303,0.000010170166,0.00015616923,0.000027481025],"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":[0.000033383723,0.000032575812,0.00006638195,0.000024325145,0.0000019306965,2.3028726e-8,0.00010054533,0.029153783,0.95865375,0.0003371777,0.00045990534,0.0111361975],"study_design_scores_gemma":[0.00028030956,0.000015984642,0.0013530063,0.0000053988074,0.0000079017545,0.0000015108062,0.00004987297,0.9336241,0.063081376,0.000023229275,0.0014221701,0.00013515091],"about_ca_topic_score_codex":0.0000021458393,"about_ca_topic_score_gemma":3.9113246e-7,"teacher_disagreement_score":0.9275877,"about_ca_system_score_codex":0.00014810156,"about_ca_system_score_gemma":0.00003930186,"threshold_uncertainty_score":0.45912528},"labels":[],"label_agreement":null},{"id":"W2148195309","doi":"10.1109/cimca.2005.1631284","title":"Artificial Intelligence and Image Processing Approaches in Damage Assessment and Material Evaluation","year":2006,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial neural network; Nondestructive testing; Signal processing; SIGNAL (programming language); Image processing; MATLAB; Sample (material); Adaptive neuro fuzzy inference system; Artificial intelligence; Wavelet; Wavelet transform; Fuzzy logic; Software; Acoustics; Computer vision; Pattern recognition (psychology); Image (mathematics); Fuzzy control system; Digital signal processing","score_opus":0.08955312181315186,"score_gpt":0.3051144592871007,"score_spread":0.21556133747394882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148195309","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":[0.9649514,0.000034608587,0.029136132,0.000012175229,0.00013851414,0.00026345608,0.0000014713478,0.000066781475,0.0053954525],"genre_scores_gemma":[0.99864113,0.0000016627994,0.0011581534,0.0000012522402,0.00015073271,0.000024073546,0.0000051654592,0.000008351237,0.000009480358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994335,0.000034029916,0.00020751797,0.000117243275,0.000116175994,0.00009153029],"domain_scores_gemma":[0.9998912,0.000011348493,0.00001920228,0.00004518374,0.000017437054,0.000015596668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005253509,0.000072233845,0.00008841055,0.00008192041,0.000038635193,0.0001488817,0.000015723392,0.000063513275,0.000026990081],"category_scores_gemma":[0.000005872358,0.00006474027,0.000006737164,0.00009630832,0.000016712575,0.00017707043,0.000011089009,0.00006360575,0.0000015050607],"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":[0.00003750747,0.00005402901,0.0014317106,0.00021156871,0.0000071952745,0.0000048431184,0.0003184923,0.025468802,0.15196902,0.00249315,0.000091765665,0.8179119],"study_design_scores_gemma":[0.000103606144,0.00002901663,0.011786944,0.000032994398,0.000008198322,0.000006246316,0.00048153332,0.92113817,0.06449715,0.0017472453,0.000030825206,0.00013807557],"about_ca_topic_score_codex":0.00012024712,"about_ca_topic_score_gemma":0.00009350013,"teacher_disagreement_score":0.89566934,"about_ca_system_score_codex":0.00004737221,"about_ca_system_score_gemma":0.000011584295,"threshold_uncertainty_score":0.26400316},"labels":[],"label_agreement":null},{"id":"W2149755688","doi":"10.24908/pceea.v0i0.3965","title":"DESIGN OF A CLOSE-UP STEREO VISION BASED SHEET METAL INSPECTION SYSTEM","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Grid; Sheet metal; Computer vision; Visual inspection; Artificial intelligence; Square tiling; Sample (material); Etching (microfabrication); Computer science; Computer graphics (images); Engineering; Materials science; Geometry; Structural engineering; Mathematics; Composite material; Physics","score_opus":0.01584982540863571,"score_gpt":0.1952880086978306,"score_spread":0.1794381832891949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149755688","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":[0.9480904,0.00024120022,0.0077268854,0.00019741128,0.019401133,0.002538107,0.00007581215,0.0016039645,0.020125104],"genre_scores_gemma":[0.998728,0.0000020506525,0.0008126984,0.000012237357,0.00016081988,0.00005501238,0.000003038539,0.00004433747,0.00018182749],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875414,0.000017507864,0.00047408466,0.0001617984,0.0003496402,0.00024282516],"domain_scores_gemma":[0.99882257,0.00003988122,0.00034551718,0.00012641562,0.0005288005,0.00013679697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075334293,0.00017381678,0.00025172174,0.000501926,0.00011451003,0.000045975165,0.00019705696,0.00025701133,0.000016443835],"category_scores_gemma":[0.00027170675,0.00016807686,0.00011501291,0.0006805938,0.00001159189,0.0002618361,0.000010511993,0.00020973054,0.000009275217],"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":[0.00029461918,0.0007135706,0.059146598,0.011172931,0.0023850468,0.000001554905,0.025229106,0.17799535,0.49125916,0.04068074,0.17657939,0.014541943],"study_design_scores_gemma":[0.0021699273,0.00042451706,0.10056873,0.0024309775,0.0005109922,0.000025150539,0.0035088954,0.51565295,0.3642498,0.00013716357,0.008792463,0.0015284279],"about_ca_topic_score_codex":0.0050349575,"about_ca_topic_score_gemma":0.0005242284,"teacher_disagreement_score":0.3376576,"about_ca_system_score_codex":0.0021079257,"about_ca_system_score_gemma":0.00029069796,"threshold_uncertainty_score":0.7611384},"labels":[],"label_agreement":null},{"id":"W2153404309","doi":"10.1109/iecon.1990.149245","title":"An experimental vision system for SMD component placement inspection","year":2002,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Centroid; Component (thermodynamics); Artificial intelligence; Orientation (vector space); Computer vision; Surface-mount technology; Computer science; Printed circuit board; Displacement (psychology); Segmentation; Soldering; Mathematics; Materials science; Geometry; Physics","score_opus":0.028210265339526298,"score_gpt":0.25753311925574296,"score_spread":0.22932285391621665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153404309","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":[0.9220178,0.00017113588,0.059944253,0.0000075981065,0.003321564,0.000859953,0.0000075494404,0.001921635,0.011748521],"genre_scores_gemma":[0.99909246,0.0000017919452,0.00023778043,0.000006104466,0.0003788803,0.000094410614,0.000006678433,0.000025495783,0.00015637498],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992888,0.00002159339,0.00023708686,0.00015253703,0.00014800801,0.0001519878],"domain_scores_gemma":[0.9997099,0.000014221705,0.00002505173,0.00015632021,0.000024023855,0.00007048927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012688496,0.00011599019,0.00013253286,0.00008821185,0.00013731791,0.00006094865,0.000045440695,0.00009298542,0.000109243614],"category_scores_gemma":[0.0000015795433,0.00010365589,0.000057434358,0.000078597266,0.0000056936246,0.00015750588,0.0000061978812,0.000056166315,0.00010239007],"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":[0.000120878125,0.0002888802,0.00003977288,0.00016708657,0.000055969216,0.0000036576569,0.0010546946,0.048925817,0.9101377,0.0011839311,0.028844152,0.00917744],"study_design_scores_gemma":[0.0012180815,0.0006407195,0.000033994973,0.000043170752,0.000006522936,0.000017680322,0.0021225442,0.6522986,0.33100528,8.638035e-7,0.012421496,0.00019103807],"about_ca_topic_score_codex":0.000029887788,"about_ca_topic_score_gemma":0.0000034005652,"teacher_disagreement_score":0.6033728,"about_ca_system_score_codex":0.00027600312,"about_ca_system_score_gemma":0.0000011516637,"threshold_uncertainty_score":0.42269644},"labels":[],"label_agreement":null},{"id":"W2154564025","doi":"10.4271/2008-01-1156","title":"Development of an Improved Cosmetic Corrosion Test for Finished Aluminum Autobody Panels","year":2008,"lang":"en","type":"article","venue":"SAE International Journal of Materials and Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Chrysler (Canada); Western University; Novelis (Canada)","funders":"","keywords":"Corrosion; Aluminium; Materials science; Forensic engineering; Metallurgy; Engineering","score_opus":0.023102759990939734,"score_gpt":0.24789622575725523,"score_spread":0.22479346576631548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154564025","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":[0.9933872,0.000023570516,0.0036366298,0.000008947298,0.0027456358,0.00012834498,0.000027338003,0.000019592891,0.000022719392],"genre_scores_gemma":[0.9960002,0.00003078175,0.0034696544,0.000008064861,0.00043998216,0.0000054934626,0.0000075362786,0.000016065604,0.000022269873],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989854,0.000014320586,0.00062995474,0.000078159625,0.00019003636,0.00010212351],"domain_scores_gemma":[0.9994418,0.00006576701,0.00025376122,0.000053805652,0.00013050647,0.000054343684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035341288,0.00010568571,0.000240159,0.00013424088,0.00005766577,0.00004294667,0.00011362994,0.000072772666,0.0000311538],"category_scores_gemma":[0.000046482684,0.000088188506,0.000045644483,0.000013498691,0.000015446305,0.00018447968,0.00002303373,0.000058105397,0.0000010719109],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011528425,0.000026772568,0.000017927781,0.00004243263,0.000038131104,0.00000926942,0.00035732755,0.00031625907,0.98873484,0.0000033344206,0.0000460527,0.010292374],"study_design_scores_gemma":[0.00096011406,0.00015409553,0.0063271495,0.000105406085,0.000010280288,0.00023703961,0.0000837249,0.0002442271,0.98891705,0.00003589694,0.0028345631,0.000090463414],"about_ca_topic_score_codex":0.000008385158,"about_ca_topic_score_gemma":0.000003196017,"teacher_disagreement_score":0.0102019105,"about_ca_system_score_codex":0.000047252222,"about_ca_system_score_gemma":0.000031610893,"threshold_uncertainty_score":0.3596223},"labels":[],"label_agreement":null},{"id":"W2159492909","doi":"10.1109/icip.1995.537440","title":"A ratio edge detector for speckled images based on maximum strength edge pruning","year":2002,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":37,"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":"Pruning; Enhanced Data Rates for GSM Evolution; Detector; Edge detection; Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Image (mathematics); Image processing; Telecommunications; Biology","score_opus":0.04675569965081514,"score_gpt":0.27488452429059723,"score_spread":0.2281288246397821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159492909","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18464561,0.00032505562,0.30558145,0.0020109012,0.0059284214,0.003684191,0.00027613336,0.0036398866,0.49390838],"genre_scores_gemma":[0.9942234,0.0000121433395,0.0037314452,0.00010592835,0.0009068787,0.00024374409,0.000015420897,0.00007935569,0.0006816646],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980901,0.000010268726,0.0004948329,0.00048392164,0.00052096334,0.000399912],"domain_scores_gemma":[0.99870276,0.000077707424,0.00020801697,0.00010887065,0.0007818494,0.0001207959],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029475582,0.00037231648,0.00030031893,0.00045387007,0.00027184177,0.000961666,0.0003255622,0.00017109426,0.00045289332],"category_scores_gemma":[0.00038434673,0.00036090007,0.0001238366,0.0002800553,0.00005113414,0.0007542433,0.00002731161,0.00041253117,0.00009331204],"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":[0.00084946223,0.0005199842,0.0005005059,0.0014009351,0.00019375262,0.00001980332,0.0019196036,0.0016536145,0.512587,0.004776114,0.017981831,0.4575974],"study_design_scores_gemma":[0.0014930541,0.0002495896,0.00007665989,0.00070682995,0.000021717233,0.000010842806,0.0002956938,0.86908084,0.12392118,0.0004107241,0.003270214,0.00046268158],"about_ca_topic_score_codex":0.000004064472,"about_ca_topic_score_gemma":0.00000106574,"teacher_disagreement_score":0.86742723,"about_ca_system_score_codex":0.00024515373,"about_ca_system_score_gemma":0.0000412352,"threshold_uncertainty_score":0.9998843},"labels":[],"label_agreement":null},{"id":"W2160137813","doi":"10.1109/imtc.2010.5488107","title":"Automatic woven fabric structure identification by using principal component analysis and fuzzy clustering","year":2010,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Yarn; Principal component analysis; Artificial intelligence; Cluster analysis; Computer science; Pattern recognition (psychology); Segmentation; Texture (cosmology); Woven fabric; Computer vision; Fuzzy logic; Identification (biology); Projection (relational algebra); Sample (material); Image segmentation; Fuzzy clustering; Orientation (vector space); Image processing; Image (mathematics); Mathematics; Engineering; Algorithm","score_opus":0.010831872533996407,"score_gpt":0.2349436840882703,"score_spread":0.2241118115542739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160137813","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":[0.952013,0.000021305328,0.046792027,0.000004135123,0.00060813763,0.00014752237,0.000011117275,0.00021041333,0.00019232281],"genre_scores_gemma":[0.9991084,0.0000012059935,0.0007075069,0.0000043358677,0.000093348026,0.0000033683295,0.000013111316,0.000015265505,0.000053501502],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992534,0.000020357445,0.00029032503,0.0001548202,0.0001462081,0.00013487788],"domain_scores_gemma":[0.9996464,0.000020294274,0.000056307577,0.00018851472,0.000025398802,0.00006306742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015976305,0.00011439553,0.00018580774,0.00021097147,0.00009276661,0.00012976116,0.000053558735,0.00013567088,0.00010917851],"category_scores_gemma":[0.000012586028,0.00010358034,0.000051450985,0.00039662237,0.000012026719,0.00012832857,0.000023673934,0.00018168853,0.000005748469],"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":[0.0000020406842,0.0000049293903,0.0016884285,0.000046367724,0.00016058702,6.8575093e-7,0.0001268522,0.012881893,0.9671701,0.000015507378,0.000080895115,0.017821683],"study_design_scores_gemma":[0.00016997672,0.0000061211567,0.014542089,0.0000070071605,0.00012801104,0.00001539354,0.000050928407,0.96356356,0.021013908,0.000015155334,0.00033649887,0.00015133161],"about_ca_topic_score_codex":0.00021499244,"about_ca_topic_score_gemma":0.00020204624,"teacher_disagreement_score":0.9506817,"about_ca_system_score_codex":0.000039404338,"about_ca_system_score_gemma":0.000004130985,"threshold_uncertainty_score":0.42238837},"labels":[],"label_agreement":null},{"id":"W2162304794","doi":"10.6000/1927-5129.2014.10.71","title":"Techniques to Identify and Test PCB Faults with Proposed Solution","year":2014,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Troubleshooting; Downtime; Printed circuit board; Reliability engineering; Task (project management); Test (biology); Fault (geology); Computer science; Engineering; Embedded system; Systems engineering; Operating system","score_opus":0.016138087762015488,"score_gpt":0.25798865412035843,"score_spread":0.24185056635834296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162304794","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":[0.964908,0.000023486411,0.030158201,0.00010066136,0.00024412487,0.00018969692,5.3748755e-7,0.00006047977,0.004314836],"genre_scores_gemma":[0.99700004,0.0000033732854,0.0026760285,0.00002954015,0.0002670229,0.0000030882272,4.9023683e-8,0.0000060581424,0.000014780538],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919474,0.000014126181,0.00024162365,0.00009598526,0.0003230102,0.0001305291],"domain_scores_gemma":[0.99961936,0.00007857817,0.00010553126,0.000058043654,0.00006108661,0.00007738135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011437754,0.000078788944,0.00015272673,0.000191373,0.00013111747,0.000087321525,0.00009991815,0.000050111285,0.0000037382474],"category_scores_gemma":[0.000058036272,0.000051178842,0.000020004964,0.00032408827,0.00006059235,0.00016051691,0.000011964973,0.00012476255,0.0000054434704],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005839693,0.000027472259,0.0012442563,0.000041620635,0.000015099519,0.0000034205102,0.00046548218,0.0012657435,0.8596805,0.00041159362,0.0021304348,0.13465598],"study_design_scores_gemma":[0.0013629358,0.0037948466,0.019675484,0.000567543,0.000053793647,0.00044574492,0.0011495744,0.0051636244,0.93824214,0.0008897032,0.028061435,0.00059317827],"about_ca_topic_score_codex":0.000006934195,"about_ca_topic_score_gemma":0.0000077399845,"teacher_disagreement_score":0.1340628,"about_ca_system_score_codex":0.000030100653,"about_ca_system_score_gemma":0.000023668592,"threshold_uncertainty_score":0.20870127},"labels":[],"label_agreement":null},{"id":"W2166704954","doi":"10.1109/nafips.2006.365494","title":"A Neuro-fuzzy Approach to Machine Vision Based Parts Inspection","year":2006,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence; Concordia University; University of Calgary","keywords":"Machine vision; Computer science; Automotive industry; Artificial intelligence; Process (computing); CLIPS; Fuzzy logic; Truck; Computer vision; Neuro-fuzzy; Machine learning; Fuzzy control system; Engineering; Automotive engineering","score_opus":0.011162814402036416,"score_gpt":0.20875651897472794,"score_spread":0.19759370457269151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166704954","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":[0.15198012,0.00004923112,0.46380213,0.000093414215,0.0018616106,0.0007356072,0.000008135052,0.0027661014,0.37870365],"genre_scores_gemma":[0.9982064,4.0613315e-7,0.0008626971,0.000079930905,0.00039035556,0.000031629603,0.000012417769,0.00003041335,0.00038578213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991821,0.000030388419,0.00023275003,0.00019206562,0.00018316317,0.00017950064],"domain_scores_gemma":[0.9996747,0.000024626346,0.000018270714,0.00018809852,0.000028065939,0.000066260494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015740175,0.00013414152,0.00013824741,0.00018681197,0.000086487235,0.00006623208,0.00004963186,0.000103458726,0.000033097152],"category_scores_gemma":[0.000013927543,0.000116237126,0.000060464892,0.0003910604,0.000005259928,0.00009260309,0.000010414949,0.00012565927,0.00015018492],"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":[0.00007041534,0.00016356198,0.0005948328,0.000059471888,0.000011939702,0.000005743916,0.000039810344,0.8076946,0.03655828,0.0016483665,0.14138143,0.0117715085],"study_design_scores_gemma":[0.0011727696,0.00025972156,0.0062546227,0.000029301793,0.000013235442,0.000024399771,0.000025438074,0.8332673,0.0250808,0.00012093293,0.13328998,0.0004615069],"about_ca_topic_score_codex":0.00057172414,"about_ca_topic_score_gemma":0.00004730986,"teacher_disagreement_score":0.8462263,"about_ca_system_score_codex":0.00006260984,"about_ca_system_score_gemma":0.000005373778,"threshold_uncertainty_score":0.47400123},"labels":[],"label_agreement":null},{"id":"W2167742641","doi":"10.1109/icma.2005.1626722","title":"Development of trinocular CCD-based optical detector for real-time monitoring of laser cladding","year":2006,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":29,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Cladding (metalworking); Optics; Laser; Projection (relational algebra); Materials science; Computer science; Computer vision; Physics; Algorithm","score_opus":0.018909733392863316,"score_gpt":0.23608905643551886,"score_spread":0.21717932304265555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167742641","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":[0.949467,0.000010346122,0.0478598,9.825079e-7,0.00027270426,0.0002515927,0.000005143967,0.00012376608,0.0020086886],"genre_scores_gemma":[0.9604161,3.432487e-7,0.03924383,3.2380214e-7,0.0001703229,0.000030213614,0.0000028869335,0.000022509052,0.00011346783],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990832,0.000009473911,0.000475937,0.00010514026,0.00016372139,0.00016253494],"domain_scores_gemma":[0.99964905,0.00007457981,0.000054305314,0.00011561617,0.00007268634,0.000033749864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024064048,0.00010571359,0.00023614836,0.00012026044,0.00003512105,0.000009166897,0.000049805614,0.00012444182,0.000026794123],"category_scores_gemma":[0.000021332935,0.000096508455,0.00009239527,0.00013964907,0.000008647282,0.000038735976,0.000007530453,0.00004985106,0.0000055580977],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048411817,0.000024185938,0.00040193336,0.00016032191,0.000033560715,7.3941544e-7,0.000029891939,0.023810754,0.9684788,0.000056886114,0.000110107125,0.0068444037],"study_design_scores_gemma":[0.00073582376,0.000042241416,0.0012275111,0.00007149699,0.000011224664,4.967129e-7,0.000031221454,0.009563979,0.9872722,0.000003724244,0.0009332311,0.00010681987],"about_ca_topic_score_codex":0.000030184083,"about_ca_topic_score_gemma":0.0000025356462,"teacher_disagreement_score":0.018793426,"about_ca_system_score_codex":0.00007041634,"about_ca_system_score_gemma":0.000029289155,"threshold_uncertainty_score":0.39355004},"labels":[],"label_agreement":null},{"id":"W2168375829","doi":"10.1109/mwscas.2004.1353934","title":"Local thresholding of composite documents using multi-layer perceptron nerual network","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Thresholding; Computer science; Pixel; Artificial intelligence; Pattern recognition (psychology); Perceptron; Artificial neural network; Feature (linguistics); Image (mathematics); Layer (electronics); Multilayer perceptron; Computer vision","score_opus":0.042500180131380794,"score_gpt":0.2795750398503095,"score_spread":0.23707485971892872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168375829","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":[0.65341794,0.00006740547,0.3446706,0.0000017122535,0.0006486613,0.00010668099,9.680523e-7,0.00011058347,0.00097543345],"genre_scores_gemma":[0.99484164,0.000003729657,0.0048386045,0.000013097167,0.00022750346,0.000002066792,0.0000016362452,0.000024615249,0.000047091697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991814,0.000016540122,0.00029310925,0.00011839387,0.00015999934,0.00023054254],"domain_scores_gemma":[0.99974126,0.000011614363,0.000035240304,0.00012681872,0.000029620593,0.000055422304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014292909,0.00012532744,0.00019663095,0.000062622006,0.00007336444,0.000026838765,0.00005831785,0.00012626422,0.000072194256],"category_scores_gemma":[0.0000023955604,0.00011239588,0.00007396114,0.00017936746,0.00001941063,0.00014132573,0.000024589222,0.00013691318,0.000026365273],"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":[0.0000118918915,0.000011711783,0.0005734228,0.000014998318,0.00002527372,0.0000022006402,0.00009631402,0.9426096,0.05473729,0.00008958586,0.00007648733,0.0017511928],"study_design_scores_gemma":[0.0066270493,0.00027000593,0.008751275,0.0006726412,0.000089705725,0.00008775401,0.000924905,0.8138597,0.16522156,0.00017350342,0.002375247,0.0009466501],"about_ca_topic_score_codex":0.0005827498,"about_ca_topic_score_gemma":0.000026196587,"teacher_disagreement_score":0.3414237,"about_ca_system_score_codex":0.00014515489,"about_ca_system_score_gemma":0.00001054173,"threshold_uncertainty_score":0.4583371},"labels":[],"label_agreement":null},{"id":"W2169890662","doi":"10.1109/icarcv.2008.4795630","title":"Image processing techniques for quality inspection of gelatin capsules in pharmaceutical applications","year":2008,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":23,"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 Windsor","funders":"","keywords":"USB; Computer science; Image processing; Automated X-ray inspection; Artificial intelligence; Computer vision; Machine vision; Digital image processing; Tracing; Computer hardware; Image quality; Embedded system; Image (mathematics); Software; Operating system","score_opus":0.06833621101630727,"score_gpt":0.35843408722773096,"score_spread":0.2900978762114237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169890662","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":[0.24059409,0.00009514773,0.747576,0.000015979122,0.00006825337,0.0009359066,0.000009106081,0.0006669049,0.010038585],"genre_scores_gemma":[0.9904495,0.000012776649,0.009152477,0.000004061316,0.00009627715,0.00023986673,0.000002726026,0.000011167061,0.00003114966],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941415,0.00001715385,0.00031510662,0.00008914462,0.00007464963,0.00008979487],"domain_scores_gemma":[0.99977654,0.000035482233,0.0000356878,0.00007078096,0.00006037629,0.000021113414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023739079,0.000061638406,0.00012808797,0.000103077815,0.000055577228,0.0000065122304,0.000030723746,0.00008521929,0.000007951503],"category_scores_gemma":[0.000022047749,0.000058106747,0.00003602359,0.00021015866,0.000026291415,0.00010654012,0.000004887171,0.000088458604,0.0000025246954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000069157264,0.00014049097,0.002095137,0.00069521257,0.00001749582,9.0373794e-7,0.0006164244,0.00044532682,0.8324049,0.0016762379,0.0011336592,0.16070502],"study_design_scores_gemma":[0.0007294898,0.000052896765,0.003852261,0.000055841105,0.000007974564,0.000017315542,0.00027875963,0.05341223,0.9291181,0.0003031382,0.011944489,0.00022752266],"about_ca_topic_score_codex":0.00007772081,"about_ca_topic_score_gemma":0.000016923763,"teacher_disagreement_score":0.7498554,"about_ca_system_score_codex":0.000053408887,"about_ca_system_score_gemma":0.000013508852,"threshold_uncertainty_score":0.23695244},"labels":[],"label_agreement":null},{"id":"W2171647728","doi":"10.1109/icsmc.1995.538259","title":"Omni-directional sensors for pipe inspection","year":2002,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lens (geology); Computer science; Surface (topology); Optics; Field of view; Computer vision; Acoustics; Artificial intelligence; Physics","score_opus":0.0383719266844384,"score_gpt":0.2409724662979272,"score_spread":0.2026005396134888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171647728","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":[0.2672929,0.00073810364,0.39603904,0.00012458077,0.063519865,0.0039593126,0.00032782054,0.012621433,0.25537696],"genre_scores_gemma":[0.98779404,0.00005744621,0.0010785433,0.0000129726095,0.0033460895,0.00026742404,0.000040107258,0.00008346087,0.0073198914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886686,0.000024889636,0.00037539416,0.00031646315,0.00019939995,0.00021700563],"domain_scores_gemma":[0.999438,0.00006448525,0.00006536585,0.00025252704,0.000113081514,0.00006654937],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019534725,0.00026186113,0.00030521496,0.0002499502,0.00012029154,0.000093795956,0.00007443576,0.0006655294,0.00036698356],"category_scores_gemma":[0.000041528532,0.000261192,0.0002524554,0.00012845584,0.00001150957,0.000057852507,0.000039414197,0.0004918689,0.00014819438],"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":[0.00010268589,0.00009609168,0.00015826461,0.0011896114,0.00057259464,0.0000064227397,0.00035206048,0.58808815,0.00936461,0.000598506,0.36574048,0.033730518],"study_design_scores_gemma":[0.0011019525,0.00013619236,0.00037417279,0.00020471524,0.00007343905,0.000060492504,0.00012099081,0.5181182,0.04290812,0.0010482064,0.43476284,0.0010907027],"about_ca_topic_score_codex":0.00010902354,"about_ca_topic_score_gemma":0.0000198812,"teacher_disagreement_score":0.7205012,"about_ca_system_score_codex":0.00026026266,"about_ca_system_score_gemma":0.000015586922,"threshold_uncertainty_score":0.999984},"labels":[],"label_agreement":null},{"id":"W2180876289","doi":"10.1007/978-1-4471-6741-9_1","title":"Industrial Inspection with Open Eyes: Advance with Machine Vision Technology","year":2015,"lang":"en","type":"book-chapter","venue":"Advances in computer vision and pattern recognition","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Machine vision; Computer vision; Artificial intelligence; Computer science; Manufacturing engineering; Engineering; Engineering drawing","score_opus":0.032762735928974204,"score_gpt":0.2727255303335905,"score_spread":0.23996279440461626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180876289","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":[0.033657514,0.01989645,0.67606556,0.00043618513,0.012962356,0.011240604,0.0007594033,0.0043893955,0.24059254],"genre_scores_gemma":[0.9595841,0.012799314,0.0137515515,0.00043896804,0.0047207517,0.00038030735,0.0015852357,0.000980837,0.0057589347],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99812555,0.000042879547,0.00054650434,0.00066401344,0.00035747775,0.00026355364],"domain_scores_gemma":[0.99905974,0.00006526449,0.0002672129,0.00032118332,0.00017392615,0.00011268324],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027423448,0.0005352702,0.0006697939,0.00067347154,0.00012238955,0.00018739008,0.0002187047,0.00068469363,0.000066736306],"category_scores_gemma":[0.000005535661,0.00041686025,0.000040287177,0.000219763,0.00009735978,0.00097589585,0.00014837093,0.0009475506,0.000057549125],"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":[0.00036900147,0.00002423532,0.00011030644,0.00008276463,0.000030911237,0.0000633503,0.00003890414,0.0014550264,0.000009040222,0.000055577973,0.00044808624,0.9973128],"study_design_scores_gemma":[0.015565831,0.012342638,0.000102822174,0.016716244,0.00015255735,0.0012472833,0.00009468604,0.046692807,0.00024067562,0.010021523,0.89383256,0.0029903394],"about_ca_topic_score_codex":0.000032002754,"about_ca_topic_score_gemma":0.00049784983,"teacher_disagreement_score":0.9943225,"about_ca_system_score_codex":0.00018281025,"about_ca_system_score_gemma":0.000031307965,"threshold_uncertainty_score":0.99982834},"labels":[],"label_agreement":null},{"id":"W2181386505","doi":"","title":"2011 Canadian Conference on Computer and Robot Vision CRV 2011 Table of Contents","year":2011,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Table (database); Computer science; Computer vision; Artificial intelligence; Robot; Computer graphics (images); Database","score_opus":0.058902583273090565,"score_gpt":0.21578158577882875,"score_spread":0.1568790025057382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2181386505","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":[0.7102657,0.00021723802,0.029194335,0.000054470787,0.0042296695,0.00085189525,0.000051104813,0.00032207282,0.25481352],"genre_scores_gemma":[0.99890554,0.000013452369,0.0003229294,0.000027924723,0.000049382725,0.0000029512296,0.0000017315098,0.000011224673,0.0006648351],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945295,0.000016178385,0.00017845762,0.00011446966,0.00008079314,0.00015714136],"domain_scores_gemma":[0.99963534,0.000011503234,0.000025269728,0.00013154259,0.00006330483,0.00013305439],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000969539,0.00009515268,0.00015832779,0.000119815886,0.000037380338,0.000015783888,0.000051569812,0.0001133447,0.0010977356],"category_scores_gemma":[0.0000024453354,0.00007957976,0.000021312628,0.000036208567,0.000015884405,0.000087337794,0.000010926152,0.00008611442,0.00019141762],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058761047,0.00036755437,0.02444263,0.0005554696,0.0006055945,0.00010618061,0.0044373567,0.005545361,0.0830408,0.03357677,0.49689087,0.34984377],"study_design_scores_gemma":[0.008880166,0.005804935,0.32848433,0.0011098337,0.00011528425,0.00011956385,0.00087246194,0.34429035,0.22998661,0.0010050309,0.076543316,0.0027881498],"about_ca_topic_score_codex":0.1306073,"about_ca_topic_score_gemma":0.014005879,"teacher_disagreement_score":0.42034757,"about_ca_system_score_codex":0.00002669044,"about_ca_system_score_gemma":0.000015442234,"threshold_uncertainty_score":0.9998154},"labels":[],"label_agreement":null},{"id":"W2182260436","doi":"","title":"Identification de cristaux dans un phoswich par la méthode de mesure de temps au dessus d'un seuil (ToT) pour le scanner LabPET II","year":2014,"lang":"fr","type":"article","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Physics; Humanities; Art","score_opus":0.012377518823696749,"score_gpt":0.20545565771297794,"score_spread":0.1930781388892812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182260436","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":[0.55356395,0.0016152668,0.43403456,0.00014895848,0.0006971848,0.0001843796,0.00002281452,0.000055827302,0.009677081],"genre_scores_gemma":[0.9874381,0.00014066775,0.00840296,0.00000884703,0.00032186334,0.0000016050044,0.00000700087,0.000030069821,0.0036489235],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99773407,0.00075606845,0.00043236767,0.00032228846,0.00031197575,0.00044324642],"domain_scores_gemma":[0.99876446,0.00016625332,0.00018567256,0.00043387315,0.00025751613,0.0001922573],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015945133,0.0002873802,0.0003940287,0.0001667125,0.001173547,0.00004163193,0.00060907233,0.0005360652,0.00003284423],"category_scores_gemma":[0.00020871042,0.0003181956,0.0003489424,0.00041689348,0.00046535404,0.00028965113,0.00020336956,0.0004374638,0.000037822643],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024439857,0.0006229141,0.013318859,0.0030128015,0.0005610279,0.000026665075,0.022834344,0.39563975,0.51019144,0.03923863,0.0034539069,0.010855258],"study_design_scores_gemma":[0.0022795412,0.0001075353,0.11394749,0.0031066304,0.0005794706,0.0002870164,0.0009961476,0.16089122,0.6823133,0.0021548527,0.032825924,0.0005108405],"about_ca_topic_score_codex":0.014798772,"about_ca_topic_score_gemma":0.008396262,"teacher_disagreement_score":0.43387413,"about_ca_system_score_codex":0.0031607265,"about_ca_system_score_gemma":0.00090962386,"threshold_uncertainty_score":0.999927},"labels":[],"label_agreement":null},{"id":"W2199961675","doi":"10.4271/2006-01-0378","title":"A Neuro-Fuzzy Approach to a Machine Vision-based Parts Inspection Problem","year":2006,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Government of Canada","keywords":"Computer science; Artificial intelligence; Neuro-fuzzy; Machine vision; Computer vision; Fuzzy logic; Machine learning; Fuzzy control system","score_opus":0.010694049850732372,"score_gpt":0.223781317408556,"score_spread":0.21308726755782362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2199961675","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39294082,0.00059396226,0.00029719193,0.0033221964,0.0021703816,0.007358078,0.00026827777,0.030731156,0.56231797],"genre_scores_gemma":[0.993948,0.000019327166,0.0033341998,0.0007824151,0.00052357395,0.000825199,0.000080384336,0.00022109764,0.00026584225],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99517876,0.00018478688,0.0013540696,0.0012236455,0.0010592712,0.0009994493],"domain_scores_gemma":[0.99774396,0.0003118055,0.00016567238,0.0012374659,0.00012034227,0.00042073103],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007431318,0.0008884553,0.0009692858,0.00051352644,0.0004855946,0.00023227504,0.00059055135,0.0010367087,0.00012279765],"category_scores_gemma":[0.00028728202,0.0007763767,0.00050050823,0.0016814767,0.0002625592,0.00038930544,0.00016060188,0.0014565923,0.00023208291],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003306962,0.00045986546,0.000074798176,0.0000945041,0.000024630857,0.000026304844,0.000012242894,0.01005621,0.9543281,0.00409274,0.026846794,0.0036530807],"study_design_scores_gemma":[0.0017616473,0.0024651254,0.74562806,0.00034440862,0.00008496115,0.00016814293,0.000037818172,0.000024246268,0.0007378117,0.001335513,0.24597864,0.0014336176],"about_ca_topic_score_codex":0.00025218786,"about_ca_topic_score_gemma":0.03200909,"teacher_disagreement_score":0.95359033,"about_ca_system_score_codex":0.000490683,"about_ca_system_score_gemma":0.0000611028,"threshold_uncertainty_score":0.99946874},"labels":[],"label_agreement":null},{"id":"W2202633017","doi":"10.1016/j.ifacol.2015.06.451","title":"A Learning-Based Approach for Automatic Defect Detection in Textile Images","year":2015,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Contourlet; Classifier (UML); Feature extraction; Textile; Naive Bayes classifier; Computer vision; Machine learning; Support vector machine","score_opus":0.025124655946076664,"score_gpt":0.24683643599075494,"score_spread":0.22171178004467829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2202633017","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":[0.6907996,0.00034997842,0.3027845,0.000031416854,0.00095244875,0.0013222692,0.000027665177,0.0012960294,0.0024360865],"genre_scores_gemma":[0.97573507,0.0000015899221,0.023460032,0.000016373524,0.0003144605,0.00018829315,0.000035944093,0.00005334018,0.00019492218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890023,0.00007832067,0.00030980227,0.00022948965,0.00019194564,0.00029019784],"domain_scores_gemma":[0.9995137,0.00010386557,0.000056619054,0.00015611308,0.0000662259,0.00010351167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057699165,0.00019687144,0.00028268778,0.00023257852,0.000065454384,0.00005132289,0.000076502554,0.00020503112,0.0000143695415],"category_scores_gemma":[0.00028457033,0.00018405556,0.0001754813,0.0003629464,0.000016396754,0.00011680101,0.000009216137,0.00028215715,0.000032470543],"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":[0.00013148274,0.00011392212,0.00042523615,0.00031021753,0.000060890277,0.000005181365,0.0006017133,0.850553,0.02096356,0.0000029819319,0.00007625393,0.12675558],"study_design_scores_gemma":[0.0020717846,0.00037383413,0.0002490301,0.000042696207,0.000024629398,0.000011994085,0.000520072,0.9858075,0.009298003,0.000009000801,0.0013453319,0.0002461413],"about_ca_topic_score_codex":0.000092430404,"about_ca_topic_score_gemma":0.000052999654,"teacher_disagreement_score":0.28493544,"about_ca_system_score_codex":0.00019958492,"about_ca_system_score_gemma":0.00003816043,"threshold_uncertainty_score":0.7505568},"labels":[],"label_agreement":null},{"id":"W2207470802","doi":"10.1109/ceidp.2015.7352008","title":"Comparison of pollution level of aged porcelain and silicon rubber insulators","year":2015,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Western University","funders":"","keywords":"Pollution; Silicon rubber; Insulator (electricity); Natural rubber; Salt lake; Silicon; Materials science; Environmental science; Artificial neural network; Composite material; Computer science; Metallurgy; Geology; Artificial intelligence","score_opus":0.16621229369164608,"score_gpt":0.3177927145531096,"score_spread":0.1515804208614635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2207470802","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":[0.99165297,0.00012579454,0.0033985963,0.000003883489,0.00027796466,0.00009421008,0.000006615671,0.000047538673,0.004392451],"genre_scores_gemma":[0.9997284,0.0000013105468,0.00008920145,0.0000022288054,0.000034518187,0.0000012113945,0.0000013138342,0.000007648959,0.00013417963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994558,0.000024487577,0.00027515634,0.00005805835,0.00011957479,0.00006688404],"domain_scores_gemma":[0.9997598,0.000015637375,0.000051469782,0.00008485214,0.000040691997,0.000047550388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023070404,0.000061577775,0.00019382585,0.00008703114,0.000011537114,0.0000045815978,0.000022560287,0.0000990733,0.000012466032],"category_scores_gemma":[0.000033722044,0.000053443142,0.00002461781,0.0001242301,0.000016265225,0.000055593875,0.000009717451,0.000059703347,0.000003131557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020274919,0.0001377529,0.0753441,0.00030034775,0.00016401656,0.0000014387902,0.0054556546,0.03658602,0.8196705,0.0031001733,0.014071362,0.04496587],"study_design_scores_gemma":[0.002422891,0.00037069272,0.1142935,0.00008173929,0.000027719301,0.000007788242,0.002512497,0.027997168,0.8498283,0.000164986,0.0020319691,0.00026073796],"about_ca_topic_score_codex":0.00033197616,"about_ca_topic_score_gemma":0.000029799456,"teacher_disagreement_score":0.044705134,"about_ca_system_score_codex":0.000023495488,"about_ca_system_score_gemma":0.000009175213,"threshold_uncertainty_score":0.2179348},"labels":[],"label_agreement":null},{"id":"W2227765095","doi":"10.6084/m9.figshare.1057731.v1","title":"Amorçage inter-sensoriel : implication des fonctions exécutives dans la sélection des traces","year":2014,"lang":"fr","type":"article","venue":"Figshare","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Humanities; Philosophy","score_opus":0.04685835057429401,"score_gpt":0.2727976600482693,"score_spread":0.22593930947397528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2227765095","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2873683,0.008615251,0.10963309,0.00042593828,0.010176615,0.0033324305,0.13015327,0.0041678096,0.4461273],"genre_scores_gemma":[0.98957735,0.000030778498,0.0003127952,0.000008079943,0.0013486219,0.0002699028,0.0032446657,0.0000734366,0.0051343525],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99849665,0.00026360672,0.00038433453,0.000330961,0.00015286788,0.00037158292],"domain_scores_gemma":[0.9988996,0.00034033455,0.00012787395,0.0002945454,0.00019706614,0.00014058167],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00018079438,0.0002788353,0.00024491368,0.0001670285,0.0004715516,0.00024250559,0.00012554097,0.00045232428,0.01833917],"category_scores_gemma":[0.0009256175,0.0003034178,0.00018005822,0.00049631926,0.000052592957,0.0005632577,0.000039166112,0.00039225232,0.0019589756],"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":[0.000047476748,0.00016740503,0.00037882122,0.0017205478,0.00019528458,0.000011021031,0.007980003,0.006640305,0.030466,0.0011444287,0.18061793,0.7706308],"study_design_scores_gemma":[0.0007217249,0.0003972778,0.037497163,0.004253987,0.00008245492,0.00033342463,0.0019866638,0.013701053,0.062429458,0.0012307741,0.8766594,0.0007066232],"about_ca_topic_score_codex":0.00041690044,"about_ca_topic_score_gemma":0.00034598442,"teacher_disagreement_score":0.76992416,"about_ca_system_score_codex":0.0003740388,"about_ca_system_score_gemma":0.000033802196,"threshold_uncertainty_score":0.99994177},"labels":[],"label_agreement":null},{"id":"W2308990644","doi":"10.1149/ma2014-02/21/1230","title":"Capturing the Morphology of the Micro-Porous Layer Using a Stochastic Approach","year":2014,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Victoria; Simon Fraser University","funders":"","keywords":"Porosity; Materials science; Layer (electronics); Thermal diffusivity; Substrate (aquarium); Economies of agglomeration; Component (thermodynamics); Work (physics); Diffusion; Nanotechnology; Composite material; Chemical engineering; Mechanical engineering; Thermodynamics; Engineering","score_opus":0.027232697903993842,"score_gpt":0.22486843753607882,"score_spread":0.19763573963208497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2308990644","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":[0.98416865,0.0000822073,0.0012131404,0.000014946314,0.0011087274,0.00019464779,0.0000018159216,0.00008060651,0.01313528],"genre_scores_gemma":[0.99917555,3.3439744e-7,0.00034047308,0.000015045644,0.00040012045,0.0000056558997,3.9510854e-7,0.00002911786,0.000033315955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903166,0.00008951653,0.00034548144,0.00013676581,0.00016822484,0.00022834836],"domain_scores_gemma":[0.9993014,0.00017927599,0.00015151735,0.00029778725,0.000038672933,0.00003135576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008367858,0.0001301276,0.00017883015,0.00004526673,0.00015938304,0.000030186824,0.0001730237,0.00013733363,0.000002890844],"category_scores_gemma":[0.00024512195,0.00008050269,0.00007380967,0.00014395532,0.000040751598,0.000034925695,0.000039294067,0.00031847422,0.000009409148],"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":[0.0000031328595,0.0000056020735,0.000048014885,0.000022509608,0.000012516243,4.0713073e-7,0.00034556683,0.75393134,0.24545895,0.0000027775857,0.00005450428,0.00011465595],"study_design_scores_gemma":[0.0013347003,0.00007420129,0.02229707,0.0006021505,0.00015904059,0.00055738405,0.0010050859,0.32440922,0.6469669,0.00018770745,0.001711938,0.0006946153],"about_ca_topic_score_codex":0.0004695243,"about_ca_topic_score_gemma":0.000005536552,"teacher_disagreement_score":0.42952216,"about_ca_system_score_codex":0.00004154569,"about_ca_system_score_gemma":0.000014061444,"threshold_uncertainty_score":0.32828042},"labels":[],"label_agreement":null},{"id":"W2320627090","doi":"10.1097/00006324-200012001-00030","title":"(VB-102)MEDIAN EFFECTIVE DOSE AND ACTION SPECTRUM FOR IN VITRO UV CATARACT FORMATION","year":2000,"lang":"en","type":"article","venue":"Optometry and Vision Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Action spectrum; Action (physics); Medicine; Spectrum (functional analysis); Ophthalmology; Optometry; Physics; Chemistry; Photochemistry","score_opus":0.013645886312316753,"score_gpt":0.354010414484416,"score_spread":0.3403645281720992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320627090","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":[0.9954146,0.00009648125,0.0030600864,0.000039600545,0.00039216285,0.0004361285,0.000007906032,0.00005436967,0.00049864553],"genre_scores_gemma":[0.9995122,0.000087157554,0.00021305993,0.000014547508,0.00010053297,0.000027296799,0.0000033196832,0.0000068558593,0.000035011828],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999155,0.000022554295,0.00018510011,0.00022245014,0.00019567629,0.00021922703],"domain_scores_gemma":[0.9996297,0.00011735386,0.000030026686,0.00010439216,0.000024092675,0.00009447391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087296544,0.00010275353,0.00013992371,0.00047233596,0.00021617794,0.00014891903,0.000059848433,0.00008661948,0.000016777702],"category_scores_gemma":[0.00006162161,0.000089421665,0.000023004803,0.0009779145,0.000068633824,0.0011166467,0.000013540142,0.00012487012,0.000013144857],"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":[0.00018027532,0.000014441455,0.00031308076,0.00006678097,0.0000021383394,9.552451e-7,0.00028976964,0.00024838562,0.12805025,0.000011811037,0.00004501288,0.8707771],"study_design_scores_gemma":[0.0029642663,0.0006532517,0.17923988,0.00020410762,0.000013069187,0.0000904262,0.00047958878,0.3080968,0.49906296,0.0002546849,0.008424037,0.00051691907],"about_ca_topic_score_codex":0.0000297565,"about_ca_topic_score_gemma":0.000004196835,"teacher_disagreement_score":0.8702602,"about_ca_system_score_codex":0.000085172636,"about_ca_system_score_gemma":0.000009622501,"threshold_uncertainty_score":0.36465096},"labels":[],"label_agreement":null},{"id":"W2324016868","doi":"10.6000/1927-5129.2012.08.02.33","title":"Troubleshooting Techniques of Complex Multi-Layered PCBs","year":2012,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Original equipment manufacturer; Troubleshooting; Downtime; Dependability; Purchasing; Reliability engineering; Manufacturing engineering; Computer science; Engineering; Risk analysis (engineering); Systems engineering; Operations management; Embedded system; Business; Operating system","score_opus":0.08688545648037733,"score_gpt":0.2954068306318579,"score_spread":0.2085213741514806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324016868","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":[0.94938105,0.0003022732,0.03690831,0.000027092201,0.0010322077,0.00018866236,0.000002072903,0.00007881257,0.012079493],"genre_scores_gemma":[0.98987037,0.0000075056078,0.009635326,0.000010892198,0.0004567391,0.0000021712704,1.0806551e-7,0.000008113012,0.000008789484],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998797,0.000023874216,0.00053557404,0.000062464715,0.00036397646,0.00021708266],"domain_scores_gemma":[0.99941534,0.000068026275,0.00029679385,0.00007320925,0.00006597881,0.00008063882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017039442,0.0000943259,0.000270078,0.00022357308,0.000096676464,0.000032799042,0.00017910705,0.000071544906,0.000024447518],"category_scores_gemma":[0.000028854838,0.00007010205,0.00008458269,0.00036764797,0.000076719145,0.00024380384,0.000017030248,0.00017221033,0.000004953696],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024403927,0.0000598262,0.0019180333,0.000042652584,0.00003071083,9.517845e-7,0.0005578753,0.0021148517,0.9376162,0.0008713304,0.0010480181,0.055715125],"study_design_scores_gemma":[0.0011125464,0.00035431315,0.005377594,0.00019871497,0.00003921431,0.00014929535,0.0032089578,0.006227894,0.9724248,0.00018102823,0.010375301,0.00035035162],"about_ca_topic_score_codex":0.000008187735,"about_ca_topic_score_gemma":0.000001356796,"teacher_disagreement_score":0.055364773,"about_ca_system_score_codex":0.00004108768,"about_ca_system_score_gemma":0.000027012471,"threshold_uncertainty_score":0.28586787},"labels":[],"label_agreement":null},{"id":"W2332432430","doi":"10.1115/msec2007-31196","title":"Robust Machine Vision-Based Parts Inspection: Intelligent Neuro-Fuzzy Versus Threshold Based Classification","year":2007,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine vision; Computer science; Artificial intelligence; Fuzzy logic; Computer vision; Neuro-fuzzy; Robustness (evolution); Fuzzy control system; Machine learning; Pattern recognition (psychology)","score_opus":0.06819444824873568,"score_gpt":0.2717236608058259,"score_spread":0.20352921255709022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332432430","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":[0.07815913,0.00017243944,0.8319104,0.0002722866,0.010170007,0.00081425917,0.000010552938,0.0029510516,0.075539924],"genre_scores_gemma":[0.9987915,0.000004999396,0.00044311865,0.00009173325,0.00047910583,0.000018066035,0.000026643214,0.00004644055,0.00009839937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985219,0.000032048134,0.00049401075,0.00029639306,0.00035184855,0.00030376422],"domain_scores_gemma":[0.99907,0.00023311793,0.00006581967,0.00038814367,0.00009281159,0.0001500915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059967715,0.00022455103,0.00019302475,0.0003004751,0.0001713061,0.00008770196,0.00010705912,0.00022094179,0.00027788006],"category_scores_gemma":[0.000065203116,0.00020361236,0.00012325626,0.00055351795,0.000026951579,0.00012809382,0.000010977732,0.00031314304,0.00020230096],"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":[0.0009142334,0.00017395054,0.0013022671,0.00007011718,0.000049295293,0.000029189454,0.000049830844,0.93404996,0.009894957,0.0017193139,0.020212883,0.03153398],"study_design_scores_gemma":[0.0015684876,0.00032010052,0.0024825523,0.00003568233,0.000019976387,0.0000038230637,0.00008623364,0.92565924,0.030822512,0.000012047989,0.038667995,0.0003213753],"about_ca_topic_score_codex":0.000097882315,"about_ca_topic_score_gemma":0.0002099934,"teacher_disagreement_score":0.92063236,"about_ca_system_score_codex":0.0002258999,"about_ca_system_score_gemma":0.00002738792,"threshold_uncertainty_score":0.8303071},"labels":[],"label_agreement":null},{"id":"W2339455181","doi":"10.14288/1.0074991","title":"An analysis of machine shape defects in British Columbia sawmills and their classification using neural networks","year":2009,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Artificial intelligence; Computer science; Machine learning","score_opus":0.012228755231433337,"score_gpt":0.18702534716220456,"score_spread":0.17479659193077124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339455181","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":[0.99647665,0.0003387887,0.0026455733,0.0000018351803,0.000099022756,0.00020240215,0.00009920943,0.00007164959,0.00006489662],"genre_scores_gemma":[0.99975604,0.000082833394,0.00007146174,0.000007759888,0.000026815926,2.2255034e-7,0.000032537762,0.000011554315,0.000010748221],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99911374,0.00008311584,0.00022342228,0.00026570354,0.00012418632,0.00018981691],"domain_scores_gemma":[0.9994841,0.000033240474,0.00011352778,0.00019245983,0.0000888186,0.00008783663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023527836,0.000047036174,0.000422219,0.00012473091,0.00012326655,0.00018896374,0.00012258098,0.00019200693,0.00003965823],"category_scores_gemma":[0.00001170909,0.00018424031,0.0001228058,0.0009859967,0.00006236075,0.00032087765,0.000018273035,0.00015937573,1.9872701e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073421616,0.00006764132,0.12967306,0.000029835366,0.00010692341,0.000033145014,0.000121378645,0.04397534,0.002828467,1.2431319e-7,0.00007489181,0.82308185],"study_design_scores_gemma":[0.00031435347,0.000051836345,0.52878165,0.000052686635,0.00007003323,0.00001702543,0.00034012855,0.47027466,6.19928e-7,0.0000058632418,0.000004065897,0.000087100125],"about_ca_topic_score_codex":0.3100454,"about_ca_topic_score_gemma":0.82008284,"teacher_disagreement_score":0.82299477,"about_ca_system_score_codex":0.00007476113,"about_ca_system_score_gemma":0.000011789132,"threshold_uncertainty_score":0.7513102},"labels":[],"label_agreement":null},{"id":"W2349363592","doi":"","title":"Design and Realization of Circuit Board Detective System Based on LabVIEW and IMAQ Vision","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; USB; Computer hardware; Printed circuit board; Fixture; Embedded system; Realization (probability); On board; Test fixture; Test (biology); Artificial intelligence; Operating system","score_opus":0.010767783022822658,"score_gpt":0.21839873795084774,"score_spread":0.20763095492802508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2349363592","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":[0.007194507,0.00016506755,0.9911601,0.0000109219045,0.000020158044,0.0008616458,0.0000053458893,0.00017853708,0.00040370654],"genre_scores_gemma":[0.9920363,0.000016271191,0.007745005,0.000033839497,0.000064285916,0.00008085174,0.0000049379732,0.000013670483,0.0000048345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993664,0.000045925983,0.00022798756,0.00018612732,0.00008082272,0.00009271643],"domain_scores_gemma":[0.99962616,0.00006627137,0.00005540462,0.00015078728,0.000056282646,0.000045116794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001613619,0.00011113432,0.00016801657,0.00013054053,0.0000814897,0.00003739469,0.000046627505,0.00009277639,0.0000010354546],"category_scores_gemma":[5.930946e-7,0.0001079551,0.000024281251,0.00021988714,0.000013763392,0.000052445303,0.000007431082,0.00007112182,0.0000051502475],"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":[0.000035218327,0.00006254041,0.000037452784,0.000361588,0.000026002064,0.0000011046076,0.00028060217,0.03719859,0.13468215,0.004536901,0.0007605996,0.82201725],"study_design_scores_gemma":[0.0026839946,0.0011281577,0.007982551,0.0010347546,0.000110446446,0.00006624178,0.00010004824,0.82560116,0.11629855,0.0012156722,0.04296665,0.0008117569],"about_ca_topic_score_codex":0.000008540282,"about_ca_topic_score_gemma":4.609721e-7,"teacher_disagreement_score":0.9848418,"about_ca_system_score_codex":0.000050985123,"about_ca_system_score_gemma":0.000007850585,"threshold_uncertainty_score":0.44022813},"labels":[],"label_agreement":null},{"id":"W2352827293","doi":"","title":"Defects segmentation for wood floor based on image fusion method","year":2014,"lang":"en","type":"article","venue":"Dianji yu kongzhi xuebao","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"Artificial intelligence; Computer vision; Segmentation; Image stitching; Image fusion; Image segmentation; Pattern recognition (psychology); Computer science; Margin (machine learning); Mathematics; Image (mathematics)","score_opus":0.01319994454038338,"score_gpt":0.26099851683433845,"score_spread":0.24779857229395508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2352827293","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":[0.1618474,0.000024922832,0.8271477,0.0000598127,0.0028343927,0.0010936246,0.000033025928,0.0008687923,0.006090302],"genre_scores_gemma":[0.9840675,0.0000022252075,0.014289782,0.00018038879,0.00091927144,0.00019672935,0.000061857165,0.000115220624,0.00016700244],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986907,0.0001399428,0.00031478383,0.0002924323,0.00024248862,0.00031962965],"domain_scores_gemma":[0.99911064,0.00031693684,0.00007051283,0.00030866193,0.00007767975,0.00011557117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081182906,0.00023029355,0.00027667655,0.00022676411,0.00012855598,0.00010785187,0.00009548194,0.00021808872,0.00006400541],"category_scores_gemma":[0.00022460042,0.00021652972,0.00017353875,0.0002525045,0.000009694284,0.00015986964,0.000010867085,0.00016256873,0.0001722753],"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":[0.000503333,0.00015492772,0.00015755817,0.0005841145,0.00009184047,0.0000067703836,0.0004349579,0.09024925,0.5551211,0.000830068,0.025289347,0.3265767],"study_design_scores_gemma":[0.0041438257,0.00060197257,0.0006675915,0.00015107957,0.00006295382,0.000003553719,0.00009202749,0.57705265,0.37722406,0.00026760498,0.039191965,0.00054072944],"about_ca_topic_score_codex":0.00003905458,"about_ca_topic_score_gemma":0.0000140141165,"teacher_disagreement_score":0.82222015,"about_ca_system_score_codex":0.00010674299,"about_ca_system_score_gemma":0.000017575494,"threshold_uncertainty_score":0.88298255},"labels":[],"label_agreement":null},{"id":"W2358835247","doi":"","title":"Experimental Study on Layout Designs for General Interface in Process Plant for Process Plants","year":2008,"lang":"en","type":"article","venue":"Machine Design and Research","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Interface (matter); Workload; Process (computing); Context (archaeology); Measure (data warehouse); Fault (geology); Computer science; Interface design; Reliability engineering; Engineering drawing; Simulation; Engineering; Data mining; Human–computer interaction; Operating system","score_opus":0.2249893359903221,"score_gpt":0.4237204076193051,"score_spread":0.19873107162898299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2358835247","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":[0.97683644,0.00023170999,0.019407557,0.000007819176,0.00015341003,0.003168688,0.000034754183,0.00006233838,0.00009726217],"genre_scores_gemma":[0.99848706,0.000005187592,0.00015893496,0.000004037602,0.00013975591,0.0010159625,0.0000079208885,0.00003392698,0.00014723741],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987583,0.00013755646,0.00021976282,0.00025431337,0.00028021395,0.00034982886],"domain_scores_gemma":[0.99951315,0.00024072519,0.000016634338,0.00009947176,0.000054793574,0.00007523647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010897657,0.00014122686,0.0002102559,0.00028151143,0.00023131842,0.000043217253,0.00010796707,0.00009579172,0.0000053151757],"category_scores_gemma":[0.000051881423,0.00011066102,0.000025758325,0.00015858767,0.000025923617,0.000065881126,0.000014009673,0.00025359172,0.0000033628921],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.03589782,0.0076197237,0.011180805,0.002099388,0.00054974126,0.0004124229,0.12734266,0.2924961,0.48037037,0.00017047313,0.020574434,0.021286096],"study_design_scores_gemma":[0.0064491294,0.0067593036,0.00064134056,0.00013598723,0.000004432082,0.00006237365,0.0046041426,0.44409493,0.53644454,0.000093784976,0.00034798152,0.00036209298],"about_ca_topic_score_codex":0.000048068785,"about_ca_topic_score_gemma":0.000007672565,"teacher_disagreement_score":0.15159884,"about_ca_system_score_codex":0.00006261679,"about_ca_system_score_gemma":0.00003322721,"threshold_uncertainty_score":0.45126256},"labels":[],"label_agreement":null},{"id":"W2365495139","doi":"","title":"EXPERIMENTAL STUDY ON THE ROLE OF MOISTURE IN DE NOVO FORMATION OF PCDD AND PCDF","year":2006,"lang":"en","type":"article","venue":"Journal of Engineering Thermophysics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Moisture; De novo synthesis; Environmental chemistry; Fly ash; Incineration; Atmosphere (unit); Environmental science; Municipal solid waste; Oxygen; Chemistry; Waste management; Meteorology; Organic chemistry; Physics","score_opus":0.00783486430620237,"score_gpt":0.20087810065684839,"score_spread":0.19304323635064602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2365495139","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":[0.9980917,0.00021044229,0.0012765955,0.0000023098894,0.00011050426,0.00009470945,0.0000011370738,0.000007942846,0.00020464101],"genre_scores_gemma":[0.9998141,0.0000021602737,0.000038356913,8.873462e-7,0.00012941206,0.0000020808006,1.13817464e-7,0.000011509119,0.0000013656561],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994656,0.000018211402,0.00029111435,0.00002738435,0.00013083182,0.00006689482],"domain_scores_gemma":[0.9997429,0.000050714676,0.00009902747,0.000066061846,0.000028687107,0.000012575603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000209691,0.00007114541,0.00015519612,0.00008158348,0.000009560402,0.000007686695,0.00005165204,0.000038817027,0.0000010063468],"category_scores_gemma":[0.00001007064,0.000051242758,0.00004268402,0.00011493322,0.000005160868,0.00007956965,0.0000063984457,0.00014649492,1.9882035e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003100297,0.0001096866,0.00039967685,0.00001806803,0.00001998346,0.000002581254,0.0011240988,0.22992174,0.76747245,0.00032238485,0.000010177322,0.000568174],"study_design_scores_gemma":[0.0009349465,0.0006288292,0.01697223,0.00018266788,0.000016764956,0.000028635566,0.0022567657,0.036431145,0.94215274,0.00020735827,0.000078104415,0.00010983548],"about_ca_topic_score_codex":0.000019633504,"about_ca_topic_score_gemma":8.921343e-7,"teacher_disagreement_score":0.1934906,"about_ca_system_score_codex":0.00004743057,"about_ca_system_score_gemma":0.000005122616,"threshold_uncertainty_score":0.2089619},"labels":[],"label_agreement":null},{"id":"W2365688570","doi":"","title":"Design of Optical System for Surface Defect Detection of Steel Strip","year":2005,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"CAE (Canada)","funders":"","keywords":"Lens (geology); Optics; Surface (topology); Line (geometry); Field of view; Computer science; Engineering; Computer vision; Physics; Mathematics","score_opus":0.02924233372161417,"score_gpt":0.23252613732573238,"score_spread":0.2032838036041182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2365688570","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":[0.3047369,0.00007892079,0.6931524,0.0000013072429,0.00035355796,0.00045288488,0.000004795703,0.000148389,0.0010708225],"genre_scores_gemma":[0.9955717,0.0000021682038,0.0041864235,6.964752e-7,0.00012208837,0.000014064211,6.30081e-7,0.000019983581,0.00008225448],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999192,0.00003571191,0.00039767122,0.00010071246,0.00013701925,0.0001369115],"domain_scores_gemma":[0.99948,0.00018727621,0.00006103803,0.00013989165,0.00009426394,0.000037543487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046209802,0.000098155244,0.00024703465,0.00007116207,0.000025418927,0.0000083317655,0.000049239923,0.00015767365,0.000007166858],"category_scores_gemma":[0.000029203573,0.000086266366,0.000115486895,0.00015401894,0.000010200421,0.00007534698,0.000005984903,0.00006445764,0.000009031061],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007779381,0.000012033075,0.000007727187,0.00020571068,0.000042586973,1.16714254e-7,0.000027384773,0.37116513,0.61734104,0.00017178326,0.0000859034,0.010862794],"study_design_scores_gemma":[0.00053548143,0.00020274287,0.00002972745,0.000043638214,0.000023517538,0.0000059062436,0.000119295386,0.22848418,0.7700794,0.0000021611104,0.00039855216,0.00007538875],"about_ca_topic_score_codex":0.000021105137,"about_ca_topic_score_gemma":0.0000058767378,"teacher_disagreement_score":0.69083476,"about_ca_system_score_codex":0.00008721705,"about_ca_system_score_gemma":0.000012304777,"threshold_uncertainty_score":0.35178402},"labels":[],"label_agreement":null},{"id":"W2365744847","doi":"","title":"Design and Implementation of a Metal Detector Based on Frequency-domain Analysis","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Detector; Computer science; Interference (communication); Frequency domain; Production line; Software; Domain (mathematical analysis); Field (mathematics); Line (geometry); Computer hardware; Electronic engineering; Telecommunications; Operating system","score_opus":0.011583906579871612,"score_gpt":0.2566647703495903,"score_spread":0.2450808637697187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2365744847","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":[0.12876697,0.0000272787,0.87048703,0.000005724889,0.000013342623,0.0005113727,0.000009210515,0.00007388388,0.00010518213],"genre_scores_gemma":[0.8378898,8.8250505e-7,0.16193163,0.000016259904,0.000051237534,0.00008805015,0.000010623333,0.00000993542,0.0000016073424],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935824,0.000024124127,0.00027179797,0.00014135076,0.000088848676,0.00011561458],"domain_scores_gemma":[0.9996389,0.00008954329,0.000052187457,0.00014088393,0.00003716974,0.000041309562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034042657,0.00009050262,0.00014738138,0.00034430792,0.000054947242,0.000017170225,0.000054478525,0.000057961377,0.000020037578],"category_scores_gemma":[1.9934222e-7,0.00008972046,0.000065896966,0.00063086324,0.000012102635,0.000033461165,0.000006871886,0.000058550217,0.000006844189],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002287925,0.000059939906,0.00135939,0.00006026837,0.0004948136,0.0000014385142,0.00047039025,0.034298863,0.7116832,0.0006949249,0.00019676694,0.2506571],"study_design_scores_gemma":[0.0023217727,0.0003556223,0.030056939,0.000027027629,0.00059782865,0.000011051017,0.0003689532,0.08076282,0.86710614,0.00084919477,0.016870083,0.0006725908],"about_ca_topic_score_codex":0.000047461384,"about_ca_topic_score_gemma":0.000027368285,"teacher_disagreement_score":0.7091228,"about_ca_system_score_codex":0.000042699947,"about_ca_system_score_gemma":0.000009167549,"threshold_uncertainty_score":0.3658694},"labels":[],"label_agreement":null},{"id":"W2366693250","doi":"","title":"Design and Realization of Computer Check and Disposal System during the Process of Leather Dyeing","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dyeing; Computer science; Automation; Realization (probability); Process (computing); Production (economics); Process engineering; Manufacturing engineering; Embedded system; Operating system; Engineering; Mechanical engineering","score_opus":0.00650698885312216,"score_gpt":0.21458631725097072,"score_spread":0.20807932839784857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2366693250","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":[0.298269,0.00008499548,0.7011039,0.0000020784123,0.000015749998,0.00042049098,0.0000016786154,0.000050887582,0.0000511822],"genre_scores_gemma":[0.99187595,0.0000052678934,0.007941663,0.0000023803757,0.00011833469,0.000034586268,0.0000015864624,0.000013766736,0.0000064658175],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994561,0.000016899328,0.00026636882,0.00010825124,0.000065178276,0.00008722429],"domain_scores_gemma":[0.999693,0.000052979238,0.00007160175,0.000104161176,0.000054854372,0.000023448942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025217765,0.000077129436,0.00012389575,0.0000681444,0.00007705091,0.000017696108,0.000057089543,0.000060116225,4.8788166e-7],"category_scores_gemma":[2.125415e-7,0.00006077317,0.000017140377,0.00017428855,0.000028422588,0.00004521382,0.00002067377,0.00005561895,6.837604e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009730746,0.000099531666,0.005267318,0.004634085,0.00025116093,0.0000022670476,0.00938034,0.10165256,0.6119494,0.006258766,0.00023722713,0.26017004],"study_design_scores_gemma":[0.0023254878,0.00016336983,0.05697409,0.0008107614,0.000121129546,0.00031023892,0.0013019625,0.3455137,0.5834021,0.00020319082,0.0081712315,0.00070271664],"about_ca_topic_score_codex":0.000012278981,"about_ca_topic_score_gemma":0.0000014860741,"teacher_disagreement_score":0.6936069,"about_ca_system_score_codex":0.000018948276,"about_ca_system_score_gemma":0.000004362435,"threshold_uncertainty_score":0.24782579},"labels":[],"label_agreement":null},{"id":"W2367822659","doi":"","title":"Designing the Management System of Origin Marks","year":2010,"lang":"en","type":"article","venue":"International Business Research","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"China; Order (exchange); Product (mathematics); Business; Country of origin; Set (abstract data type); Management system; International trade; Marketing; Computer science; Operations management; Political science; Economics; Law; Mathematics; Finance","score_opus":0.05704326036751204,"score_gpt":0.3368683749833876,"score_spread":0.27982511461587556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2367822659","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":[0.73111576,0.00006453477,0.045572393,0.00032448195,0.010449836,0.0007912469,0.000015303,0.00024678267,0.21141967],"genre_scores_gemma":[0.99842906,0.000009303266,0.0004226101,0.000001881169,0.000460739,0.000050183673,0.000002973573,0.000014235642,0.0006090062],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988489,0.000045198558,0.00020370807,0.00010007196,0.0006497839,0.00015231999],"domain_scores_gemma":[0.9991048,0.00012748761,0.000024369589,0.00019398326,0.00052459596,0.000024787912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012067097,0.00006173054,0.000077684424,0.00026753664,0.000085441156,0.000077024815,0.00032353314,0.00006860077,0.00010512736],"category_scores_gemma":[0.000052123174,0.000043530465,0.00002952631,0.0004901663,0.000043910513,0.00008315013,0.00006412786,0.00034533796,0.00008296543],"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":[0.00043244325,0.0001529175,0.009292082,0.001902785,0.0011751631,0.00019491557,0.0007501897,0.039137937,0.6391235,0.07250262,0.037466135,0.1978693],"study_design_scores_gemma":[0.0024727983,0.000068201,0.117389716,0.0011850494,0.00003581749,0.00023470854,0.0028937606,0.06877844,0.099479996,0.0007416179,0.7060852,0.0006347098],"about_ca_topic_score_codex":0.00012760145,"about_ca_topic_score_gemma":0.0000118306325,"teacher_disagreement_score":0.66861904,"about_ca_system_score_codex":0.000070934664,"about_ca_system_score_gemma":0.000015767468,"threshold_uncertainty_score":0.17751208},"labels":[],"label_agreement":null},{"id":"W2375177230","doi":"","title":"Research for the Reduction of Noise of Automobile Gear Box","year":2000,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Noise (video); Non-circular gear; Noise reduction; Process (computing); Workmanship; Automotive engineering; Engineering; Computer science; Spiral bevel gear; Artificial intelligence; Operations management","score_opus":0.05514993539557761,"score_gpt":0.3217447864099523,"score_spread":0.2665948510143747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2375177230","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":[0.96519715,0.00022288847,0.0033235033,0.000048118043,0.0006451802,0.00081677287,0.000010271224,0.00011188943,0.029624216],"genre_scores_gemma":[0.9970917,0.000025626694,0.00010231848,6.705357e-7,0.00017643828,0.00003677113,5.801304e-7,0.000008950058,0.0025569287],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949527,0.000026842974,0.00017920852,0.000055760214,0.00014622041,0.00009671139],"domain_scores_gemma":[0.99961984,0.00010809113,0.000013351923,0.00015440672,0.00008994874,0.000014341707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067907874,0.000035541983,0.00008344121,0.00006282241,0.00004778635,0.0000064459923,0.0000536911,0.000065171815,0.0004739543],"category_scores_gemma":[0.000018678176,0.000023423427,0.000049618742,0.00021851371,0.00002564673,0.000038885108,0.0000039866245,0.000083896004,0.000019688468],"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":[0.00027364443,0.000066412686,0.000020597792,0.00024679807,0.00010469549,2.7411176e-7,0.0009783591,0.13392918,0.31512418,0.0012915812,0.08233319,0.4656311],"study_design_scores_gemma":[0.00067991356,0.0003728196,0.0003773798,0.000057233432,0.000014360926,0.000008615763,0.0011001511,0.055231113,0.78000903,0.00019029116,0.1618597,0.000099410645],"about_ca_topic_score_codex":0.00022734526,"about_ca_topic_score_gemma":0.0000034385996,"teacher_disagreement_score":0.46553168,"about_ca_system_score_codex":0.000018501443,"about_ca_system_score_gemma":0.000010450277,"threshold_uncertainty_score":0.5189467},"labels":[],"label_agreement":null},{"id":"W2376061728","doi":"","title":"Talking About the Effective Way to do Red Dry Hot Regional test","year":2011,"lang":"en","type":"article","venue":"Seed","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Test (biology); Environmental science; Biology; Botany","score_opus":0.02175002271799621,"score_gpt":0.223633092032626,"score_spread":0.2018830693146298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2376061728","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":[0.95275116,0.00014941019,0.002676392,0.00004394507,0.0021190974,0.00087461405,0.000006546869,0.00041622965,0.040962603],"genre_scores_gemma":[0.9991074,0.0000019431347,0.000071577386,0.00004770615,0.0004398533,0.000058210524,0.000001044831,0.000023517525,0.00024874526],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99938726,0.000036106667,0.0001449883,0.0001289486,0.00013271134,0.00016997165],"domain_scores_gemma":[0.99955714,0.00012337473,0.000025709916,0.00019959817,0.000039469327,0.000054717046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026348766,0.000106433145,0.00011841475,0.000066301516,0.00010484839,0.000032849548,0.000093303584,0.00009748339,0.000032774635],"category_scores_gemma":[0.000085782995,0.00007489063,0.000061300096,0.0002240924,0.000013448632,0.000056266555,0.000019598292,0.00016895459,0.00019816121],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011477999,0.00023998001,0.05488558,0.00026315486,0.00071605184,0.00012230022,0.04484096,0.006219815,0.51795727,0.0029295105,0.041274235,0.32940337],"study_design_scores_gemma":[0.0007536736,0.00027763852,0.9278034,0.0001948935,0.000023423187,0.000021163554,0.00054571545,0.0014129526,0.0345384,0.0002188738,0.033854894,0.00035499048],"about_ca_topic_score_codex":0.0001459135,"about_ca_topic_score_gemma":0.000019493953,"teacher_disagreement_score":0.8729178,"about_ca_system_score_codex":0.00005589339,"about_ca_system_score_gemma":0.0000042629567,"threshold_uncertainty_score":0.30539513},"labels":[],"label_agreement":null},{"id":"W2379436792","doi":"","title":"Development of Self-programming Smart Toys","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Executable; Software; Flexibility (engineering); Icon; Embedded system; Code (set theory); Software development; Software engineering; Human–computer interaction; Operating system; Programming language","score_opus":0.017780299845316254,"score_gpt":0.21435879537005564,"score_spread":0.19657849552473938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2379436792","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":[0.22455914,0.00007260514,0.77338517,0.0000037861928,0.000050302897,0.0004427431,0.0000018737826,0.00044726895,0.0010371346],"genre_scores_gemma":[0.5115393,0.000005410434,0.48799732,0.000008408705,0.00014144239,0.00023912886,0.000008264157,0.000017823657,0.00004291425],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999405,0.0000058296196,0.0002808629,0.000111108464,0.000077734214,0.000119439785],"domain_scores_gemma":[0.9997376,0.000012875303,0.00003359288,0.000133669,0.00004304762,0.000039169372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000065968714,0.000083708925,0.00011502923,0.00007714338,0.0001272561,0.000009568529,0.00008922784,0.000063402505,0.0000070637907],"category_scores_gemma":[1.07650166e-7,0.000086349566,0.00004152595,0.00022136063,0.000010448082,0.000036547463,0.00002237173,0.00007035541,0.00009221016],"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":[0.0000023816415,0.00010837703,0.0004148638,0.00010407528,0.000095680756,0.0000012837837,0.0027253916,0.00078833656,0.0737704,0.00022454081,0.0030863713,0.9186783],"study_design_scores_gemma":[0.00014616894,0.0000058454716,0.0007706554,0.0000088206625,0.0000037526868,0.000023509028,0.000019906478,0.00063701393,0.049726572,0.000004443088,0.9485533,0.00009997485],"about_ca_topic_score_codex":0.000004441926,"about_ca_topic_score_gemma":0.0000021520887,"teacher_disagreement_score":0.945467,"about_ca_system_score_codex":0.000044054887,"about_ca_system_score_gemma":0.000023325785,"threshold_uncertainty_score":0.35212332},"labels":[],"label_agreement":null},{"id":"W2385791096","doi":"","title":"The development of a curometer for testing rubber based on the 80C196 single chip microprocessor","year":2003,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Microprocessor; Stylus; Natural rubber; Liquid-crystal display; Chip; Computer hardware; ARM architecture; Engineering; Computer science; Embedded system; Electrical engineering; Materials science; Operating system; Composite material","score_opus":0.06670586267153589,"score_gpt":0.2309765872187551,"score_spread":0.16427072454721922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2385791096","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":[0.84541065,0.00006270681,0.099551596,0.00008109014,0.0009581251,0.0010696875,0.0000038834296,0.00019105649,0.052671224],"genre_scores_gemma":[0.9944175,8.985027e-8,0.005119827,0.00003503746,0.00003908407,0.00006792145,3.6522687e-7,0.000017664093,0.00030251784],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993916,0.00002521696,0.00024362582,0.00008052177,0.00011078186,0.0001482313],"domain_scores_gemma":[0.99908906,0.0006507328,0.00004179726,0.0001325228,0.00006749598,0.000018367886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057407795,0.00008689245,0.000086337655,0.000035565718,0.00019541821,0.000038321687,0.00006481361,0.000047695456,0.00001927904],"category_scores_gemma":[0.00042480015,0.00004334669,0.00004078502,0.0001904204,0.00001046709,0.000018555387,0.0000039632,0.000069519825,0.000009894459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013053411,0.00017745706,0.00056505314,0.00032588918,0.00016382086,6.989727e-7,0.0012394367,0.015960764,0.80673975,0.001927432,0.007378212,0.16539098],"study_design_scores_gemma":[0.00043203332,0.00011182022,0.00012736148,0.00007766247,0.000007152158,0.000001823543,0.00019473181,0.014461785,0.8980744,0.00007110504,0.0863006,0.00013956128],"about_ca_topic_score_codex":0.000002270208,"about_ca_topic_score_gemma":0.0000065124696,"teacher_disagreement_score":0.16525142,"about_ca_system_score_codex":0.000032629796,"about_ca_system_score_gemma":0.000029081462,"threshold_uncertainty_score":0.17676267},"labels":[],"label_agreement":null},{"id":"W2390654529","doi":"","title":"Comparison between the Chinese and Canadian Visual Grading Rules for Dimensional Lumber","year":2008,"lang":"en","type":"article","venue":"Mucai gongye","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Grading (engineering); Engineering; Engineering drawing; Forensic engineering; Artificial intelligence; Mathematics; Computer science; Civil engineering","score_opus":0.030195263616560424,"score_gpt":0.28277218889133365,"score_spread":0.2525769252747732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2390654529","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":[0.99763477,0.00017210389,0.00037680363,0.00006265978,0.0006139016,0.00023435213,0.000028292958,0.00007455655,0.00080256566],"genre_scores_gemma":[0.99910533,0.0000024685075,0.000068221874,0.000026209525,0.00062599126,0.000021668722,0.000018603252,0.000021546864,0.00010997309],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99936455,0.000020602523,0.00018032361,0.00011151857,0.00010895228,0.0002140217],"domain_scores_gemma":[0.9996161,0.00012345499,0.000021920496,0.000086470296,0.0000306197,0.0001214651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017537191,0.000112811875,0.0001785398,0.000078138335,0.00036648705,0.000033194934,0.00004915892,0.00010754952,0.000018105244],"category_scores_gemma":[0.00003420086,0.00007983695,0.000048790233,0.00011358956,0.000027709908,0.00006283842,0.000010485945,0.00014573694,0.000022505013],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031183023,0.00002422787,0.8993798,0.00007600704,0.00022108406,0.000014041402,0.0019637605,0.0034352574,0.0014155189,0.00019376492,0.06310165,0.030143691],"study_design_scores_gemma":[0.0010325057,0.00011621987,0.8416795,0.00004319974,0.00003604732,0.00007840329,0.00012858436,0.058023352,0.0009192329,0.0001820791,0.09731427,0.0004466089],"about_ca_topic_score_codex":0.011027099,"about_ca_topic_score_gemma":0.0055480837,"teacher_disagreement_score":0.057700317,"about_ca_system_score_codex":0.00006137909,"about_ca_system_score_gemma":0.00002685472,"threshold_uncertainty_score":0.99555856},"labels":[],"label_agreement":null},{"id":"W2394016627","doi":"","title":"Structured Light Projection Technique for BGA Void Inspection","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ball grid array; Void (composites); Computer science; Light-emitting diode; Optics; Projection (relational algebra); Materials science; Optoelectronics; Physics; Algorithm; Composite material","score_opus":0.013586106265475562,"score_gpt":0.2218284746243972,"score_spread":0.20824236835892163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2394016627","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":[0.011077244,0.00005269075,0.98433626,0.000028939898,0.00016429946,0.002650947,0.00001634473,0.001037588,0.00063570845],"genre_scores_gemma":[0.9129325,0.000018750501,0.07869397,0.00003453158,0.0016670611,0.006352226,0.00005339766,0.0000654571,0.00018210334],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992858,0.00001221976,0.00025209447,0.00021664765,0.00007361507,0.0001596083],"domain_scores_gemma":[0.9996208,0.000017313023,0.000045133987,0.00018479806,0.00009031394,0.000041625324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007478956,0.00013671923,0.00013660842,0.00017424332,0.0003343754,0.00002903319,0.000088965564,0.00017176579,0.0000048340353],"category_scores_gemma":[5.520975e-7,0.00013836817,0.000084511106,0.00034868484,0.000014873428,0.000098053526,0.000013161053,0.00013953767,0.000033730492],"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":[0.000012539418,0.000035391688,0.00005326168,0.000068665744,0.0000352493,4.6884134e-7,0.00026557548,0.0011689019,0.91951406,0.00028214336,0.029082747,0.049481012],"study_design_scores_gemma":[0.0003062307,0.00004159347,0.00034606498,0.0000100599855,0.000008198788,0.0001463694,0.000016394877,0.001947869,0.3631635,0.00020651272,0.63363177,0.00017544613],"about_ca_topic_score_codex":0.000019539362,"about_ca_topic_score_gemma":0.000011105152,"teacher_disagreement_score":0.9056423,"about_ca_system_score_codex":0.000119708544,"about_ca_system_score_gemma":0.00001777038,"threshold_uncertainty_score":0.56424904},"labels":[],"label_agreement":null},{"id":"W2394142146","doi":"","title":"The Sensitivity Performance of Cross Voltage Measurement Protocol on EIT","year":2009,"lang":"es","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Sensitivity (control systems); Voltage; Protocol (science); Materials science; Physics; Computer science; Electronic engineering; Electrical engineering; Engineering; Medicine","score_opus":0.03552729362100752,"score_gpt":0.291428876061157,"score_spread":0.2559015824401495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2394142146","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":[0.8246133,0.000024011488,0.0024921508,0.000103770886,0.0012288252,0.087290265,0.0000100125035,0.0002698462,0.08396783],"genre_scores_gemma":[0.99658924,0.0000026883536,0.000007544048,0.000018941813,0.0003168094,0.0026069984,1.784843e-7,0.000013755177,0.00044384316],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983763,0.00008725348,0.00045267158,0.00017246172,0.0006439586,0.0002673369],"domain_scores_gemma":[0.99912745,0.000070955255,0.00012301335,0.00036334404,0.00026290078,0.000052351654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019602696,0.0001928939,0.00022639759,0.00005321304,0.00025821626,0.00016226424,0.0000739189,0.0001667639,0.000021585776],"category_scores_gemma":[0.000109741566,0.00012298158,0.00011376991,0.00018217153,0.000033641274,0.00011301067,0.000013767724,0.00026622924,0.00008352591],"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":[0.0024896446,0.00038364026,0.00404079,0.00091035495,0.00019731226,0.000015501304,0.00024250163,0.051690053,0.29260474,0.0061331396,0.004575827,0.6367165],"study_design_scores_gemma":[0.0018380086,0.0023926417,0.076623335,0.00074648164,0.00001753869,0.000011518036,0.000034875455,0.045841772,0.7519627,0.000019768555,0.12012055,0.00039076325],"about_ca_topic_score_codex":0.000047487218,"about_ca_topic_score_gemma":0.0000068832082,"teacher_disagreement_score":0.6363257,"about_ca_system_score_codex":0.00014766557,"about_ca_system_score_gemma":0.000035279274,"threshold_uncertainty_score":0.50150436},"labels":[],"label_agreement":null},{"id":"W2401542805","doi":"","title":"Acceptability ratings for simulated image distortions of static images corresponding to different viewing angles for a flat panel display.","year":2012,"lang":"en","type":"article","venue":"International Conference on Computer Graphics, Imaging and Visualisation","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Flat panel; Computer vision; Viewing angle; Artificial intelligence; Image (mathematics); Flat panel display; Computer science; Computer graphics (images); Liquid-crystal display","score_opus":0.07250743596603466,"score_gpt":0.3403279704811911,"score_spread":0.26782053451515647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401542805","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":[0.5232668,0.000015601534,0.47482756,0.0001205835,0.0011755343,0.00039448217,0.000113656024,0.00006903508,0.000016766715],"genre_scores_gemma":[0.99758637,0.000009413076,0.0018037762,0.000055116005,0.0003323399,0.00007211326,0.000111086025,0.000022745786,0.000007065138],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988723,0.00005281729,0.00043960422,0.00021053104,0.00022244772,0.00020229827],"domain_scores_gemma":[0.99898916,0.0003760544,0.00012870149,0.000114501985,0.0003045976,0.00008698801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004848024,0.00017882924,0.00023365211,0.00030854423,0.00012758859,0.00016945005,0.00010965091,0.00005376363,0.000012162843],"category_scores_gemma":[0.00013244993,0.00016779873,0.00010271333,0.00011172158,0.000039016533,0.00035838303,0.000032090553,0.00009439524,0.0000010396982],"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":[0.0015979127,0.0008754527,0.15607792,0.0022432795,0.00090584817,0.000002193447,0.029138478,0.009893691,0.33087948,0.1750592,0.006388526,0.286938],"study_design_scores_gemma":[0.00074129197,0.00013373882,0.020691996,0.00021138701,0.000032086755,0.00000250434,0.00028595893,0.9662522,0.009947823,0.00095407263,0.0005088007,0.00023810867],"about_ca_topic_score_codex":0.00007543814,"about_ca_topic_score_gemma":0.0000058598284,"teacher_disagreement_score":0.95635855,"about_ca_system_score_codex":0.00007408909,"about_ca_system_score_gemma":0.000009531793,"threshold_uncertainty_score":0.68426335},"labels":[],"label_agreement":null},{"id":"W2405596045","doi":"10.21611/qirt.2000.032","title":"Defect Depth Estimation Using Neuro-Fuzzy System in TNDE","year":2000,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"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 Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Neuro-fuzzy; Fuzzy logic; Computer vision; Fuzzy control system","score_opus":0.021589698642837052,"score_gpt":0.23530958268360036,"score_spread":0.2137198840407633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405596045","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":[0.93239176,0.00004346575,0.015225866,0.0000033859974,0.00051125995,0.00022354296,9.258812e-7,0.0005321977,0.051067606],"genre_scores_gemma":[0.9992978,0.000002237597,0.00048891763,0.000019941124,0.00007642919,0.000007989705,0.0000013753962,0.000023316446,0.00008198434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927807,0.000042667805,0.00026131867,0.00012639127,0.000122002544,0.00016952705],"domain_scores_gemma":[0.99976236,0.000036598274,0.000016099728,0.0001348169,0.000010731222,0.000039373743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018779008,0.00010641962,0.00014528375,0.0001338451,0.00004167218,0.000044091496,0.000040781273,0.00009496938,0.00009538692],"category_scores_gemma":[0.000012683449,0.00010001873,0.000056814828,0.00030105023,0.000004115082,0.00014504915,0.00000407073,0.00012091872,0.00019611444],"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":[0.000013019087,0.0000063987777,0.00044917042,0.000055642016,0.000008817766,0.000014000754,0.000054497134,0.93050617,0.003572072,0.00013361973,0.0005284802,0.06465811],"study_design_scores_gemma":[0.00038699477,0.000023456685,0.0011369612,0.000092413386,0.000007068745,0.00006743412,0.000053190197,0.99392873,0.0020032695,0.000011258769,0.0021428585,0.00014636297],"about_ca_topic_score_codex":0.00042498013,"about_ca_topic_score_gemma":0.000055553304,"teacher_disagreement_score":0.06690606,"about_ca_system_score_codex":0.00015145351,"about_ca_system_score_gemma":0.000007902481,"threshold_uncertainty_score":0.40786457},"labels":[],"label_agreement":null},{"id":"W2421979979","doi":"10.5006/c2016-07463","title":"New and Old Challenges in Analyzing ECDA Indirect Inspections Data","year":2016,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"TransCanada (Canada); Spectra Energy (Canada); Union Gas (Canada)","funders":"","keywords":"Computer science; Process engineering; Reliability engineering; Engineering","score_opus":0.10892754311408864,"score_gpt":0.265297647184139,"score_spread":0.15637010407005036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2421979979","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":[0.8698734,0.0077047893,0.034238674,0.0010168481,0.0026913697,0.00053232815,0.00003467678,0.0015915955,0.08231632],"genre_scores_gemma":[0.9981323,0.0008785569,0.00015095944,0.00000312217,0.00022663584,0.00000255782,9.0988266e-7,0.000011746018,0.0005932226],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99950355,0.000016711574,0.00014301964,0.00016507314,0.00005792084,0.00011372559],"domain_scores_gemma":[0.9996052,0.000055855486,0.000012253791,0.00026839811,0.0000064314786,0.000051902538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018599871,0.00007176305,0.00011389678,0.00017104147,0.000025453275,0.000021351825,0.000070834205,0.000078410085,0.00006977833],"category_scores_gemma":[0.00003537287,0.00004909084,0.0000111627,0.00014867209,0.000005056374,0.0002341343,0.00004127423,0.00006909638,0.000030687628],"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":[0.0000070627025,0.0000066909815,0.004656378,0.0000172684,0.000039363196,0.000004403972,0.00018668521,0.00006644021,0.004507926,0.00089264417,0.0037241203,0.98589104],"study_design_scores_gemma":[0.0091154585,0.00045499118,0.27524355,0.0014328893,0.00009909341,0.0002197136,0.0016786356,0.01711445,0.03600335,0.002402412,0.65388876,0.0023466845],"about_ca_topic_score_codex":0.00029905586,"about_ca_topic_score_gemma":0.0014240054,"teacher_disagreement_score":0.98354435,"about_ca_system_score_codex":0.000036306228,"about_ca_system_score_gemma":0.000008148655,"threshold_uncertainty_score":0.20018663},"labels":[],"label_agreement":null},{"id":"W2460796372","doi":"","title":"Image Analysis and Recognition: 8th International Conference, ICIAR 2011, Burnaby, BC, Canada, June 22-24, 2011. Proceedings, Part I (Lecture Notes in ... Vision, Pattern Recognition, and Graphics)","year":2011,"lang":"en","type":"book","venue":"Springer eBooks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Biometrics; Artificial intelligence; Coding (social sciences); Computer vision; Graphics; Image processing; Facial recognition system; Feature (linguistics); Feature extraction; Pattern recognition (psychology); Image (mathematics); Computer graphics (images); Mathematics","score_opus":0.023948962134596987,"score_gpt":0.21099817182965103,"score_spread":0.18704920969505404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460796372","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1749001,0.0034695517,0.0137768965,0.00048398998,0.01985375,0.0055417763,0.0046851193,0.0014397408,0.7758491],"genre_scores_gemma":[0.8781725,0.004203881,0.0015407356,0.0006502358,0.007971927,0.0007573254,0.002805854,0.0008549644,0.103042565],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99770755,0.000034848585,0.0007857388,0.0006587545,0.00043926018,0.00037384612],"domain_scores_gemma":[0.9987004,0.00007576091,0.00030132825,0.00021018652,0.0005148616,0.00019747995],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041326435,0.0005481856,0.0007407376,0.0010773625,0.000121895486,0.00022749956,0.0001694894,0.00067742093,0.0015200581],"category_scores_gemma":[0.000038273945,0.0005562149,0.00014673697,0.000112996844,0.00007865034,0.00017003085,0.000091766975,0.0009112003,0.000073143645],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055502175,0.00016861265,0.012631381,0.0027783005,0.0095220795,0.0005780772,0.0035145418,0.000032134125,0.0031668409,0.0002311142,0.24178036,0.72504157],"study_design_scores_gemma":[0.010344442,0.0009334022,0.020888548,0.010966664,0.007482067,0.00060217135,0.00053348875,0.0115831,0.019330537,0.059877586,0.8440681,0.013389897],"about_ca_topic_score_codex":0.048960265,"about_ca_topic_score_gemma":0.17266572,"teacher_disagreement_score":0.7116516,"about_ca_system_score_codex":0.00021395982,"about_ca_system_score_gemma":0.0001983216,"threshold_uncertainty_score":0.9996889},"labels":[],"label_agreement":null},{"id":"W2461693811","doi":"10.1016/j.procir.2016.04.023","title":"A Bi-criterion Flexible Registration Method for Fixtureless Inspection of Compliant Parts","year":2016,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Industrial Vision Systems and Defect Detection","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":"École de Technologie Supérieure","funders":"","keywords":"CAD; Residual stress; Sheet metal; Residual; Engineering drawing; Mechanical engineering; Engineering; Computer science; Structural engineering; Algorithm; Materials science","score_opus":0.03591029070178994,"score_gpt":0.28801290032253246,"score_spread":0.2521026096207425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461693811","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":[0.04429973,0.00012329739,0.9514341,0.00010034689,0.0014104568,0.0006177638,0.000032210657,0.0004771515,0.0015049836],"genre_scores_gemma":[0.99662024,0.00001847231,0.0026007951,0.0000076388615,0.00042482093,0.000116381016,0.0000055737414,0.00002269855,0.00018340287],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926656,0.000019369882,0.00029596002,0.00015171044,0.00012324218,0.00014315019],"domain_scores_gemma":[0.99954027,0.00008062284,0.00008934118,0.00013152293,0.00011885643,0.000039400747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003032295,0.00010225515,0.0001788359,0.00010172215,0.000056354227,0.000019517985,0.000048401023,0.00013004037,0.000010026289],"category_scores_gemma":[0.00009777225,0.00007641875,0.00006592528,0.00014952988,0.000012438427,0.00014877706,0.000005938837,0.000052590127,0.000009799763],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020782574,0.000043100015,0.00033806832,0.000693574,0.000053743828,9.0792514e-7,0.0004464829,0.00074509846,0.81332225,0.0028181162,0.013436099,0.16789477],"study_design_scores_gemma":[0.0019552624,0.0004159731,0.0013793758,0.00048727967,0.000040014595,0.000039278242,0.0001121509,0.024929367,0.9071618,0.0019977132,0.06110756,0.0003742514],"about_ca_topic_score_codex":0.000024389565,"about_ca_topic_score_gemma":0.00001228048,"teacher_disagreement_score":0.9523205,"about_ca_system_score_codex":0.00006017523,"about_ca_system_score_gemma":0.000019010746,"threshold_uncertainty_score":0.3116266},"labels":[],"label_agreement":null},{"id":"W2492246656","doi":"10.1142/9789814273398_0016","title":"OPTIMIZATION OF REJECTION PARAMETERS TO ENHANCE RELIABILITY IN HANDWRITING RECOGNITION","year":2009,"lang":"en","type":"book-chapter","venue":"WORLD SCIENTIFIC eBooks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Handwriting; Reliability (semiconductor); Computer science; Speech recognition; Pattern recognition (psychology); Artificial intelligence; Reliability engineering; Engineering; Physics","score_opus":0.02569936363401142,"score_gpt":0.24168631545203673,"score_spread":0.2159869518180253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2492246656","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015012342,0.00007021462,0.0056870743,0.000011673401,0.0041167615,0.0013343059,0.000032242315,0.00026135208,0.973474],"genre_scores_gemma":[0.36131227,0.0000026505109,0.0037609187,0.000015722728,0.00027874304,0.00005688656,0.000058858906,0.00008114483,0.6344328],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979615,0.00006107105,0.0007768519,0.0005123647,0.00043933475,0.00024886383],"domain_scores_gemma":[0.9989873,0.00009801409,0.0001604631,0.00040756838,0.00027422997,0.000072430834],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019959696,0.00022056144,0.0003728484,0.0014363982,0.00014191573,0.00011584044,0.00010626684,0.00028345073,0.00004913856],"category_scores_gemma":[0.00006537956,0.0002515115,0.00011292794,0.00028088925,0.00007695496,0.00008006777,0.0000212793,0.000502222,0.00005843614],"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":[0.00012255614,0.000030043342,0.0000065918744,0.00035268196,0.00002986522,0.000008526194,0.0010277892,0.4557575,0.009710885,0.0005333843,0.0077922847,0.52462786],"study_design_scores_gemma":[0.0015010302,0.0007829856,0.00006396314,0.018350491,0.00016352368,0.000024726402,0.00028181702,0.052856974,0.29648584,0.023722684,0.6022861,0.0034798929],"about_ca_topic_score_codex":0.000029453426,"about_ca_topic_score_gemma":0.00092425,"teacher_disagreement_score":0.5944938,"about_ca_system_score_codex":0.00034058938,"about_ca_system_score_gemma":0.000033822827,"threshold_uncertainty_score":0.99999374},"labels":[],"label_agreement":null},{"id":"W2496496917","doi":"10.1007/978-1-84882-406-5_7","title":"Spatial Partitioning Methods","year":2009,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Victoria","funders":"","keywords":"Computer science","score_opus":0.03550162955661053,"score_gpt":0.28182079755186584,"score_spread":0.24631916799525533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2496496917","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000028345858,0.00017192519,0.1435194,0.000005322428,0.0012993573,0.00012073394,0.0000033280514,0.00049266906,0.8543844],"genre_scores_gemma":[0.008471314,0.000055410073,0.0053536943,0.0000470034,0.0022287413,0.000007936011,0.000021218682,0.000093190036,0.9837215],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930567,0.000013516493,0.00028364186,0.00014355213,0.00012638308,0.00012724733],"domain_scores_gemma":[0.99966174,0.000035926503,0.000041323256,0.00017637262,0.00003100429,0.00005361623],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021796442,0.00019638475,0.00026651434,0.00011477943,0.000046745627,0.000043288248,0.000046508085,0.00046134094,0.0017926131],"category_scores_gemma":[0.000009105728,0.00018556444,0.00012967651,0.000015951176,0.000007549194,0.00003973253,0.000008346779,0.0003173789,0.00044637694],"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":[0.0000039574525,0.0000011613032,2.5969857e-7,0.000018688472,0.000047167174,0.0000067535825,0.000014393947,0.0008453097,0.00028148395,0.017022151,0.012034174,0.9697245],"study_design_scores_gemma":[0.00011367222,0.00004578744,0.000004090992,0.000094538016,0.00002389608,0.000012195437,0.0000018537221,0.0023505697,0.0017308936,0.0044290842,0.99092835,0.0002650716],"about_ca_topic_score_codex":0.000026826672,"about_ca_topic_score_gemma":0.00001655472,"teacher_disagreement_score":0.9788942,"about_ca_system_score_codex":0.00006625233,"about_ca_system_score_gemma":0.000009324975,"threshold_uncertainty_score":0.9991199},"labels":[],"label_agreement":null},{"id":"W2503931548","doi":"10.1007/978-1-4471-6741-9","title":"Integrated Imaging and Vision Techniques for Industrial Inspection","year":2015,"lang":"en","type":"book","venue":"Advances in computer vision and pattern recognition","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":270,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science","score_opus":0.02950227043850592,"score_gpt":0.2887707138581019,"score_spread":0.259268443419596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2503931548","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":[0.014083951,0.012554766,0.9157635,0.00011637629,0.018039389,0.006172276,0.00073284266,0.0033147023,0.0292222],"genre_scores_gemma":[0.80922055,0.057155527,0.04661883,0.0021685727,0.053662952,0.0020539877,0.014449636,0.002498539,0.012171376],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985488,0.00006683914,0.0005374474,0.00045044103,0.00019116828,0.00020530223],"domain_scores_gemma":[0.99932915,0.00014149572,0.00015478043,0.00013355393,0.00015097122,0.00009002895],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043646808,0.00036827082,0.00045887785,0.00051329593,0.00009360272,0.00016905305,0.00006396037,0.0005065351,0.000008976013],"category_scores_gemma":[0.000017650607,0.00034377296,0.00006449152,0.000115278206,0.000048686594,0.00060002273,0.000052767416,0.00054244616,0.0000079790725],"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":[0.000064555235,0.000010699431,0.000027334094,0.00015806696,0.000008735174,0.0000054370003,0.000057338384,0.00003053581,0.000034008946,0.0000027267106,0.0127660865,0.98683447],"study_design_scores_gemma":[0.0028998842,0.0012307251,0.000036866102,0.0058548977,0.00005458312,0.00015310937,0.00005373632,0.15583175,0.0003078753,0.006089725,0.82647026,0.0010165923],"about_ca_topic_score_codex":0.000018111476,"about_ca_topic_score_gemma":0.000048150796,"teacher_disagreement_score":0.9858179,"about_ca_system_score_codex":0.00022247247,"about_ca_system_score_gemma":0.000029994915,"threshold_uncertainty_score":0.9999014},"labels":[],"label_agreement":null},{"id":"W2510758617","doi":"10.1109/icip.2016.7532453","title":"Shape-included label-consistent discriminative dictionary learning: An approach to detect and segment multi-class objects in images","year":2016,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"","keywords":"Discriminative model; Artificial intelligence; Pattern recognition (psychology); Segmentation; Computer science; Representation (politics); Sparse approximation; Object (grammar); Dictionary learning; Image segmentation; Computer vision","score_opus":0.0382952671877906,"score_gpt":0.25996379615584286,"score_spread":0.22166852896805225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510758617","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":[0.93731636,0.000115365336,0.049980044,0.00007153844,0.00043223225,0.00083541556,0.00001211045,0.0005268229,0.010710122],"genre_scores_gemma":[0.99667495,0.000016474612,0.0021609818,0.000021561053,0.000076663484,0.000107087995,0.0000025704542,0.000030422523,0.00090930256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988778,0.0001162153,0.00026961265,0.00031399864,0.00017298892,0.00024936142],"domain_scores_gemma":[0.99956334,0.000080972466,0.000029796285,0.000140514,0.000042764816,0.00014260162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030675074,0.00018747078,0.00022228206,0.00022502968,0.00009159155,0.000045594017,0.00006090299,0.00012007313,0.000022058692],"category_scores_gemma":[0.00006896151,0.0001276837,0.00003355799,0.00019046571,0.000024047315,0.00021986764,0.000058303496,0.00015784353,0.0000251659],"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":[0.0002710706,0.00029059045,0.0024929708,0.00015871847,0.0001403551,0.00002733868,0.004743156,0.009013229,0.46118325,0.00019076843,0.0006786988,0.5208098],"study_design_scores_gemma":[0.020738121,0.0039743474,0.120477885,0.001019999,0.00012478842,0.0002576536,0.019449666,0.44642153,0.37656283,0.00025717297,0.0072969194,0.003419097],"about_ca_topic_score_codex":0.00009196896,"about_ca_topic_score_gemma":0.000055263266,"teacher_disagreement_score":0.5173907,"about_ca_system_score_codex":0.00018535719,"about_ca_system_score_gemma":0.0000115532375,"threshold_uncertainty_score":0.520679},"labels":[],"label_agreement":null},{"id":"W2518957036","doi":"10.4133/sageep.29-067","title":"Near Surface Data Analysis","year":2016,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique; Polytechnique Montréal","funders":"","keywords":"Computer science; Surface (topology); Mathematics; Geometry","score_opus":0.0447502565560149,"score_gpt":0.25217757587094736,"score_spread":0.20742731931493247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2518957036","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":[0.69170505,0.00005615896,0.28607213,0.00006398731,0.0005983457,0.000078704696,0.00004355767,0.0005846995,0.02079734],"genre_scores_gemma":[0.9978438,0.0000059835597,0.0004230752,0.000004925072,0.000069169844,4.162185e-7,0.0000041009457,0.00000694082,0.0016415762],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961156,0.000011276862,0.000104242325,0.00010823291,0.00007837923,0.00008633683],"domain_scores_gemma":[0.99943775,0.000033937315,0.000008244995,0.00047592667,0.00001203093,0.0000320822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016680668,0.000046885885,0.00009322812,0.000023709343,0.000025866724,0.000028459914,0.00010672371,0.000048444923,0.0005690077],"category_scores_gemma":[0.000016394753,0.000027023878,0.000031209896,0.0003152561,0.000005840677,0.00012500094,0.000028026778,0.000024877825,0.00043881845],"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":[0.000048992388,0.000043710912,0.04255161,0.000043741624,0.004180839,0.000020782652,0.00019150112,0.13619877,0.2040148,0.0010807995,0.27785468,0.3337698],"study_design_scores_gemma":[0.00081141765,0.000039246865,0.007901094,0.000021565584,0.00029907454,0.000004848275,0.000051169387,0.37399894,0.019493423,0.000042351578,0.5968934,0.00044342937],"about_ca_topic_score_codex":0.000083951774,"about_ca_topic_score_gemma":0.000039265473,"teacher_disagreement_score":0.33332637,"about_ca_system_score_codex":0.000015477743,"about_ca_system_score_gemma":0.0000038933795,"threshold_uncertainty_score":0.6230235},"labels":[],"label_agreement":null},{"id":"W2524597532","doi":"10.1109/aim.2016.7576769","title":"Performance evaluation of MVI for fault detection in automated assembly machines","year":2016,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Queen's University","funders":"","keywords":"Downtime; Computer science; Robustness (evolution); Machine vision; Fault detection and isolation; Real-time computing; Programmable logic controller; Simulation; Artificial intelligence; Operating system","score_opus":0.03190330398238825,"score_gpt":0.286983493341718,"score_spread":0.25508018935932975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2524597532","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":[0.9864546,0.000014418455,0.010743898,0.0000053574427,0.0005588892,0.00039272703,0.0000029758683,0.00031972726,0.0015073906],"genre_scores_gemma":[0.99969774,0.000004223825,0.00007385075,0.0000012272362,0.000066129156,0.00007892793,9.62067e-7,0.0000114931245,0.00006546314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937665,0.00003170781,0.00024245918,0.00008043119,0.00017092953,0.000097797136],"domain_scores_gemma":[0.9996814,0.00005161185,0.00003624362,0.00008438675,0.00013180636,0.000014508894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076534535,0.00006683693,0.00010656932,0.00014455205,0.000019707179,0.000005993705,0.000028765604,0.00009502612,0.000023924544],"category_scores_gemma":[0.00008901424,0.00004437276,0.0000315221,0.00016117228,0.0000042193506,0.00014922567,0.000003511713,0.00002777355,0.000011327093],"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":[0.000030997096,0.000007020611,0.0005195825,0.000033768636,0.000009038922,3.0964646e-8,0.000029297886,0.018711,0.40013862,0.0000066922958,0.00014312787,0.58037084],"study_design_scores_gemma":[0.0008631986,0.000060691487,0.007784295,0.00004441519,0.0000065667095,0.0000012010905,0.000008844926,0.736821,0.25417432,0.0000095876485,0.0001715381,0.000054358272],"about_ca_topic_score_codex":0.000044962348,"about_ca_topic_score_gemma":0.00009794821,"teacher_disagreement_score":0.71810997,"about_ca_system_score_codex":0.000100047335,"about_ca_system_score_gemma":0.000011261272,"threshold_uncertainty_score":0.18094686},"labels":[],"label_agreement":null},{"id":"W2526095533","doi":"10.2316/journal.206.2016.5.206-4698","title":"A MACHINE-LEARNING-BASED ALGORITHM FOR DETECTING A MOVING OBJECT","year":2016,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Object (grammar); Computer vision; Machine learning; Algorithm","score_opus":0.012460027360904345,"score_gpt":0.2451358960149698,"score_spread":0.23267586865406545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2526095533","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":[0.020172575,0.00014172753,0.97797346,0.00014160544,0.0013926564,0.000072393086,0.000006950862,0.00005687281,0.00004175244],"genre_scores_gemma":[0.9818175,0.00003313031,0.017639685,0.000012658617,0.00045199355,0.000002766882,0.0000013629322,0.000014657603,0.000026281723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993219,0.0000221253,0.00032051807,0.000054818356,0.00020190972,0.00007874134],"domain_scores_gemma":[0.99929535,0.00018365732,0.00018492162,0.00002929532,0.00027101723,0.000035739788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035393023,0.00007308549,0.00011216586,0.00020117934,0.00004681197,0.00007033128,0.00006406917,0.000058664173,0.0000063206367],"category_scores_gemma":[0.00015361467,0.00005181164,0.00007314496,0.000047222227,0.000007440831,0.00017111674,0.000007544334,0.00008319989,0.0000015291745],"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":[0.000018211458,0.000007884656,0.0001596833,0.0000109173725,0.00006620327,0.0000044324397,0.00004768614,0.088864066,0.0067477864,0.000059712478,0.000039399958,0.903974],"study_design_scores_gemma":[0.0011192916,0.0001334476,0.00022941649,0.00019850081,0.000013732985,0.00007481894,0.000021860758,0.9899477,0.0060505853,0.00019562451,0.0019366344,0.000078386416],"about_ca_topic_score_codex":0.0000042484426,"about_ca_topic_score_gemma":0.0000016547529,"teacher_disagreement_score":0.9616449,"about_ca_system_score_codex":0.0000850458,"about_ca_system_score_gemma":0.000019677544,"threshold_uncertainty_score":0.21128173},"labels":[],"label_agreement":null},{"id":"W2536201449","doi":"10.1007/s00170-016-9581-5","title":"Fault detection and classification in automated assembly machines using machine vision","year":2016,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Machine vision; Robustness (evolution); Artificial intelligence; Programmable logic controller; Fault detection and isolation; Computer science; Automation; Engineering; Computer vision","score_opus":0.013110648444945111,"score_gpt":0.27617412902001204,"score_spread":0.2630634805750669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2536201449","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":[0.9825514,0.00017088442,0.015224197,0.00056726305,0.0011410477,0.00008880707,0.00000239139,0.00021706587,0.00003696778],"genre_scores_gemma":[0.99898005,0.00016815294,0.0007060399,0.000009189349,0.00010696525,0.0000029552364,2.4593882e-7,0.00001577443,0.000010639964],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914986,0.000027555467,0.00040868207,0.00009738903,0.00019993847,0.00011658343],"domain_scores_gemma":[0.9994803,0.000083582185,0.00021212528,0.00011040946,0.00009206442,0.000021485044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027123003,0.00010933177,0.0001529787,0.0006046381,0.000042695825,0.00002818162,0.0002280314,0.00013172714,0.0000047658928],"category_scores_gemma":[0.00009321596,0.00006454808,0.000036321253,0.000114038645,0.00003822128,0.00028441538,0.00004631092,0.00023288741,0.0000029974594],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007386777,0.0000071786835,0.0001499096,0.0000034677917,0.000025900585,0.000009567473,0.00001559495,0.006898572,0.7755694,0.000038206046,0.00000807122,0.21720028],"study_design_scores_gemma":[0.0013079491,0.00011312057,0.0068403934,0.0002265726,0.000010142534,0.00071002316,0.0000670879,0.077779,0.9094964,0.0017278,0.0015966842,0.00012480284],"about_ca_topic_score_codex":0.000015348582,"about_ca_topic_score_gemma":0.00004762021,"teacher_disagreement_score":0.21707548,"about_ca_system_score_codex":0.00023016753,"about_ca_system_score_gemma":0.00000743338,"threshold_uncertainty_score":0.26321945},"labels":[],"label_agreement":null},{"id":"W2568282499","doi":"","title":"Proceedings, International Conference on Parallel Processing Workshops, 18-21 August 2002, Vancouver, B.C., Canada","year":2002,"lang":"en","type":"book","venue":"IEEE Computer Society eBooks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Library science; Regional science; History; Geography; Political science; Media studies; Sociology; Computer science","score_opus":0.03343432911402876,"score_gpt":0.22016604817900498,"score_spread":0.1867317190649762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568282499","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009615728,0.00024735494,0.02251626,0.000023153512,0.02782149,0.000776898,0.000070671034,0.0007936828,0.9476543],"genre_scores_gemma":[0.011002404,0.00007058431,0.001781695,0.0004314674,0.008479692,0.00008608505,0.0000307449,0.0002207882,0.9778965],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975242,0.000012140824,0.00061221624,0.0005737187,0.0008026614,0.00047503633],"domain_scores_gemma":[0.9989613,0.000039160048,0.00025855994,0.0002379604,0.00032643168,0.00017658029],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014001099,0.0006277896,0.0005831601,0.0000843819,0.00020539411,0.0003262166,0.00048144895,0.00080851704,0.00020319455],"category_scores_gemma":[0.000002943991,0.0006515128,0.0003095194,0.00006245603,0.00005173831,0.00011065305,0.00006512571,0.001290175,0.000034846926],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008534471,0.000008520669,8.6863304e-7,0.00017126359,0.00017129899,0.00001295712,0.00024901514,0.0033367332,0.000013033675,0.000069941125,0.9652685,0.030689359],"study_design_scores_gemma":[0.0006751736,0.000056296292,0.0000023869504,0.0013282972,0.000037009613,0.000018559822,0.000058913243,0.18345585,0.000054692762,0.00008174543,0.8134628,0.00076827494],"about_ca_topic_score_codex":0.001977692,"about_ca_topic_score_gemma":0.0073755994,"teacher_disagreement_score":0.18011913,"about_ca_system_score_codex":0.0014320897,"about_ca_system_score_gemma":0.0005940797,"threshold_uncertainty_score":0.9995936},"labels":[],"label_agreement":null},{"id":"W2583491955","doi":"10.1007/978-3-319-46454-1","title":"Computer Vision – ECCV 2016","year":2016,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":232,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of California, Irvine; University of Illinois at Urbana-Champaign; Rheinische Friedrich-Wilhelms-Universität Bonn; Technische Universität München; University of Crete; Sapienza Università di Roma; Lunds Universitet; Albert-Ludwigs-Universität Freiburg; Carnegie Mellon University; Università Degli Studi di Modena e Reggio Emila; Institute of Science and Technology Austria; Université de Sherbrooke; University College London; Imperial College London; Yale University; Eberhard Karls Universität Tübingen; University of North Carolina at Chapel Hill; Seoul National University; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; University of Oxford","keywords":"Computer science; Computer graphics (images); Volume (thermodynamics); Computer vision; Set (abstract data type); Artificial intelligence; Programming language","score_opus":0.010947100727419844,"score_gpt":0.23206690241408442,"score_spread":0.2211198016866646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2583491955","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":[0.00021492026,0.00029683363,0.98294383,0.00004950916,0.010482805,0.0003428805,0.000010212735,0.00034256425,0.005316423],"genre_scores_gemma":[0.7866874,0.00040456114,0.13163103,0.0020549744,0.05756408,0.00010085552,0.00005787302,0.0007536343,0.020745631],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759793,0.0000390776,0.00047382948,0.0007190139,0.00062699994,0.0005431331],"domain_scores_gemma":[0.99868643,0.00030269625,0.00010452374,0.00065363216,0.00011858444,0.00013411976],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00065121596,0.00042498787,0.00048247588,0.00068280776,0.0001415794,0.00025209002,0.00066791935,0.00057685317,0.000052692787],"category_scores_gemma":[0.00002416329,0.000309331,0.00012916506,0.00046555387,0.00020822692,0.00028724942,0.00023533245,0.0006177823,0.00034825807],"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":[0.000006438182,0.0000065066215,0.0000047230815,0.000048810998,0.0000086316195,0.000029601648,0.00007872499,0.05490268,0.00059399166,0.000030597977,0.0073404945,0.9369488],"study_design_scores_gemma":[0.0012177865,0.00072555966,0.00013375127,0.004316764,0.000017888311,0.00019712155,1.15442695e-7,0.7764641,0.0060870284,0.011620664,0.19739673,0.0018224982],"about_ca_topic_score_codex":0.000008606289,"about_ca_topic_score_gemma":0.000012513846,"teacher_disagreement_score":0.9351263,"about_ca_system_score_codex":0.00061002467,"about_ca_system_score_gemma":0.00027505984,"threshold_uncertainty_score":0.99993587},"labels":[],"label_agreement":null},{"id":"W2593116849","doi":"10.1016/j.microrel.2017.02.013","title":"Fault detection using thermal image based on soft computing methods: Comparative study","year":2017,"lang":"en","type":"article","venue":"Microelectronics Reliability","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Soft computing; Computer science; Principal component analysis; Fault detection and isolation; Histogram; Printed circuit board; Artificial intelligence; Adaptive neuro fuzzy inference system; Artificial neural network; Fuzzy logic; Finite element method; Support vector machine; Image processing; Engineering; Pattern recognition (psychology); Image (mathematics); Fuzzy control system","score_opus":0.04081144518267854,"score_gpt":0.35470485705526633,"score_spread":0.3138934118725878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593116849","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":[0.78115547,0.00003100439,0.21645369,0.0000061232067,0.00067597,0.0007037492,0.000004275918,0.00023632467,0.0007334295],"genre_scores_gemma":[0.99650115,5.725521e-7,0.0032369066,0.000009463593,0.00018692491,0.000014233984,0.0000017834049,0.00003889666,0.000010047199],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792045,0.00047710145,0.00045706634,0.00044810225,0.00022805369,0.00046922013],"domain_scores_gemma":[0.99840575,0.00022446694,0.00019272567,0.0009408675,0.00016273574,0.000073425916],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001952648,0.00029506514,0.00044229144,0.000105280174,0.000921412,0.00028523005,0.0002758289,0.00018428804,0.00001694033],"category_scores_gemma":[0.00020911686,0.00027807386,0.00015448144,0.00013833735,0.000067752684,0.00019666449,0.000054613396,0.000720336,0.000025243455],"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":[0.00019738817,0.00024409982,0.000989047,0.0000477933,0.000067531284,0.0000034801808,0.000573237,0.3510203,0.6132501,0.0000030275935,0.000027295777,0.033576738],"study_design_scores_gemma":[0.0008539137,0.00041670233,0.0026358746,0.00002379612,0.000030606636,0.0000030168783,0.00016528611,0.7764388,0.21870518,0.000023436254,0.00045255982,0.00025085834],"about_ca_topic_score_codex":0.00033326732,"about_ca_topic_score_gemma":0.000070640075,"teacher_disagreement_score":0.4254185,"about_ca_system_score_codex":0.0006901452,"about_ca_system_score_gemma":0.000061203966,"threshold_uncertainty_score":0.99996716},"labels":[],"label_agreement":null},{"id":"W2595054758","doi":"10.5061/dryad.b12kk","title":"Data from: Spatial detection of outlier loci with Moran eigenvector maps (MEM)","year":2016,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Industrial Vision Systems and Defect Detection","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":"","keywords":"Anomaly detection; Outlier; Spatial analysis; Cartography; Computer science; Geography; Data mining; Artificial intelligence; Pattern recognition (psychology); Statistics; Mathematics; Remote sensing","score_opus":0.02703185640872413,"score_gpt":0.22173402096756295,"score_spread":0.19470216455883882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595054758","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":[0.89500123,0.00025667038,0.09960299,0.000011989173,0.0006086301,0.00022582158,0.0037059917,0.00020869076,0.00037800844],"genre_scores_gemma":[0.9974657,0.0001140229,0.00027602428,0.000009898733,0.00083546655,0.0000059255804,0.001214126,0.000045345598,0.000033485132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859035,0.000095351694,0.0003384831,0.0004826184,0.00022716796,0.00026600045],"domain_scores_gemma":[0.9981433,0.0001734487,0.00010942934,0.0014490044,0.000022172306,0.0001026113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035342784,0.00021825465,0.00029471153,0.00008128048,0.00012029871,0.000057539928,0.00070966344,0.000101115955,0.00002540698],"category_scores_gemma":[0.000009193678,0.00014344061,0.000021480759,0.00016053767,0.000048203772,0.00046946155,0.00037263057,0.00015518097,0.000019991623],"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":[0.0012409154,0.00008740629,0.030219762,0.0007103114,0.0008728892,0.000030393272,0.0022976187,0.0042588837,0.25654492,0.000010659422,0.0023533548,0.70137286],"study_design_scores_gemma":[0.0037582708,0.0004324127,0.065700755,0.0020379652,0.0003557254,0.000049023984,0.0008231645,0.84329975,0.015887873,0.00016088356,0.06627272,0.0012214858],"about_ca_topic_score_codex":0.0032509258,"about_ca_topic_score_gemma":0.00920503,"teacher_disagreement_score":0.8390408,"about_ca_system_score_codex":0.00001932533,"about_ca_system_score_gemma":0.000011797066,"threshold_uncertainty_score":0.5849338},"labels":[],"label_agreement":null},{"id":"W2597349539","doi":"10.3390/robotics6010005","title":"Visual Tracking of Deformation and Classification of Non-Rigid Objects with Robot Hand Probing","year":2017,"lang":"en","type":"article","venue":"Robotics","topic":"Industrial Vision Systems and Defect Detection","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":"Université du Québec en Outaouais; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Innovation Trust","keywords":"Computer vision; Artificial intelligence; Computer science; GRASP; Image warping; Robot; Rigidity (electromagnetism); RGB color model; Process (computing); Object (grammar); Engineering","score_opus":0.02880622201144877,"score_gpt":0.2642463509011376,"score_spread":0.23544012888968885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597349539","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":[0.8703202,0.000020625039,0.12829202,0.0000052143623,0.00019635973,0.00019542356,0.0000013160529,0.000025010198,0.0009438181],"genre_scores_gemma":[0.9988688,0.0000063523785,0.0010409268,5.539544e-7,0.00005363853,0.0000029336343,0.0000022039644,0.000012437154,0.000012152392],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995024,0.0000089623445,0.00022435657,0.000067067165,0.00011453249,0.00008264803],"domain_scores_gemma":[0.999532,0.000018154533,0.00019113583,0.00015150852,0.00008325698,0.000023979565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014747457,0.00007343067,0.0001558016,0.000066260196,0.00012171246,0.00006423679,0.00004596281,0.000084486484,8.1367915e-7],"category_scores_gemma":[0.000026702295,0.000061671846,0.0000188539,0.000048837243,0.000039532417,0.00025505517,0.000010219188,0.000074112664,0.0000012539928],"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":[0.000062163745,0.000034486962,0.007446883,0.00070730154,0.00006460787,0.0000012311908,0.0019930885,0.43338212,0.5164559,0.0002531895,0.000019122414,0.039579894],"study_design_scores_gemma":[0.0010621515,0.00030155564,0.124533564,0.0006004906,0.00005158793,0.000017936252,0.000420501,0.5803081,0.29245523,0.000025786298,0.000025649735,0.00019744574],"about_ca_topic_score_codex":0.000021129174,"about_ca_topic_score_gemma":0.000021433158,"teacher_disagreement_score":0.22400068,"about_ca_system_score_codex":0.000021885222,"about_ca_system_score_gemma":0.000013984147,"threshold_uncertainty_score":0.2514905},"labels":[],"label_agreement":null},{"id":"W2600283457","doi":"10.1109/ram.2017.7889679","title":"Predictive maintenance applications for machine learning","year":2017,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PTC (Canada)","funders":"","keywords":"Computer science; Machine learning; Predictive maintenance; Artificial intelligence; Predictive modelling; Reliability engineering; Engineering","score_opus":0.01910937837673475,"score_gpt":0.25070455556256893,"score_spread":0.23159517718583417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600283457","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":[0.0025428433,0.000037608046,0.93753123,0.000034022585,0.00036318201,0.00055465056,0.00001642893,0.00034539963,0.05857463],"genre_scores_gemma":[0.99515855,0.0000071656823,0.0004608932,0.0000049243627,0.0002664066,0.0002664359,0.000003793756,0.000013531944,0.0038182854],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99969244,0.000003845822,0.000087148954,0.000078159086,0.000041711162,0.00009667297],"domain_scores_gemma":[0.99970555,0.00002614386,0.000030990945,0.00017439839,0.000036018653,0.000026905513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010801335,0.000051989173,0.00007145208,0.00002183436,0.00032662414,0.000060185714,0.00007686768,0.00005159843,0.000019527808],"category_scores_gemma":[0.00005147466,0.000043960525,0.000036844907,0.000017051228,0.0000104714545,0.00008193539,0.000010870351,0.00008230607,0.00003357579],"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":[0.0002655457,0.00007561523,0.009621755,0.000410044,0.00039290224,0.0000035846058,0.0005718107,0.123564005,0.032265715,0.035384998,0.05107336,0.7463707],"study_design_scores_gemma":[0.0006242532,0.0000760608,0.0012109985,0.000018203042,0.000009481577,0.0000040874997,0.000056711167,0.3459017,0.005047223,0.00042278942,0.6464935,0.00013499787],"about_ca_topic_score_codex":0.0000367661,"about_ca_topic_score_gemma":0.000013683709,"teacher_disagreement_score":0.9926157,"about_ca_system_score_codex":0.000022981223,"about_ca_system_score_gemma":0.000003271919,"threshold_uncertainty_score":0.25121617},"labels":[],"label_agreement":null},{"id":"W2607317540","doi":"10.1149/ma2010-03/1/734","title":"Using Image Analysis to Extract Quantitative Data from Li-Ion Battery Micrographs","year":2010,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Micrograph; Electron micrographs; Ion; Artificial intelligence; Battery (electricity); Materials science; Computer vision; Computer science; Chemistry; Optics; Physics; Composite material; Scanning electron microscope; Electron microscope","score_opus":0.06871697189631523,"score_gpt":0.3171986859374736,"score_spread":0.24848171404115835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607317540","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":[0.9886754,0.00006592935,0.0028168904,0.000019863995,0.0020538252,0.00017209965,0.00016101997,0.00026924754,0.005765686],"genre_scores_gemma":[0.9772334,0.0000028620054,0.021999164,0.000018463468,0.0005771972,0.0000036971562,0.000107141546,0.00004432459,0.000013760055],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984141,0.000052436142,0.0005247755,0.00043208626,0.00025721212,0.00031941573],"domain_scores_gemma":[0.9985359,0.00032713948,0.00014421614,0.00074464624,0.00008408614,0.00016403418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009240165,0.00022005799,0.000329553,0.000444741,0.00015042338,0.0002506085,0.00029956276,0.00021218845,0.000084173786],"category_scores_gemma":[0.00042147792,0.00022531034,0.000121822275,0.0008229326,0.000020678735,0.00037662705,0.00008068955,0.0005155492,0.00021819481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014363069,0.000017578337,0.00079455564,0.000008481249,0.00023870813,0.000016644946,0.00018742695,0.08108257,0.916169,3.3458534e-7,0.000864595,0.0006057474],"study_design_scores_gemma":[0.00060281705,0.000067243,0.045600586,0.00023527363,0.0008388442,0.000018576166,0.00077165425,0.22539976,0.7111287,0.00004289595,0.014252304,0.0010413475],"about_ca_topic_score_codex":0.0023281244,"about_ca_topic_score_gemma":0.00054344424,"teacher_disagreement_score":0.2050403,"about_ca_system_score_codex":0.00003257486,"about_ca_system_score_gemma":0.000018410985,"threshold_uncertainty_score":0.9187889},"labels":[],"label_agreement":null},{"id":"W2615859282","doi":"","title":"Classification algorithms for virtual metrology","year":2014,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"","keywords":"Metrology; Computer science; Algorithm; Process (computing); Sampling (signal processing); Dimensional metrology; Quality (philosophy); Measurement uncertainty; Data mining; Mathematics; Computer vision; Statistics","score_opus":0.020471203136089924,"score_gpt":0.22981892768203055,"score_spread":0.20934772454594064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615859282","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":[0.253143,0.000040232164,0.7417617,0.00008845135,0.0021729146,0.00021547712,0.000008252536,0.0003164821,0.0022534765],"genre_scores_gemma":[0.99660623,0.0000052468286,0.001945096,0.000058457983,0.00096522,0.00006605784,0.000016682421,0.000026022086,0.00031099946],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993872,0.000043033033,0.00018473726,0.00013739207,0.000081518956,0.00016612813],"domain_scores_gemma":[0.99960274,0.00013641312,0.00003399823,0.00014119645,0.0000378835,0.000047803376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030238807,0.000098455464,0.0001518358,0.00010317656,0.00012211614,0.000027145039,0.00006364122,0.00025775004,0.000013089636],"category_scores_gemma":[0.00009014761,0.000093977156,0.00007206297,0.00010513786,0.00003129049,0.000058443686,0.0000037012733,0.00014847587,0.00003960154],"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":[0.000106914005,0.00003320051,0.00038867266,0.000069068636,0.00014923439,0.0000016585227,0.0008984151,0.0047211857,0.0439849,0.17434566,0.0238869,0.7514142],"study_design_scores_gemma":[0.0017522438,0.0004959831,0.0021788096,0.000010426745,0.00003874017,0.00002167503,0.0003376708,0.34380177,0.008149473,0.0024649126,0.64035815,0.00039018254],"about_ca_topic_score_codex":0.000012918542,"about_ca_topic_score_gemma":0.0000040437126,"teacher_disagreement_score":0.751024,"about_ca_system_score_codex":0.00008359973,"about_ca_system_score_gemma":0.0000047172143,"threshold_uncertainty_score":0.38322774},"labels":[],"label_agreement":null},{"id":"W2615908833","doi":"10.1109/tase.2017.2696748","title":"Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":130,"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 du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Pattern recognition (psychology); Preprocessor; Contourlet; Computer science; Classifier (UML); Feature extraction; Statistical model; Computer vision; Wavelet transform","score_opus":0.018210426169232352,"score_gpt":0.24989466265221602,"score_spread":0.23168423648298367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615908833","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":[0.46796912,0.0000076281294,0.53119427,0.000023602533,0.0004251695,0.00015816011,0.0000028022907,0.00018647728,0.00003275075],"genre_scores_gemma":[0.99967706,0.0000021993626,0.0002037182,0.00000873235,0.00004107652,0.0000499109,6.5971386e-7,0.0000139857875,0.000002652704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880975,0.000042461063,0.00026616696,0.00019963896,0.0003971017,0.00028485514],"domain_scores_gemma":[0.9994171,0.000102325976,0.00006429676,0.00027125125,0.000077567034,0.000067430105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010167296,0.00016343007,0.00015358388,0.00039608407,0.0011500027,0.00045462165,0.00019945297,0.00009872819,0.000007766357],"category_scores_gemma":[0.0000740836,0.00013617973,0.00006868748,0.00056998536,0.00013650478,0.00063940807,0.0000015998326,0.00036324142,0.000009054631],"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":[0.000003192384,0.000010812232,0.00001617087,0.00002743579,0.0000058287187,0.0000023047855,0.00021517147,0.9328064,0.015833031,0.000015357527,0.0000017504054,0.051062543],"study_design_scores_gemma":[0.00038389757,0.00005047483,0.0040231706,0.00006899957,0.0000116005585,0.000034561624,0.00019721796,0.98279476,0.012122807,0.000013955804,0.00013994305,0.00015862907],"about_ca_topic_score_codex":0.00010690512,"about_ca_topic_score_gemma":0.000051901105,"teacher_disagreement_score":0.53170794,"about_ca_system_score_codex":0.00038047545,"about_ca_system_score_gemma":0.000055155255,"threshold_uncertainty_score":0.88450074},"labels":[],"label_agreement":null},{"id":"W2618540799","doi":"10.5815/ijisa.2017.06.06","title":"Batik Classification with Artificial Neural Network Based on Texture-Shape Feature of Main Ornament","year":2017,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Texture (cosmology); Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Computer vision; Image (mathematics)","score_opus":0.03132037076273415,"score_gpt":0.2769539758217607,"score_spread":0.24563360505902654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618540799","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":[0.2879997,0.0009790747,0.695286,0.0018918727,0.0061180517,0.0018421102,0.00016376123,0.00009437789,0.0056250994],"genre_scores_gemma":[0.9981557,0.00004027589,0.0001984134,0.000017773786,0.0014606849,0.00004095958,0.0000070751835,0.00001651527,0.00006259962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987936,0.00003199578,0.00052791875,0.00011662749,0.00042249687,0.00010734978],"domain_scores_gemma":[0.9986089,0.00008128679,0.00062898087,0.00024060073,0.00036628672,0.00007392947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035255266,0.00013078414,0.000222683,0.00014185147,0.00014653117,0.00019676411,0.00028776843,0.00009935586,0.00001482271],"category_scores_gemma":[0.000027169237,0.000096954696,0.00007983417,0.000065244836,0.000046823207,0.0001160333,0.000013940042,0.00021819028,0.0000048489833],"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":[0.00064388075,0.00025261048,0.005710576,0.00012964361,0.000525159,0.000023356839,0.0001682785,0.7815873,0.009443555,0.02584632,0.0067901723,0.16887914],"study_design_scores_gemma":[0.0011406025,0.00068219943,0.016118288,0.0013051555,0.000103952414,0.00028367576,0.00078713667,0.88927996,0.0046448233,0.0004303799,0.08478139,0.00044244897],"about_ca_topic_score_codex":0.000018344968,"about_ca_topic_score_gemma":0.000010133202,"teacher_disagreement_score":0.710156,"about_ca_system_score_codex":0.00009632404,"about_ca_system_score_gemma":0.00003202472,"threshold_uncertainty_score":0.39536977},"labels":[],"label_agreement":null},{"id":"W2623385463","doi":"10.1109/mis.2017.2581328","title":"Robust and Fast Segmentation Based on Fuzzy clustering combined with Unsupervised Histogram analysis","year":2017,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Nova Scotia Community College","funders":"","keywords":"Histogram; Thresholding; Artificial intelligence; Balanced histogram thresholding; Computer science; Image segmentation; Pattern recognition (psychology); Segmentation; Fuzzy logic; Image histogram; Histogram matching; Region growing; Partition (number theory); Cluster analysis; Scale-space segmentation; Computer vision; Mathematics; Image (mathematics); Image texture","score_opus":0.03972770714145255,"score_gpt":0.24489925206956897,"score_spread":0.20517154492811643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2623385463","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":[0.50834835,0.00010207552,0.48258457,0.000023665332,0.0034781748,0.0010873763,0.000023159471,0.00039345093,0.003959184],"genre_scores_gemma":[0.9992567,0.000010065737,0.00011825013,0.0000099091685,0.00020348858,0.00008547982,0.000014148731,0.00004069039,0.00026127748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986631,0.00006894315,0.0004094751,0.00029681608,0.0003270285,0.00023458207],"domain_scores_gemma":[0.9989994,0.00005883995,0.00016543288,0.0005759918,0.000079807985,0.00012048139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034240016,0.0002507193,0.0004162522,0.0003657436,0.00034050987,0.0004567061,0.0001537648,0.00014658757,0.000013361745],"category_scores_gemma":[0.0000142042145,0.00020859914,0.00010475077,0.00021465372,0.00003742313,0.00015407744,0.000011601872,0.00016255373,0.000031374373],"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":[0.00015445682,0.00002526119,0.0032623722,0.00014704412,0.00030538708,0.000011357233,0.00019293823,0.9896232,0.0014894515,0.000011412517,0.0001559027,0.0046212487],"study_design_scores_gemma":[0.0008664598,0.00041484088,0.0012536208,0.00026074858,0.00021544601,0.0000075326598,0.00042081956,0.9906836,0.0047797086,0.0000010871186,0.0007682954,0.00032779702],"about_ca_topic_score_codex":0.0007828637,"about_ca_topic_score_gemma":0.0002896613,"teacher_disagreement_score":0.49090835,"about_ca_system_score_codex":0.0001878433,"about_ca_system_score_gemma":0.000011430213,"threshold_uncertainty_score":0.8506426},"labels":[],"label_agreement":null},{"id":"W2641324795","doi":"","title":"Multichannel Intraluminal Impedance Monitoring","year":2005,"lang":"en","type":"article","venue":"Pharma Medica","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.02775749172412693,"score_gpt":0.2843599571314695,"score_spread":0.25660246540734255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2641324795","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":[0.98524845,0.00095396384,0.004149242,0.00013307296,0.005043788,0.00016265325,0.0000032334638,0.0005761796,0.003729425],"genre_scores_gemma":[0.9946814,0.000086678294,0.00031694566,0.000019406962,0.0047256695,0.000023745693,7.7292344e-7,0.000020465912,0.00012489251],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924797,0.000016734683,0.00019769114,0.000104272905,0.0002107823,0.00022257655],"domain_scores_gemma":[0.99969745,0.000032764547,0.000020724752,0.00009971855,0.000021358745,0.00012798689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020666319,0.00009874842,0.00011487358,0.000058503447,0.000054373904,0.00001788906,0.000080941274,0.000069693066,0.00015586155],"category_scores_gemma":[0.000038209888,0.00009280924,0.000039953407,0.0001113881,0.000013929456,0.0001310901,0.000011870352,0.00022246157,0.00028970817],"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":[0.000022698745,0.000024621891,0.00026219373,0.00005857972,0.00005106089,0.000018305507,0.0010679149,0.0067145396,0.10210256,0.0000117514655,0.012432752,0.877233],"study_design_scores_gemma":[0.0015428077,0.000045059856,0.00045420777,0.00013041825,0.00002758813,0.000066645676,0.00023087188,0.19442901,0.47444198,0.000008497081,0.32828465,0.0003382933],"about_ca_topic_score_codex":0.000011332065,"about_ca_topic_score_gemma":6.8593744e-7,"teacher_disagreement_score":0.8768947,"about_ca_system_score_codex":0.00006794902,"about_ca_system_score_gemma":0.000009231339,"threshold_uncertainty_score":0.3784651},"labels":[],"label_agreement":null},{"id":"W267981117","doi":"10.5006/c2004-04183","title":"Methodology for Statistically Designing and Assessing Metal Loss In-line Inspection Validation Programs","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BP (Canada)","funders":"","keywords":"Reliability engineering; Line (geometry); Computer science; Engineering; Forensic engineering; Mathematics","score_opus":0.13223895281094028,"score_gpt":0.34928591819179033,"score_spread":0.21704696538085005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W267981117","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":[0.30427566,0.000018961086,0.6947801,0.000012442303,0.00028863887,0.000266172,7.8567683e-7,0.0001755835,0.00018162104],"genre_scores_gemma":[0.8503858,0.0000031766756,0.14942798,0.0000046641735,0.00010176852,0.00004397806,0.000008365474,0.000014543229,0.000009727014],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99933535,0.000061574494,0.00025038762,0.00013623263,0.00006992496,0.00014651429],"domain_scores_gemma":[0.9996871,0.0001564984,0.000030028887,0.000050546383,0.000043187913,0.00003263886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074722694,0.00008673316,0.00016315603,0.00012310124,0.000059805618,0.00006888741,0.000018642635,0.00011317231,0.0000033371198],"category_scores_gemma":[0.00015439144,0.00007972241,0.00002314152,0.0001649073,0.00001630769,0.00020652774,0.000005993447,0.00009865941,0.0000019437475],"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":[0.00014798588,0.00011241821,0.0018976929,0.00031943605,0.00011943058,0.000020101115,0.0009095448,0.13357712,0.30402473,0.024418859,0.000033689936,0.534419],"study_design_scores_gemma":[0.010266697,0.0017996158,0.0069102887,0.0003614045,0.00016294184,0.00030768305,0.001857333,0.12689458,0.8110868,0.038003996,0.0012134633,0.0011352013],"about_ca_topic_score_codex":0.000110618406,"about_ca_topic_score_gemma":0.00005021896,"teacher_disagreement_score":0.54611015,"about_ca_system_score_codex":0.000086946246,"about_ca_system_score_gemma":0.000014724897,"threshold_uncertainty_score":0.32509857},"labels":[],"label_agreement":null},{"id":"W2705660411","doi":"10.1109/ccece.2017.7946699","title":"Using ga to optimize the explicitly defined skin regions for human skincolor detection","year":2017,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Color space; Artificial intelligence; Computer science; Skin color; Perceptron; Pattern recognition (psychology); Computer vision; Image (mathematics); Artificial neural network","score_opus":0.11760881265510054,"score_gpt":0.3336677725601044,"score_spread":0.21605895990500384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2705660411","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":[0.31638536,0.000008642,0.6739262,0.000108391905,0.0014484522,0.00097166194,0.000004552743,0.00025277786,0.00689397],"genre_scores_gemma":[0.9964246,9.732714e-7,0.0020700726,0.00003745915,0.0006534117,0.00016110165,8.0777363e-7,0.000034245088,0.0006173302],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999284,0.000019496583,0.00022022391,0.00015835858,0.00010406338,0.00021382447],"domain_scores_gemma":[0.9992489,0.000061759376,0.00006477878,0.00049928477,0.00006171563,0.00006353859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030109702,0.00012757145,0.00015665939,0.0000812823,0.0012073063,0.00022950799,0.00018320898,0.00012945352,0.000020961954],"category_scores_gemma":[0.000105385516,0.000092891525,0.000113257294,0.00007416123,0.000015366519,0.0001370681,0.000036131954,0.00010503092,0.000026330928],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000289315,0.00006597409,0.00007499128,0.00010811169,0.00024682877,0.0000058367696,0.0012168046,0.22307952,0.6705313,0.014341753,0.018275855,0.07176374],"study_design_scores_gemma":[0.004088395,0.00073870976,0.0022238833,0.00018971405,0.00015734449,0.000085196,0.0011790313,0.2994892,0.55296373,0.0028573035,0.13479728,0.0012302289],"about_ca_topic_score_codex":0.0006284626,"about_ca_topic_score_gemma":0.0005647873,"teacher_disagreement_score":0.6800392,"about_ca_system_score_codex":0.00010302809,"about_ca_system_score_gemma":0.000008415135,"threshold_uncertainty_score":0.9285746},"labels":[],"label_agreement":null},{"id":"W2737457785","doi":"10.1016/j.chemolab.2017.07.008","title":"Multi-class classification for steel surface defects based on machine learning with quantile hyper-spheres","year":2017,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"State Key Laboratory of Robotics and System","keywords":"Quantile; Classifier (UML); Artificial intelligence; Mathematics; Pattern recognition (psychology); Boundary (topology); Computer science; One-class classification; Machine learning; Statistics; Mathematical analysis","score_opus":0.05711096008556061,"score_gpt":0.2670004874774562,"score_spread":0.2098895273918956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737457785","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":[0.9412377,0.0023889546,0.05182659,0.000012887683,0.0019833236,0.001196212,0.00011555635,0.000299509,0.0009393049],"genre_scores_gemma":[0.9990425,0.000091228816,0.00023506192,0.000011353481,0.00016816576,0.00006284533,0.0000198753,0.0000688218,0.00030011838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868566,0.00005924779,0.00035879004,0.00034159215,0.0002859703,0.0002687217],"domain_scores_gemma":[0.99856734,0.0002490016,0.00024855227,0.0004649133,0.0003309988,0.0001391915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062065903,0.00026478228,0.00036718982,0.00026306315,0.0004751933,0.00048972055,0.0001715593,0.00023573179,0.0000070655024],"category_scores_gemma":[0.00042837913,0.00021721778,0.000059274444,0.00049491593,0.000042229065,0.0001642531,0.000016812097,0.00026652668,0.000019843383],"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":[0.0007624768,0.0004947624,0.14502835,0.0038572175,0.00040856624,0.00001959714,0.00042070184,0.6379746,0.18052936,0.001181393,0.005049086,0.02427391],"study_design_scores_gemma":[0.0009141943,0.00047915283,0.0012682197,0.00019263956,0.000024219058,0.0000019411061,0.0003971438,0.8754333,0.031871013,4.505853e-7,0.08909367,0.00032405506],"about_ca_topic_score_codex":0.000077778146,"about_ca_topic_score_gemma":0.000021322416,"teacher_disagreement_score":0.2374587,"about_ca_system_score_codex":0.0001291108,"about_ca_system_score_gemma":0.000039833976,"threshold_uncertainty_score":0.8857884},"labels":[],"label_agreement":null},{"id":"W2743059945","doi":"10.1061/9780784480885.027","title":"Real-Time Defect Detection in Sewer Closed Circuit Television Inspection Videos","year":2017,"lang":"en","type":"article","venue":"Pipelines 2017","topic":"Industrial Vision Systems and Defect Detection","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":true,"ca_institutions":"Concordia University","funders":"","keywords":"Closed circuit; Computer science; Computer vision; Artificial intelligence; Computer graphics (images); Telecommunications","score_opus":0.02983236087864779,"score_gpt":0.2653460049317278,"score_spread":0.23551364405307998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743059945","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":[0.9719981,0.000078575395,0.0029931979,0.000017968863,0.0033512325,0.0004824216,0.000007735681,0.0007959424,0.020274824],"genre_scores_gemma":[0.99787694,0.00013524009,0.00003587592,0.0000049853684,0.0013426504,0.000037433972,0.0000059885297,0.000054337263,0.0005065668],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985592,0.000066785025,0.00046906187,0.00032880815,0.0002546128,0.00032158228],"domain_scores_gemma":[0.9988038,0.000059393995,0.00018303521,0.00076658797,0.00010144316,0.00008576104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006312216,0.00024569008,0.0003364736,0.00029940385,0.00046989272,0.00024734696,0.00022673012,0.00034284673,0.000047161757],"category_scores_gemma":[0.00035306116,0.00023790282,0.000142233,0.00013902163,0.00003490276,0.0005271958,0.00004229556,0.0003129311,0.0007672108],"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":[0.00016250115,0.00007274346,0.0035421073,0.0001234655,0.00005401774,0.000047201524,0.00027560507,0.0028036772,0.7407866,0.000028939594,0.0046865637,0.24741657],"study_design_scores_gemma":[0.010310096,0.000622598,0.3306475,0.0010961082,0.00016159826,0.0002798287,0.0002097303,0.33318463,0.27141714,0.0011865871,0.04821268,0.0026715],"about_ca_topic_score_codex":0.0009896028,"about_ca_topic_score_gemma":0.00032188013,"teacher_disagreement_score":0.46936947,"about_ca_system_score_codex":0.00020239732,"about_ca_system_score_gemma":0.000018608076,"threshold_uncertainty_score":0.98611987},"labels":[],"label_agreement":null},{"id":"W2744748126","doi":"10.1149/ma2009-01/24/991","title":"Feature Scale Modeling for Through-Silicon-Via Packaging Applications","year":2009,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Industrial Vision Systems and Defect Detection","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":"Mitel (Canada)","funders":"","keywords":"Feature (linguistics); Scale (ratio); Silicon; Computer science; Artificial intelligence; Materials science; Pattern recognition (psychology); Optoelectronics; Cartography; Geography","score_opus":0.019648090734674824,"score_gpt":0.25424809353998895,"score_spread":0.23460000280531412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744748126","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":[0.64785653,0.0010252179,0.18898351,0.0006989055,0.0016441707,0.0019178487,0.000028186834,0.0021162303,0.1557294],"genre_scores_gemma":[0.9945191,0.0000075153357,0.0041749016,0.000051771287,0.0009867911,0.00006824208,0.000013619845,0.000034019173,0.0001440054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990375,0.000011425259,0.00027597163,0.00022220923,0.00014477818,0.00030812057],"domain_scores_gemma":[0.9994961,0.000071454044,0.00006557686,0.0002224198,0.00007313402,0.000071323106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003299032,0.00016748384,0.0001902006,0.000062904626,0.00023111976,0.000086393455,0.00009487723,0.00020267717,0.0000023537739],"category_scores_gemma":[0.00004219765,0.00017127776,0.00009696414,0.00017107553,0.000006508722,0.0001636274,0.0000066401235,0.00025407271,0.000035955894],"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":[0.0000075549838,0.000014009659,0.000015349025,0.000041168867,0.000011457204,7.381138e-7,0.00019566662,0.908166,0.08063201,0.000011327741,0.0023777618,0.008526954],"study_design_scores_gemma":[0.00097268797,0.00008909739,0.0003146855,0.000327361,0.000052077427,0.000036994206,0.00036747902,0.71588916,0.20491952,0.0022608482,0.07407837,0.00069173245],"about_ca_topic_score_codex":0.00002466582,"about_ca_topic_score_gemma":0.000005157725,"teacher_disagreement_score":0.3466626,"about_ca_system_score_codex":0.000062809726,"about_ca_system_score_gemma":0.000012178994,"threshold_uncertainty_score":0.69845045},"labels":[],"label_agreement":null},{"id":"W2750932963","doi":"10.1167/17.10.1029","title":"Preserved eye sensitivity of the N170 ERP component across face size","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Component (thermodynamics); Face (sociological concept); Sensitivity (control systems); Physics; Linguistics; Engineering","score_opus":0.024583214158728866,"score_gpt":0.301647747341673,"score_spread":0.2770645331829441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750932963","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":[0.99597156,0.00007437497,0.0011960511,0.00014158941,0.0021928144,0.000105404455,0.000007943043,0.000012282759,0.00029800742],"genre_scores_gemma":[0.9995169,0.000015557023,0.000050115952,0.0000050460226,0.00029402526,2.9264612e-7,7.8919385e-8,0.000011524662,0.00010644561],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99891454,0.00012175043,0.00040938135,0.000060480368,0.000365641,0.0001281804],"domain_scores_gemma":[0.998797,0.0001463641,0.00048111376,0.00036915144,0.00015306905,0.000053325257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013917371,0.00009071343,0.00025710757,0.000023407747,0.00023865177,0.000090078276,0.00020326178,0.00011062371,0.000009538016],"category_scores_gemma":[0.0004805697,0.000056453508,0.00020042298,0.00004634815,0.000035986013,0.00025113297,0.00009128549,0.0002991207,0.000005041067],"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":[0.00018550648,0.00006641149,0.0069501507,0.00007731643,0.000083067745,0.000026093137,0.0005358014,0.021801692,0.951617,0.000007158644,0.0029851769,0.0156646],"study_design_scores_gemma":[0.0013054268,0.00019850962,0.8525807,0.00056656974,0.000022232316,0.00006514305,0.00018508859,0.015168958,0.122789614,0.000035492412,0.0069531295,0.00012910951],"about_ca_topic_score_codex":0.00006853344,"about_ca_topic_score_gemma":0.000016919972,"teacher_disagreement_score":0.8456306,"about_ca_system_score_codex":0.000050032006,"about_ca_system_score_gemma":0.000013999848,"threshold_uncertainty_score":0.23021072},"labels":[],"label_agreement":null},{"id":"W2751915841","doi":"10.1167/17.10.1086","title":"Texture density aftereffect is bidirectional","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Directionality; Texture (cosmology); Fixation (population genetics); Contrast (vision); Adaptation (eye); Mathematics; Computer science; Artificial intelligence; Optics; Physics; Image (mathematics); Chemistry; Biology","score_opus":0.015538799830539142,"score_gpt":0.27540385860476907,"score_spread":0.25986505877422994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751915841","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":[0.99126166,0.0002097553,0.0021201381,0.00009895563,0.004023455,0.000057872974,0.0000022506142,0.000030732434,0.0021951566],"genre_scores_gemma":[0.99855065,0.000035149573,0.00009868774,0.00002000601,0.0011140361,4.013725e-7,1.5270831e-7,0.000011867448,0.0001690707],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99930364,0.000025936843,0.00025509307,0.0000616805,0.00025947962,0.000094158575],"domain_scores_gemma":[0.99938613,0.000030967647,0.0001999279,0.00019073034,0.00012290219,0.00006933532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044492682,0.00008573437,0.00018426581,0.000106387284,0.00020760916,0.00013430805,0.000121514844,0.00013093994,0.00008059788],"category_scores_gemma":[0.0000867451,0.00006404208,0.00014904195,0.000038247927,0.000013471778,0.00030709748,0.000019935545,0.0002706226,0.00004800249],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048564863,0.000096805634,0.013169803,0.0001226806,0.00027897113,0.00021047353,0.00039949213,0.0013484193,0.2712903,0.000039851027,0.23165548,0.48090205],"study_design_scores_gemma":[0.0028046481,0.0011423427,0.67239666,0.00088293775,0.00008475873,0.0013190359,0.00005302095,0.0078129405,0.09430879,0.0005546443,0.21816978,0.00047043926],"about_ca_topic_score_codex":0.000011141337,"about_ca_topic_score_gemma":0.0000031738882,"teacher_disagreement_score":0.65922683,"about_ca_system_score_codex":0.000061032344,"about_ca_system_score_gemma":0.000013361301,"threshold_uncertainty_score":0.26115602},"labels":[],"label_agreement":null},{"id":"W2767015367","doi":"","title":"Stacking With Auxiliary Features for Combining Supervised and Unsupervised Ensembles","year":2016,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Stacking; Computer science; Artificial intelligence; Pattern recognition (psychology); Machine learning; Chemistry","score_opus":0.010011617066141452,"score_gpt":0.22136740188459617,"score_spread":0.2113557848184547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767015367","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":[0.80106926,0.0010955594,0.19601507,0.000050193055,0.000047343554,0.0006040349,0.000040626113,0.00015266439,0.00092526164],"genre_scores_gemma":[0.9992925,0.00008187346,0.00023972245,0.000006062528,0.00006131895,0.00017816106,0.000004140784,0.000015642392,0.00012056198],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959236,0.000024965833,0.00013046128,0.00010763066,0.0000503005,0.00009430194],"domain_scores_gemma":[0.99938196,0.00037580883,0.000027623057,0.00012698628,0.00005431623,0.00003332383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002659178,0.00009055942,0.00014253124,0.00005620068,0.00014525384,0.000022115482,0.000041688472,0.000058948845,0.000005154674],"category_scores_gemma":[0.000015984944,0.000057558133,0.000016898015,0.00008072677,0.000105304964,0.00010250489,0.000010712015,0.00003533677,5.372405e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044778257,0.000017684259,0.00049367524,0.00031087236,0.00009316046,2.4654025e-7,0.0011254548,0.00006924365,0.09910187,0.6666089,0.00015118562,0.23157987],"study_design_scores_gemma":[0.0067963013,0.00075285614,0.0036136953,0.0004948837,0.00023454614,0.00007098565,0.010389691,0.0003966894,0.625063,0.3197133,0.031437792,0.0010362663],"about_ca_topic_score_codex":0.000005542425,"about_ca_topic_score_gemma":0.0000032759147,"teacher_disagreement_score":0.5259611,"about_ca_system_score_codex":0.0000059693984,"about_ca_system_score_gemma":0.000008963297,"threshold_uncertainty_score":0.23471525},"labels":[],"label_agreement":null},{"id":"W2769145937","doi":"10.1109/pn.2017.8090586","title":"Optical detection of defects in high quality surface composites","year":2017,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Western University","funders":"","keywords":"Collimated light; Materials science; Composite number; Composite material; Surface (topology); Laser beams; Focus (optics); Optics; Laser; Beam (structure); Geometry; Physics; Mathematics","score_opus":0.031048738494762803,"score_gpt":0.2790753049913387,"score_spread":0.2480265664965759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769145937","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":[0.98497623,0.00001893826,0.0057623587,0.0000056729964,0.00049544685,0.00010817165,0.0000015030598,0.000084171894,0.008547534],"genre_scores_gemma":[0.99971044,0.0000025833058,0.00016618839,0.0000015020032,0.000051076167,0.0000020523373,4.8138037e-7,0.000008647983,0.000057023506],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941444,0.00003161201,0.0002465121,0.000089391695,0.000109239874,0.00010880501],"domain_scores_gemma":[0.99956375,0.000070596776,0.000050311246,0.00025542238,0.00002873727,0.000031149848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033615684,0.000071363174,0.00018205919,0.000042063133,0.00006428797,0.000038948732,0.00007569023,0.00011953195,0.00001497756],"category_scores_gemma":[0.00007986935,0.00006479859,0.000042128213,0.000050330582,0.000021081021,0.00011776221,0.000019276218,0.00011219238,0.000021289397],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035507706,0.000018446168,0.013946424,0.00005330844,0.000011513353,0.0000017544027,0.000032773612,0.011925716,0.9681428,0.000788105,0.00002665028,0.0050169816],"study_design_scores_gemma":[0.00049158576,0.00004775717,0.17833182,0.000026891841,0.000003297779,0.0000019252166,0.000025759393,0.0061687184,0.8146362,0.00007301994,0.000097134616,0.000095929834],"about_ca_topic_score_codex":0.0013516679,"about_ca_topic_score_gemma":0.00047694906,"teacher_disagreement_score":0.1643854,"about_ca_system_score_codex":0.000038710525,"about_ca_system_score_gemma":0.000004047905,"threshold_uncertainty_score":0.26424098},"labels":[],"label_agreement":null},{"id":"W2782909894","doi":"","title":"Influence of Cure Monitoring on the Curing Cycle Time and the Associated Economic Impact","year":2017,"lang":"en","type":"article","venue":"Journal of Mineral Metal and Material Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Curing (chemistry); Environmental science; Econometrics; Economics; Operations management; Materials science; Composite material","score_opus":0.007536578814359828,"score_gpt":0.21465406689189803,"score_spread":0.20711748807753821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782909894","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":[0.99893147,0.000084963074,0.0000050269005,0.000030708263,0.0008160749,0.00006814445,0.000008757088,0.000011818314,0.000043033026],"genre_scores_gemma":[0.9993938,0.000046678186,0.0000125121105,0.0000010557343,0.0005203379,0.000001533407,1.3005531e-7,0.000012476515,0.0000114794575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994471,0.00002189916,0.00028859646,0.00004760504,0.00008795983,0.00010684742],"domain_scores_gemma":[0.9995595,0.00007666306,0.00019538117,0.000104113635,0.000025020992,0.000039293154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053736405,0.00011055595,0.00027051292,0.000049429083,0.00009795434,0.000121826655,0.0001092507,0.000055847886,0.000005698762],"category_scores_gemma":[0.00012430066,0.000058079604,0.00007521443,0.000016513843,0.000030862728,0.0002159296,0.00003255251,0.00015120274,0.00000115285],"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":[0.00014978102,0.000003831813,0.0002010343,0.000026289663,0.00020525944,0.0000045571255,0.000112543035,0.54906034,0.44938904,0.00013687502,0.000026098833,0.0006843719],"study_design_scores_gemma":[0.00939772,0.0011324106,0.20716389,0.0022848074,0.00043862424,0.00045230595,0.00014626145,0.25885612,0.5176604,0.00034700966,0.0011234956,0.000996967],"about_ca_topic_score_codex":0.00008491484,"about_ca_topic_score_gemma":7.288178e-7,"teacher_disagreement_score":0.2902042,"about_ca_system_score_codex":0.00003951219,"about_ca_system_score_gemma":0.000006252618,"threshold_uncertainty_score":0.23684175},"labels":[],"label_agreement":null},{"id":"W2792849756","doi":"10.21307/ijssis-2017-877","title":"Surface Following With An Rgb-D Vision-Guided Robotic System For Automated And Rapid Vehicle Inspection","year":2016,"lang":"en","type":"article","venue":"International Journal on Smart Sensing and Intelligent Systems","topic":"Industrial Vision Systems and Defect Detection","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 Ottawa","funders":"","keywords":"Computer vision; Artificial intelligence; Automotive industry; RGB color model; Process (computing); Computer science; Robotics; Robot; Engineering; Machine vision","score_opus":0.026664576714077227,"score_gpt":0.2759558805211185,"score_spread":0.2492913038070413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792849756","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":[0.89244884,0.00025704753,0.09751357,0.00009788682,0.0084221605,0.00034141773,0.000007519565,0.000656153,0.00025542136],"genre_scores_gemma":[0.9987999,0.00006179264,0.00024200325,0.000013002678,0.0007335423,0.0000028570637,0.0000030272051,0.000046943635,0.00009690726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843043,0.00011986084,0.0005607298,0.00024671713,0.00042161366,0.00022067272],"domain_scores_gemma":[0.99905086,0.00018993308,0.00016690469,0.00012470302,0.00029692132,0.0001706747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007336624,0.00021681412,0.00030650012,0.0002374089,0.00025632657,0.0004380309,0.000077566474,0.0001323343,0.0000018744336],"category_scores_gemma":[0.000048485035,0.0001411744,0.00008175891,0.00009202975,0.000025523754,0.00034873158,0.000013146909,0.00014832085,0.000011713095],"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":[0.0030667926,0.00024178736,0.004414342,0.0006312766,0.0039633154,0.00067737576,0.0025879997,0.48553112,0.29434207,0.0019396208,0.009609031,0.19299528],"study_design_scores_gemma":[0.0020533395,0.0011219885,0.0003734974,0.0034099587,0.00005246869,0.003049325,0.0011665823,0.96957624,0.01205491,0.000014462395,0.0067127994,0.00041439934],"about_ca_topic_score_codex":0.0000900821,"about_ca_topic_score_gemma":0.0000076010874,"teacher_disagreement_score":0.48404515,"about_ca_system_score_codex":0.00034499186,"about_ca_system_score_gemma":0.000020999834,"threshold_uncertainty_score":0.57569253},"labels":[],"label_agreement":null},{"id":"W2799622443","doi":"10.1117/12.2309957","title":"A low-cost machine vision system for the recognition and sorting of small parts","year":2018,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Sorting; Automation; Machine vision; Computer science; Software; Conveyor system; Matching (statistics); Identification (biology); Set (abstract data type); Artificial intelligence; Machine learning; Engineering; Algorithm","score_opus":0.04251319771889598,"score_gpt":0.24625631774955842,"score_spread":0.20374312003066244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799622443","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":[0.73782486,0.00012320388,0.25581652,0.00001755841,0.0013865068,0.0010422302,0.000027006712,0.00024877244,0.003513349],"genre_scores_gemma":[0.9993764,0.0000044123626,0.0001937219,0.000006149816,0.00031753196,0.00003681702,0.000003661629,0.000013122505,0.000048187707],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950755,0.000016388112,0.00023613486,0.00008353084,0.000058253314,0.000098171746],"domain_scores_gemma":[0.99963284,0.00012195947,0.000052328,0.00009453283,0.000074762895,0.000023600658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040712798,0.000067471505,0.00010984458,0.000039786366,0.00009491951,0.000026003127,0.00002830874,0.000067430214,0.000012462119],"category_scores_gemma":[0.000039465867,0.00004300873,0.000035822184,0.00007622528,0.000015591282,0.00004486831,0.0000101789,0.00004587382,0.000013327114],"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":[0.00017112246,0.00001841491,0.00018708546,0.00077762484,0.00007650436,7.390072e-7,0.0006505752,0.00070535985,0.037450485,0.00014296622,0.001213196,0.95860595],"study_design_scores_gemma":[0.0018457051,0.00048847916,0.00055731036,0.0007927354,0.00007579166,0.000042764113,0.0012306372,0.5800047,0.4066463,0.00010586158,0.0079137245,0.00029601465],"about_ca_topic_score_codex":0.00007862069,"about_ca_topic_score_gemma":0.000071975366,"teacher_disagreement_score":0.9583099,"about_ca_system_score_codex":0.00001625489,"about_ca_system_score_gemma":0.0000030799824,"threshold_uncertainty_score":0.1753845},"labels":[],"label_agreement":null},{"id":"W2802665380","doi":"10.1117/12.2309556","title":"Wire connector classification with machine vision and a novel hybrid SVM","year":2018,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Queen's University","funders":"","keywords":"Support vector machine; Artificial intelligence; Computer science; Machine learning; Class (philosophy); Layer (electronics); Supervised learning; False positive paradox; Pattern recognition (psychology); One-class classification; Artificial neural network","score_opus":0.017835418243916018,"score_gpt":0.22835600828651492,"score_spread":0.2105205900425989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802665380","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":[0.8332145,0.00004080228,0.15345882,0.000056322326,0.0004151407,0.00022521307,0.0000092794435,0.00037194704,0.01220794],"genre_scores_gemma":[0.9990256,0.000003192605,0.00037718876,0.000021546179,0.00025675318,0.000007637655,0.000004340931,0.00001878728,0.00028495342],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951977,0.000008684392,0.0001286707,0.00013713093,0.00010110655,0.0001046635],"domain_scores_gemma":[0.99971676,0.000023332097,0.00002251861,0.00013260776,0.0000523076,0.00005248052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010728491,0.000097659504,0.000105899715,0.00006316766,0.00007617087,0.000048133883,0.00002904156,0.00005241442,0.00008444889],"category_scores_gemma":[0.000011150186,0.000066709115,0.0000141215105,0.00010751449,0.000029995052,0.00011387909,0.000008563802,0.000073070245,0.00005452725],"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":[0.00044399154,0.00010428496,0.0022305294,0.00014567081,0.00015196502,0.000008306614,0.00042310855,0.00049884577,0.83178246,0.006604516,0.01618444,0.14142187],"study_design_scores_gemma":[0.0034371684,0.0014948094,0.013884555,0.00017771877,0.000036872127,0.0002629924,0.0002544555,0.7691382,0.11433448,0.00006281113,0.0962691,0.00064683653],"about_ca_topic_score_codex":0.00007536324,"about_ca_topic_score_gemma":0.000045836943,"teacher_disagreement_score":0.7686393,"about_ca_system_score_codex":0.000025207353,"about_ca_system_score_gemma":0.00000579475,"threshold_uncertainty_score":0.27203187},"labels":[],"label_agreement":null},{"id":"W2809944199","doi":"10.1007/s42243-018-0103-6","title":"Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere","year":2018,"lang":"en","type":"article","venue":"Journal of Iron and Steel Research International","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Support vector machine; Pattern recognition (psychology); Binary classification; Mathematics; One-class classification; Surface (topology); Artificial intelligence; Feature vector; Classifier (UML); Structured support vector machine; Minification; Multiclass classification; Computer science; Algorithm; Mathematical optimization; Geometry","score_opus":0.09428210083464171,"score_gpt":0.3749240949677056,"score_spread":0.2806419941330639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809944199","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":[0.6591235,0.00025209403,0.32350737,0.0009615549,0.0033395654,0.0012919271,0.00021328253,0.00009424002,0.011216492],"genre_scores_gemma":[0.98954105,0.00002510254,0.0081476215,0.00003231362,0.00063697295,0.000011874113,0.00001045617,0.0000320802,0.0015625256],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814504,0.00017015602,0.00038976138,0.00017721365,0.00085266924,0.00026515938],"domain_scores_gemma":[0.99809843,0.00045768006,0.00014086129,0.00013820188,0.0010003005,0.00016449882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022120788,0.0001390092,0.00022023653,0.0002701527,0.00014675906,0.00013783202,0.00018126145,0.00013155151,0.0001623973],"category_scores_gemma":[0.0003061993,0.000103249855,0.000078059056,0.00019402688,0.00005532437,0.00020330692,0.0000181802,0.00047112964,0.000012863514],"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":[0.014193506,0.0012873996,0.008159707,0.00071964663,0.0007094151,0.00009541335,0.0009429474,0.1190562,0.6327148,0.0024618548,0.0802874,0.13937171],"study_design_scores_gemma":[0.004501219,0.0029027706,0.0073758606,0.00017446949,0.000018250508,0.00006253691,0.00038498943,0.87041116,0.0111296475,0.000015112176,0.10285421,0.0001698066],"about_ca_topic_score_codex":0.000025413297,"about_ca_topic_score_gemma":0.000059772206,"teacher_disagreement_score":0.75135493,"about_ca_system_score_codex":0.00027420506,"about_ca_system_score_gemma":0.00015242214,"threshold_uncertainty_score":0.42104068},"labels":[],"label_agreement":null},{"id":"W2831321715","doi":"10.1109/access.2018.2852663","title":"Automatic Visual Defect Detection Using Texture Prior and Low-Rank Representation","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":54,"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":"National University of Defense Technology; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Computer vision; Rank (graph theory); Visual inspection; Pattern recognition (psychology); Texture (cosmology); Representation (politics); Image texture; Process (computing); Image (mathematics); Feature extraction; Image processing; Mathematics","score_opus":0.03361194343589494,"score_gpt":0.3292761522339615,"score_spread":0.29566420879806654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2831321715","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":[0.9600022,0.000050424653,0.03692186,0.0000022787135,0.0021133032,0.00028471713,0.0000014394656,0.0003400833,0.00028368988],"genre_scores_gemma":[0.99884146,0.0000042986553,0.00004809806,0.000013783383,0.0010339863,0.0000133386875,9.265936e-7,0.000028994686,0.000015094992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991909,0.00005303058,0.00023572789,0.00019192042,0.00015891153,0.00016951047],"domain_scores_gemma":[0.9996255,0.000047621917,0.00006114261,0.00014475425,0.00006668948,0.000054262146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001736705,0.00013000384,0.00015840569,0.00015866161,0.00018410389,0.00021280072,0.000069604364,0.00016462128,0.000041697007],"category_scores_gemma":[0.00004475955,0.00012223043,0.000052473795,0.0003637783,0.00002911167,0.0005116742,0.000018950219,0.00012961723,0.000031181997],"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":[0.00006933432,0.000026826589,0.0024575545,0.00028423537,0.0001177331,0.0000104944575,0.0006601468,0.004742189,0.47020125,0.00000362781,0.0004210928,0.5210055],"study_design_scores_gemma":[0.00059346517,0.00009987533,0.0078071966,0.000115060924,0.00004420613,0.000061961175,0.00005550614,0.67587423,0.31484678,0.00004923103,0.00022384404,0.00022864524],"about_ca_topic_score_codex":0.0001809374,"about_ca_topic_score_gemma":0.000068351044,"teacher_disagreement_score":0.671132,"about_ca_system_score_codex":0.00007020077,"about_ca_system_score_gemma":0.0000105417985,"threshold_uncertainty_score":0.49844122},"labels":[],"label_agreement":null},{"id":"W28424106","doi":"10.1017/s0954579417000505","title":"Methods and Sample Size Effect Evaluation for Wafer Level Statistical Bin Limits Determination with Poisson Distributions","year":2004,"lang":"en","type":"article","venue":"IE interfaces","topic":"Industrial Vision Systems and Defect Detection","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":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Canadian Institutes of Health Research","keywords":"Bin; Percentile; Poisson distribution; Statistics; Sample size determination; Transformation (genetics); Mathematics; Standard deviation; Monte Carlo method; Statistical model; Normal distribution; Exponential distribution; Computer science; Algorithm","score_opus":0.05734522576882654,"score_gpt":0.3752386324511694,"score_spread":0.31789340668234284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W28424106","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":[0.43991363,0.00004850807,0.5590876,0.00002619106,0.00025279052,0.0004255567,0.00014016342,0.000065153276,0.000040439038],"genre_scores_gemma":[0.95672613,0.0000027016708,0.042981714,0.0000033279607,0.00007284475,0.0001608757,0.00002161594,0.00001813775,0.000012640445],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922675,0.000107057785,0.00019619106,0.00016786417,0.0001499021,0.00015223214],"domain_scores_gemma":[0.99810797,0.0016055979,0.000038417744,0.000093247974,0.000106211315,0.000048559647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007694605,0.00013140001,0.00016994859,0.00005564637,0.00009627071,0.00007720648,0.000037221133,0.00010340682,0.000020998425],"category_scores_gemma":[0.0014009768,0.000102849604,0.000023835639,0.00010364322,0.000025753998,0.0001335858,0.0000073267647,0.00007685028,0.000004922941],"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":[0.00026237717,0.00003598948,0.00015478877,0.00015923184,0.00008512992,0.0000012172627,0.00040485524,0.008277179,0.07110176,0.0003537616,0.00023221153,0.9189315],"study_design_scores_gemma":[0.0040713847,0.0017811195,0.0073234304,0.00025537418,0.00020161053,0.0000328824,0.000122234,0.07055198,0.9088934,0.004501072,0.0018611088,0.0004043725],"about_ca_topic_score_codex":0.00011873735,"about_ca_topic_score_gemma":0.00010220668,"teacher_disagreement_score":0.9185271,"about_ca_system_score_codex":0.00015901079,"about_ca_system_score_gemma":0.00001968543,"threshold_uncertainty_score":0.41940853},"labels":[],"label_agreement":null},{"id":"W2883175617","doi":"10.1007/s10845-018-1438-3","title":"A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Support vector machine; Artificial intelligence; Machine learning; Robustness (evolution); Machine vision; Artificial neural network; Computer science; Pattern recognition (psychology); Flexibility (engineering); Feature extraction; Engineering","score_opus":0.028921766810149244,"score_gpt":0.24545270851084816,"score_spread":0.21653094170069892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883175617","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":[0.29207033,0.00010504825,0.6990655,0.000018108161,0.004851125,0.00047344895,0.000010820824,0.0002879077,0.0031177304],"genre_scores_gemma":[0.99611354,0.0000104115525,0.0012827083,0.00002539788,0.0023953856,0.000015321899,0.000004419824,0.000053506337,0.00009933471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983383,0.00005668472,0.00080327253,0.00018922632,0.00033246746,0.0002800863],"domain_scores_gemma":[0.9990632,0.000098648976,0.00030858422,0.0002236839,0.00017248654,0.00013339204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091651763,0.00024137241,0.00037509593,0.0005093968,0.00018687976,0.00011959262,0.00016297374,0.0001254914,0.000023169918],"category_scores_gemma":[0.000047473055,0.0001918077,0.00029199343,0.00011396051,0.000020213594,0.00014510467,0.000015880061,0.00035641692,0.000037137837],"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":[0.0034393156,0.00043658513,0.00008037052,0.0018765909,0.0005009652,0.00006142753,0.0007209463,0.74432564,0.0107746245,0.00010438489,0.014164448,0.22351474],"study_design_scores_gemma":[0.000701965,0.0010276773,0.000032322307,0.00055243104,0.00003837522,0.00016449403,0.00017172805,0.17639865,0.78929013,0.000015892201,0.031409904,0.00019641353],"about_ca_topic_score_codex":0.000015136967,"about_ca_topic_score_gemma":0.000002956416,"teacher_disagreement_score":0.7785155,"about_ca_system_score_codex":0.00042711638,"about_ca_system_score_gemma":0.00002485144,"threshold_uncertainty_score":0.7821691},"labels":[],"label_agreement":null},{"id":"W2884473836","doi":"10.21611/qirt.2018.p27","title":"Application of Deep Learning in Infrared Non-Destructive Testing","year":2018,"lang":"en","type":"article","venue":"Proceedings of the 2018 International Conference on Quantitative InfraRed Thermography","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":45,"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 Laval","funders":"Universidad de Oviedo; Université Laval","keywords":"Computer science; Infrared; Deep learning; Artificial intelligence; Optics; Physics","score_opus":0.03236050638024989,"score_gpt":0.2732233282198822,"score_spread":0.2408628218396323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884473836","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":[0.88207334,0.000015602023,0.0021097916,0.000016771815,0.00044485234,0.00034848074,0.000013416593,0.00006343983,0.114914276],"genre_scores_gemma":[0.9985249,0.000006879733,0.001270076,0.000008481934,0.000081183156,0.000044499928,0.0000019102504,0.000020294628,0.00004174181],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988623,0.00001724133,0.0004567115,0.0001925678,0.0003248591,0.00014631118],"domain_scores_gemma":[0.9983739,0.00010928521,0.0003840092,0.00008845189,0.0010168385,0.000027469576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031024092,0.0001605211,0.00022241975,0.0004637994,0.00006499144,0.00003339161,0.00037595656,0.00011104306,0.000028409146],"category_scores_gemma":[0.00031840167,0.00013158511,0.000094285926,0.00088586187,0.00021303518,0.00024818382,0.00005185053,0.00027296355,0.0000082658735],"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":[0.0005036444,0.00010183118,0.08107189,0.00013595758,0.00028297538,2.5490596e-7,0.005582711,0.001005155,0.7575409,0.123507574,0.00012538624,0.030141732],"study_design_scores_gemma":[0.002023732,0.0016814705,0.28602892,0.0015459558,0.000041419178,0.000007783465,0.0076636462,0.3065004,0.29168764,0.10147723,0.0006458248,0.0006959797],"about_ca_topic_score_codex":0.00005361541,"about_ca_topic_score_gemma":0.00000538704,"teacher_disagreement_score":0.46585324,"about_ca_system_score_codex":0.000051140836,"about_ca_system_score_gemma":0.000016253933,"threshold_uncertainty_score":0.5365885},"labels":[],"label_agreement":null},{"id":"W2885236043","doi":"10.1016/j.neucom.2018.07.061","title":"SliceNet: A proficient model for real-time 3D shape-based recognition","year":2018,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Computer science; Convolution (computer science); Artificial intelligence; Variance (accounting); Feature (linguistics); Object (grammar); Pattern recognition (psychology); Kernel (algebra); Inference; Cognitive neuroscience of visual object recognition; Independence (probability theory); Computer vision; Mathematics; Artificial neural network; Statistics","score_opus":0.040691641539423465,"score_gpt":0.2516258020895056,"score_spread":0.21093416055008213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885236043","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":[0.7561602,0.0000038724966,0.23860414,0.000011944614,0.0006733383,0.00069663586,0.000011119691,0.0007176861,0.00312107],"genre_scores_gemma":[0.9923028,4.3560598e-7,0.006484592,0.00007255637,0.0009762655,0.000044626344,0.000010229802,0.000050135463,0.00005841319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908626,0.000025505044,0.00026747983,0.00022635504,0.00012995934,0.00026444023],"domain_scores_gemma":[0.9995597,0.00007861191,0.000058129142,0.00013238093,0.00011739898,0.000053781645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025339745,0.00013280292,0.00014445119,0.00010616996,0.00017496718,0.000053009666,0.00006611249,0.00010241378,0.000018913752],"category_scores_gemma":[0.00003579558,0.0001358545,0.00006888201,0.00017674295,0.000012302458,0.000052248135,0.000014214215,0.0000975483,0.000121656565],"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":[0.000104200975,0.000041714,0.000028785742,0.00015507461,0.000017503658,0.000002155877,0.00037366906,0.4545242,0.24920425,0.000012079592,0.0027902776,0.2927461],"study_design_scores_gemma":[0.0004905258,0.00014712305,0.000037798836,0.00006177653,0.000008738388,0.0000034313196,0.0000047936637,0.9789809,0.019055214,0.000017787597,0.0010477108,0.00014418682],"about_ca_topic_score_codex":0.000009444609,"about_ca_topic_score_gemma":0.000001616545,"teacher_disagreement_score":0.52445674,"about_ca_system_score_codex":0.000046425215,"about_ca_system_score_gemma":0.00002036409,"threshold_uncertainty_score":0.55399853},"labels":[],"label_agreement":null},{"id":"W2893380747","doi":"10.1177/0040517518803792","title":"A machine vision system for the braid angle measurement of tubular braided structures","year":2018,"lang":"en","type":"article","venue":"Textile Research Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":26,"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":"Braid; Mandrel; Fiber; Materials science; Quality assurance; Orientation (vector space); Process (computing); Machine vision; Composite material; Computer science; Computer vision; Engineering; Geometry; Mathematics","score_opus":0.088208731950314,"score_gpt":0.3529116564233318,"score_spread":0.2647029244730178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893380747","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":[0.6823128,0.018959118,0.26146567,0.0006449685,0.011636206,0.0061623305,0.00016400185,0.0006529606,0.018001923],"genre_scores_gemma":[0.9981706,0.000031642445,0.00010706039,0.0000031220354,0.0015596055,0.000021463984,6.7846736e-7,0.000030399582,0.00007543677],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975294,0.00024130079,0.00042628412,0.00012523588,0.0012865288,0.00039129268],"domain_scores_gemma":[0.9981793,0.0002528053,0.000078694466,0.00027842142,0.001087897,0.00012284605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005840514,0.00011599033,0.00021567995,0.00026782192,0.00056933943,0.00015033987,0.00028204283,0.000109697634,0.00014572895],"category_scores_gemma":[0.00039228337,0.00007085341,0.00013392055,0.00035255868,0.000093515984,0.00011664404,0.000043283126,0.0004928651,0.000024383238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070110284,0.00005326424,0.000057400797,0.0005536225,0.00044599432,0.000016912385,0.0010062767,0.0030361926,0.7152498,0.0012520723,0.06255683,0.21507053],"study_design_scores_gemma":[0.0053827534,0.0041398606,0.002122332,0.001418411,0.000074407755,0.0010750279,0.002720835,0.14916128,0.559614,0.0016577187,0.2721086,0.00052478974],"about_ca_topic_score_codex":0.00004797658,"about_ca_topic_score_gemma":0.00002513809,"teacher_disagreement_score":0.31585777,"about_ca_system_score_codex":0.00026231413,"about_ca_system_score_gemma":0.00008574556,"threshold_uncertainty_score":0.43789563},"labels":[],"label_agreement":null},{"id":"W2896403646","doi":"10.2351/1.5060212","title":"Development of trinocular optical ccd-based detectors for real-time measurement of clad geometry","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Detector; Optics; Cladding (metalworking); Laser; Position (finance); Computer science; Computer vision; Geometry; Materials science; Physics; Mathematics","score_opus":0.030862357806080435,"score_gpt":0.2319896554960959,"score_spread":0.20112729769001547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896403646","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":[0.8055337,0.000024444782,0.19237997,0.0000033268998,0.00021007421,0.00040867677,0.0000031710556,0.000107373584,0.0013292801],"genre_scores_gemma":[0.9724946,9.3174066e-7,0.02739406,0.0000017920225,0.000039771774,0.000031952026,0.0000018894682,0.000020200827,0.000014791945],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878836,0.000009606077,0.0005319267,0.00011868804,0.00038065974,0.00017075268],"domain_scores_gemma":[0.9995324,0.000033123582,0.000058614747,0.0001535283,0.00016109078,0.000061263134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005850742,0.00011970432,0.00027771387,0.00018642802,0.000030397872,0.000006275849,0.00006362296,0.00013852201,0.00003811362],"category_scores_gemma":[0.000060371178,0.00010424327,0.000118169635,0.00022745134,0.000012741992,0.00003015285,0.000008070239,0.000056139885,0.000011218031],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008020072,0.000052817144,0.00003101318,0.00021539284,0.00009222007,5.654056e-7,0.00004874086,0.027126651,0.9592989,0.00016538113,0.000049095357,0.012839034],"study_design_scores_gemma":[0.0015884724,0.00013157104,0.00042053516,0.00008679457,0.00001732101,7.143353e-7,0.000028299057,0.0013953364,0.995158,0.000011258068,0.0010395714,0.0001220996],"about_ca_topic_score_codex":0.00003209674,"about_ca_topic_score_gemma":0.000011099242,"teacher_disagreement_score":0.16696092,"about_ca_system_score_codex":0.00022081955,"about_ca_system_score_gemma":0.00010485541,"threshold_uncertainty_score":0.4250917},"labels":[],"label_agreement":null},{"id":"W2903254680","doi":"10.1049/el.2018.6934","title":"Real‐time comprehensive glass container inspection system based on deep learning framework","year":2018,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Container (type theory); Computer science; Embedded system; Real-time computing; Engineering; Systems engineering; Reliability engineering; Mechanical engineering","score_opus":0.00632211137480294,"score_gpt":0.20883209604643999,"score_spread":0.20250998467163706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903254680","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":[0.87847716,0.00009355706,0.10895397,0.00018659134,0.0020953652,0.00046216237,0.0000021380936,0.0035293235,0.0061997157],"genre_scores_gemma":[0.99808896,0.0000075912194,0.00020839572,0.00026440408,0.0012913182,0.000026202617,0.00000988292,0.000070743044,0.00003249169],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985605,0.00013672857,0.0002794297,0.0002707223,0.00027850902,0.0004741199],"domain_scores_gemma":[0.99935204,0.00016049242,0.000080119404,0.00024856156,0.0000862638,0.00007251266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020375302,0.00023397327,0.00026791866,0.00019706285,0.0002775507,0.000077990546,0.000097830416,0.00023896933,0.00002711343],"category_scores_gemma":[0.000033312583,0.00023472478,0.000101131,0.00036340702,0.00003479074,0.00007890023,0.000009889979,0.0007026388,0.0004936879],"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":[0.0005015035,0.00003974159,0.00031500307,0.00017893375,0.000260735,0.000042758034,0.0005351405,0.34972703,0.6229382,0.0049272445,0.0139956605,0.006538023],"study_design_scores_gemma":[0.0009528703,0.0009307757,0.0004204527,0.00025685428,0.000034036577,0.000023307046,0.0001329716,0.93930346,0.023977779,0.000012730307,0.033457283,0.000497476],"about_ca_topic_score_codex":0.00004597964,"about_ca_topic_score_gemma":0.000009541722,"teacher_disagreement_score":0.5989604,"about_ca_system_score_codex":0.00087813934,"about_ca_system_score_gemma":0.000019791103,"threshold_uncertainty_score":0.95717984},"labels":[],"label_agreement":null},{"id":"W2903300004","doi":"10.1130/abs/2018am-320183","title":"EVALUATING ATRYPIDE BRACHIOPOD FEEDING USING 3D PRINTED MODELS","year":2018,"lang":"en","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; 3d printed; Geology; Artificial intelligence; Engineering; Biomedical engineering","score_opus":0.08322323520980514,"score_gpt":0.313496811453864,"score_spread":0.23027357624405886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903300004","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":[0.9386276,0.00006642892,0.057099123,0.000031950673,0.00017754725,0.00052871904,0.0000017812553,0.000479921,0.0029869205],"genre_scores_gemma":[0.8730355,0.000008457349,0.1265157,0.00006004217,0.0003030888,0.000020525113,0.000005489921,0.000030883268,0.000020310734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805814,0.000052683532,0.0005808342,0.00034181366,0.00044369503,0.0005228612],"domain_scores_gemma":[0.99900246,0.00010031475,0.00028677002,0.00022976448,0.00024903825,0.00013164373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053946755,0.00026636085,0.0004362743,0.00003247804,0.00023326902,0.00005753298,0.00015760965,0.00028997683,0.00006410758],"category_scores_gemma":[0.00005291526,0.00019780472,0.00019409276,0.00048991514,0.00041298303,0.00023324494,0.000062652594,0.00043461926,0.000017585551],"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":[0.0001770069,0.00024247388,0.0021602865,0.00019634317,0.00045274477,0.0000058802907,0.0018187731,0.4892423,0.026144607,0.000029951385,0.00022688964,0.47930276],"study_design_scores_gemma":[0.0009443546,0.0024993022,0.0028498075,0.0003033826,0.00008501607,0.000054950764,0.0013639131,0.98421735,0.0038922098,0.00018322874,0.0030796838,0.0005268035],"about_ca_topic_score_codex":0.0004820062,"about_ca_topic_score_gemma":0.000003761488,"teacher_disagreement_score":0.49497506,"about_ca_system_score_codex":0.00008562135,"about_ca_system_score_gemma":0.000035524878,"threshold_uncertainty_score":0.80662423},"labels":[],"label_agreement":null},{"id":"W2904276454","doi":"10.1109/coase.2018.8560559","title":"Vision-based deformation and wrinkle detection for semi-finished fiber products on curved surfaces","year":2018,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Wrinkle; Deformation (meteorology); Fiber; Materials science; Composite material; Computer science; Computer vision","score_opus":0.017407185742109213,"score_gpt":0.24083109136098982,"score_spread":0.2234239056188806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904276454","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":[0.9760753,0.000018287306,0.017898526,0.00005017297,0.001255978,0.0007224282,0.0000072945822,0.00045269655,0.003519354],"genre_scores_gemma":[0.9987561,0.0000013898634,0.00035596476,0.0000285514,0.0004095306,0.00004462252,0.000007809769,0.000023255898,0.00037278183],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992769,0.000027459644,0.00022594856,0.00017795345,0.00013041815,0.00016132524],"domain_scores_gemma":[0.99952805,0.00008886243,0.000041309104,0.00015688532,0.00014083752,0.000044051707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003639312,0.00012854196,0.00013158511,0.00011280496,0.00016145493,0.000057886224,0.00003299781,0.0001371219,0.00005688617],"category_scores_gemma":[0.000092128044,0.00010496737,0.00003296808,0.00018520208,0.000019503295,0.00019272877,0.000006351425,0.00007608287,0.00011339901],"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":[0.0014844392,0.00017369026,0.0005117687,0.0010373837,0.00016044214,0.0000012760261,0.0007895045,0.059869397,0.42047727,0.00019352404,0.030106096,0.4851952],"study_design_scores_gemma":[0.0010263913,0.0008544501,0.0013002966,0.000054946737,0.000010963355,0.0000032257822,0.000042143296,0.28185895,0.6742365,0.00003955015,0.040350325,0.00022231475],"about_ca_topic_score_codex":0.0000313927,"about_ca_topic_score_gemma":0.00004187822,"teacher_disagreement_score":0.4849729,"about_ca_system_score_codex":0.00004985657,"about_ca_system_score_gemma":0.000010918042,"threshold_uncertainty_score":0.4280445},"labels":[],"label_agreement":null},{"id":"W2905168744","doi":"10.1109/crv.2018.00047","title":"Automotive Semi-specular Surface Defect Detection System","year":2018,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Automotive industry; Specular reflection; Surface (topology); Computer science; Materials science; Optics; Engineering; Physics; Aerospace engineering","score_opus":0.008791748725092228,"score_gpt":0.2004210728154376,"score_spread":0.1916293240903454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905168744","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":[0.73314476,0.000077740486,0.19243693,0.0000032960427,0.0033510376,0.00030675295,0.0000033492229,0.0022855562,0.068390556],"genre_scores_gemma":[0.9985897,0.0000016205522,0.00014312132,0.000008589883,0.0007102431,0.00000766269,9.803903e-7,0.000035426696,0.0005026635],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910057,0.00005856174,0.00023482625,0.00019209173,0.00018183095,0.00023212735],"domain_scores_gemma":[0.999492,0.000031197596,0.00003143076,0.0002278219,0.00014269994,0.000074847645],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003008669,0.00015561447,0.00018049471,0.0000802918,0.00013911953,0.00005534651,0.0000640229,0.00019380654,0.000096346164],"category_scores_gemma":[0.00002426782,0.00013757993,0.00011850662,0.0003372353,0.000021883514,0.00012061799,0.0000152003395,0.00014120791,0.0011345949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017197427,0.000058416237,0.0008105945,0.0005867542,0.0008078242,0.00006583077,0.0016055774,0.055347487,0.8771178,0.0016649633,0.016631862,0.04513089],"study_design_scores_gemma":[0.000543044,0.0002742161,0.00037232327,0.000093348106,0.000033544333,0.000102196274,0.00063567923,0.12501334,0.84912425,0.000012576734,0.023432171,0.00036333097],"about_ca_topic_score_codex":0.00013998743,"about_ca_topic_score_gemma":0.000059183094,"teacher_disagreement_score":0.2654449,"about_ca_system_score_codex":0.0002378577,"about_ca_system_score_gemma":0.000008430145,"threshold_uncertainty_score":0.99964315},"labels":[],"label_agreement":null},{"id":"W2905374808","doi":"10.1007/978-3-030-04375-9_39","title":"EREL-Net: A Remedy for Industrial Bottle Defect Detection","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Bottle; Artificial intelligence; Classifier (UML); Bounding overwatch; Process (computing); Computer vision; Pattern recognition (psychology); Training set; Manufacturing process; Engineering","score_opus":0.03274848581710834,"score_gpt":0.2401842213640418,"score_spread":0.20743573554693345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905374808","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":[0.0019424603,0.00016527562,0.98426175,0.000013276873,0.010397208,0.00090499263,0.000015890051,0.00028560654,0.002013548],"genre_scores_gemma":[0.98059297,0.000015253427,0.007834727,0.000106650645,0.010664507,0.00006528982,0.000010987536,0.00014685788,0.0005627534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978813,0.000020261776,0.00048128024,0.0006809405,0.0004674629,0.00046875793],"domain_scores_gemma":[0.998794,0.00029491173,0.00013713431,0.00049809733,0.00016799444,0.000107870386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008715068,0.0004032362,0.0004475708,0.0006494163,0.00021325491,0.00021233049,0.00040083716,0.00087785,0.00005827313],"category_scores_gemma":[0.00016759585,0.0003786641,0.00021739538,0.00035208135,0.00018970425,0.00017454063,0.00009322736,0.0007177819,0.0000686129],"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":[0.00006658441,0.000007861834,0.000007852012,0.000065016604,0.00003980408,0.000007623566,0.00025397548,0.03978271,0.0025816576,0.00007181049,0.00055795483,0.95655715],"study_design_scores_gemma":[0.0031871,0.0022895175,0.000030035724,0.001604496,0.00010863795,0.00018375977,0.0000013692251,0.70092726,0.09143782,0.03091626,0.16688494,0.0024287954],"about_ca_topic_score_codex":0.000025628051,"about_ca_topic_score_gemma":0.00018630683,"teacher_disagreement_score":0.9786505,"about_ca_system_score_codex":0.00043240705,"about_ca_system_score_gemma":0.00012674338,"threshold_uncertainty_score":0.99986655},"labels":[],"label_agreement":null},{"id":"W2906084246","doi":"10.25103/jestr.115.19","title":"Smoothing and Reconstruction Strategy for Part Repair Using a Laser Displacement Sensor","year":2018,"lang":"en","type":"article","venue":"Journal of Engineering Science and Technology Review","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Smoothness; Smoothing; Interpolation (computer graphics); Curve fitting; Displacement (psychology); Measure (data warehouse); Observational error; Computer science; Mathematics; Algorithm; Artificial intelligence; Computer vision; Statistics; Data mining; Mathematical analysis; Motion (physics)","score_opus":0.0296449475426924,"score_gpt":0.28069764947436987,"score_spread":0.25105270193167745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906084246","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":[0.9615658,0.021602355,0.014400508,0.0001268911,0.0016312638,0.00040616535,0.0000021073602,0.00018127251,0.00008366201],"genre_scores_gemma":[0.98316437,0.007383897,0.00895723,0.000028866745,0.00042115577,0.0000091715465,8.934758e-8,0.000018172357,0.000017057017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999321,0.000004885795,0.00031993305,0.000098683246,0.000110246096,0.00014524456],"domain_scores_gemma":[0.99950725,0.000020210142,0.00009514447,0.000082060695,0.00024763768,0.000047707385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010844397,0.00008176826,0.00021762386,0.00024398183,0.00011639824,0.000033927387,0.000054454824,0.000073188756,0.0000032258843],"category_scores_gemma":[0.0002674563,0.00006555258,0.000031013045,0.0004911171,0.000111660505,0.00023215997,0.000014087872,0.0001344062,3.1560765e-7],"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":[0.000047359004,0.000038144175,0.0017064847,0.0077550407,0.000316157,0.000033327586,0.00014017255,0.007800419,0.19242789,0.0014874311,0.004211424,0.78403616],"study_design_scores_gemma":[0.0029851184,0.0035548112,0.00046638437,0.040589396,0.0007008046,0.016870175,0.00074865436,0.5305978,0.08889685,0.0005574399,0.31251907,0.0015134824],"about_ca_topic_score_codex":8.5390997e-7,"about_ca_topic_score_gemma":2.926444e-7,"teacher_disagreement_score":0.7825227,"about_ca_system_score_codex":0.000051271847,"about_ca_system_score_gemma":0.000033949054,"threshold_uncertainty_score":0.26731566},"labels":[],"label_agreement":null},{"id":"W2907506903","doi":"","title":"The effects of terrain discrepancy and the role of texture in enhanced/synthetic vision systems","year":2000,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministère de la Défense Nationale","keywords":"Terrain; Texture (cosmology); Artificial intelligence; Computer vision; Computer science; Geography; Cartography; Image (mathematics)","score_opus":0.002187262445092559,"score_gpt":0.2389657871441977,"score_spread":0.23677852469910513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907506903","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":[0.96058697,0.022364808,0.000042110947,0.000008321551,0.0012534513,0.0010915644,0.0000033264373,0.000040451505,0.014609025],"genre_scores_gemma":[0.9970356,0.00069474865,0.0000016256994,5.209951e-7,0.00008320195,0.00007606056,0.000007472657,0.000030155858,0.0020706186],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989914,0.00016069748,0.00036588067,0.00013876447,0.00020467484,0.00013862389],"domain_scores_gemma":[0.9990544,0.00045624175,0.00016291832,0.0002772624,0.000028507917,0.000020719319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000355605,0.0001777473,0.00040533463,0.00009082872,0.0000590104,0.000030981442,0.00010889592,0.00028149676,0.0000047580875],"category_scores_gemma":[0.00009641211,0.00009820899,0.000083812265,0.00017398628,0.000039653663,0.000029470191,0.0000075716557,0.00028200887,0.000002704752],"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":[0.0038492733,0.00010136741,0.00004463163,0.008804888,0.00044142638,0.000007641616,0.10536043,0.0075167115,0.32203296,0.0034437678,0.0007623762,0.54763454],"study_design_scores_gemma":[0.012918791,0.0015334843,0.007412591,0.037467137,0.0006714741,0.000048524656,0.13123089,0.051202353,0.6986706,0.0024376311,0.054142945,0.002263562],"about_ca_topic_score_codex":0.0007028332,"about_ca_topic_score_gemma":0.000271551,"teacher_disagreement_score":0.54537094,"about_ca_system_score_codex":0.000028106944,"about_ca_system_score_gemma":0.000015893555,"threshold_uncertainty_score":0.40048465},"labels":[],"label_agreement":null},{"id":"W2910197297","doi":"10.3166/acsm.42.563-573","title":"Improvement of radial basis function neural network with accelerated particle swarm optimization for corrosion rate prediction of 3C steel in seawater environment","year":2018,"lang":"en","type":"article","venue":"Annales de Chimie Science des Matériaux","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Seawater; Radial basis function; Artificial neural network; Corrosion; Basis (linear algebra); Function (biology); Particle (ecology); Biological system; Materials science; Computer science; Mathematical optimization; Artificial intelligence; Metallurgy; Mathematics; Ecology; Biology","score_opus":0.037681063225035796,"score_gpt":0.23048448846935765,"score_spread":0.19280342524432187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910197297","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":[0.8486982,0.00001990036,0.15040313,0.000008230934,0.00037211904,0.00041473028,0.0000098348055,0.000035898705,0.00003795649],"genre_scores_gemma":[0.9983746,0.00002246653,0.0013340508,0.0000130617545,0.00017164087,0.00004684095,0.00000907808,0.000014386509,0.000013875545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989393,0.000032044965,0.00034039433,0.00019564881,0.00020132598,0.00029132288],"domain_scores_gemma":[0.99954534,0.000026887205,0.00010865181,0.00014266856,0.000114218295,0.00006225589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007818101,0.000111493435,0.0001567553,0.0001116761,0.00013441223,0.000037513753,0.00008409125,0.00006398898,0.000029783123],"category_scores_gemma":[0.00002320594,0.00009169401,0.000034114546,0.0004454053,0.00019973506,0.0003562609,0.00002020159,0.000059659127,0.0000023779596],"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":[0.00030230786,0.000024467365,0.0019562335,0.000035330715,0.0000054471016,1.718501e-7,0.00021067796,0.7810464,0.21265198,0.000009140252,0.00005560202,0.0037022615],"study_design_scores_gemma":[0.000682011,0.0009537614,0.013154115,0.000056754616,0.000014332163,0.000001728638,0.000105474865,0.69212145,0.292765,0.000022346943,0.000053798372,0.00006925701],"about_ca_topic_score_codex":0.000034783676,"about_ca_topic_score_gemma":0.000020680183,"teacher_disagreement_score":0.1496764,"about_ca_system_score_codex":0.00012949728,"about_ca_system_score_gemma":0.000040242307,"threshold_uncertainty_score":0.37391734},"labels":[],"label_agreement":null},{"id":"W2910660926","doi":"10.1139/tcsme-2017-1022","title":"DESIGN OPTIMIZATION OF VERTICAL NEEDLE GEOMETRY FOR BUMP WAFER-LEVEL PROBING","year":2017,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Rectangle; Taguchi methods; Wafer; Offset (computer science); Orthogonal array; Materials science; Geometry; Stress (linguistics); Optics; Structural engineering; Acoustics; Mathematics; Composite material; Engineering; Physics; Nanotechnology; Computer science","score_opus":0.04561660548376049,"score_gpt":0.22988884395442055,"score_spread":0.18427223847066007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910660926","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":[0.004093333,0.00002091337,0.99397725,0.00007577854,0.0009406539,0.0007292354,0.00009929788,0.0000494956,0.000014066363],"genre_scores_gemma":[0.95936143,0.0000042500596,0.04038116,0.00000692982,0.00007110443,0.000095830495,0.000002534571,0.00004055715,0.000036194808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922836,0.00000583266,0.00028795408,0.00011255432,0.000113878865,0.00025142962],"domain_scores_gemma":[0.9993196,0.000111062065,0.00004468944,0.00031119262,0.00009592054,0.00011754364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034246323,0.00012033888,0.0002163961,0.00006174994,0.0003730664,0.00003558928,0.0002398264,0.00023976603,0.000009839688],"category_scores_gemma":[0.00010806463,0.00011199465,0.00044172312,0.00012266061,0.000023665965,0.0001222281,0.0000043895466,0.00015575797,2.6977446e-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":[0.0000097172515,0.0000055419287,3.969528e-7,0.00015561613,0.00010155325,2.6869582e-8,0.00007223962,0.9824892,0.015746122,0.00038846798,0.000091064285,0.0009400457],"study_design_scores_gemma":[0.0005166929,0.000054808468,0.00000803299,0.00008169141,0.00007445636,0.0000018408333,0.00004530167,0.88577396,0.112974435,0.000042694995,0.000310385,0.000115684605],"about_ca_topic_score_codex":0.0014984554,"about_ca_topic_score_gemma":0.0005811984,"teacher_disagreement_score":0.9552681,"about_ca_system_score_codex":0.00022505404,"about_ca_system_score_gemma":0.00009638901,"threshold_uncertainty_score":0.45670092},"labels":[],"label_agreement":null},{"id":"W2913199598","doi":"10.1007/s11760-019-01435-2","title":"Automatic visual inspection of thermoelectric metal pipes","year":2019,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Preprocessor; Artificial intelligence; Computer vision; Computer science; Cartesian coordinate system; Image processing; Classifier (UML); Thermoelectric effect; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.007594227519139477,"score_gpt":0.23545888060266715,"score_spread":0.22786465308352766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913199598","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":[0.98242205,0.0010865071,0.013483891,0.0000030195831,0.00013931662,0.00015097506,8.1037683e-7,0.00025181918,0.0024616232],"genre_scores_gemma":[0.99970555,0.000010981574,0.000102592,0.0000062931867,0.00010364664,0.000003509581,9.2933016e-7,0.000019052683,0.000047419184],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935126,0.00002924501,0.00023117726,0.00011949159,0.00013554934,0.00013328742],"domain_scores_gemma":[0.99976665,0.000034085057,0.00006373971,0.000051679526,0.000053545748,0.00003031983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002275156,0.000105762454,0.00019384107,0.00014725179,0.00006245871,0.00006563675,0.000032216878,0.00007382998,0.00008047678],"category_scores_gemma":[0.000013375163,0.000090020956,0.000041248295,0.00028902435,0.000018647886,0.00037367284,0.000009353809,0.00011913469,0.000022197948],"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":[0.000013303403,0.00001149666,0.00015768324,0.00030333598,0.000019304225,0.0000012832791,0.00022152172,0.00012669389,0.64679635,0.00000540307,0.000035378172,0.35230824],"study_design_scores_gemma":[0.0005920897,0.00029404918,0.0011653756,0.00028481908,0.000042978594,0.000040743937,0.00032700703,0.5603406,0.43641198,0.00012468472,0.00014632531,0.00022939239],"about_ca_topic_score_codex":0.000023065366,"about_ca_topic_score_gemma":8.390254e-7,"teacher_disagreement_score":0.56021386,"about_ca_system_score_codex":0.000022025044,"about_ca_system_score_gemma":0.00001877169,"threshold_uncertainty_score":0.3670948},"labels":[],"label_agreement":null},{"id":"W2914197704","doi":"","title":"Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition","year":2009,"lang":"en","type":"article","venue":"Energy Minimization Methods in Computer Vision and Pattern Recognition","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Energy minimization; Minification; Artificial intelligence; Computer vision; Pattern recognition (psychology); World Wide Web; Chemistry","score_opus":0.049758156958531086,"score_gpt":0.33309265836383245,"score_spread":0.28333450140530136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914197704","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":[0.06183626,0.000043389617,0.9353447,0.00020466442,0.0016715144,0.00024793143,0.000021273134,0.000097420656,0.0005328411],"genre_scores_gemma":[0.763514,0.0007358216,0.2339412,0.0008971955,0.0005947486,0.000039540297,0.00019467052,0.000059490878,0.00002335039],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972655,0.0006349768,0.0009693831,0.000565695,0.00031990145,0.00024454042],"domain_scores_gemma":[0.998839,0.00030563408,0.0003417225,0.0001595321,0.00026606378,0.00008802096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008002109,0.00036551704,0.00046548073,0.0007311676,0.00010049506,0.00018624957,0.00016609466,0.00039303955,0.000080909034],"category_scores_gemma":[0.000051617797,0.00030940474,0.00009208123,0.00057940226,0.000049751463,0.0004419932,0.00008681939,0.00029039965,0.0000017281313],"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":[0.000075211545,0.000094838666,0.0002421737,0.000041464118,0.000016344446,0.0000019227273,0.00033409463,0.0036555775,0.002366046,0.000043637083,0.0003013211,0.99282736],"study_design_scores_gemma":[0.0016948342,0.0005630336,0.007046337,0.0011757215,0.000021053698,0.00004089777,0.00006925189,0.9759604,0.010255776,0.0018014126,0.0009583916,0.0004128972],"about_ca_topic_score_codex":0.00007871561,"about_ca_topic_score_gemma":0.000031267533,"teacher_disagreement_score":0.9924145,"about_ca_system_score_codex":0.00009294547,"about_ca_system_score_gemma":0.000013849455,"threshold_uncertainty_score":0.9999358},"labels":[],"label_agreement":null},{"id":"W2914270305","doi":"","title":"Proceedings of the 2nd International Workshop on Conducting Empirical Studies in Industry","year":2014,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Data science; Management science; Regional science; Engineering; Geography","score_opus":0.17573821083283006,"score_gpt":0.36124252530938,"score_spread":0.18550431447654994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914270305","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":[0.9602034,0.000010019104,0.00001874127,0.00013594935,0.0012697742,0.00007743318,2.7335537e-7,0.000044054123,0.038240366],"genre_scores_gemma":[0.9991546,0.0000015544363,0.000020448128,0.00003963913,0.0002625817,0.000006525372,7.010915e-8,0.0000069853936,0.00050755334],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950075,0.000008620659,0.000198132,0.00007804297,0.00013706573,0.000077392666],"domain_scores_gemma":[0.999764,0.00009271276,0.000034614102,0.000046080826,0.00004917106,0.000013375555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029094378,0.00006243165,0.000110471716,0.00007151994,0.000022072258,0.000012833227,0.00007795243,0.00014415708,0.00002261067],"category_scores_gemma":[0.00034267173,0.000039566854,0.000029938215,0.00019040969,0.00002067851,0.000051009007,0.000027806223,0.00034349214,0.000004125207],"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":[0.0002718439,0.00034139864,0.4242289,0.00063152966,0.0008314417,0.000004407921,0.018990023,0.07870126,0.12109426,0.02115568,0.20290823,0.13084102],"study_design_scores_gemma":[0.006870411,0.0004631677,0.0720812,0.0052435794,0.00005956875,0.000090534864,0.06976463,0.08724334,0.6067026,0.002013875,0.14784385,0.0016232006],"about_ca_topic_score_codex":0.0000069438615,"about_ca_topic_score_gemma":0.000006078032,"teacher_disagreement_score":0.4856084,"about_ca_system_score_codex":0.00005722978,"about_ca_system_score_gemma":0.000003622172,"threshold_uncertainty_score":0.16134894},"labels":[],"label_agreement":null},{"id":"W2914582798","doi":"10.1007/s10664-018-9679-5","title":"The impact of feature reduction techniques on defect prediction models","year":2019,"lang":"en","type":"article","venue":"Empirical Software Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":99,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Alberta","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Reduction (mathematics); Feature (linguistics); Computer science; Pattern recognition (psychology); Artificial intelligence; Data mining; Mathematics","score_opus":0.015841601350863765,"score_gpt":0.25694013528353327,"score_spread":0.2410985339326695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914582798","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":[0.95257866,0.00027284113,0.043523204,0.000011911919,0.0013894514,0.000494054,0.000021885382,0.0012150686,0.00049292645],"genre_scores_gemma":[0.9991565,0.000023094497,0.00032150198,0.0000017165326,0.00031744785,0.00003017338,0.0000066963744,0.000044764995,0.00009812917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991366,0.000022960521,0.00023598976,0.00016034066,0.00021812269,0.00022594113],"domain_scores_gemma":[0.99941957,0.0001565247,0.000040577983,0.00027239547,0.000051054943,0.000059870697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023745756,0.00018405882,0.00020371628,0.00011807307,0.000059306734,0.000034111614,0.0000928402,0.00023321922,0.000010044647],"category_scores_gemma":[0.00009053442,0.00012449914,0.0002559493,0.00032686713,0.0000090701915,0.00014846127,0.000015050552,0.0003854626,0.00001858393],"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":[0.000039505576,0.000011327257,0.0016384523,0.000037212936,0.00008112747,5.741479e-7,0.000073983894,0.97878677,0.006662637,0.000017892593,0.0057075634,0.006942984],"study_design_scores_gemma":[0.0014085592,0.00280703,0.042194027,0.00097309024,0.00007965819,0.00016429796,0.00009634214,0.81451374,0.110881574,0.0004520755,0.025140736,0.0012888818],"about_ca_topic_score_codex":0.000018750909,"about_ca_topic_score_gemma":2.1952773e-7,"teacher_disagreement_score":0.16427301,"about_ca_system_score_codex":0.00023985512,"about_ca_system_score_gemma":0.0000147229775,"threshold_uncertainty_score":0.5076928},"labels":[],"label_agreement":null},{"id":"W2915242072","doi":"10.1149/ma2018-02/15/2186","title":"(Invited) Using Image Recognition to Identify Platinum Surfaces with Cyclic Voltammetry Scans","year":2018,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Platinum; Durability; Commercialization; Cyclic voltammetry; Electrolyte; Materials science; Fuel cells; Electrode; Catalysis; Computer science; Chemical engineering; Electrochemistry; Business; Composite material; Chemistry; Engineering; Organic chemistry","score_opus":0.033466443449096085,"score_gpt":0.2778287006784321,"score_spread":0.244362257229336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915242072","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":[0.98394644,0.0000225494,0.00067158986,0.000031074396,0.0011988536,0.0002564647,0.000015767337,0.00042116386,0.013436088],"genre_scores_gemma":[0.99545664,0.000001361804,0.0033722182,0.00005549728,0.0010064851,0.000006815442,0.000007886918,0.0000644245,0.000028645622],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985972,0.00004358443,0.00041228486,0.00027351637,0.00029843644,0.0003749707],"domain_scores_gemma":[0.99924785,0.00010814494,0.00012398708,0.00020324465,0.00016617643,0.00015058497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006488891,0.00021138995,0.00021530593,0.00023234898,0.00022634541,0.0002037189,0.000089402834,0.00017073487,0.00002728313],"category_scores_gemma":[0.00024429584,0.00020109426,0.00004534907,0.000515169,0.000032915876,0.00027668543,0.00002266606,0.00024212486,0.00035549077],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042598203,0.000014247548,0.0006879823,0.000054135104,0.000037963742,0.00001801721,0.00042258142,0.07539675,0.9183232,4.6404402e-8,0.0025863308,0.0024161157],"study_design_scores_gemma":[0.00054515054,0.00018236958,0.014171573,0.0008832115,0.000042378582,0.00006692871,0.0005174737,0.011524484,0.9690153,0.000021747224,0.0025219135,0.0005074495],"about_ca_topic_score_codex":0.0007486381,"about_ca_topic_score_gemma":0.00014208516,"teacher_disagreement_score":0.06387226,"about_ca_system_score_codex":0.0001113472,"about_ca_system_score_gemma":0.000012632435,"threshold_uncertainty_score":0.82003856},"labels":[],"label_agreement":null},{"id":"W2920647791","doi":"10.17577/ijertv7is060085","title":"Object Recognition Using Deep Learning","year":2018,"lang":"en","type":"article","venue":"International Journal of Engineering Research and","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Cognitive neuroscience of visual object recognition; Object (grammar); Pattern recognition (psychology)","score_opus":0.07558359506352623,"score_gpt":0.3443859601039807,"score_spread":0.26880236504045446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920647791","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":[0.9663612,0.00032010855,0.031303223,0.000011043974,0.0014335325,0.00004170952,0.0000012018987,0.00003462583,0.00049332104],"genre_scores_gemma":[0.99671304,0.0001068559,0.0012602384,0.0000014959976,0.0018856713,8.2661234e-7,7.066901e-7,0.00001582551,0.00001532031],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991157,0.000026672298,0.00022596678,0.000058539787,0.00042328326,0.00014980452],"domain_scores_gemma":[0.9989501,0.00011149079,0.000037152015,0.000035407877,0.0007891712,0.0000766599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008043585,0.00006129482,0.000092520386,0.0005126695,0.000055777746,0.00010491837,0.000096591924,0.000060486334,0.000045158708],"category_scores_gemma":[0.00032503536,0.000057853576,0.00003193832,0.00014041636,0.00003085103,0.00020314509,0.000023840592,0.00040811868,0.000016095255],"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":[0.0005004837,0.000068372305,0.0021700815,0.00011629213,0.00085448724,0.00033336168,0.0015106209,0.16816573,0.4506201,0.0001509826,0.0010469054,0.37446257],"study_design_scores_gemma":[0.002295502,0.0018026172,0.0027339018,0.0010098428,0.000020740275,0.0024119169,0.00053203036,0.86000395,0.089900926,0.0007889737,0.03804239,0.00045722417],"about_ca_topic_score_codex":0.000012970009,"about_ca_topic_score_gemma":0.000001153512,"teacher_disagreement_score":0.6918382,"about_ca_system_score_codex":0.000109630244,"about_ca_system_score_gemma":0.00001533144,"threshold_uncertainty_score":0.23592004},"labels":[],"label_agreement":null},{"id":"W2922217543","doi":"10.4095/313749","title":"User manual, source code, and test set for MSBASv3 (Multidimensional Small Baseline Subset version 3) for one- and two-dimensional deformation analysis","year":2019,"lang":"en","type":"report","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Baseline (sea); Computer science; Code (set theory); Set (abstract data type); Programming language; Deformation (meteorology); Source code; Physics","score_opus":0.04328230742573391,"score_gpt":0.28172313136887145,"score_spread":0.23844082394313754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922217543","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":[0.7770729,0.0014316812,0.19916315,0.00012395003,0.004186004,0.0069775586,0.009212232,0.00079644343,0.0010360935],"genre_scores_gemma":[0.9325222,0.00040476213,0.018010303,0.0002207817,0.0025581603,0.0005306234,0.019524202,0.00041271647,0.025816265],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976224,0.000048403137,0.00084096077,0.00054945407,0.0005781228,0.0003607095],"domain_scores_gemma":[0.9969459,0.0014292354,0.00027990065,0.00031583203,0.00086732896,0.0001617583],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001907373,0.0004600203,0.0008981928,0.0006798248,0.0002260222,0.0000903238,0.00006711344,0.00068430323,0.00006739765],"category_scores_gemma":[0.0004761996,0.0004096071,0.00029266044,0.00034534672,0.000025879976,0.0001831429,0.00007041219,0.0002888429,0.000018931232],"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":[0.0013641061,0.0002532011,0.00919035,0.007069048,0.005810544,0.0000068274812,0.00030338785,0.44017652,0.006823607,0.00007889986,0.5074932,0.02143034],"study_design_scores_gemma":[0.0035456207,0.00037419327,0.0013769162,0.0003691248,0.0019803206,0.00007101074,0.00011718948,0.6937906,0.0018049774,0.000009228335,0.29566285,0.0008979801],"about_ca_topic_score_codex":0.00032860078,"about_ca_topic_score_gemma":0.00042700866,"teacher_disagreement_score":0.25361407,"about_ca_system_score_codex":0.00022587446,"about_ca_system_score_gemma":0.00014421302,"threshold_uncertainty_score":0.99983555},"labels":[],"label_agreement":null},{"id":"W2945647361","doi":"10.5006/c2018-10657","title":"Improvements in the Use of Indirect Inspection Tools and Data Interpretation","year":2018,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TransCanada (Canada)","funders":"","keywords":"Computer science; Interpretation (philosophy); Reliability engineering; Process engineering; Forensic engineering; Artificial intelligence; Engineering; Programming language","score_opus":0.1374847528981216,"score_gpt":0.2983023352325804,"score_spread":0.16081758233445878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945647361","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":[0.98933035,0.000013749465,0.008378142,0.0000065862864,0.00036635748,0.0001908996,0.0000150218375,0.000052858177,0.0016460407],"genre_scores_gemma":[0.99976987,0.0000055695814,0.000096905824,0.00001875428,0.00006742158,0.0000039594333,0.0000100780035,0.000004094974,0.00002336926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963343,0.000028223178,0.00014409084,0.00007747331,0.00006887424,0.00004788181],"domain_scores_gemma":[0.99971116,0.000050455197,0.000021257058,0.00019280959,0.000017467035,0.000006847002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002553476,0.00003907908,0.00005883153,0.00006712903,0.000019793004,0.00004706762,0.000055333072,0.000039437506,0.000011824305],"category_scores_gemma":[0.00006745103,0.00002658055,0.0000062392623,0.00014041769,0.000014542519,0.0003943874,0.000023407836,0.000051191422,0.000004680501],"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":[0.000101178535,0.000035568108,0.005301292,0.000058791797,0.000052638927,0.0000010535917,0.0030590638,0.00045658523,0.048034146,0.00012852708,0.011223852,0.9315473],"study_design_scores_gemma":[0.0021282756,0.00092671457,0.1205182,0.0002475261,0.000042477724,0.000023933151,0.0020534112,0.7024661,0.11811417,0.0001303673,0.05291061,0.00043823826],"about_ca_topic_score_codex":0.0002627338,"about_ca_topic_score_gemma":0.00036727192,"teacher_disagreement_score":0.9311091,"about_ca_system_score_codex":0.0000131929055,"about_ca_system_score_gemma":0.0000028681395,"threshold_uncertainty_score":0.10839234},"labels":[],"label_agreement":null},{"id":"W2950307208","doi":"","title":"Stealing From Stealing","year":2012,"lang":"en","type":"article","venue":"Dalhousie review","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Computer security","score_opus":0.02854997316188623,"score_gpt":0.2545016065343168,"score_spread":0.22595163337243057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950307208","genre_codex":"review","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":[0.12605311,0.83618027,0.008220509,0.00004092956,0.0054668044,0.00089444016,0.000047417238,0.0011900455,0.021906503],"genre_scores_gemma":[0.9559146,0.040800817,0.00035024466,0.00029105964,0.0023488966,0.000048326787,0.000031885782,0.00007634527,0.00013783506],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99902695,0.0000648283,0.000361164,0.00010549856,0.00016437627,0.00027717277],"domain_scores_gemma":[0.99949634,0.000060420476,0.000051566425,0.0002556423,0.000022162083,0.000113841044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005197799,0.00014093885,0.0003303879,0.00003804504,0.000055368477,0.000022778677,0.00007883839,0.00009128122,0.0003342305],"category_scores_gemma":[0.00004777952,0.00012258362,0.00010629968,0.00019673744,0.0000049625924,0.00016961173,0.000019368808,0.00017456255,0.00064550585],"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":[0.0000068920704,0.000053231557,0.0031536608,0.0060646776,0.00023208364,0.000010484677,0.0006908571,0.000876868,0.0051861852,0.0003422435,0.055348612,0.9280342],"study_design_scores_gemma":[0.0001600835,0.000009649534,0.00040709096,0.0034061093,0.00007861954,0.0000119198885,0.000029382576,0.00045369528,0.00083452096,0.000009573112,0.994342,0.0002573304],"about_ca_topic_score_codex":0.000066341854,"about_ca_topic_score_gemma":0.000002393784,"teacher_disagreement_score":0.9389934,"about_ca_system_score_codex":0.000044426855,"about_ca_system_score_gemma":0.00000662432,"threshold_uncertainty_score":0.82968867},"labels":[],"label_agreement":null},{"id":"W2950810492","doi":"10.21608/iceeng.2006.33689","title":"SUBOPTIMAL DATA ASSOCIATION TECHNIQUE FOR MULTIPLE-TARGET TRACKING IN DENSE CLUTTER ENVIRONMENT","year":2006,"lang":"en","type":"article","venue":"The International Conference on Electrical Engineering/The International Conference on Electrical Engineering ","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Data association; Clutter; Association (psychology); Computer science; Tracking (education); Artificial intelligence; Psychology; Radar; Telecommunications","score_opus":0.04049232729740962,"score_gpt":0.26103795505216637,"score_spread":0.22054562775475675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950810492","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":[0.09966576,0.00034578072,0.8630543,0.011431805,0.00699784,0.005419425,0.0005575571,0.0017862214,0.010741352],"genre_scores_gemma":[0.99655783,0.000086809974,0.00089003565,0.00014884552,0.0010821534,0.00055263354,0.00018383731,0.00009924951,0.0003985835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99624026,0.000057843215,0.0009350629,0.00068596733,0.0012681345,0.00081271806],"domain_scores_gemma":[0.997661,0.0011897034,0.00019746105,0.00059291616,0.00024916936,0.000109726294],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012661858,0.00056387065,0.00044113738,0.0005840069,0.0001242657,0.0003874867,0.0018741324,0.0003837703,0.00016355539],"category_scores_gemma":[0.0009675825,0.0004506147,0.0001944288,0.00047167193,0.000025685586,0.0003185776,0.00013158338,0.0015166872,0.00006806703],"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":[0.00035878518,0.00022709643,0.00042678617,0.000021263188,0.00027695464,0.000022675003,0.000045813365,0.8059437,0.13191794,0.054256056,0.0033314372,0.0031714845],"study_design_scores_gemma":[0.0009174773,0.00018321295,0.0010484272,0.00010165044,0.000022209839,0.000028490613,0.000007749486,0.9715944,0.017837325,0.00067586766,0.0070842444,0.00049896573],"about_ca_topic_score_codex":0.00011850042,"about_ca_topic_score_gemma":0.000022822996,"teacher_disagreement_score":0.8968921,"about_ca_system_score_codex":0.0016742004,"about_ca_system_score_gemma":0.000096588614,"threshold_uncertainty_score":0.99979454},"labels":[],"label_agreement":null},{"id":"W2953958484","doi":"10.1109/access.2019.2925561","title":"Real-Time Tiny Part Defect Detection System in Manufacturing Using Deep Learning","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":141,"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":"Petroleum Technology Research Centre; Guizhou Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Deep learning; Artificial intelligence; Real-time computing","score_opus":0.019047450405254025,"score_gpt":0.24829375896179495,"score_spread":0.22924630855654093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953958484","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":[0.9875604,0.000033486256,0.0035654402,4.1268765e-7,0.0027798468,0.0003684986,8.4389427e-7,0.0005787976,0.0051122988],"genre_scores_gemma":[0.9993393,0.000008160466,0.000024885618,0.0000019355102,0.00044922758,0.000018713177,0.0000015393612,0.00005592143,0.00010035175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99873006,0.000107351574,0.00037930606,0.00025575576,0.00020078037,0.00032676884],"domain_scores_gemma":[0.9995404,0.00007409967,0.000091459195,0.0002120798,0.000025072293,0.00005690702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044015746,0.0001885654,0.00029353515,0.0002850434,0.000092487244,0.00016585403,0.00013876702,0.00021944683,0.00005796543],"category_scores_gemma":[0.000012800216,0.000193105,0.00010087366,0.00027957623,0.0000056078384,0.0004672552,0.000027307338,0.00034400617,0.00035962343],"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":[0.0000388807,0.0000064888586,0.0031042618,0.00030563556,0.000037555452,0.000018567429,0.00013174232,0.7723311,0.20976281,0.0000017449207,0.000021142847,0.014240033],"study_design_scores_gemma":[0.0009078254,0.00006831716,0.0027289134,0.00053033757,0.000028256434,0.00007242285,0.00022822939,0.44939122,0.54376644,0.0000050055924,0.0017826002,0.00049042044],"about_ca_topic_score_codex":0.00065292296,"about_ca_topic_score_gemma":0.00005621953,"teacher_disagreement_score":0.33400366,"about_ca_system_score_codex":0.0003895984,"about_ca_system_score_gemma":0.000008701575,"threshold_uncertainty_score":0.7874593},"labels":[],"label_agreement":null},{"id":"W2956119553","doi":"10.25046/aj040323","title":"An Efficient Automotive Paint Defect Detection System","year":2019,"lang":"en","type":"article","venue":"Advances in Science Technology and Engineering Systems Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automotive industry; Computer science; Automotive engineering; Embedded system; Engineering; Aerospace engineering","score_opus":0.0033412294100529844,"score_gpt":0.21682833498125442,"score_spread":0.21348710557120143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956119553","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":[0.91017777,0.0029489563,0.0808174,0.0000030365982,0.0048336936,0.0002567928,9.156428e-7,0.0006427537,0.00031866223],"genre_scores_gemma":[0.999549,0.00005689606,0.00023144971,0.0000010368212,0.000116470364,0.000021108073,1.0426108e-7,0.000018831284,0.000005125874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985864,0.000031572286,0.000399708,0.00027535288,0.00027414048,0.00043282995],"domain_scores_gemma":[0.9994305,0.000041278025,0.00008124616,0.00023256207,0.00010747657,0.00010694451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016368674,0.00017989712,0.00028201405,0.0021013943,0.00018598096,0.00012716994,0.00023811994,0.00021204662,0.0000014226661],"category_scores_gemma":[0.00008081203,0.00015879895,0.00003773104,0.0020565866,0.00009845534,0.00064945867,0.000024221703,0.0005766345,0.000015177865],"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":[0.0000046078676,0.0000054128686,0.0010761004,0.00009585095,0.000005613281,0.000012647691,0.000091638816,0.95278966,0.03942451,0.0011904183,4.147853e-7,0.005303107],"study_design_scores_gemma":[0.00047712593,0.0002723322,0.0008818622,0.00055967295,0.0000065459362,0.0019129809,0.0021071653,0.9822492,0.009989649,0.000024805955,0.0012258863,0.00029275607],"about_ca_topic_score_codex":0.000004738333,"about_ca_topic_score_gemma":0.0000017879258,"teacher_disagreement_score":0.08937119,"about_ca_system_score_codex":0.0004357955,"about_ca_system_score_gemma":0.000025421372,"threshold_uncertainty_score":0.64756334},"labels":[],"label_agreement":null},{"id":"W2966830196","doi":"10.1130/abs/2019am-337942","title":"SHOCK DEFORMATION STAGE CLASSIFICATION IN ORDINARY CHONDRITES USING IN SITU 2D XRD SIMPLE DIFFRACTION PEAK WIDTH MEASUREMENT OF OLIVINE","year":2019,"lang":"en","type":"article","venue":"Abstracts with programs - Geological Society of America","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Chondrite; Olivine; Diffraction; Stage (stratigraphy); Materials science; In situ; Shock (circulatory); X-ray crystallography; Deformation (meteorology); Geology; Mineralogy; Optics; Meteorite; Chemistry; Composite material; Physics; Astrobiology","score_opus":0.043143194425346594,"score_gpt":0.25775252459709425,"score_spread":0.21460933017174766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966830196","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":[0.9957306,0.000068557776,0.00279026,0.000027492752,0.00008776821,0.00077606813,0.0000018691223,0.00008052647,0.00043688845],"genre_scores_gemma":[0.99705553,0.00003610842,0.0027810507,0.000012555991,0.000032287433,0.000030903975,0.000026891168,0.000015482532,0.000009217698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99829704,0.00006806956,0.00069941033,0.00021891581,0.00042460117,0.00029199175],"domain_scores_gemma":[0.99918336,0.00006396413,0.00037843446,0.00018258549,0.00014154991,0.000050108527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005847818,0.00018284531,0.0004060493,0.00007663629,0.00003426402,0.000018865972,0.000086558684,0.00023362212,0.00003219008],"category_scores_gemma":[0.00003619422,0.00014417096,0.0001082265,0.0005748094,0.00008014664,0.0003204017,0.000022780454,0.00034523907,0.000006775054],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038115532,0.0010268976,0.16043523,0.0009239637,0.00013296338,0.0000036440574,0.0016595116,0.39374208,0.27807263,0.000014050686,0.00010343537,0.16350445],"study_design_scores_gemma":[0.0019006423,0.0016433237,0.8273045,0.0004475828,0.00002884815,0.00000984638,0.004999972,0.15399393,0.007037929,0.00005155606,0.0021496476,0.00043222329],"about_ca_topic_score_codex":0.0013664842,"about_ca_topic_score_gemma":0.00007928068,"teacher_disagreement_score":0.6668693,"about_ca_system_score_codex":0.00026447905,"about_ca_system_score_gemma":0.000032021762,"threshold_uncertainty_score":0.58791214},"labels":[],"label_agreement":null},{"id":"W2973001551","doi":"10.32604/iasc.2020.010100","title":"A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images","year":2020,"lang":"en","type":"article","venue":"Intelligent Automation & Soft Computing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Concordia University","funders":"","keywords":"Computer science; Segmentation; Weed; Artificial intelligence; Distress; Weed science; Computer vision; Machine learning; Ecology","score_opus":0.03601393799993522,"score_gpt":0.29808607318016184,"score_spread":0.2620721351802266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973001551","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":[0.014760348,0.000053842323,0.98317146,0.000031219763,0.00046007583,0.0009682366,0.000040768136,0.0004576594,0.000056380566],"genre_scores_gemma":[0.79533076,0.0000030397591,0.20432808,0.00002552478,0.00016957909,0.00004015299,0.000068061934,0.000028394003,0.0000063943385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883723,0.00008421873,0.0005698605,0.00019967728,0.00016606996,0.00014295308],"domain_scores_gemma":[0.9989677,0.00040517093,0.00024029918,0.00008281327,0.00024841432,0.000055575976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027104074,0.00015272609,0.00024387742,0.00010825373,0.00009210887,0.000055149252,0.00008082933,0.000097313634,0.000016478085],"category_scores_gemma":[0.00038397525,0.0001601054,0.000107905165,0.00030885154,0.000012224732,0.00016997497,0.000024373901,0.00009354132,0.000008399476],"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":[0.000019111047,0.000013144086,0.000038639955,0.0001740606,0.000047404454,2.6912747e-7,0.0020615575,0.87254095,0.0689231,0.000012266752,0.0001245753,0.056044906],"study_design_scores_gemma":[0.0003408286,0.000062437364,0.00009470513,0.000073665986,0.000019265486,0.0000012893744,0.0007288543,0.71355134,0.28495735,0.000007600367,0.000055819466,0.00010684741],"about_ca_topic_score_codex":0.00003737062,"about_ca_topic_score_gemma":0.0000032460255,"teacher_disagreement_score":0.78057045,"about_ca_system_score_codex":0.0000949162,"about_ca_system_score_gemma":0.000019856247,"threshold_uncertainty_score":0.65289086},"labels":[],"label_agreement":null},{"id":"W2974422157","doi":"10.18287/2412-6179-2019-43-4-632-646","title":"Analysis of a robust edge detection system in different color spaces using color and depth images","year":2019,"lang":"en","type":"article","venue":"Computer Optics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fields Institute for Research in Mathematical Sciences","funders":"","keywords":"Artificial intelligence; Edge detection; Computer vision; Computer science; Robustness (evolution); Color image; Image gradient; Color space; Pattern recognition (psychology); Image processing; Mathematics; Image (mathematics)","score_opus":0.020043368869894995,"score_gpt":0.21855528869999802,"score_spread":0.19851191983010302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974422157","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":[0.8616033,0.000063995365,0.13721974,8.836226e-7,0.00075092097,0.00021463804,0.000004356349,0.0000638682,0.00007827386],"genre_scores_gemma":[0.99850506,0.0000050805006,0.0013504593,0.0000014073438,0.00011128286,0.000003994987,0.0000016667061,0.000013400064,0.000007673825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993025,0.000037245063,0.0002814195,0.00014592381,0.000104529,0.0001283592],"domain_scores_gemma":[0.9996446,0.000064018925,0.0000700994,0.00013977959,0.000046072637,0.000035424266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013142319,0.00012313214,0.000394508,0.0003945736,0.000024516206,0.00005518832,0.0000466598,0.00011464209,0.000001865393],"category_scores_gemma":[0.000004026988,0.00010725982,0.00006900844,0.00047862023,0.000011790978,0.00006888449,0.000034941466,0.00009501944,0.0000022619968],"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":[0.00002240371,0.000017050561,0.011872729,0.0002637833,0.00027352225,0.000003744955,0.00018334764,0.9616634,0.02158677,0.000024740246,0.0000068384843,0.0040816152],"study_design_scores_gemma":[0.00035266532,0.00009092084,0.019227844,0.000092952854,0.00014191537,0.0000066050284,0.00013945594,0.971502,0.008315514,3.7254546e-7,0.000023136185,0.00010660738],"about_ca_topic_score_codex":0.000052928488,"about_ca_topic_score_gemma":0.0000512377,"teacher_disagreement_score":0.1369017,"about_ca_system_score_codex":0.00011697865,"about_ca_system_score_gemma":0.0000052896767,"threshold_uncertainty_score":0.43739283},"labels":[],"label_agreement":null},{"id":"W2976519442","doi":"10.36001/phmconf.2019.v11i1.868","title":"Automating Visual Inspection with Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Industrial Vision Systems and Defect Detection","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":"Lockheed Martin (Canada)","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Context (archaeology); Segmentation; Object detection; Deep learning; Computer vision; Task (project management); Set (abstract data type); Convolution (computer science); Contextual image classification; Image (mathematics); Artificial neural network","score_opus":0.010700467205941325,"score_gpt":0.21091557566802552,"score_spread":0.20021510846208418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976519442","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":[0.98999304,0.000027746548,0.007604831,0.000030409723,0.00088139396,0.00020181383,0.000008241364,0.00019704759,0.0010554581],"genre_scores_gemma":[0.9996214,0.000002780047,0.000044322675,0.000021815002,0.0001642495,0.000005845813,0.0000029339653,0.000012544629,0.00012409742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933225,0.000032849937,0.0001768131,0.00010741868,0.0001992694,0.00015137056],"domain_scores_gemma":[0.9995719,0.000037659498,0.00007767725,0.00012690961,0.00015912771,0.000026678194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015040582,0.00010338335,0.00014451536,0.000012467089,0.00010034594,0.000030106989,0.0000986042,0.00011176604,0.00003940083],"category_scores_gemma":[0.000008904641,0.000070476926,0.00011572554,0.00018392401,0.000048981823,0.0001813401,0.000032181586,0.00024487168,0.0000082977585],"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":[0.00015035237,0.00011375897,0.057044376,0.00032585775,0.0006105083,0.0000013121306,0.011646101,0.87008876,0.02106443,0.0028007485,0.011602529,0.02455128],"study_design_scores_gemma":[0.00039370303,0.00011240039,0.017900625,0.00006916204,0.00001267763,0.0000115975145,0.0015716237,0.9786266,0.0009871845,0.00001557659,0.00017664688,0.00012216836],"about_ca_topic_score_codex":0.00006008611,"about_ca_topic_score_gemma":0.000006353497,"teacher_disagreement_score":0.10853789,"about_ca_system_score_codex":0.00004938402,"about_ca_system_score_gemma":0.000027908854,"threshold_uncertainty_score":0.28739655},"labels":[],"label_agreement":null},{"id":"W2982273098","doi":"10.4095/219689","title":"Comparison of Surface Reflectance Retrieved from Hyperspectral Data Using MODTRAN4 and CAM5S","year":2000,"lang":"en","type":"report","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Hyperspectral imaging; Reflectivity; Remote sensing; Surface (topology); Geography; Geology; Mathematics; Optics; Physics; Geometry","score_opus":0.25830971944275805,"score_gpt":0.3968977568694529,"score_spread":0.13858803742669484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982273098","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":[0.9415608,0.009399998,0.0028218487,0.0000030263611,0.0019790826,0.00037219102,0.0006164527,0.0002673104,0.0429793],"genre_scores_gemma":[0.99516976,0.001047181,0.0021893042,0.0000015258688,0.0005884217,7.8801173e-7,0.00021439185,0.000075141405,0.0007134723],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793017,0.000045298686,0.0007604121,0.00045852008,0.0005720962,0.0002335098],"domain_scores_gemma":[0.9987009,0.000085552885,0.00017244201,0.00085607904,0.00010918091,0.00007580579],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039541308,0.00030431198,0.0009489483,0.00005390918,0.000054188728,0.000050679806,0.0002328005,0.0006741921,0.00016683503],"category_scores_gemma":[0.00005143677,0.0002918683,0.00006819729,0.00024983246,0.00003128242,0.00014585836,0.000048375532,0.000548656,0.000005672894],"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":[0.0009267638,0.00027089776,0.012145258,0.0025346885,0.0030777575,0.00010632137,0.002093,0.15701154,0.5995689,0.000023385484,0.15266828,0.069573194],"study_design_scores_gemma":[0.0019614398,0.00028274802,0.0015223357,0.0019305524,0.0007286732,0.00013350102,0.00068709766,0.7930075,0.071987525,0.000044703946,0.1255095,0.0022044508],"about_ca_topic_score_codex":0.0058686896,"about_ca_topic_score_gemma":0.00027434967,"teacher_disagreement_score":0.6359959,"about_ca_system_score_codex":0.00021265572,"about_ca_system_score_gemma":0.00017794191,"threshold_uncertainty_score":0.9999533},"labels":[],"label_agreement":null},{"id":"W2982683857","doi":"10.1108/aa-05-2019-0094","title":"Wrinkle and boundary detection of fiber products in robotic composites manufacturing","year":2019,"lang":"en","type":"article","venue":"Assembly Automation","topic":"Industrial Vision Systems and Defect Detection","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 British Columbia","funders":"","keywords":"Modular design; Bulkhead (partition); Computer science; Yarn; RGB color model; Process (computing); Machine vision; Textile; Image processing; Mechanical engineering; Artificial intelligence; Computer vision; Engineering; Materials science; Structural engineering; Composite material; Image (mathematics)","score_opus":0.009160642510823764,"score_gpt":0.20782295387361613,"score_spread":0.19866231136279236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982683857","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":[0.9973344,0.00009562624,0.00086102076,0.000007711803,0.0004620236,0.00029253922,5.046485e-7,0.00014899873,0.00079714536],"genre_scores_gemma":[0.9996835,0.0000043490627,0.00016870521,0.0000017978979,0.00004457345,0.000007761898,0.0000033375882,0.000013274573,0.00007268292],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994167,0.000030102801,0.0002304792,0.000115081115,0.00010882782,0.0000987943],"domain_scores_gemma":[0.9997622,0.000036112724,0.00005262387,0.00011063673,0.000022932085,0.000015543334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018058106,0.00008120347,0.00014444455,0.00016848359,0.00002521579,0.000023815126,0.0000260187,0.000087843786,0.000010382397],"category_scores_gemma":[0.000011921154,0.00008280622,0.000017451826,0.00014016425,0.000005959289,0.0002634121,0.000010930003,0.000090713445,0.000053837583],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014251044,0.000014063434,0.0015408009,0.00038783226,0.00001714749,8.967735e-7,0.00021979026,0.08698762,0.8724954,0.000011839487,0.000023711786,0.038286623],"study_design_scores_gemma":[0.00033182115,0.000057992827,0.10815785,0.00009868118,0.0000059285007,0.000011147364,0.000033769138,0.106286384,0.78453904,0.000025006528,0.00035110235,0.00010125005],"about_ca_topic_score_codex":0.0000461752,"about_ca_topic_score_gemma":0.000016246744,"teacher_disagreement_score":0.106617056,"about_ca_system_score_codex":0.000058171972,"about_ca_system_score_gemma":0.0000067680035,"threshold_uncertainty_score":0.33767396},"labels":[],"label_agreement":null},{"id":"W2985872997","doi":"10.1109/imtc.2005.1604348","title":"Non-Contact 3D Coordinates Measurement of Cross-Cutting Feature Points on the Surface of Large-Scale Workpiece Based on Machine Vision Method","year":2006,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Industrial Vision Systems and Defect Detection","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 Sherbrooke","funders":"Harbin Institute of Technology","keywords":"Computer vision; Artificial intelligence; Feature (linguistics); Computer science; Monocular vision; Machine vision; Scale (ratio); Stereopsis; Coordinate-measuring machine; Point (geometry); Camera resectioning; Correctness; Surface (topology); Calibration; Stereoscopy; System of measurement; Mathematics; Engineering; Algorithm; Geometry; Mechanical engineering","score_opus":0.020437724129546916,"score_gpt":0.2691754428949657,"score_spread":0.24873771876541878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985872997","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":[0.9718057,0.000120526594,0.019855319,0.0010336223,0.0006463167,0.0013423926,0.000044922184,0.00030350767,0.0048477035],"genre_scores_gemma":[0.9967499,0.000010058804,0.002983394,0.000043613818,0.000055106273,0.00007399264,0.0000064000487,0.00003619196,0.000041387626],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99718225,0.00003195717,0.0007020934,0.0003975706,0.0012639831,0.00042212624],"domain_scores_gemma":[0.9980294,0.000056839923,0.00042297228,0.00023617367,0.0012059762,0.000048624035],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024382907,0.0003588779,0.00046282087,0.00033313019,0.00022161214,0.00007657599,0.00029735643,0.0003889619,0.00005285302],"category_scores_gemma":[0.0001317956,0.00027583344,0.00011290125,0.0006145354,0.00008974593,0.00019547611,0.000025133808,0.0005000225,0.000008957949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041653262,0.00039800594,0.029583046,0.00031626076,0.00015122323,0.0000012593682,0.00020541142,0.008464222,0.9429589,0.0021854308,0.003760287,0.011559455],"study_design_scores_gemma":[0.0023792074,0.0004891847,0.005255271,0.0010619591,0.000048805832,0.0000040933996,0.00039246422,0.060084082,0.9285968,0.00029500714,0.0011029973,0.0002901147],"about_ca_topic_score_codex":0.00005013345,"about_ca_topic_score_gemma":0.0000391842,"teacher_disagreement_score":0.051619858,"about_ca_system_score_codex":0.0003705137,"about_ca_system_score_gemma":0.000091261616,"threshold_uncertainty_score":0.99996936},"labels":[],"label_agreement":null},{"id":"W2987908520","doi":"10.1007/s11837-019-03893-y","title":"A Machine Vision Sensor for Quality Control of Green Anode Paste Material","year":2019,"lang":"en","type":"article","venue":"JOM","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Alcoa (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Partial least squares regression; Anode; Texture (cosmology); Raw material; Materials science; Coke; Measure (data warehouse); Process engineering; Composite material; Mathematics; Computer science; Artificial intelligence; Metallurgy; Image (mathematics); Engineering; Statistics; Data mining; Electrode","score_opus":0.013515234081125836,"score_gpt":0.25942117387229147,"score_spread":0.24590593979116562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987908520","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":[0.98595476,0.000026130341,0.010426209,0.000024709589,0.001967322,0.00056027906,0.00026132519,0.00010048874,0.0006787654],"genre_scores_gemma":[0.99909705,9.1428586e-7,0.00010676591,0.000013372454,0.00031035455,0.000011629793,0.0000096346,0.000018482504,0.0004317794],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99933916,0.000039768463,0.00029361888,0.00009841308,0.00011341838,0.00011562146],"domain_scores_gemma":[0.9996315,0.00007262072,0.00006332416,0.00016219431,0.00004202651,0.000028301216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031122795,0.00008331217,0.00024822034,0.000046089583,0.00001813151,0.000015034407,0.000040055973,0.0001115796,0.000090937945],"category_scores_gemma":[0.000019410387,0.00006959237,0.00008386259,0.000050800074,0.0000049087525,0.000056763554,0.0000073453866,0.0000514804,0.000042433552],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007996809,0.00002546607,0.0017326107,0.00043447665,0.000071565184,8.2079055e-7,0.00014869343,0.0029043416,0.9782296,0.00022131052,0.0008898819,0.014541589],"study_design_scores_gemma":[0.025660891,0.002435795,0.029178476,0.00045358547,0.00013777033,0.000027314916,0.00036281717,0.3241732,0.4078679,0.0005056153,0.2078699,0.0013267181],"about_ca_topic_score_codex":0.00036283082,"about_ca_topic_score_gemma":0.000012681224,"teacher_disagreement_score":0.5703617,"about_ca_system_score_codex":0.000023411814,"about_ca_system_score_gemma":0.0000050294075,"threshold_uncertainty_score":0.2837895},"labels":[],"label_agreement":null},{"id":"W2988663399","doi":"10.1109/imtc.2002.1007124","title":"Implementation of a mechanics-based system for estimating the strength of a board","year":2003,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Finite element method; Stress (linguistics); Grading (engineering); Structural engineering; Size effect on structural strength; Compressive strength; Strength of materials; Feature (linguistics); Computer science; Engineering; Materials science; Composite material","score_opus":0.022106025851427586,"score_gpt":0.27371448500364426,"score_spread":0.2516084591522167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988663399","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":[0.18192688,0.000014100614,0.8162859,0.0000029138391,0.0005125956,0.00049206713,0.000013288171,0.00007045469,0.0006818025],"genre_scores_gemma":[0.9917186,8.113141e-8,0.008194275,0.0000016672464,0.000026385153,0.00004287569,0.0000012142596,0.000009490834,0.0000054047046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947166,0.000027191867,0.00028567313,0.000049927126,0.00009066672,0.00007486936],"domain_scores_gemma":[0.9996585,0.00009402935,0.00007935872,0.00010021432,0.000056061297,0.000011838805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036514722,0.000053600495,0.00011750915,0.000041730018,0.000029905414,0.000006787013,0.000031481497,0.00003692799,0.00001913274],"category_scores_gemma":[0.000026234842,0.000037012007,0.000057896505,0.00010478683,0.0000025784213,0.000023893006,0.0000019155634,0.000027178397,6.6798793e-7],"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":[0.00011269546,0.00006813131,0.00054864085,0.0050483746,0.00042097172,8.1141957e-7,0.0015189269,0.48564917,0.25420368,0.13892974,0.0029374214,0.11056146],"study_design_scores_gemma":[0.00070565206,0.00010033932,0.00000876896,0.000055878885,0.000020074818,9.5219616e-7,0.0022841834,0.45002213,0.54645526,0.000040339055,0.0002562044,0.000050237468],"about_ca_topic_score_codex":0.00013433605,"about_ca_topic_score_gemma":0.000030083089,"teacher_disagreement_score":0.80979174,"about_ca_system_score_codex":0.000030024612,"about_ca_system_score_gemma":0.000018177387,"threshold_uncertainty_score":0.15093058},"labels":[],"label_agreement":null},{"id":"W2989732077","doi":"10.18280/i2m.180505","title":"End Position Detection of Industrial Robots Based on Laser Tracker","year":2019,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Baoji University of Arts And Sciences","keywords":"Laser tracker; Position (finance); Robot; Artificial intelligence; Computer vision; Laser; Computer science; Optics; Physics; Business","score_opus":0.024210296320215027,"score_gpt":0.2453851577711057,"score_spread":0.22117486145089066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989732077","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":[0.9906232,0.000008012001,0.004055955,0.000046886264,0.0025939983,0.00050095096,0.000022359516,0.00022044078,0.0019282035],"genre_scores_gemma":[0.99952734,0.000002267902,0.000095101175,0.000058913865,0.00019945725,0.000029200288,0.000036485322,0.000023597031,0.000027625509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876595,0.00013360391,0.00039417486,0.0001968321,0.00032535277,0.00018407327],"domain_scores_gemma":[0.9994738,0.00010276146,0.00012146902,0.00020217297,0.000057241596,0.00004255732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038272064,0.0001656242,0.00022356707,0.00025338473,0.000045434484,0.000029511997,0.00006941467,0.00032984288,0.00021897882],"category_scores_gemma":[0.000039002014,0.00015688721,0.00009355588,0.00028431925,0.000018317833,0.00019389066,0.000006263913,0.00025929644,0.00012551427],"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":[0.0009111011,0.0000934277,0.012331251,0.000059538816,0.00008621519,0.000004168329,0.000103926664,0.4785851,0.35101077,0.00006192296,0.00053358276,0.156219],"study_design_scores_gemma":[0.0051518516,0.0013690108,0.016707668,0.00009046369,0.000046320973,0.000007512215,0.00013249482,0.05239401,0.92293555,0.000093588096,0.00074789923,0.00032362534],"about_ca_topic_score_codex":0.00002889703,"about_ca_topic_score_gemma":0.000008367343,"teacher_disagreement_score":0.5719248,"about_ca_system_score_codex":0.00014652846,"about_ca_system_score_gemma":0.000027161135,"threshold_uncertainty_score":0.63976747},"labels":[],"label_agreement":null},{"id":"W2990943125","doi":"10.48550/arxiv.1911.10608","title":"AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Industrial Vision Systems and Defect Detection","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":"University of Waterloo","funders":"","keywords":"Initialization; Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Pattern recognition (psychology); Convolutional neural network; Computation; Enhanced Data Rates for GSM Evolution; Algorithm","score_opus":0.047647046343240296,"score_gpt":0.16703806675853877,"score_spread":0.11939102041529848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990943125","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":[0.98324686,0.00016074986,0.007899228,0.000003203478,0.0025477167,0.00058738637,0.000025417523,0.00040282926,0.0051266314],"genre_scores_gemma":[0.9987212,0.00012095282,0.000013687569,0.0000060890115,0.00016368793,0.0000018705483,0.000017238546,0.000053631462,0.0009016627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985261,0.0001274586,0.0003005988,0.00061956403,0.000089852205,0.0003364426],"domain_scores_gemma":[0.999103,0.00006091377,0.00010093678,0.0005771014,0.00007029495,0.00008776285],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026331935,0.0003581445,0.00046504027,0.00051403855,0.00006083746,0.00007298355,0.0002899723,0.00085285876,0.00007847239],"category_scores_gemma":[0.000018752009,0.00042674673,0.00018101324,0.0006173402,0.000026049234,0.00020445952,0.00015832037,0.00086362555,0.0002341038],"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":[0.00008407755,0.000021598964,0.0039017696,0.00015536719,0.000066224355,0.0000631741,0.000101448204,0.9890901,0.0054857936,0.00006599632,0.0001441329,0.00082030706],"study_design_scores_gemma":[0.0015794876,0.00011291465,0.014413099,0.00028386767,0.000074889365,0.000007807245,0.0003661302,0.9708767,0.008967592,0.00042485617,0.001951918,0.0009407708],"about_ca_topic_score_codex":0.0008023171,"about_ca_topic_score_gemma":0.0007868595,"teacher_disagreement_score":0.018213443,"about_ca_system_score_codex":0.00042230598,"about_ca_system_score_gemma":0.000056487173,"threshold_uncertainty_score":0.99981844},"labels":[],"label_agreement":null},{"id":"W2992408230","doi":"10.2495/safe-v9-n4-371-380","title":"Office building occupancy monitoring through image recognition sensors","year":2019,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Occupancy; Computer science; Artificial intelligence; Computer vision; Engineering; Architectural engineering","score_opus":0.010662732556394636,"score_gpt":0.23882492198803734,"score_spread":0.2281621894316427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992408230","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":[0.97786456,0.00038455206,0.015536571,0.00003142208,0.0053533562,0.00007558023,0.000013117996,0.00007269504,0.00066816795],"genre_scores_gemma":[0.9969158,0.0004031159,0.0016172357,0.000005318827,0.0010257569,7.01555e-7,0.0000020909763,0.000020843163,0.000009108347],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990121,0.000015434203,0.0004474427,0.000091374015,0.0002978282,0.0001357905],"domain_scores_gemma":[0.99946976,0.00009979471,0.00010641065,0.000060775077,0.00020822418,0.000055015164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002978193,0.0001289659,0.00020564633,0.00016774861,0.000026706555,0.000074866206,0.00011029336,0.000100773614,0.00003714946],"category_scores_gemma":[0.000069900416,0.00013027294,0.000092095426,0.00010055876,0.0000068648615,0.00063012063,0.000023240453,0.0003562018,0.000020434863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015783038,0.00012076021,0.0037325784,0.00072519743,0.0017748949,0.00041545476,0.006916241,0.4116594,0.4965746,0.0015438241,0.0004330683,0.07452568],"study_design_scores_gemma":[0.012681878,0.0008366516,0.02432206,0.0076668076,0.00023580753,0.0058029094,0.0032759982,0.26329157,0.55650324,0.001733112,0.12078699,0.0028630116],"about_ca_topic_score_codex":0.000014323119,"about_ca_topic_score_gemma":2.6721085e-7,"teacher_disagreement_score":0.14836784,"about_ca_system_score_codex":0.00011127295,"about_ca_system_score_gemma":0.000009535832,"threshold_uncertainty_score":0.5312376},"labels":[],"label_agreement":null},{"id":"W2992410263","doi":"10.2175/193864710798285813","title":"Developing a Cip Using a Deterioration Modeling and Field Sampling Approach","year":2010,"lang":"en","type":"article","venue":"Proceedings of the Water Environment Federation","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada","keywords":"Field (mathematics); Sampling (signal processing); Computer science; Environmental science; Mathematics","score_opus":0.035376580858066865,"score_gpt":0.2184535229502945,"score_spread":0.18307694209222763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992410263","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":[0.89498955,0.0000058008036,0.1043779,0.00004096374,0.00018680733,0.00021107179,2.7854338e-7,0.000033615685,0.00015401402],"genre_scores_gemma":[0.9906714,0.000004922449,0.009151098,0.000011314378,0.00012100305,0.000014267135,0.0000010430673,0.000014660993,0.000010317255],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994136,0.0000032548844,0.00021865405,0.00012384326,0.00012726629,0.00011337573],"domain_scores_gemma":[0.9998721,0.0000051784323,0.00003897022,0.000047652895,0.000017074997,0.00001902164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022361099,0.000096694734,0.00009656957,0.000043734046,0.00020859091,0.000120907134,0.00004851861,0.000108026645,0.000003864439],"category_scores_gemma":[0.00001144396,0.000059315265,0.000027247073,0.000030692707,0.00001004478,0.00024264309,0.00004354269,0.00014734425,9.906223e-7],"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":[0.0000060872835,0.0000031155241,0.00024798454,0.00004548499,0.0000066232474,9.686105e-9,0.0004595989,0.018269176,0.9796409,0.00007318893,0.0000018899569,0.0012459233],"study_design_scores_gemma":[0.00011780269,0.000012219318,0.00004863299,0.000024313904,0.000010190587,0.000010082201,0.00012530334,0.5277783,0.47145054,0.0002436013,0.00008935106,0.00008964063],"about_ca_topic_score_codex":0.000013068191,"about_ca_topic_score_gemma":0.0000013848361,"teacher_disagreement_score":0.50950915,"about_ca_system_score_codex":0.000039028087,"about_ca_system_score_gemma":0.0000027991962,"threshold_uncertainty_score":0.24188064},"labels":[],"label_agreement":null},{"id":"W2997070389","doi":"10.1007/s10845-019-01530-8","title":"An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction","year":2020,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Task (project management); Quality (philosophy); Artificial intelligence; Embedding; Adversarial system; Decoding methods; Product (mathematics); Machine learning; Algorithm; Mathematics","score_opus":0.05655607398016439,"score_gpt":0.29941750173827375,"score_spread":0.24286142775810937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997070389","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":[0.35346812,0.000082492996,0.6400524,0.00016583368,0.0056557697,0.00034076176,0.00002473222,0.00014858673,0.00006131258],"genre_scores_gemma":[0.9848124,0.000019068937,0.0077297525,0.00007524266,0.0072947256,0.000011074045,0.000008262661,0.000039502273,0.000010015479],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99820906,0.00008996115,0.0009010592,0.00019604972,0.00038680906,0.00021704717],"domain_scores_gemma":[0.9990096,0.0001499244,0.00031887332,0.00014757943,0.00016304491,0.00021094656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074572896,0.00019759909,0.00035675263,0.00015612636,0.00011389765,0.000097049284,0.00016336031,0.00018092286,0.00010492453],"category_scores_gemma":[0.00028432132,0.00017709102,0.0002736511,0.00011033055,0.000014259248,0.0003301714,0.000009741563,0.00047395716,0.000010822214],"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":[0.001628746,0.00006865543,0.00019808831,0.00020341353,0.00018867056,0.000005711333,0.0005160802,0.9596888,0.019331126,0.000116461815,0.0017462965,0.016307965],"study_design_scores_gemma":[0.0012874772,0.0008882952,0.001019975,0.00018098531,0.00007857971,0.00003316275,0.00033860994,0.03167994,0.9274452,0.0005530121,0.036146224,0.00034853508],"about_ca_topic_score_codex":0.0000133219955,"about_ca_topic_score_gemma":0.0000023197929,"teacher_disagreement_score":0.92800885,"about_ca_system_score_codex":0.000178408,"about_ca_system_score_gemma":0.000059802824,"threshold_uncertainty_score":0.72215617},"labels":[],"label_agreement":null},{"id":"W3003525420","doi":"10.1109/iscc47284.2019.8969682","title":"Deep Learning for the Detection of Tabular Information from Electronic Component Datasheets","year":2019,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"University of Ottawa","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Object detection; Domain (mathematical analysis); Component (thermodynamics); Precision and recall; Feature (linguistics); Machine learning; Backbone network; Feature learning; Feature extraction; Transfer of learning; Pyramid (geometry); Pattern recognition (psychology)","score_opus":0.0070411729747962785,"score_gpt":0.19392751659857804,"score_spread":0.18688634362378176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003525420","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":[0.3414975,0.00018155249,0.6566521,0.0000047499097,0.0006617162,0.00045561415,0.0000043982773,0.000109853914,0.00043251627],"genre_scores_gemma":[0.99976534,0.000020932797,0.00003942577,0.000006432671,0.00007425959,0.00002148822,0.00004439827,0.000008044221,0.000019686733],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994989,0.000015740543,0.00020967734,0.000051706375,0.000105205596,0.00011879334],"domain_scores_gemma":[0.9996489,0.00011545129,0.000053061125,0.0001326886,0.000037154747,0.00001271858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020432059,0.00006442806,0.00009855461,0.000049510712,0.00005253999,0.000025822563,0.000058041605,0.00006990436,0.00006819607],"category_scores_gemma":[0.000020620419,0.000046245492,0.000045617133,0.000086195745,0.0000037233713,0.0002417995,0.00000976979,0.00013086275,0.00008230369],"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":[0.00011653726,0.000007924398,0.0003147729,0.000075881624,0.00015493756,3.80833e-8,0.00034629414,0.4736289,0.2078871,0.00037169392,0.00019941814,0.3168965],"study_design_scores_gemma":[0.0005299373,0.000104224746,0.0009253171,0.000010974597,0.000015903579,7.7703635e-7,0.00032702705,0.8244589,0.06564009,0.000036501184,0.10787092,0.0000794443],"about_ca_topic_score_codex":0.0002308598,"about_ca_topic_score_gemma":0.00004067292,"teacher_disagreement_score":0.6582678,"about_ca_system_score_codex":0.00004999041,"about_ca_system_score_gemma":0.0000050098865,"threshold_uncertainty_score":0.18858364},"labels":[],"label_agreement":null},{"id":"W3006488808","doi":"10.3390/s20041057","title":"Detection System for U-Shaped Bellows Convolution Pitches Based on a Laser Line Scanner","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China; Government of Jiangsu Province","keywords":"Bellows; Convolution (computer science); Expansion joint; Laser scanning; Scanner; Sample (material); Computer science; Line (geometry); Laser; Engineering; Mechanical engineering; Artificial intelligence; Structural engineering; Mathematics; Optics","score_opus":0.026411813459069646,"score_gpt":0.2154899225173082,"score_spread":0.18907810905823855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006488808","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":[0.9147606,0.000027013244,0.07992419,0.00013482381,0.002047191,0.00091734604,0.00004810275,0.0011960415,0.000944693],"genre_scores_gemma":[0.9987312,7.515871e-7,0.000108460335,0.0000717994,0.0008893396,0.00005319485,0.000010755514,0.00004646878,0.00008801028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912673,0.00004539666,0.00026212586,0.0002095662,0.00015900348,0.00019718046],"domain_scores_gemma":[0.9995609,0.00008228732,0.000050127615,0.00012901994,0.000074511656,0.000103172526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016322377,0.00016112738,0.00020911967,0.000093243994,0.00010660228,0.000031769683,0.000042724572,0.00018715562,0.000010911119],"category_scores_gemma":[0.00008816639,0.00015080809,0.00012360599,0.0002321313,0.000009578464,0.000045056313,0.000004589784,0.00014434198,0.00011691764],"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":[0.0005350229,0.000018241495,0.000027892114,0.0004676706,0.00005201488,0.0000066063685,0.0002153798,0.8283792,0.16088864,0.000020354644,0.00091796165,0.00847099],"study_design_scores_gemma":[0.0008946523,0.0002808997,0.000028213657,0.00006045931,0.00002072196,0.0000021532132,0.0001755254,0.80869704,0.18194105,0.0000015914167,0.0077484306,0.00014925297],"about_ca_topic_score_codex":0.00003227862,"about_ca_topic_score_gemma":0.000013768296,"teacher_disagreement_score":0.08397062,"about_ca_system_score_codex":0.00013286632,"about_ca_system_score_gemma":0.000011339152,"threshold_uncertainty_score":0.61497754},"labels":[],"label_agreement":null},{"id":"W3007295782","doi":"10.1109/tsm.2020.2976714","title":"Color Difference Detection of Polysilicon Wafers Using Optimized Support Vector Machine by Magnetic Bacteria Optimization Algorithm With Elitist Strategy","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Jiangsu Key Laboratory of Precision and Micro-Manufacturing Technology; Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Support vector machine; Wafer; Artificial intelligence; Feature (linguistics); Algorithm; Computer science; Pattern recognition (psychology); Engineering","score_opus":0.01826643047564976,"score_gpt":0.20895079848174258,"score_spread":0.1906843680060928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007295782","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":[0.53525084,0.000026803807,0.46337637,0.0000063675698,0.00055630575,0.0003421264,0.00019082506,0.00022594658,0.000024385059],"genre_scores_gemma":[0.9972874,0.000046123358,0.0023306215,0.000022958548,0.00011669531,0.00003511798,0.000025547171,0.000091115064,0.000044434753],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982723,0.00008287051,0.0005784562,0.00041877478,0.00030360933,0.00034396944],"domain_scores_gemma":[0.9992843,0.000058184847,0.0001512956,0.0002390906,0.000061874496,0.00020525455],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008619717,0.0004002152,0.00050073717,0.00020796258,0.000157225,0.00008694369,0.00013616151,0.00024331658,0.00042421184],"category_scores_gemma":[0.0000032815467,0.0003806208,0.00012761494,0.00028407335,0.000053309504,0.00030018465,0.000002038207,0.000433487,0.0000089466685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015047462,0.000021123184,5.549823e-7,0.00005256332,0.00005515276,0.000002832768,0.00009996335,0.43943435,0.54883605,6.864293e-8,0.0000047960575,0.011342059],"study_design_scores_gemma":[0.0011006974,0.0005127013,0.000015538744,0.00004255402,0.00007939627,0.000023245242,0.00010795572,0.31592762,0.68186915,3.090161e-7,0.000045593155,0.00027521496],"about_ca_topic_score_codex":0.0003229796,"about_ca_topic_score_gemma":0.000016045004,"teacher_disagreement_score":0.46203652,"about_ca_system_score_codex":0.00020081684,"about_ca_system_score_gemma":0.000039935134,"threshold_uncertainty_score":0.9998646},"labels":[],"label_agreement":null},{"id":"W3010874397","doi":"10.5220/0009168005060513","title":"Defect Detection using Deep Learning from Minimal Annotations","year":2020,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence","score_opus":0.042309713242607405,"score_gpt":0.2295030740828518,"score_spread":0.1871933608402444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010874397","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":[0.7122907,0.00007226403,0.2850224,0.000017079303,0.00038554094,0.000085325504,0.0000014086819,0.0005296734,0.0015956193],"genre_scores_gemma":[0.9988091,0.000002862134,0.00067714404,0.00007469204,0.00038807685,0.000005125501,0.000004309454,0.000025872883,0.000012809603],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993782,0.000041787553,0.00019052903,0.00014504648,0.00011196794,0.00013243673],"domain_scores_gemma":[0.99974954,0.000054871143,0.000026729667,0.000059663453,0.000029294859,0.0000799262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060500606,0.00010091366,0.000121333294,0.00005362365,0.00012250063,0.000056224995,0.000035039215,0.0000920694,0.00015978013],"category_scores_gemma":[0.00007325951,0.00010211017,0.00008661733,0.0002754399,0.000005935334,0.00013923824,0.000010589345,0.0002146329,0.00015106887],"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":[0.000033643082,0.0000051048414,0.0008452576,0.000013912656,0.00007560964,0.0000058332703,0.0008371998,0.40550736,0.5432759,0.000010195526,0.00022301893,0.049166955],"study_design_scores_gemma":[0.00029871412,0.00007362688,0.00070082414,0.000008613495,0.000021992677,0.000004200385,0.0004607572,0.9487475,0.039024513,0.000008025515,0.010491786,0.00015944852],"about_ca_topic_score_codex":0.00017589527,"about_ca_topic_score_gemma":0.00003215975,"teacher_disagreement_score":0.54324013,"about_ca_system_score_codex":0.000045093497,"about_ca_system_score_gemma":0.0000050838316,"threshold_uncertainty_score":0.4163932},"labels":[],"label_agreement":null},{"id":"W3012176411","doi":"10.1038/s41524-020-00380-w","title":"Quantifying defects in thin films using machine vision","year":2020,"lang":"en","type":"preprint","venue":"npj Computational Materials","topic":"Industrial Vision Systems and Defect Detection","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":"Canadian Institute for Advanced Research; University of British Columbia","funders":"Natural Resources Canada; Canada Research Chairs; Canada First Research Excellence Fund; University of British Columbia; Western Canada Research Grid; Compute Canada","keywords":"Convolutional neural network; Computer science; Thin film; Personalization; Software; Artificial intelligence; Image processing; Sensitivity (control systems); Machine vision; Materials science; Computer vision; Image (mathematics); Nanotechnology; Electronic engineering; Engineering","score_opus":0.077918200655731,"score_gpt":0.3148924281072619,"score_spread":0.2369742274515309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012176411","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":[0.9677131,0.00027427747,0.02474465,0.000028334769,0.0057839393,0.00063087716,0.00022525355,0.00039330352,0.00020624802],"genre_scores_gemma":[0.99334294,0.000008564324,0.0057160365,0.000034989564,0.00051828683,0.000028127693,0.00026853915,0.000078614605,0.0000039080664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978921,0.00020816189,0.0008431472,0.00042211165,0.00039065978,0.000243818],"domain_scores_gemma":[0.99933827,0.00013422601,0.00019365393,0.00018482277,0.00007147523,0.00007753942],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052797626,0.00035510215,0.00064104993,0.0003040903,0.000074493095,0.00029700072,0.00016817162,0.0004610905,0.00020207811],"category_scores_gemma":[0.00007616232,0.00037181584,0.000108150336,0.00021089743,0.000015061926,0.0001175899,0.0001772594,0.00044364925,0.00010135712],"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":[0.000040993335,0.00001251307,0.000112782735,0.0004586982,0.000029307981,0.000020000978,0.00014627379,0.85789055,0.14072013,0.00016576886,0.00022328005,0.00017968057],"study_design_scores_gemma":[0.0009416509,0.00006294986,0.0032237405,0.001298714,0.00003522026,0.000031864158,0.00003689462,0.9435159,0.045133147,0.004660227,0.0003282152,0.0007314672],"about_ca_topic_score_codex":0.00037087238,"about_ca_topic_score_gemma":0.000017121482,"teacher_disagreement_score":0.095586985,"about_ca_system_score_codex":0.00017292447,"about_ca_system_score_gemma":0.000073753814,"threshold_uncertainty_score":0.9998734},"labels":[],"label_agreement":null},{"id":"W3013782022","doi":"10.18280/ts.370111","title":"A Deep Learning Model for Striae Identification in End Images of Float Glass","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Float glass; Convolutional neural network; Artificial intelligence; Float (project management); Computer science; Process (computing); Artificial neural network; Deep learning; Pattern recognition (psychology); Computer vision; Materials science; Engineering; Composite material","score_opus":0.03158263414958313,"score_gpt":0.23672378451552,"score_spread":0.2051411503659369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013782022","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":[0.2900877,0.000062962215,0.7090592,0.000034516393,0.00010768029,0.00039797334,0.000014785521,0.0000836996,0.00015150032],"genre_scores_gemma":[0.9993743,0.000005448938,0.00034872335,0.000008645763,0.00015072632,0.000055839046,0.000015803878,0.000017017292,0.000023554616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919015,0.000027122445,0.00038192284,0.00012788324,0.00014675656,0.00012618265],"domain_scores_gemma":[0.9997645,0.0000458643,0.000063457635,0.000048489925,0.00003802298,0.000039638493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027025715,0.000088858666,0.00015988864,0.00007541529,0.000030445994,0.000025340027,0.000058507136,0.000062528576,0.000045795838],"category_scores_gemma":[0.000032268832,0.000092482696,0.00006403492,0.00014403029,0.000007846896,0.00011347453,0.000007447976,0.00010259302,0.000005859809],"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":[0.000058429676,0.000012137318,0.00011562911,0.000086019056,0.000016456792,4.4544188e-7,0.0008190861,0.76229084,0.22143191,0.00007036592,0.00017926272,0.014919417],"study_design_scores_gemma":[0.0009106752,0.000081126986,0.00033927348,0.000021155825,0.000011293948,3.3299222e-7,0.00019574966,0.9509996,0.04697443,0.000036578444,0.0003446565,0.00008509598],"about_ca_topic_score_codex":0.000010604912,"about_ca_topic_score_gemma":0.0000067068204,"teacher_disagreement_score":0.7092865,"about_ca_system_score_codex":0.00004063477,"about_ca_system_score_gemma":0.0000093262925,"threshold_uncertainty_score":0.3771335},"labels":[],"label_agreement":null},{"id":"W3022019328","doi":"10.17613/0qq4-xp47","title":"A comparison of top-down and bottom-up approaches to recognizing component assemblies in image mining electronic circuits","year":2020,"lang":"en","type":"article","venue":"Humanities Commons CORE (Modern Language Association / Columbia University)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Top-down and bottom-up design; Component (thermodynamics); Computer science; Electronic circuit; Artificial intelligence; Data mining; Engineering; Electrical engineering; Physics; Software engineering","score_opus":0.10316138961652993,"score_gpt":0.23535241280059874,"score_spread":0.1321910231840688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022019328","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":[0.9910648,0.00015110854,0.0014335996,0.000046606365,0.0001261131,0.00034040192,0.000041070587,0.00018545604,0.0066108857],"genre_scores_gemma":[0.999249,0.000007173305,0.00007681042,0.000028097338,0.00006102854,0.0000037056736,0.000028707691,0.000024531157,0.00052096735],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9988326,0.00011940221,0.00030747382,0.00021448544,0.00022657328,0.00029949224],"domain_scores_gemma":[0.99938065,0.00019502733,0.00016487908,0.00012279737,0.000063738844,0.00007291524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021994121,0.00014305275,0.0004213749,0.00027688505,0.00016126531,0.00012895741,0.00014561995,0.00015002796,0.000031249194],"category_scores_gemma":[0.00009389767,0.00022272645,0.00006575036,0.0004561002,0.000020911912,0.00023357467,0.000078938574,0.0003329556,0.0000041713897],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031571605,0.00038928137,0.38353258,0.0010832024,0.0009873079,0.00015434246,0.43247974,0.0070001404,0.09558848,0.0032199915,0.010281055,0.06496814],"study_design_scores_gemma":[0.01285125,0.0021325408,0.110137016,0.0012292645,0.00063297956,0.000025162364,0.5343271,0.28509215,0.016087975,0.00028101096,0.03330162,0.0039019363],"about_ca_topic_score_codex":0.0008129962,"about_ca_topic_score_gemma":0.010622537,"teacher_disagreement_score":0.278092,"about_ca_system_score_codex":0.00054747576,"about_ca_system_score_gemma":0.00003468055,"threshold_uncertainty_score":0.9082521},"labels":[],"label_agreement":null},{"id":"W3033261402","doi":"10.1109/vts48691.2020.9107619","title":"Innovative Practice on Wafer Test Innovations","year":2020,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Powertech Labs (Canada)","funders":"","keywords":"Wafer; Randomness; Interposer; Upstream (networking); Test (biology); Computer science; Reliability engineering; Wafer testing; Engineering; Layer (electronics); Electrical engineering; Materials science; Mathematics; Telecommunications","score_opus":0.03745831262280773,"score_gpt":0.2588599241217111,"score_spread":0.22140161149890336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033261402","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0551298,0.000016810234,0.04953415,0.0057743834,0.001150675,0.0005070129,0.000019104842,0.001613413,0.88625467],"genre_scores_gemma":[0.9972086,0.0000012368934,0.00033612014,0.0018161183,0.00035506318,0.000011431813,0.0000036966314,0.000015861282,0.0002518823],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99949574,0.000012317865,0.00018607696,0.00009565861,0.00012000948,0.000090203626],"domain_scores_gemma":[0.9994763,0.00019041661,0.00002495903,0.000081786886,0.00019126205,0.00003528058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009707829,0.00007771004,0.00008310315,0.000064288724,0.000044458342,0.000033073266,0.00003806469,0.00006832829,0.00014327717],"category_scores_gemma":[0.00093834754,0.00006658799,0.000013751585,0.0012977677,0.0000055717533,0.00016328318,0.000008798293,0.00020403818,0.0006775217],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014385357,0.00017424196,0.0006793144,0.00008127122,0.00021178127,0.000037498037,0.002687712,0.019241316,0.11300596,0.07407689,0.71123236,0.07842779],"study_design_scores_gemma":[0.0005964726,0.00040454627,0.0005329084,0.0000196243,0.0000067204787,0.0000079469255,0.000792966,0.011589596,0.043716908,0.00003425183,0.94204324,0.00025480875],"about_ca_topic_score_codex":0.000009632031,"about_ca_topic_score_gemma":6.09329e-7,"teacher_disagreement_score":0.94207877,"about_ca_system_score_codex":0.000030932904,"about_ca_system_score_gemma":0.000012598355,"threshold_uncertainty_score":0.87083966},"labels":[],"label_agreement":null},{"id":"W3036411632","doi":"10.1007/978-3-030-50347-5_26","title":"Detecting Defects in Materials Using Deep Convolutional Neural Networks","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Discriminative model; Preprocessor; Mutual information; Convolutional neural network; Artificial intelligence; Entropy (arrow of time); Architecture; Pattern recognition (psychology); Filter (signal processing); Data mining; Machine learning; Computer vision","score_opus":0.02588918408818469,"score_gpt":0.2311149405400483,"score_spread":0.2052257564518636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036411632","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":[0.02309707,0.00039100155,0.97046614,0.00000943542,0.0052683526,0.0003509522,0.000003997549,0.00016410186,0.00024895396],"genre_scores_gemma":[0.9946175,0.0000049256705,0.0036411942,0.000102693586,0.0015713747,0.000004550698,0.000003602901,0.000050489623,0.0000036489262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979885,0.000038382557,0.00054842455,0.0005822338,0.00039249702,0.00044992377],"domain_scores_gemma":[0.99924856,0.00021681604,0.00012627046,0.00024992984,0.0000616069,0.00009680346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052070146,0.0003710156,0.0004960827,0.00048533044,0.00012858155,0.00021554295,0.0003333396,0.00046544272,0.000028146418],"category_scores_gemma":[0.00007589173,0.00037559285,0.00007988013,0.0004337776,0.00012074101,0.0001866652,0.0001684425,0.0008135549,0.000009821261],"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":[0.000008989187,0.0000015340869,0.00003630479,0.000032510383,0.000004720407,0.000043859956,0.00007457117,0.95428616,0.0034437426,0.00006822353,0.0000011604428,0.041998245],"study_design_scores_gemma":[0.00023555415,0.00005061448,0.0000690358,0.00030093038,0.000006145557,0.00007334922,2.7225363e-7,0.994206,0.003274426,0.0013522072,0.000053899337,0.0003775707],"about_ca_topic_score_codex":0.000047600053,"about_ca_topic_score_gemma":0.00009690161,"teacher_disagreement_score":0.9715204,"about_ca_system_score_codex":0.0004473172,"about_ca_system_score_gemma":0.00006905398,"threshold_uncertainty_score":0.9998696},"labels":[],"label_agreement":null},{"id":"W3037764089","doi":"10.1109/tnnls.2020.3001602","title":"A Wide-Deep-Sequence Model-Based Quality Prediction Method in Industrial Process Analysis","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Redundancy (engineering); Process (computing); Data mining; Quality (philosophy); Data-driven; Artificial intelligence; Industrial engineering; Machine learning; Engineering","score_opus":0.060822887611934814,"score_gpt":0.2935010786457989,"score_spread":0.23267819103386406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037764089","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":[0.17295411,0.000077847406,0.8254651,0.000041322357,0.0006750377,0.00036791028,0.000011403682,0.00036494192,0.000042347812],"genre_scores_gemma":[0.9994719,0.000010690164,0.00005344293,0.00004395448,0.0002552051,0.00009610057,0.000007012185,0.000034073895,0.000027633343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977705,0.00061484834,0.000651596,0.00039743196,0.00027874636,0.00028690128],"domain_scores_gemma":[0.9992119,0.00030804754,0.00011976034,0.00013728476,0.000054520864,0.00016848449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008031625,0.00024555853,0.000516614,0.00027745936,0.00021953577,0.00012980463,0.00008207226,0.00035801565,0.000008190664],"category_scores_gemma":[0.000030929215,0.00023659291,0.00015740914,0.0014699256,0.000021821816,0.00018781287,7.4444335e-7,0.0013568192,0.000001825592],"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":[0.00013087253,0.000012901861,0.00076515175,0.00005999085,0.00009079833,0.0000033037197,0.00026688748,0.9897477,0.00017506139,0.0000015460702,0.000012227992,0.008733562],"study_design_scores_gemma":[0.0007950414,0.0001710282,0.000102907215,0.00006509208,0.00012158241,0.00000320978,0.00027065296,0.998066,0.000114910596,7.4136625e-7,0.00007532229,0.00021351392],"about_ca_topic_score_codex":0.00025059583,"about_ca_topic_score_gemma":0.00004432453,"teacher_disagreement_score":0.82651776,"about_ca_system_score_codex":0.00007796594,"about_ca_system_score_gemma":0.000019257757,"threshold_uncertainty_score":0.9647979},"labels":[],"label_agreement":null},{"id":"W3037845764","doi":"10.1061/9780784483077.004","title":"Calibration of Availability and Safety of a Video-Based Detection System for Airport APMs","year":2020,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Computer science; Reliability (semiconductor); Sensitivity (control systems); Calibration; Real-time computing; Point (geometry); Train; Object detection; Artificial intelligence; Engineering; Pattern recognition (psychology)","score_opus":0.02086271086060346,"score_gpt":0.2102808105387605,"score_spread":0.18941809967815704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037845764","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":[0.30443084,0.000022029479,0.69419074,0.000016768374,0.00022060731,0.0005422294,0.00002191629,0.00015920853,0.00039567918],"genre_scores_gemma":[0.9995137,6.24996e-7,0.00037333727,0.000005743295,0.00006764459,0.000019921754,0.0000030463736,0.000010210791,0.000005759851],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993267,0.000024040753,0.00038996898,0.00010737976,0.00009061626,0.00006131243],"domain_scores_gemma":[0.99966335,0.000067248395,0.00007390961,0.00008778744,0.00006596863,0.00004174396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000248367,0.00006891235,0.00020635234,0.000037732818,0.00002445396,0.0000061666497,0.000021837363,0.00009838556,0.000009311054],"category_scores_gemma":[0.00006146239,0.0000613752,0.00005723017,0.00013182167,0.000012142187,0.000069842565,0.0000047019366,0.00004143978,5.993677e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012686078,0.000027178829,0.0021714612,0.005898026,0.000088392146,5.024606e-7,0.00035778788,0.051807586,0.9129534,0.00035029807,0.00039820347,0.024678553],"study_design_scores_gemma":[0.00061640533,0.00018680337,0.00029381382,0.000035205587,0.000013825574,9.099181e-7,0.00012267065,0.4013525,0.5968767,0.0000036119047,0.00044137824,0.000056225526],"about_ca_topic_score_codex":0.000065050524,"about_ca_topic_score_gemma":0.000015383433,"teacher_disagreement_score":0.6950829,"about_ca_system_score_codex":0.000031528816,"about_ca_system_score_gemma":0.00001400737,"threshold_uncertainty_score":0.2502808},"labels":[],"label_agreement":null},{"id":"W3039752361","doi":"10.1017/s1431927600028361","title":"Modified Flat-BED Scanner for Macroscopic Study of Large Specimens","year":2001,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Scanner; Magnification; Lens (geology); Depth of field; Distortion (music); Microscope; Optics; Computer vision; Field of view; Artificial intelligence; Resolution (logic); Computer science; Materials science; Computer graphics (images); Physics; Optoelectronics","score_opus":0.018026258105195633,"score_gpt":0.2769205106945649,"score_spread":0.25889425258936927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039752361","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":[0.9899149,0.0005150742,0.008343181,0.0000070776123,0.0002964096,0.0004963859,0.000053340398,0.00007136907,0.00030224174],"genre_scores_gemma":[0.9987937,0.000052989843,0.00020162995,0.000013317981,0.00011325549,0.00003479541,0.000013283653,0.000028907729,0.00074812165],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998883,0.000035394856,0.00043771617,0.00025654785,0.00010129438,0.0002860404],"domain_scores_gemma":[0.99948555,0.00003813675,0.0000763814,0.00026050326,0.000074314485,0.00006508626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027639934,0.00018740902,0.0004560568,0.00023879937,0.00014932583,0.000050187162,0.00008524334,0.00012596454,0.00005578121],"category_scores_gemma":[0.0000117727695,0.00017329655,0.000132589,0.00041641356,0.000019471243,0.00007229688,0.000025820123,0.00009998665,0.0000073895053],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012358597,0.00013693028,0.0029349187,0.00006440112,0.00045602297,0.000003950201,0.0009776152,0.0010579381,0.99121886,0.00001761025,0.0013681368,0.0016400489],"study_design_scores_gemma":[0.010017609,0.00091510586,0.0029795086,0.000102998696,0.001131747,0.000025592002,0.0046868795,0.013842672,0.9319434,0.00006358294,0.03343034,0.0008605718],"about_ca_topic_score_codex":0.00018411702,"about_ca_topic_score_gemma":0.000268775,"teacher_disagreement_score":0.059275452,"about_ca_system_score_codex":0.000038934755,"about_ca_system_score_gemma":0.000008122317,"threshold_uncertainty_score":0.70668286},"labels":[],"label_agreement":null},{"id":"W3042176045","doi":"","title":"A Data Collection System for Future Automation Applications","year":2020,"lang":"en","type":"article","venue":"The Atrium (University of Guelph)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Data collection; Automation; Computer science; Engineering; Mathematics","score_opus":0.032951903873220466,"score_gpt":0.21866123378120453,"score_spread":0.18570932990798406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042176045","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":[0.040182076,0.00020118117,0.9534674,0.0014807975,0.0006934146,0.0014559549,0.00028746043,0.00072652544,0.0015051876],"genre_scores_gemma":[0.99880594,0.000010880888,0.00060503365,0.000006867016,0.0004528114,0.0000018918679,0.000054074702,0.000008325915,0.000054188862],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996212,0.000024405637,0.00008764274,0.000108788336,0.000088353845,0.000069593276],"domain_scores_gemma":[0.9995484,0.000037124795,0.000059930673,0.00026239065,0.000058289013,0.00003386002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001593766,0.00005538468,0.00010714138,0.000038848,0.00021346762,0.000011017212,0.0002559794,0.00007664545,0.000009058391],"category_scores_gemma":[0.000007151147,0.00005399658,0.000047752652,0.00035175934,0.00001319975,0.00013736778,0.000042918797,0.0000685587,0.000027319224],"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":[0.00085068675,0.000060101447,0.00012906558,0.0032879037,0.000708617,0.000004562748,0.010348034,0.02981996,0.530462,0.0073867086,0.33433723,0.08260509],"study_design_scores_gemma":[0.0010206023,0.00009796195,0.0018509541,0.000027201113,0.00014772631,0.000008515234,0.01458308,0.62390023,0.00087310874,0.00002122383,0.35730708,0.00016234635],"about_ca_topic_score_codex":0.000051359933,"about_ca_topic_score_gemma":0.000018647726,"teacher_disagreement_score":0.9586238,"about_ca_system_score_codex":0.00004896358,"about_ca_system_score_gemma":0.000016839002,"threshold_uncertainty_score":0.22019166},"labels":[],"label_agreement":null},{"id":"W3042308382","doi":"10.1097/der.0000000000000628","title":"Patch Testing Interrupted: Virtual Patch Test Readings during the COVID-19 Pandemic","year":2020,"lang":"en","type":"article","venue":"Dermatitis","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Coronavirus disease 2019 (COVID-19); Patch testing; Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Patch test; Test (biology); Virology; Internal medicine; Immunology; Contact dermatitis; Geology; Allergy","score_opus":0.05705027670715116,"score_gpt":0.25292081725734616,"score_spread":0.195870540550195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042308382","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":[0.98031914,0.00009439438,0.012179567,0.0012002239,0.00075485394,0.0003886631,0.000059781887,0.001477368,0.0035260336],"genre_scores_gemma":[0.99708736,0.000015872793,0.000047155914,0.0020752547,0.000625785,0.00003555129,0.000005902233,0.000038669794,0.000068460315],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99892414,0.000055643948,0.000381281,0.00018115703,0.00020434824,0.00025342597],"domain_scores_gemma":[0.999063,0.0004609873,0.000071252645,0.00016612619,0.00003931713,0.00019933081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018680654,0.00017149816,0.00019605663,0.00005221612,0.00024231884,0.0001228896,0.0001687425,0.00013046309,0.00011724145],"category_scores_gemma":[0.0011759638,0.00014068729,0.000063743864,0.0004175727,0.000026464446,0.00016600266,0.000055997225,0.0003729721,0.00022461009],"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":[0.00001889028,0.00003075534,0.6587276,0.0008977394,0.00016280799,0.00028578375,0.02650265,0.018557465,0.04770993,0.0000622912,0.19705698,0.049987126],"study_design_scores_gemma":[0.0048075323,0.00066929345,0.06324198,0.0005833793,0.00011643179,0.0027081147,0.009385757,0.30379874,0.025728425,0.00014138164,0.58656913,0.0022498597],"about_ca_topic_score_codex":0.0007601763,"about_ca_topic_score_gemma":0.00007491754,"teacher_disagreement_score":0.5954856,"about_ca_system_score_codex":0.00014891439,"about_ca_system_score_gemma":0.000025402636,"threshold_uncertainty_score":0.57370615},"labels":[],"label_agreement":null},{"id":"W3045318356","doi":"10.23977/cpcs.2020.41002","title":"Study on the Collaborative Acceleration Method Between CPU and GPU in Image Processing of Mask Detectio","year":2020,"lang":"en","type":"article","venue":"Computing Performance and Communication systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Software portability; CUDA; Coprocessor; Image processing; Digital signal processing; Field-programmable gate array; Digital image processing; Parallel processing; Parallel computing; Central processing unit; Computer hardware; Embedded system; Image (mathematics); Artificial intelligence; Operating system","score_opus":0.06246547221489107,"score_gpt":0.30097750935355067,"score_spread":0.2385120371386596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045318356","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":[0.9902035,0.0003836905,0.008071085,0.00006439254,0.000050973606,0.00060792844,0.0000015978948,0.000062675084,0.0005541313],"genre_scores_gemma":[0.99964535,0.000037282003,0.00019629531,0.0000110188785,0.00007039075,0.000024079272,0.0000015427515,0.000011377589,0.0000026364046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897265,0.00030607756,0.00039254373,0.00012023961,0.00012212459,0.00008637429],"domain_scores_gemma":[0.99934196,0.00021899636,0.00013694487,0.000192933,0.00008179313,0.000027341035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094195054,0.00010341751,0.0002267135,0.00007219185,0.00021262158,0.00011460282,0.000116259755,0.000058028603,4.5159504e-7],"category_scores_gemma":[0.00003200466,0.00008036171,0.000011202074,0.00044439101,0.000023542094,0.0001605787,0.00005554896,0.00023052396,0.0000016605994],"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":[0.0002342603,0.00014766885,0.25597176,0.0015656814,0.00023811228,0.0000020671328,0.1963758,0.12314137,0.01480429,0.00034775698,0.0003651293,0.4068061],"study_design_scores_gemma":[0.0008924784,0.00042992423,0.06653174,0.00048205903,0.000019234529,0.0000046596488,0.025583733,0.90167123,0.0037268307,0.000004139971,0.00044925947,0.00020470095],"about_ca_topic_score_codex":0.000029282064,"about_ca_topic_score_gemma":0.000003517983,"teacher_disagreement_score":0.7785299,"about_ca_system_score_codex":0.0000241169,"about_ca_system_score_gemma":0.000012170025,"threshold_uncertainty_score":0.32770553},"labels":[],"label_agreement":null},{"id":"W3047479023","doi":"10.1186/s10086-020-01904-0","title":"Application of decision tree-based techniques to veneer processing","year":2020,"lang":"en","type":"article","venue":"Journal of Wood Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Centre for Advancing Health Outcomes; University of British Columbia","funders":"Mitacs","keywords":"Veneer; Tree (set theory); Range (aeronautics); Process (computing); Computer science; Process engineering; Materials science; Mathematics; Engineering; Composite material","score_opus":0.02019480122141451,"score_gpt":0.2730221091938468,"score_spread":0.25282730797243225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047479023","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":[0.4259329,0.00004135122,0.5733997,0.00009717289,0.000120042256,0.00008776327,4.605719e-7,0.000028712539,0.0002918963],"genre_scores_gemma":[0.9903224,0.0000012076907,0.009477085,0.000046394947,0.00014522336,0.0000016420588,2.8529863e-8,0.000005169267,8.4372994e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991446,0.000006871155,0.00030570262,0.00006853074,0.00039385093,0.00008041337],"domain_scores_gemma":[0.9994601,0.000020542506,0.00012366226,0.000064962675,0.00022872098,0.00010201693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056806975,0.000047498397,0.00012169539,0.0001852029,0.000040255,0.000032478805,0.00017287406,0.00003384201,0.0000017400812],"category_scores_gemma":[0.00014842289,0.000037728663,0.000036754573,0.0009280731,0.00002145032,0.00024042687,0.000010330273,0.00009275468,0.0000028861573],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025418352,0.0000057773723,0.000053433134,0.000016022259,9.225125e-7,0.0000010123301,0.00010943593,0.013645632,0.69639707,0.00000503369,0.00012517085,0.2896151],"study_design_scores_gemma":[0.00016399604,0.00030827086,0.00027229654,0.00015077303,0.0000049854402,0.000009542371,0.000050307215,0.052912373,0.9411849,0.000021987309,0.0048571755,0.0000634187],"about_ca_topic_score_codex":0.0000017142996,"about_ca_topic_score_gemma":3.775532e-7,"teacher_disagreement_score":0.5643895,"about_ca_system_score_codex":0.000051438426,"about_ca_system_score_gemma":0.00008059623,"threshold_uncertainty_score":0.15385301},"labels":[],"label_agreement":null},{"id":"W3081106937","doi":"10.1117/12.2570605","title":"Evaluation of deep learning-generated lens design starting points","year":2020,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Lens (geology); Computer science; Deep learning; Artificial neural network; Artificial intelligence; Quality (philosophy); Field (mathematics); Through-the-lens metering; Machine learning; Computer engineering; Engineering; Mathematics","score_opus":0.11469273990973103,"score_gpt":0.26871213366570224,"score_spread":0.1540193937559712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081106937","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":[0.6198601,0.00014368538,0.37132686,0.000051032017,0.00032512084,0.00037478597,7.130985e-7,0.0004151303,0.0075025493],"genre_scores_gemma":[0.9992434,0.0000025177374,0.00057336385,0.000021299737,0.0001222298,0.000006831471,0.000001923081,0.000015133022,0.000013344953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901754,0.00021578766,0.00024074041,0.000089695655,0.00033970637,0.00009654996],"domain_scores_gemma":[0.99961525,0.000036295183,0.000042976168,0.000053598367,0.0002147051,0.00003717517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012023532,0.00006914835,0.0001156763,0.000039484265,0.000034629957,0.000014466394,0.00002937773,0.00007025585,0.00032839234],"category_scores_gemma":[0.0003738307,0.00006401227,0.00003158413,0.00022393257,0.000004077772,0.000084585845,0.0000065544914,0.000107547006,0.0000662023],"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":[0.000010888465,0.000002890831,0.00006934971,0.00001131795,0.000028364508,3.1967775e-7,0.0003670078,0.8481311,0.11310481,0.00001237916,0.00045336154,0.037808184],"study_design_scores_gemma":[0.00034799665,0.00008357804,0.000059360973,0.000006485818,0.00002429052,4.0233047e-7,0.00013761318,0.81424576,0.18471497,0.000015931753,0.00030216877,0.00006141371],"about_ca_topic_score_codex":0.000014561132,"about_ca_topic_score_gemma":0.0000028508375,"teacher_disagreement_score":0.37938324,"about_ca_system_score_codex":0.000040368046,"about_ca_system_score_gemma":0.000015328678,"threshold_uncertainty_score":0.3595666},"labels":[],"label_agreement":null},{"id":"W3086969873","doi":"10.5281/zenodo.3764633","title":"sslf: A simple spectral-line finder","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","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 British Columbia","funders":"","keywords":"Simple (philosophy); Line (geometry); Computer science; Mathematics; Philosophy; Geometry","score_opus":0.05648245703623314,"score_gpt":0.2354308709334243,"score_spread":0.17894841389719116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086969873","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33136198,0.0003482068,0.1461822,0.0025838264,0.001082844,0.0019456209,0.0005463607,0.01381015,0.5021388],"genre_scores_gemma":[0.99797124,0.000018103818,0.000057767975,0.00012899793,0.0005337525,2.2512653e-8,0.00024339106,0.00088876847,0.00015797853],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99898547,0.00009286279,0.00021603734,0.00022171889,0.00022757503,0.00025634604],"domain_scores_gemma":[0.99942887,0.000011292563,0.00003252173,0.00020597062,0.0001398394,0.00018152503],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00022866802,0.00011526368,0.00013197119,0.00009331426,0.0006439123,0.000357942,0.00031589088,0.000075913595,0.009339562],"category_scores_gemma":[0.00023680032,0.00012061747,0.000052081923,0.0004948693,0.000027340084,0.00014959631,0.00018731235,0.00026864588,0.009353359],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009889202,0.000047731595,0.0000032675002,0.00014059451,0.00008729037,0.000031761734,0.0017929936,0.009586345,0.04790305,0.0013927568,0.7912833,0.147632],"study_design_scores_gemma":[0.00043037624,0.00019092836,0.00007699475,0.000009320045,0.0000063885163,0.000035207428,0.00015180932,0.011346981,0.0038711308,0.000053766355,0.9836814,0.00014573043],"about_ca_topic_score_codex":0.000007093512,"about_ca_topic_score_gemma":1.2410878e-7,"teacher_disagreement_score":0.6666092,"about_ca_system_score_codex":0.000075979755,"about_ca_system_score_gemma":0.0000013243549,"threshold_uncertainty_score":0.99156606},"labels":[],"label_agreement":null},{"id":"W3089223520","doi":"10.1002/sdtp.14267","title":"P‐130: A Full Screen Biometric Identification Approach for OLED Displays by Using Near‐Infrared OLED","year":2020,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Subpixel rendering; OLED; Thin-film transistor; Pixel; Computer science; Optoelectronics; Biometrics; Materials science; Responsivity; Layer (electronics); Computer vision; Photodetector; Nanotechnology","score_opus":0.02187476161149325,"score_gpt":0.23836507836326354,"score_spread":0.21649031675177027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089223520","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":[0.6855888,0.0015838677,0.09461939,0.00076940336,0.0021913003,0.009930334,0.0013928789,0.004386715,0.1995373],"genre_scores_gemma":[0.9987128,0.000016138089,0.0007450451,0.000032157677,0.0001680434,0.000086478685,0.000118474745,0.000067074616,0.000053789627],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99813884,0.000042920197,0.0007303831,0.00040095576,0.00036198666,0.0003248911],"domain_scores_gemma":[0.99909854,0.00011116505,0.0001824872,0.00033185596,0.000087355824,0.00018862139],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031712058,0.0002533775,0.00045902122,0.00019306701,0.00014095992,0.00009989981,0.00027320438,0.00038198542,0.000012256706],"category_scores_gemma":[0.0002001628,0.0002464717,0.00025531673,0.0013210721,0.00007549804,0.00016150578,0.000048422862,0.00023041962,0.000008798973],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011795906,0.00004341013,0.00006640381,0.00015582738,0.000045667064,5.6526557e-7,0.000037836155,0.008406258,0.9896812,0.000023662828,0.0011404613,0.0002807907],"study_design_scores_gemma":[0.006300474,0.0017415329,0.0016364822,0.00025868707,0.0005093794,0.000041258703,0.00039819567,0.16287038,0.7974738,0.000019142926,0.026743663,0.002006996],"about_ca_topic_score_codex":0.0000332715,"about_ca_topic_score_gemma":0.0000010283172,"teacher_disagreement_score":0.313124,"about_ca_system_score_codex":0.00014071102,"about_ca_system_score_gemma":0.000031031144,"threshold_uncertainty_score":0.99999875},"labels":[],"label_agreement":null},{"id":"W3091468429","doi":"10.1016/j.procs.2020.09.033","title":"Automatic Anode Rod Inspection in Aluminum Smelters using Deep-Learning Techniques: A Case Study","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Bell (Canada)","funders":"","keywords":"Anode; Computer science; Convolutional neural network; Deep learning; Artificial intelligence; Task (project management); Process (computing); Machine learning","score_opus":0.028578779481107822,"score_gpt":0.26057816769458236,"score_spread":0.23199938821347454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091468429","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":[0.8273156,0.000014284651,0.17086239,0.0000081477965,0.0004536154,0.00043616325,1.4088388e-7,0.0008718233,0.000037808797],"genre_scores_gemma":[0.99020916,7.3497614e-7,0.009440946,0.000028566745,0.0002799528,0.000023683695,1.3028234e-7,0.000016357586,4.3603816e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987524,0.00004250379,0.0003060412,0.00034382968,0.0002928692,0.00026232604],"domain_scores_gemma":[0.99962556,0.000025308225,0.000055283934,0.00011817125,0.00006460145,0.00011106551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055561546,0.00013888787,0.0001805687,0.00030268606,0.00019891809,0.00018025254,0.00016635518,0.00005466797,0.000002734374],"category_scores_gemma":[0.00006295324,0.00013924703,0.000026471145,0.0017360281,0.000043058866,0.0004995225,0.00008493294,0.0002822942,0.000008128895],"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":[0.00002118612,0.00020879478,0.029674113,0.00041733732,0.000032424192,0.0022929662,0.07398975,0.27599096,0.04545125,0.000012618863,0.00006897924,0.57183963],"study_design_scores_gemma":[0.00023146473,0.00022647265,0.0004582061,0.00004665831,0.0000046756923,0.00057000754,0.0012291402,0.9931967,0.0038449457,0.00000250287,0.000029940542,0.00015924106],"about_ca_topic_score_codex":0.00013935173,"about_ca_topic_score_gemma":0.000025202326,"teacher_disagreement_score":0.7172058,"about_ca_system_score_codex":0.00019837133,"about_ca_system_score_gemma":0.00005260712,"threshold_uncertainty_score":0.5678329},"labels":[],"label_agreement":null},{"id":"W3092018880","doi":"","title":"Facelift for National Office","year":2005,"lang":"en","type":"article","venue":"Journal of the Royal Astronomical Society of Canada","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.008143810883324856,"score_gpt":0.19051920883245702,"score_spread":0.18237539794913216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092018880","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":[0.99395066,0.000099116376,0.0033432075,0.00090857525,0.0010985566,0.000103651546,0.000034250414,0.0000055728187,0.00045641552],"genre_scores_gemma":[0.99718374,1.8412845e-7,0.001549187,0.00004246834,0.0008068932,9.5295735e-7,2.4970925e-7,0.000007076961,0.00040921947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928933,0.000010497552,0.00031658288,0.00003336481,0.00024427136,0.00010594996],"domain_scores_gemma":[0.9995833,0.000071110306,0.00013842229,0.00004182439,0.000113011294,0.00005235591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025732338,0.000057460144,0.00013927366,0.000007562216,0.000051640898,0.000008242252,0.00011928669,0.000049027643,0.000040112045],"category_scores_gemma":[0.000024544557,0.000041024814,0.0002516664,0.000030963754,0.000012435489,0.000033526827,0.000010406735,0.00014825306,3.8758358e-7],"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":[0.00001930715,0.0000049605824,0.00009556209,0.000008370103,0.000059461214,7.373874e-9,0.000016126467,0.6671442,0.00035035852,0.000011090122,0.3308943,0.0013963167],"study_design_scores_gemma":[0.0010153493,0.000061013827,0.005841575,0.000036615093,0.000019826184,0.0000019666663,0.00012901353,0.16735819,0.009517615,0.0000149268235,0.81589854,0.000105367464],"about_ca_topic_score_codex":0.001641863,"about_ca_topic_score_gemma":0.0049196803,"teacher_disagreement_score":0.49978596,"about_ca_system_score_codex":0.000416184,"about_ca_system_score_gemma":0.00032779964,"threshold_uncertainty_score":0.27452973},"labels":[],"label_agreement":null},{"id":"W3092373601","doi":"10.1109/3dv50981.2020.00126","title":"Deep SVBRDF Estimation on Real Materials","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Université Laval","funders":"Scan|Design Foundation by Inger and Jens Bruun","keywords":"Computer science; Code (set theory); Deep learning; Artificial intelligence; Synthetic data; Machine learning; Data mining; Network architecture","score_opus":0.034623200651416816,"score_gpt":0.25893086125250103,"score_spread":0.2243076606010842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092373601","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":[0.6442559,0.00004793273,0.18003881,0.00019052651,0.022406938,0.0018102569,0.00008197772,0.004778333,0.14638935],"genre_scores_gemma":[0.9978871,0.000023676404,0.0007399125,0.000028866367,0.0009722942,0.00005679421,0.00008398519,0.000050571234,0.00015680886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998993,0.00004418668,0.00037593555,0.00024897125,0.00019718084,0.00014074243],"domain_scores_gemma":[0.9995367,0.000030097017,0.00006495,0.00027321948,0.000027050968,0.00006799834],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015399736,0.00022717833,0.00032867788,0.000100097124,0.000034606088,0.0001390518,0.00009790898,0.0004788833,0.0003964111],"category_scores_gemma":[0.000034598237,0.00020763566,0.000075824435,0.00007180794,0.000005188311,0.00003772756,0.00007225986,0.00033346118,0.0008539471],"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":[0.00015863455,0.000023782577,0.000009936347,0.0009499662,0.00018979127,0.000027898926,0.00038014207,0.88659114,0.045504928,0.0032217426,0.02307391,0.039868157],"study_design_scores_gemma":[0.0006306953,0.000256453,0.00039757494,0.00044730084,0.00006285061,0.000009751584,0.00005355407,0.77812016,0.21271248,0.0020164575,0.0043428335,0.00094988],"about_ca_topic_score_codex":0.00019274812,"about_ca_topic_score_gemma":0.000006020778,"teacher_disagreement_score":0.3536312,"about_ca_system_score_codex":0.0001188666,"about_ca_system_score_gemma":0.00001530528,"threshold_uncertainty_score":0.999924},"labels":[],"label_agreement":null},{"id":"W3098186591","doi":"10.1007/s00170-020-06338-y","title":"Dimension reduction and 2D-visualization for early change of state detection in a machining process with a variational autoencoder approach","year":2020,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Autoencoder; Machining; Process (computing); Dimension (graph theory); Dimensionality reduction; Principal component analysis; Visualization; Reduction (mathematics); Computer science; Artificial intelligence; Pattern recognition (psychology); Engineering; Mechanical engineering; Artificial neural network; Mathematics","score_opus":0.0187409859785668,"score_gpt":0.2546833140718278,"score_spread":0.23594232809326102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098186591","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":[0.79525995,0.000051590137,0.20396823,0.0001977357,0.000231808,0.00023300498,0.000003013792,0.000044237,0.000010418524],"genre_scores_gemma":[0.99684286,0.000028349754,0.0029363907,0.000012100126,0.00013159218,0.000029572102,0.0000017103458,0.000015885495,0.0000015321385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924475,0.000016216556,0.00034116593,0.00010058969,0.00020929749,0.000087967106],"domain_scores_gemma":[0.9994504,0.000032815715,0.00028037978,0.00004751994,0.00016848078,0.000020399537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018429144,0.00009070096,0.00016141255,0.00032279576,0.00003352177,0.000016767715,0.00012479747,0.0000741168,0.0000010054143],"category_scores_gemma":[0.000049268547,0.00006689556,0.000025575631,0.00015264306,0.000026759091,0.00027443297,0.000019602257,0.00020331239,1.3142358e-7],"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":[0.0019414511,0.00004851186,0.00044060496,0.00014106871,0.00019769922,0.0000061342334,0.004721103,0.77285516,0.08717717,0.00038173865,0.00000528653,0.13208407],"study_design_scores_gemma":[0.003146324,0.0009798792,0.0043451516,0.00026322991,0.00003633614,0.00034990103,0.0012656474,0.22004776,0.7651787,0.004016446,0.00016513724,0.0002055443],"about_ca_topic_score_codex":0.000009599574,"about_ca_topic_score_gemma":0.0000039337006,"teacher_disagreement_score":0.67800146,"about_ca_system_score_codex":0.00006575946,"about_ca_system_score_gemma":0.000011587463,"threshold_uncertainty_score":0.2727922},"labels":[],"label_agreement":null},{"id":"W3100010788","doi":"10.5267/j.dsl.2020.10.005","title":"Monitoring image-based processes using a PCA-based control chart and a classification technique","year":2020,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Support vector machine; Control chart; Process (computing); Linear discriminant analysis; Computer science; Quadratic classifier; Statistical process control; Artificial intelligence; Data mining; Machine learning; Pattern recognition (psychology); Discriminant; Image processing; Chart; Control (management); Image (mathematics); Mathematics; Statistics","score_opus":0.0536185091060022,"score_gpt":0.2874532635020164,"score_spread":0.2338347543960142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3100010788","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":[0.41988474,0.000021718231,0.5789866,0.0004351905,0.00020557735,0.00027858836,0.0000024969181,0.00016390168,0.00002117941],"genre_scores_gemma":[0.98785263,0.0000014421443,0.011352864,0.0005543729,0.00017475654,0.000045843364,3.194479e-7,0.00001752484,2.4782753e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985946,0.000025982663,0.00028106978,0.00034031086,0.0005220006,0.00023606433],"domain_scores_gemma":[0.9993148,0.00016686956,0.00006949507,0.00016694982,0.00012934572,0.0001525451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061719544,0.00014004495,0.00017037285,0.00026714065,0.00022495462,0.0002775889,0.00018059864,0.0000708962,0.000004914113],"category_scores_gemma":[0.0004898673,0.00012584096,0.00003465637,0.0013117134,0.00012768868,0.00042735858,0.000011271425,0.00016031704,0.000014778258],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027630338,0.0000033174322,0.0007762092,0.000029945006,0.0000013110739,0.000003869256,0.000047613426,0.008107654,0.98635375,0.0000012405841,0.0000931813,0.0045542736],"study_design_scores_gemma":[0.0007324593,0.000049012022,0.0017086025,0.00019259646,0.000008296539,0.000005995756,0.0000642713,0.44299966,0.55349255,0.00000558441,0.0005413413,0.00019965175],"about_ca_topic_score_codex":0.0000056766653,"about_ca_topic_score_gemma":2.0426755e-7,"teacher_disagreement_score":0.5679679,"about_ca_system_score_codex":0.00010089284,"about_ca_system_score_gemma":0.000084393745,"threshold_uncertainty_score":0.5131646},"labels":[],"label_agreement":null},{"id":"W3101971338","doi":"10.1109/iemcon51383.2020.9284921","title":"Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"National Research Council Canada; Western University","funders":"Government of Canada","keywords":"Convolutional neural network; Anomaly detection; Artificial intelligence; Computer science; Residual; Metric (unit); Anomaly (physics); Pattern recognition (psychology); Task (project management); Similarity (geometry); Artificial neural network; Deep learning; Surface (topology); Machine learning; Image (mathematics); Algorithm; Engineering; Mathematics","score_opus":0.09690244408331108,"score_gpt":0.2638881365110323,"score_spread":0.16698569242772124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101971338","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":[0.12499425,0.00018627875,0.8617862,0.000023277444,0.010185229,0.0015537259,0.000081990765,0.00084995787,0.00033913038],"genre_scores_gemma":[0.9950716,0.000006607803,0.00088920136,0.00002257791,0.0036552758,0.00013431987,0.00007192403,0.00010561619,0.00004287433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981283,0.000084020954,0.0006855368,0.0005014971,0.00023661017,0.00036402716],"domain_scores_gemma":[0.9990872,0.00015028314,0.00019589548,0.00033151355,0.00009947197,0.0001356388],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003993267,0.00043057368,0.0006394592,0.00016332696,0.00014997249,0.00024874162,0.00017083655,0.0013170165,0.000036091435],"category_scores_gemma":[0.000097673714,0.00043049018,0.00035196042,0.00031314587,0.000019595243,0.00008260075,0.00006383164,0.0009513905,0.0000052885184],"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":[0.00023844553,0.000007621218,0.0001013282,0.00014381242,0.00008590015,0.0000019003668,0.000014127844,0.9871749,0.0022330594,0.000010259248,0.0013385011,0.008650101],"study_design_scores_gemma":[0.0014081736,0.000104933286,0.000026291431,0.00015835474,0.000085482454,0.0000015919325,0.00002829733,0.96997076,0.013679011,0.000052488747,0.014008892,0.00047572848],"about_ca_topic_score_codex":0.00034359816,"about_ca_topic_score_gemma":0.00010850472,"teacher_disagreement_score":0.8700774,"about_ca_system_score_codex":0.00035693264,"about_ca_system_score_gemma":0.00010461985,"threshold_uncertainty_score":0.9999795},"labels":[],"label_agreement":null},{"id":"W3105475673","doi":"10.1007/s001700200104","title":"An Automated Inspection System","year":2002,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; University of Alberta","funders":"","keywords":"CAD; Point cloud; Range (aeronautics); Automated optical inspection; Coordinate-measuring machine; Computer science; Cloud computing; Engineering drawing; Automated X-ray inspection; Engineering; Computer vision; Artificial intelligence; Computer graphics (images); Image processing; Mechanical engineering","score_opus":0.010089985654725445,"score_gpt":0.24343203582232573,"score_spread":0.2333420501676003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105475673","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":[0.98756224,0.00017500209,0.0038748304,0.00027794114,0.0038660022,0.00007868981,0.0000019625395,0.003440801,0.000722558],"genre_scores_gemma":[0.9989637,0.00005964599,0.00053756975,0.000012751102,0.00039213873,0.0000038507096,4.074387e-7,0.000018192322,0.000011738232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991494,0.000019209512,0.0003750415,0.000075820266,0.000258578,0.00012194991],"domain_scores_gemma":[0.99944955,0.00002719712,0.00018626753,0.00017529265,0.00013124534,0.00003045781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016122368,0.00010216323,0.00014890601,0.0004327732,0.00007116357,0.000043108554,0.00048818148,0.00012213615,0.000012376899],"category_scores_gemma":[0.000025137006,0.00007336486,0.00005797156,0.00011206697,0.00003133495,0.00025713732,0.000023310158,0.00033793243,0.000029437291],"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":[0.00014086337,0.00008358699,0.00007393478,0.000034103035,0.00045830492,0.00023620848,0.00038082592,0.52797675,0.27677992,0.0019773084,0.007867127,0.18399107],"study_design_scores_gemma":[0.0009613723,0.00025439408,0.0002856563,0.00014079742,0.000019133142,0.002563771,0.0009298272,0.15084685,0.8315853,0.00030483905,0.0119385775,0.00016947357],"about_ca_topic_score_codex":0.0000042444512,"about_ca_topic_score_gemma":0.0000027698743,"teacher_disagreement_score":0.5548054,"about_ca_system_score_codex":0.00026556168,"about_ca_system_score_gemma":0.0000033593146,"threshold_uncertainty_score":0.29917324},"labels":[],"label_agreement":null},{"id":"W3106104831","doi":"","title":"Describing the Fracture Process Using Surface Roughness Parameters","year":2020,"lang":"en","type":"article","venue":"54th U.S. Rock Mechanics/Geomechanics Symposium","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Process (computing); Surface roughness; Fracture (geology); Computer science; Geology; Materials science; Composite material; Geotechnical engineering","score_opus":0.04820775102299901,"score_gpt":0.23589310336876287,"score_spread":0.18768535234576386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106104831","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":[0.10028495,0.00046089722,0.89124346,0.0007814544,0.004094165,0.0014625891,0.00006784551,0.0013529209,0.00025171755],"genre_scores_gemma":[0.9977811,0.00006675462,0.00029009135,0.00095672935,0.0006169028,0.000041490086,0.000018213677,0.00021287675,0.00001583968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99667287,0.00016237001,0.00079911656,0.00071981066,0.00074423646,0.0009016222],"domain_scores_gemma":[0.99829274,0.0001309945,0.00025304483,0.00064218906,0.00023615203,0.00044488406],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005863753,0.0006660067,0.00065379706,0.000111980764,0.00062763505,0.0003184516,0.00071543024,0.0006117347,0.0000769023],"category_scores_gemma":[0.00013874876,0.0005508552,0.00028364707,0.0012265769,0.0000054458883,0.00049821235,0.00016142259,0.0010837857,0.0002609991],"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":[0.000054186807,0.000017560258,0.0000028160007,0.00012595822,0.000097565484,0.000014531124,0.0012368688,0.8946963,0.10249143,0.00041907423,0.0006369601,0.00020677657],"study_design_scores_gemma":[0.00061108306,0.00016482224,1.453996e-7,0.00014683648,0.00011529503,0.000049810402,0.0014278888,0.88657886,0.105564654,0.0006118569,0.004064442,0.00066428474],"about_ca_topic_score_codex":0.000059847982,"about_ca_topic_score_gemma":0.0000080048185,"teacher_disagreement_score":0.89749616,"about_ca_system_score_codex":0.000260265,"about_ca_system_score_gemma":0.00012789674,"threshold_uncertainty_score":0.9996943},"labels":[],"label_agreement":null},{"id":"W3107850185","doi":"10.1109/tsm.2021.3062943","title":"A Graph-Theoretic Approach for Spatial Filtering and Its Impact on Mixed-Type Spatial Pattern Recognition in Wafer Bin Maps","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Semiconductor Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Pattern recognition (psychology); Spatial filter; Bin; Metric (unit); Spatial analysis; Filter (signal processing); Common spatial pattern; Wafer; Pattern matching","score_opus":0.029727616802533646,"score_gpt":0.24600464012126463,"score_spread":0.216277023318731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107850185","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":[0.827054,0.000040323455,0.17024122,0.000007490981,0.0017841452,0.00047766033,0.00016991574,0.00015408106,0.000071145834],"genre_scores_gemma":[0.99928766,0.000051653064,0.00012205127,0.00002029152,0.00024867873,0.00010706229,0.00005386946,0.00007809841,0.00003063833],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99848646,0.00009220383,0.0004074525,0.0004466374,0.00018994435,0.00037729062],"domain_scores_gemma":[0.99942255,0.00013742087,0.000055465774,0.0002194237,0.000051655596,0.000113512644],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002015463,0.00034375887,0.00035826614,0.0004238536,0.00013062607,0.00009527941,0.00007051726,0.00027343733,0.00012957743],"category_scores_gemma":[0.000010422474,0.00033062682,0.00017773906,0.00017445418,0.000016498116,0.00019520333,0.0000022208926,0.0004939829,0.000019522598],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007086402,0.000247258,0.000028827595,0.00071514276,0.00028935706,0.000041609506,0.0009101324,0.25475672,0.44936508,0.0000019539425,0.000100649086,0.29283464],"study_design_scores_gemma":[0.0013965425,0.00023673604,0.00024165088,0.0001988102,0.000042691496,0.00005961063,0.00014876216,0.026867647,0.9702916,0.00006279763,0.00005597149,0.00039713964],"about_ca_topic_score_codex":0.00026301056,"about_ca_topic_score_gemma":0.00012764365,"teacher_disagreement_score":0.52092654,"about_ca_system_score_codex":0.00019216043,"about_ca_system_score_gemma":0.000022841934,"threshold_uncertainty_score":0.9999146},"labels":[],"label_agreement":null},{"id":"W3112300971","doi":"10.18280/ts.370513","title":"End Image Defect Detection of Float Glass Based on Faster Region-Based Convolutional Neural Network","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature extraction; Feature (linguistics); Generalizability theory; Process (computing); Image (mathematics); Set (abstract data type); Artificial neural network; Layer (electronics); Deep learning; Float glass; Materials science; Mathematics","score_opus":0.02240940103769463,"score_gpt":0.20368834571703792,"score_spread":0.1812789446793433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112300971","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":[0.49207127,0.000063700885,0.5040663,0.00021381884,0.0011751477,0.00074994116,0.000042659085,0.00043619797,0.0011809581],"genre_scores_gemma":[0.99849427,4.9228606e-7,0.00013078385,0.00029334187,0.0009820997,0.000037313566,0.000020680098,0.00003451532,0.0000064884866],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998552,0.00012151317,0.00042379848,0.00023856053,0.00039416197,0.00026991262],"domain_scores_gemma":[0.99943084,0.0001558329,0.000095734715,0.0001273377,0.000069719026,0.00012054532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021789095,0.00021857549,0.00025909636,0.00009375137,0.000093811344,0.000036510126,0.000087365675,0.00012570551,0.0002657455],"category_scores_gemma":[0.000020902067,0.00020994464,0.00023839306,0.00030426309,0.000036895286,0.000096723124,0.000008479309,0.00022373963,0.00003713682],"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":[0.00056407455,0.000039579998,0.00043343194,0.00011765439,0.00005110306,0.000012708003,0.000054684886,0.9174054,0.0746571,0.000030215131,0.001764355,0.004869657],"study_design_scores_gemma":[0.001962268,0.00072332023,0.0016747647,0.000075336015,0.000038100974,0.0000031645168,0.00002081327,0.9501679,0.0425837,0.0000062698196,0.0025247626,0.00021962382],"about_ca_topic_score_codex":0.000016734919,"about_ca_topic_score_gemma":0.0000062252466,"teacher_disagreement_score":0.506423,"about_ca_system_score_codex":0.000095644886,"about_ca_system_score_gemma":0.000029185796,"threshold_uncertainty_score":0.8561294},"labels":[],"label_agreement":null},{"id":"W3114397635","doi":"10.18280/i2m.190603","title":"Perceptron Neural Network Based Machine Learning Approaches for Leather Defect Detection and Classification","year":2020,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"VIT University; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Artificial intelligence; Artificial neural network; Confusion matrix; Pattern recognition (psychology); Multilayer perceptron; Computer science; Classifier (UML); Identification (biology); Confusion; Automation; Texture (cosmology); Perceptron; Contextual image classification; Machine learning; Engineering; Image (mathematics)","score_opus":0.09528528073475946,"score_gpt":0.2612151454596665,"score_spread":0.16592986472490706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114397635","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":[0.77688265,0.00023461225,0.22053428,0.00032910038,0.00049865345,0.0006947543,0.0000096968,0.0005823007,0.00023393224],"genre_scores_gemma":[0.9983263,0.000011851159,0.0009155011,0.00011188865,0.00041056232,0.00012995607,0.00004970419,0.00003495682,0.000009295086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989455,0.0001679732,0.00029189,0.00025126289,0.0001306551,0.00021274832],"domain_scores_gemma":[0.99960417,0.00012885027,0.00009082438,0.00007834256,0.000029299534,0.000068488145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041291106,0.00017506683,0.00022780868,0.00006144393,0.00018717947,0.000076791126,0.00005007659,0.00017523649,0.000019257655],"category_scores_gemma":[0.00008997443,0.00016010446,0.00011590363,0.00018063473,0.00002270928,0.00015945599,0.000009301375,0.00024023879,0.000008880102],"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":[0.00033573774,0.000010789888,0.020022275,0.00011133786,0.000083112034,6.2717766e-7,0.00035970876,0.6503645,0.026043022,0.00005553305,0.0002557367,0.3023576],"study_design_scores_gemma":[0.0010351612,0.0004372196,0.016718587,0.0000070184874,0.000053950014,0.0000031320121,0.00020466014,0.97738826,0.0018053467,0.000036549445,0.0021453705,0.00016475635],"about_ca_topic_score_codex":0.000007854659,"about_ca_topic_score_gemma":0.00001263936,"teacher_disagreement_score":0.3270237,"about_ca_system_score_codex":0.00006151953,"about_ca_system_score_gemma":0.000009203612,"threshold_uncertainty_score":0.65288705},"labels":[],"label_agreement":null},{"id":"W3119306060","doi":"10.47893/ijipvs.2012.1004","title":"Similarity Measures for Automatic Defect Detection on Patterned Textures","year":2012,"lang":"en","type":"article","venue":"International Journal of Image Processing and Vision Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"University of Hong Kong","keywords":"Jaccard index; Similarity (geometry); Bhattacharyya distance; Pattern recognition (psychology); Histogram; Artificial intelligence; Pearson product-moment correlation coefficient; Correlation coefficient; Mathematics; Computer science; Similarity measure; Euclidean distance; Image (mathematics); Statistics","score_opus":0.024573203655876286,"score_gpt":0.34934505948069416,"score_spread":0.3247718558248179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119306060","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":[0.72999793,0.00039318265,0.26689836,0.00008786241,0.0022362901,0.000094995055,0.000003075817,0.000050941544,0.00023732228],"genre_scores_gemma":[0.9979645,0.000012128998,0.0014433445,0.000049190072,0.00051215553,0.000003158327,1.9520367e-7,0.000009031382,0.000006321721],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998781,0.000023035205,0.0003135071,0.00009558749,0.00062429433,0.00016256998],"domain_scores_gemma":[0.99901336,0.000093150426,0.0001715196,0.000053651573,0.0005674299,0.00010085496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016498367,0.00009397408,0.00012662,0.00034762337,0.00017072057,0.0003330745,0.00017743137,0.00005029865,0.0000043126397],"category_scores_gemma":[0.0004864327,0.000069350426,0.000073264455,0.00016984748,0.000073965726,0.0010468838,0.000018373385,0.00015205877,0.0000025601094],"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":[0.00005316585,0.000029182616,0.00019220708,0.000030827312,0.000014184429,0.0000013151041,0.00027450098,0.0001822628,0.14354955,0.000008699728,0.0001383776,0.85552573],"study_design_scores_gemma":[0.0022452576,0.00080896565,0.019695062,0.0010945556,0.00005900767,0.00070330064,0.00045030395,0.19439973,0.77268445,0.0007003927,0.006702125,0.00045683736],"about_ca_topic_score_codex":0.0000025644688,"about_ca_topic_score_gemma":7.184911e-7,"teacher_disagreement_score":0.85506886,"about_ca_system_score_codex":0.00009476244,"about_ca_system_score_gemma":0.000034292607,"threshold_uncertainty_score":0.32118452},"labels":[],"label_agreement":null},{"id":"W3129002924","doi":"10.3390/electronics10040446","title":"Improved Intelligent Image Segmentation Algorithm for Mechanical Sensors in Industrial IoT: A Joint Learning Approach","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Industrial Vision Systems and Defect Detection","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":"École de Technologie Supérieure","funders":"Key Research and Development Program of Jiangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Segmentation; Joint (building); Computer vision; Kernel (algebra); Image segmentation; Pixel; Task (project management); Convolution (computer science); Inference; Artificial neural network; Engineering; Mathematics","score_opus":0.027416524512150237,"score_gpt":0.24725069338460295,"score_spread":0.21983416887245272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129002924","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":[0.16712755,0.0010228893,0.82912654,0.000027790398,0.0010975432,0.0009674328,0.000009556647,0.00027740147,0.00034327523],"genre_scores_gemma":[0.94082594,0.0006054806,0.05402757,0.000045179277,0.002394297,0.0005801538,0.00027717234,0.00023940964,0.0010047843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867153,0.00010176945,0.00041191146,0.00025931094,0.00015037146,0.0004051126],"domain_scores_gemma":[0.9996327,0.000054963846,0.000057512916,0.00012373086,0.00007313682,0.0000579484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005320513,0.00016320594,0.00025941597,0.000114161034,0.00007050771,0.00007097521,0.000048287504,0.0002796267,0.000019979736],"category_scores_gemma":[0.0001292917,0.00017485234,0.00011512559,0.00035256977,0.000006606667,0.00006621965,0.000020586336,0.0006803618,0.0000070810147],"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":[0.000058168655,0.00008909356,0.000004835367,0.00006792259,0.000097249715,0.0000096615,0.00036911434,0.032665387,0.35077968,0.00017469855,0.00044141445,0.6152428],"study_design_scores_gemma":[0.0011046019,0.0001760514,0.0000012603504,0.000017100867,0.000013717897,0.000021717575,0.00041204225,0.59749377,0.3905106,0.000057351528,0.010021538,0.00017027925],"about_ca_topic_score_codex":0.000010741458,"about_ca_topic_score_gemma":0.00001574412,"teacher_disagreement_score":0.775099,"about_ca_system_score_codex":0.00048237902,"about_ca_system_score_gemma":0.00010318898,"threshold_uncertainty_score":0.7130271},"labels":[],"label_agreement":null},{"id":"W3132618259","doi":"10.17559/tv-20200906191853","title":"Deep Learning-Guided Production Quality Estimation for Virtual Environment-Based Applications","year":2020,"lang":"en","type":"article","venue":"Tehnicki vjesnik - Technical Gazette","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"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":"Information Technology Research Centre; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Institute for Information and Communications Technology Promotion; Electronics and Telecommunications Research Institute; National Research Foundation","keywords":"Quality (philosophy); Computer science; Production (economics); Estimation; Artificial intelligence; Deep learning; Human–computer interaction; Systems engineering; Engineering; Economics","score_opus":0.04173916467161047,"score_gpt":0.2809084364326264,"score_spread":0.23916927176101593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132618259","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":[0.011552403,0.000075733406,0.9828954,0.0008567577,0.00023519933,0.0017915142,0.000012273376,0.0021001538,0.00048057747],"genre_scores_gemma":[0.99157506,0.0000122878855,0.0062558753,0.00016841879,0.0006253233,0.0010785195,0.000100611906,0.00007337068,0.000110533896],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981208,0.000094331335,0.00068126264,0.00047084942,0.00032902486,0.00030377027],"domain_scores_gemma":[0.9991129,0.00015944743,0.00014996484,0.0003587851,0.000052387826,0.00016652454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057980645,0.00024836118,0.00033136533,0.000086312015,0.00020018539,0.000049338327,0.00017399217,0.00031092987,0.000061047074],"category_scores_gemma":[0.00045871333,0.00025772947,0.00018271676,0.00033638827,0.000059177673,0.00014600794,0.000031730233,0.00046467446,0.00021795573],"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":[0.00009943828,0.000070033435,0.00007032418,0.00015818798,0.000034783356,6.8039566e-7,0.000061138555,0.76912105,0.13735227,0.00083551113,0.0058908737,0.08630568],"study_design_scores_gemma":[0.0019418901,0.0007146559,0.0012003195,0.0000535803,0.00011497509,0.000012339339,0.00007752828,0.6909388,0.050939802,0.0003348179,0.2527195,0.0009518185],"about_ca_topic_score_codex":0.000008561606,"about_ca_topic_score_gemma":0.0000024181231,"teacher_disagreement_score":0.98002267,"about_ca_system_score_codex":0.00017943452,"about_ca_system_score_gemma":0.000019715508,"threshold_uncertainty_score":0.9999875},"labels":[],"label_agreement":null},{"id":"W3133317383","doi":"10.1109/iccvw54120.2021.00390","title":"Blocks World Revisited: The Effect of Self-Occlusion on Classification by Convolutional Neural Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Convolutional neural network; Artificial intelligence; Occlusion; Computer science; Object (grammar); Context (archaeology); Deep learning; Artificial neural network; Computer vision; Orientation (vector space); Pattern recognition (psychology); Mathematics","score_opus":0.012687737141894069,"score_gpt":0.23392448171974442,"score_spread":0.22123674457785036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133317383","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":[0.921692,0.0045457985,0.037096072,0.00025575672,0.011079753,0.0032517733,0.00008851845,0.0014424439,0.02054788],"genre_scores_gemma":[0.9984235,0.00006930764,0.000024543371,0.000032069664,0.00067333935,0.00006102152,0.00020867764,0.000039108552,0.0004684296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981085,0.00033911297,0.00057757157,0.00035737266,0.00040595728,0.00021148872],"domain_scores_gemma":[0.9986329,0.00042913924,0.00019530253,0.0005807806,0.00010305106,0.000058810692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074560725,0.00032591957,0.00047874046,0.00014725506,0.00012382364,0.000099997844,0.0002083683,0.0005009389,0.00017014188],"category_scores_gemma":[0.000047494686,0.00022062166,0.000269982,0.00036480892,0.000023989722,0.000042233904,0.00015804218,0.0011343956,0.0000081868875],"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":[0.00018356,0.000053244203,0.0019898203,0.0005668621,0.00041399588,0.000006878562,0.00009929052,0.8595551,0.005023895,0.00020861767,0.11477649,0.01712222],"study_design_scores_gemma":[0.00045847427,0.00015310456,0.0011996216,0.0003918123,0.00009481891,0.000009308832,0.000021870304,0.9867858,0.004822696,0.0000049647088,0.0057706614,0.00028681994],"about_ca_topic_score_codex":0.00006856535,"about_ca_topic_score_gemma":0.000013630291,"teacher_disagreement_score":0.12723073,"about_ca_system_score_codex":0.00019429314,"about_ca_system_score_gemma":0.000018831452,"threshold_uncertainty_score":0.89966905},"labels":[],"label_agreement":null},{"id":"W3140080171","doi":"","title":"4 - Modélisation et intégration de connaissances métier pour l'identification de défauts par règles linguistiques floues","year":2004,"lang":"fr","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Computer science; Expert system; Knowledge base; Field (mathematics); Exploit; Inference; Inference engine; Artificial intelligence; Fuzzy logic; Knowledge representation and reasoning; Node (physics); Natural language processing; Engineering; Mathematics","score_opus":0.03029423396711655,"score_gpt":0.28304915563504146,"score_spread":0.2527549216679249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140080171","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":[0.5416161,0.0015526612,0.4531651,0.0009556532,0.001506064,0.00043740144,0.000034515313,0.00023582441,0.0004966693],"genre_scores_gemma":[0.99395597,0.00021756956,0.0029878966,0.00011039997,0.002247626,0.00010189271,0.000072236166,0.000049069145,0.00025732454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978764,0.0003041196,0.00073681434,0.00032219006,0.00034332016,0.00041714963],"domain_scores_gemma":[0.9991658,0.000092310256,0.00022987883,0.00016801829,0.00020530574,0.00013873947],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015207262,0.00029317927,0.00026502836,0.00019459618,0.00022924662,0.00032494773,0.00012208225,0.0003432502,0.0002484],"category_scores_gemma":[0.000160941,0.00032837215,0.00013772304,0.00021505798,0.000069846006,0.0004640411,0.00001525227,0.0003024383,0.000104207276],"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":[0.000113231836,0.00028545051,0.0015259748,0.00036204708,0.00019817395,0.000023745051,0.010982905,0.71641076,0.1927447,0.029705945,0.0035033615,0.04414373],"study_design_scores_gemma":[0.0037909246,0.00063971954,0.049982082,0.0017570619,0.00031386243,0.00008942141,0.0020308928,0.23126587,0.65358746,0.033279207,0.022043373,0.0012201155],"about_ca_topic_score_codex":0.0019517948,"about_ca_topic_score_gemma":0.0002664548,"teacher_disagreement_score":0.48514485,"about_ca_system_score_codex":0.00084244774,"about_ca_system_score_gemma":0.00023176637,"threshold_uncertainty_score":0.99991685},"labels":[],"label_agreement":null},{"id":"W31432420","doi":"10.1007/978-1-4939-9706-0_18","title":"Fuzzy machine vision based inspection","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Machine vision; Automotive industry; Artificial intelligence; Engineering; Fuzzy logic; Component (thermodynamics); Identification (biology); Machine learning; Computer science; Computer vision; Engineering drawing","score_opus":0.006967319658095187,"score_gpt":0.24010644911698475,"score_spread":0.23313912945888957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W31432420","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26581323,0.00035318258,0.0026351777,0.0000154483,0.031016575,0.0008654464,0.000033947566,0.0043485845,0.6949184],"genre_scores_gemma":[0.9904438,0.000014912021,0.00017045275,0.000014103903,0.00081443373,0.000040655043,0.00073167385,0.000106604784,0.0076633515],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99889266,0.000022930435,0.0003705774,0.00024316474,0.0002859996,0.00018465848],"domain_scores_gemma":[0.9994478,0.000029187795,0.000076488715,0.00028466602,0.000086032145,0.00007577501],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018165329,0.00029030713,0.00028937013,0.00037211445,0.00012407497,0.000091223264,0.000089162044,0.001040198,0.00040580615],"category_scores_gemma":[0.000025820169,0.00026205793,0.00015286323,0.0002614505,0.000005830214,0.00010623404,0.0000036450003,0.0009026503,0.00036862836],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063347927,0.00020326965,0.0001301505,0.0016930477,0.0002498888,0.00003502825,0.0005351044,0.011353986,0.4686307,0.0012284332,0.10813076,0.40717614],"study_design_scores_gemma":[0.0038448113,0.00076573645,0.0064725764,0.0009519815,0.00024136354,0.00003350993,0.00058655324,0.17223904,0.41570577,0.00062618597,0.3953519,0.0031805933],"about_ca_topic_score_codex":0.00024521258,"about_ca_topic_score_gemma":0.0018143648,"teacher_disagreement_score":0.7246306,"about_ca_system_score_codex":0.00008400886,"about_ca_system_score_gemma":0.000030231502,"threshold_uncertainty_score":0.9999832},"labels":[],"label_agreement":null},{"id":"W3144183659","doi":"10.18280/ts.380119","title":"Feature Extraction and Retrieval of Ecommerce Product Images Based on Image Processing","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Feature extraction; Feature (linguistics); Artificial intelligence; Image retrieval; Pattern recognition (psychology); Product (mathematics); Image processing; Metric (unit); Information retrieval; Computer vision; Data mining; Image (mathematics); Mathematics; Engineering","score_opus":0.014019996364853047,"score_gpt":0.24465506094047315,"score_spread":0.2306350645756201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144183659","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":[0.9627222,0.0008212529,0.027762098,0.00073388935,0.000652845,0.00058938545,0.00004587051,0.0003101674,0.0063623074],"genre_scores_gemma":[0.9988168,0.000008071739,0.0007763766,0.000025055635,0.00022917158,0.000005027992,0.000011610707,0.000018813033,0.00010908119],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925953,0.000047489993,0.00017810463,0.00017743385,0.00021324557,0.0001241775],"domain_scores_gemma":[0.99967474,0.000038912196,0.000054207652,0.00010013877,0.000092258015,0.00003971753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025528495,0.00011939154,0.00015795534,0.0000797581,0.0000651648,0.000058370857,0.000029212168,0.00006809293,0.00009741854],"category_scores_gemma":[0.000029064997,0.0001114127,0.00004452608,0.0001994382,0.000016768672,0.00015166051,0.000006384414,0.00017878185,0.000003278388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014484672,0.00006551079,0.00015135588,0.00031583165,0.000020143954,0.000017313265,0.000089692454,0.004309876,0.9503637,0.000007766037,0.0032354135,0.041278545],"study_design_scores_gemma":[0.0009867715,0.00016133486,0.005576582,0.00021942698,0.00003190732,0.000026708944,0.000135197,0.03145587,0.9545273,0.0000075055623,0.0066831103,0.0001882495],"about_ca_topic_score_codex":0.000002930866,"about_ca_topic_score_gemma":0.0000010475924,"teacher_disagreement_score":0.041090295,"about_ca_system_score_codex":0.00003956697,"about_ca_system_score_gemma":0.000027155147,"threshold_uncertainty_score":0.45432782},"labels":[],"label_agreement":null},{"id":"W3150466290","doi":"10.18280/ts.380106","title":"Detection of Dense Small Rigid Targets Based on Convolutional Neural Network and Synthetic Images","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Department of Education of Zhejiang Province","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Set (abstract data type); Image (mathematics); Bottleneck; Pattern recognition (psychology); Orientation (vector space); Training set; Computer vision; Artificial neural network; Perspective (graphical); Test set; Mathematics","score_opus":0.014366898808927631,"score_gpt":0.19839931954779627,"score_spread":0.18403242073886864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150466290","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":[0.94665533,0.00061917206,0.050730713,0.000033228633,0.0008475878,0.00024104914,0.000023972927,0.00013520334,0.0007137358],"genre_scores_gemma":[0.999431,0.0000068508875,0.00016771475,0.00003367244,0.00029836048,0.000017034929,0.0000066313546,0.00001647361,0.000022238342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991241,0.00008187983,0.0002745088,0.00016216037,0.0001812941,0.00017605549],"domain_scores_gemma":[0.99962145,0.00011306427,0.000048242204,0.00009179523,0.000068059075,0.000057377343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023137494,0.00012826303,0.00017721484,0.00006859774,0.0000792742,0.00002632582,0.000030283787,0.00008014768,0.00014479482],"category_scores_gemma":[0.000019604879,0.00012592197,0.0000730635,0.00014029216,0.000025960046,0.00004258668,0.000008890197,0.00012216406,0.000006122953],"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":[0.00024317755,0.00008592422,0.00086960307,0.00015085883,0.00008448192,0.000038826507,0.000054544016,0.69915605,0.267879,0.00012511984,0.00056140206,0.030751016],"study_design_scores_gemma":[0.001898199,0.00045280711,0.015301097,0.00020563704,0.000063138075,0.000052864227,0.000052339372,0.68816507,0.2905857,0.00008779059,0.0028097755,0.00032557006],"about_ca_topic_score_codex":0.000014553634,"about_ca_topic_score_gemma":0.000012447298,"teacher_disagreement_score":0.052775685,"about_ca_system_score_codex":0.00004269207,"about_ca_system_score_gemma":0.00002080872,"threshold_uncertainty_score":0.51349485},"labels":[],"label_agreement":null},{"id":"W3157881939","doi":"10.23977/jemm.2021.060103","title":"Item detection inside shared car based on Image processing and Infrared","year":2021,"lang":"en","type":"article","venue":"Journal of Engineering Mechanics and Machinery","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Process (computing); Set (abstract data type); Order (exchange); Computer security; Business; Operating system","score_opus":0.00871284374757635,"score_gpt":0.20286214512103598,"score_spread":0.19414930137345962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157881939","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":[0.50487375,0.0016845237,0.4911401,0.000032201948,0.0017176305,0.00011092161,0.000009616386,0.00013473674,0.00029650336],"genre_scores_gemma":[0.99776524,0.000068852736,0.0018403572,0.000023152346,0.00025246592,0.000001686432,0.0000012486493,0.00003260721,0.000014359962],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992809,0.0000188116,0.00031261327,0.00009957446,0.00016085716,0.00012719119],"domain_scores_gemma":[0.99956656,0.00006352954,0.000080445876,0.00007957128,0.00011063745,0.000099286386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027782941,0.00014156892,0.00023261244,0.00022023701,0.000047894027,0.000116119416,0.00003162371,0.000112143294,0.0000051145043],"category_scores_gemma":[0.00018032361,0.0001297942,0.000058521542,0.00018054564,0.0000020990278,0.00018804049,0.000014215729,0.0003396999,4.759876e-7],"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":[0.00006849369,0.000025570354,0.00002332882,0.0004992502,0.00006919936,0.0001923379,0.00016475623,0.18448529,0.76808393,0.0001382414,0.00017254458,0.046077043],"study_design_scores_gemma":[0.0006574118,0.00013716244,0.00022283252,0.00030309655,0.000027817272,0.00029403757,0.000043077394,0.9618626,0.034265615,0.00008468861,0.0019404673,0.00016118541],"about_ca_topic_score_codex":0.0000021889966,"about_ca_topic_score_gemma":0.000001486361,"teacher_disagreement_score":0.7773773,"about_ca_system_score_codex":0.000048898786,"about_ca_system_score_gemma":0.000025726124,"threshold_uncertainty_score":0.5292854},"labels":[],"label_agreement":null},{"id":"W3161646182","doi":"10.1109/access.2021.3078411","title":"RHN: A Residual Holistic Neural Network for Edge Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":19,"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":"Computer science; Convolutional neural network; Residual; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Edge detection; Computational complexity theory; Deep learning; Artificial neural network; Pattern recognition (psychology); Machine learning; Image processing; Image (mathematics); Algorithm","score_opus":0.07776004354620641,"score_gpt":0.31463927503026523,"score_spread":0.23687923148405882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161646182","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":[0.8803342,0.00039328742,0.10120014,0.000028165508,0.015864534,0.00039751307,0.000022859134,0.00047588805,0.00128343],"genre_scores_gemma":[0.9955041,0.0000067392875,0.00006615334,0.0000429134,0.004051088,0.000076587654,0.000007363424,0.000033012835,0.00021205228],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992099,0.00003601811,0.0002201982,0.00017352465,0.000112898575,0.00024746006],"domain_scores_gemma":[0.9995345,0.00010248752,0.000033943576,0.000181624,0.000093120245,0.000054351094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001521946,0.000114426985,0.00016394437,0.000048905273,0.00013421783,0.0001867371,0.000097771015,0.00014134223,0.000022756745],"category_scores_gemma":[0.00006333364,0.00011712452,0.00007504675,0.00032335488,0.0000079210995,0.00021724557,0.000016760125,0.00014780325,0.000018315717],"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":[0.00021778158,0.000036940295,0.00044023627,0.00036133578,0.00015710508,0.00006942278,0.00018175377,0.706737,0.051160913,0.00012663679,0.08881134,0.15169957],"study_design_scores_gemma":[0.0034834712,0.0003011072,0.006261166,0.00022219418,0.0001706751,0.00024479884,0.0001409709,0.28344166,0.5533891,0.0015296837,0.14959365,0.0012215413],"about_ca_topic_score_codex":0.000041171606,"about_ca_topic_score_gemma":0.00014763397,"teacher_disagreement_score":0.50222814,"about_ca_system_score_codex":0.00005241058,"about_ca_system_score_gemma":0.000016594122,"threshold_uncertainty_score":0.47761992},"labels":[],"label_agreement":null},{"id":"W3161912265","doi":"10.1007/s10845-021-01776-1","title":"Automated visual detection of geometrical defects in composite manufacturing processes using deep convolutional neural networks","year":2021,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Convolutional neural network; Intersection (aeronautics); Artificial intelligence; Computer science; Deep learning; Process (computing); Composite number; Artificial neural network; Pattern recognition (psychology); Binary number; Benchmark (surveying); Algorithm; Mathematics; Engineering","score_opus":0.020620405072238644,"score_gpt":0.2609941369438242,"score_spread":0.24037373187158553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161912265","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":[0.8911035,0.0012131794,0.10601778,0.0000025157674,0.0014243135,0.00011401625,0.0000012908281,0.000089029425,0.0000343818],"genre_scores_gemma":[0.9991654,0.00010182005,0.0002817411,0.000007910871,0.00040071338,0.0000017561034,0.0000024260798,0.000034351313,0.0000038410967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977773,0.00010800184,0.0011668833,0.00017087995,0.00044198046,0.00033496352],"domain_scores_gemma":[0.9988756,0.00025313505,0.0004013501,0.00010555112,0.00024588555,0.00011843951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040943114,0.00023667184,0.00051966053,0.0008405301,0.000076844735,0.00007431566,0.00012138555,0.00022241968,0.00003114537],"category_scores_gemma":[0.00012936782,0.00022580955,0.00020468394,0.000553794,0.000025057321,0.0002698372,0.00004852694,0.00061862066,0.0000022547779],"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":[0.00009431564,0.00004788102,0.00073584163,0.00020913273,0.0000859355,0.000115231705,0.0000754412,0.9578729,0.032215733,7.962204e-7,0.0000036020192,0.008543161],"study_design_scores_gemma":[0.00032420782,0.0000680592,0.0041179503,0.00020571072,0.0000275133,0.0004987217,0.00009931592,0.3783522,0.61611706,0.000009924045,0.00004531333,0.00013402883],"about_ca_topic_score_codex":0.000033982422,"about_ca_topic_score_gemma":0.00002815875,"teacher_disagreement_score":0.5839013,"about_ca_system_score_codex":0.00040659079,"about_ca_system_score_gemma":0.000051739702,"threshold_uncertainty_score":0.92082465},"labels":[],"label_agreement":null},{"id":"W3162115923","doi":"","title":"Design of a Primary Test Calibration System for Price Industries' Testing Department","year":2019,"lang":"en","type":"article","venue":"Mspace (University of Manitoba)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Test (biology); Calibration; Reliability engineering; Computer science; Engineering; Statistics; Mathematics","score_opus":0.031354903351824864,"score_gpt":0.18159441065059675,"score_spread":0.1502395072987719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162115923","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":[0.75285745,0.000034230045,0.2435187,0.00001649952,0.00038882153,0.0011583971,0.000037002912,0.0001915783,0.0017973073],"genre_scores_gemma":[0.9968136,0.0000016259737,0.0030380972,0.0000014591437,0.000045038698,0.0000012868213,0.00000770215,0.000013879409,0.00007733808],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994493,0.000032383123,0.00012328016,0.00011864864,0.00014968264,0.00012667001],"domain_scores_gemma":[0.99929506,0.0002773461,0.00012869417,0.00014584993,0.00011807468,0.000034953366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000239423,0.00009101384,0.00022488518,0.0001078199,0.000064743566,0.000009673391,0.00008943784,0.00013859132,0.0000017224996],"category_scores_gemma":[0.00004018969,0.000110976915,0.000040855888,0.000273356,0.000014180043,0.00017334582,0.000025025516,0.00007852547,0.0000096285],"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":[0.0006687875,0.000226918,0.10816051,0.0058076764,0.0003993608,0.000027321506,0.000788506,0.36919782,0.48961797,0.00042039127,0.011874521,0.012810234],"study_design_scores_gemma":[0.008384206,0.003254818,0.075885236,0.001852634,0.00033467103,0.00006712471,0.023353277,0.73933667,0.13392793,0.000025558033,0.012335967,0.0012418852],"about_ca_topic_score_codex":0.00019885432,"about_ca_topic_score_gemma":0.00018572391,"teacher_disagreement_score":0.37013888,"about_ca_system_score_codex":0.00020603139,"about_ca_system_score_gemma":0.000043948956,"threshold_uncertainty_score":0.4525507},"labels":[],"label_agreement":null},{"id":"W3162192441","doi":"10.1186/s40537-021-00539-2","title":"Integration of image segmentation and fuzzy theory to improve the accuracy of damage detection areas in traffic accidents","year":2021,"lang":"en","type":"article","venue":"Journal Of Big Data","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Machine vision; Computer science; Process (computing); Field (mathematics); Artificial intelligence; Fuzzy logic; Segmentation; Image processing; Computer vision; Machine learning; Image (mathematics)","score_opus":0.04525610434908552,"score_gpt":0.29229943292188365,"score_spread":0.24704332857279812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162192441","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":[0.98118705,0.00013774798,0.01769603,0.000021690012,0.0007562448,0.000121859666,0.0000310109,0.000004435761,0.000043962373],"genre_scores_gemma":[0.99949133,0.00007237541,0.00024221452,0.0000062025874,0.00016369512,0.0000013183005,0.000008941298,0.0000072886464,0.0000066117145],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991015,0.00011724193,0.0004744474,0.000075523705,0.0001719561,0.00005930476],"domain_scores_gemma":[0.9992156,0.00015552323,0.00023393372,0.00023635068,0.00013150914,0.00002706752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009911434,0.000062662926,0.00015741179,0.00015403728,0.000028541415,0.000030493957,0.0001272222,0.00005250784,0.0000050732338],"category_scores_gemma":[0.0006186928,0.000045120705,0.000029820678,0.00026583718,0.000010583487,0.00042043402,0.00006348621,0.00016446461,6.7423923e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000078223384,0.000014709469,0.000050620845,0.000027944136,0.000021691312,0.0000037923055,0.0005151375,0.0009081808,0.669711,0.0000032767705,0.0000778398,0.32858762],"study_design_scores_gemma":[0.0015275507,0.0002828808,0.025145393,0.00045084226,0.00007751134,0.00011327742,0.00516154,0.008226726,0.95832604,0.00019252411,0.00036739337,0.00012830048],"about_ca_topic_score_codex":0.000026502776,"about_ca_topic_score_gemma":0.00016318334,"teacher_disagreement_score":0.3284593,"about_ca_system_score_codex":0.000038075104,"about_ca_system_score_gemma":0.00002849735,"threshold_uncertainty_score":0.18399689},"labels":[],"label_agreement":null},{"id":"W3163178460","doi":"10.18280/ts.380225","title":"Design of Automated Visual Inspection System for Beverage Industry Production Line","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Production line; Conveyor belt; Visual inspection; Computer science; Automated X-ray inspection; Image processing; Computer vision; Bottle; Line (geometry); Production (economics); Perspective (graphical); Grayscale; Engineering drawing; Artificial intelligence; Beverage industry; Image (mathematics); Engineering; Mechanical engineering; Mathematics","score_opus":0.0290596956529038,"score_gpt":0.25680728106814493,"score_spread":0.22774758541524112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163178460","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":[0.5940549,0.000082164086,0.40044704,0.000019450923,0.0021437837,0.0008789381,0.000016250196,0.0021955646,0.00016196037],"genre_scores_gemma":[0.99852866,0.0000028997213,0.0004853504,0.000004242828,0.00077454903,0.000097897195,0.000026933962,0.00003063781,0.000048848295],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988407,0.00008332363,0.00047638736,0.00020312755,0.00022050733,0.00017594869],"domain_scores_gemma":[0.9995035,0.00004565193,0.0000943628,0.000105984254,0.00020279789,0.00004770468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044994865,0.00014607159,0.00023653709,0.00012575026,0.000096217605,0.000031072745,0.000040638028,0.00026117614,0.000034044988],"category_scores_gemma":[0.000030821768,0.0001488423,0.00007302268,0.00034146063,0.0000115867615,0.000107368825,0.000008533043,0.00017785924,0.0000074863647],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013124876,0.000077890414,0.000054566237,0.000417768,0.00012770381,0.0000067189203,0.00019635416,0.42619365,0.56599534,0.000065848755,0.0023890785,0.0043438477],"study_design_scores_gemma":[0.0007981828,0.00023713842,0.00020993545,0.0001485651,0.000031319192,0.000031402968,0.0003047041,0.47931814,0.5184607,0.0000041311287,0.00032250912,0.00013324479],"about_ca_topic_score_codex":0.0000095049345,"about_ca_topic_score_gemma":0.0000015582207,"teacher_disagreement_score":0.40447378,"about_ca_system_score_codex":0.00017786672,"about_ca_system_score_gemma":0.000047465273,"threshold_uncertainty_score":0.6069613},"labels":[],"label_agreement":null},{"id":"W3164859240","doi":"10.1088/1742-6596/1914/1/012037","title":"DCSNet: A Surface Defect Classification and Segmentation Model by One-Class Learning","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Emergent BioSolutions (Canada)","funders":"","keywords":"Segmentation; Class (philosophy); Task (project management); Artificial intelligence; Computer science; Surface (topology); Code (set theory); Product (mathematics); Machine learning; Pattern recognition (psychology); Engineering; Mathematics; Programming language; Set (abstract data type)","score_opus":0.040359018683790646,"score_gpt":0.2430841442793627,"score_spread":0.20272512559557204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164859240","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":[0.77397287,0.00026875327,0.22456442,0.00006191192,0.000223693,0.000053646694,0.0000053871936,0.000033709173,0.0008156465],"genre_scores_gemma":[0.9987344,0.00026394674,0.00064776296,0.000006366455,0.000107765816,0.0000012604562,0.0000061944525,0.000013646886,0.00021861798],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993114,0.00006016569,0.00026516404,0.00008065189,0.00018681765,0.0000957968],"domain_scores_gemma":[0.9994283,0.00003315517,0.00015575888,0.00006220344,0.0002724407,0.000048155394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016217276,0.00009412622,0.00018862315,0.000022818294,0.000074168616,0.00012475783,0.00003581194,0.00007041588,0.0000101790165],"category_scores_gemma":[0.000028666444,0.00009415796,0.000057485795,0.00011777731,0.000019993066,0.0005064815,0.000010169325,0.0002570941,0.0000029275427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026001091,0.000016440776,0.00026899247,0.000047124115,0.0000642492,0.0000019077363,0.00064244587,0.07441988,0.8920361,0.0012901787,0.00033404387,0.030852681],"study_design_scores_gemma":[0.00062537764,0.0001900446,0.00034181215,0.00015028949,0.00006256704,0.00004944683,0.0024355757,0.14434294,0.84879297,0.0017127581,0.0010756854,0.00022053414],"about_ca_topic_score_codex":0.000003195548,"about_ca_topic_score_gemma":0.0000031214122,"teacher_disagreement_score":0.2247616,"about_ca_system_score_codex":0.000047065747,"about_ca_system_score_gemma":0.00006667185,"threshold_uncertainty_score":0.383965},"labels":[],"label_agreement":null},{"id":"W3169054151","doi":"","title":"A Step Toward Accurate Automated Dimensional inspection of Non Rigid Mechanical Parts","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Engineering drawing; Engineering","score_opus":0.02643818252545563,"score_gpt":0.24854605832767152,"score_spread":0.2221078758022159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169054151","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":[0.7388593,0.00071040966,0.22280692,0.00072857947,0.0039306865,0.0015137818,0.00021538606,0.0042932094,0.026941702],"genre_scores_gemma":[0.9940568,0.00007233011,0.0049351905,0.000009216729,0.00007189661,0.00006896102,0.00024229809,0.00006536251,0.0004779368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99590397,0.0018377058,0.0008509004,0.00052362157,0.0005785051,0.0003052728],"domain_scores_gemma":[0.99578434,0.00040477994,0.00044169326,0.0011743515,0.001989215,0.00020561874],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0041710944,0.00036559073,0.0005799004,0.00027825832,0.00014106628,0.00016602807,0.0004698009,0.00064783596,0.000054758635],"category_scores_gemma":[0.00064053235,0.0003760096,0.00023275317,0.00042826493,0.00007967757,0.0001414709,0.00050823356,0.00075815944,0.0000566457],"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":[0.0007602055,0.0038940948,0.0012526651,0.0056922357,0.0029810043,0.00010657618,0.036416076,0.272904,0.2522423,0.036856946,0.21096922,0.17592466],"study_design_scores_gemma":[0.00089292414,0.0000019429917,0.0005962496,0.0019142716,0.00006203039,0.000019782323,0.000084846455,0.87879187,0.11114672,0.0005748876,0.0054342467,0.0004802556],"about_ca_topic_score_codex":0.001014253,"about_ca_topic_score_gemma":0.0003008335,"teacher_disagreement_score":0.60588783,"about_ca_system_score_codex":0.0002659587,"about_ca_system_score_gemma":0.00022181333,"threshold_uncertainty_score":0.99986917},"labels":[],"label_agreement":null},{"id":"W3176263350","doi":"10.5281/zenodo.15431577","title":"AUTOMATIC DETECTION AND CLASSIFICATION OF DEFECTS IN WOVEN COMPOSITES MANUFACTURED BY RTM BASED ON X-RAY MICROTOMOGRAPHY","year":2017,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"X-ray microtomography; Composite material; Materials science; Medicine","score_opus":0.009308259566386886,"score_gpt":0.21689977334479996,"score_spread":0.20759151377841306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176263350","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":[0.9209388,0.00020808596,0.07705527,0.0000813487,0.0001634826,0.0008676936,0.000028558947,0.00035478448,0.00030201382],"genre_scores_gemma":[0.99806553,0.000021481235,0.0014989363,0.000049412076,0.000037629383,0.00025964732,0.000009800082,0.000042861153,0.000014696294],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987568,0.00009249267,0.00041297052,0.00025037417,0.00020629581,0.00028104524],"domain_scores_gemma":[0.9989054,0.000100697915,0.00023796904,0.0006163107,0.000037537808,0.00010206723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043450217,0.00023621917,0.00032613485,0.0005654717,0.00020009847,0.00015996907,0.00019506243,0.00033861643,0.0000069410817],"category_scores_gemma":[0.00008664691,0.00023760293,0.00010034325,0.00019697618,0.00005661873,0.0002015742,0.00002588787,0.00030792947,0.0000033556178],"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":[0.00010298657,0.000102534985,0.03328962,0.00016139736,0.000028824526,0.0000054579837,0.00007710028,0.008269689,0.9078527,0.00006488742,0.0004360073,0.0496088],"study_design_scores_gemma":[0.000646148,0.00013881203,0.27955404,0.00013689582,0.000013279245,0.0000048462725,0.000017065298,0.47302407,0.24595,0.00005724713,0.00027141912,0.00018620443],"about_ca_topic_score_codex":0.0017296927,"about_ca_topic_score_gemma":0.0009127347,"teacher_disagreement_score":0.66190267,"about_ca_system_score_codex":0.00018899898,"about_ca_system_score_gemma":0.000018697921,"threshold_uncertainty_score":0.96891665},"labels":[],"label_agreement":null},{"id":"W3180159491","doi":"10.32920/ryerson.14649096.v1","title":"Statistics Based Neural Networks Method for Industrial Image Inspection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Artificial intelligence; Computer science; Image (mathematics); Pixel; Computer vision; Pattern recognition (psychology); Set (abstract data type); Image processing","score_opus":0.045816508315605325,"score_gpt":0.29572064492814343,"score_spread":0.2499041366125381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180159491","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":[0.0018303366,0.000065782864,0.98116016,0.000019794705,0.014102384,0.0009854723,0.00019526658,0.0008878351,0.00075296924],"genre_scores_gemma":[0.696804,0.000018092513,0.2907581,0.000106057014,0.009663811,0.00061599613,0.0014288682,0.00026678477,0.00033828517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983342,0.00015488482,0.00058586255,0.000404823,0.00021300468,0.00030718828],"domain_scores_gemma":[0.9988481,0.00032699507,0.00012571986,0.00037351772,0.00022656578,0.00009913353],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005832171,0.00034167795,0.00052136334,0.00017547113,0.00010968409,0.0003574172,0.000117957934,0.0010946994,0.00013661994],"category_scores_gemma":[0.0001827532,0.00034879637,0.00023448264,0.00019811196,0.000013069063,0.00007832149,0.00007940483,0.0011472839,0.000003292903],"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":[0.00005632908,0.000015074209,0.000011538846,0.00013593952,0.00008598874,0.0000074375334,0.00002315736,0.92548364,0.00045878897,0.00006277308,0.032123066,0.041536245],"study_design_scores_gemma":[0.00094766007,0.00006578097,0.000018980729,0.000068633984,0.00007566496,0.0000051116667,0.00006427113,0.9927485,0.0026193708,0.00004904592,0.0029790557,0.00035791437],"about_ca_topic_score_codex":0.00031245136,"about_ca_topic_score_gemma":0.00008784667,"teacher_disagreement_score":0.69497365,"about_ca_system_score_codex":0.00022399798,"about_ca_system_score_gemma":0.0000867402,"threshold_uncertainty_score":0.9998964},"labels":[],"label_agreement":null},{"id":"W3181114435","doi":"10.48550/arxiv.2107.04902","title":"Industry and Academic Research in Computer Vision","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Industrial Vision Systems and Defect Detection","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":"York University","funders":"","keywords":"Citation; Field (mathematics); Preference; Computer science; Work (physics); Distribution (mathematics); Data science; Join (topology); Set (abstract data type); Operations research; Library science; Engineering; Economics","score_opus":0.19332258868155391,"score_gpt":0.26268807299835334,"score_spread":0.06936548431679943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181114435","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":[0.98978585,0.00021160726,0.0077848444,0.00001637322,0.0009437954,0.00024188077,0.0000046638056,0.00012194277,0.0008890341],"genre_scores_gemma":[0.9991511,0.00031984728,0.00003741577,0.000008440325,0.00029544576,9.319323e-7,0.000007799787,0.00002510522,0.00015388978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863315,0.00023488906,0.00020661038,0.00053021556,0.00010296082,0.00029219812],"domain_scores_gemma":[0.9993029,0.00011176351,0.000038631908,0.0003470774,0.0000842027,0.00011539883],"candidate_categories":["research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00068720145,0.0001834283,0.00027988068,0.0005466222,0.000072529765,0.00007860415,0.00019722489,0.002435552,0.000027116612],"category_scores_gemma":[0.000014489483,0.00022111303,0.00006066958,0.00068878726,0.000056638153,0.0001526288,0.00058250973,0.0062769786,0.000021317002],"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":[0.000050222363,0.00002607271,0.006545097,0.00031775527,0.00005262874,0.0007829486,0.0003015864,0.98098385,0.00058697246,0.0006278761,0.0010167225,0.008708249],"study_design_scores_gemma":[0.0011187975,0.00010482471,0.009511753,0.0016733108,0.000024019982,0.000029352592,0.0006365682,0.9824944,0.00064738793,0.0017157582,0.0014231596,0.0006206256],"about_ca_topic_score_codex":0.0002343757,"about_ca_topic_score_gemma":0.000044809603,"teacher_disagreement_score":0.009365266,"about_ca_system_score_codex":0.0002761996,"about_ca_system_score_gemma":0.000060104143,"threshold_uncertainty_score":0.9988595},"labels":[],"label_agreement":null},{"id":"W3189560484","doi":"10.3390/s21165401","title":"Steel Wire Rope Surface Defect Detection Based on Segmentation Template and Spatiotemporal Gray Sample Set","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Taif University; National Natural Science Foundation of China","keywords":"Wire rope; Segmentation; Rope; Artificial intelligence; Pixel; Computer vision; Image segmentation; Computer science; Structural engineering; Engineering; Pattern recognition (psychology)","score_opus":0.020847870209880805,"score_gpt":0.2366309627363069,"score_spread":0.2157830925264261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189560484","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":[0.99012434,0.000044163116,0.008198589,0.000016336784,0.000772484,0.0002105106,0.00008065464,0.00022739166,0.0003255235],"genre_scores_gemma":[0.9991218,0.000009729649,0.000549336,0.000023461755,0.000112144415,0.000005210759,0.000063739,0.00003420443,0.00008037909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99907076,0.00012977699,0.00021557267,0.00022242297,0.00019304309,0.00016841722],"domain_scores_gemma":[0.9995232,0.00013444526,0.00004685152,0.00016977108,0.00005927237,0.00006642454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020314554,0.00015713766,0.00016641375,0.000078276804,0.00013296542,0.00007430128,0.000021761682,0.00014171937,0.00004195229],"category_scores_gemma":[0.000060502614,0.00015643159,0.000063448504,0.00025229983,0.0000117851705,0.00008256747,0.000007583662,0.00016582484,0.000049106944],"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":[0.00013915566,0.00002813922,0.0060684104,0.00016359315,0.000080779224,0.000043016273,0.00038078884,0.7664833,0.19572759,0.000011552876,0.00085891096,0.030014774],"study_design_scores_gemma":[0.001713489,0.000302713,0.01027916,0.00011258051,0.000047396974,0.00004112395,0.0005227075,0.43115655,0.5467906,0.000031828058,0.008502958,0.0004989041],"about_ca_topic_score_codex":0.00031353417,"about_ca_topic_score_gemma":0.00029767898,"teacher_disagreement_score":0.351063,"about_ca_system_score_codex":0.00008755398,"about_ca_system_score_gemma":0.00001715072,"threshold_uncertainty_score":0.6379095},"labels":[],"label_agreement":null},{"id":"W3193440158","doi":"10.1007/978-3-030-75506-5_64","title":"Deep Learning-Based Automatic Detection of Defective Tablets in Pharmaceutical Manufacturing","year":2021,"lang":"en","type":"book-chapter","venue":"World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Artificial intelligence; Computer science; Support vector machine; Convolutional neural network; Hyperparameter; Transfer of learning; Deep learning; Machine learning; Pattern recognition (psychology); Set (abstract data type); AdaBoost","score_opus":0.011405629049924898,"score_gpt":0.2342520444241091,"score_spread":0.2228464153741842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193440158","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20610511,0.043270092,0.19765456,0.0008481941,0.0670305,0.013633437,0.0008172812,0.010522477,0.46011835],"genre_scores_gemma":[0.9880572,0.00040206185,0.000060690894,0.0001154536,0.0010563775,0.00007615405,0.00012252903,0.00024642845,0.009863115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961732,0.0000984727,0.0011043329,0.00062293763,0.0013703385,0.00063068833],"domain_scores_gemma":[0.9981169,0.0005276122,0.00023664428,0.0004226357,0.00008060786,0.0006156115],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0006182759,0.0007664346,0.0012360445,0.000663783,0.00008411225,0.00006137529,0.00027744588,0.0010062654,0.00086331565],"category_scores_gemma":[0.00011294787,0.0007431916,0.00032643438,0.0002760665,0.00023432153,0.00009493288,0.00012802455,0.0028540306,0.000051986677],"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":[0.00032707647,0.00089362654,0.000055495355,0.0087423,0.002393661,0.0022961625,0.00033088922,0.15945454,0.005753605,0.0035233065,0.004758015,0.81147134],"study_design_scores_gemma":[0.0023623256,0.00022656392,0.00010060599,0.003735424,0.00015699843,0.00003834369,0.000010489255,0.69739145,0.007530523,0.000105870015,0.28736636,0.00097504724],"about_ca_topic_score_codex":0.000029649034,"about_ca_topic_score_gemma":0.000102055754,"teacher_disagreement_score":0.8104963,"about_ca_system_score_codex":0.00025097193,"about_ca_system_score_gemma":0.000092621216,"threshold_uncertainty_score":0.9995019},"labels":[],"label_agreement":null},{"id":"W3196201121","doi":"10.1155/2021/4428964","title":"A Novel Deep Convolutional Neural Network Based on ResNet‐18 and Transfer Learning for Detection of Wood Knot Defects","year":2021,"lang":"en","type":"article","venue":"Journal of Sensors","topic":"Industrial Vision Systems and Defect Detection","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 Toronto; Canadian Institute for Advanced Research","funders":"Natural Sciences and Engineering Research Council of Canada; Harbin Institute of Technology; China Postdoctoral Science Foundation","keywords":"Convolutional neural network; Artificial intelligence; Transfer of learning; Deep learning; Knot (papermaking); Pattern recognition (psychology); Computer science; Residual; Artificial neural network; Block (permutation group theory); Pooling; Feature extraction; Engineering; Algorithm; Mathematics","score_opus":0.023635791849737624,"score_gpt":0.2287787644999611,"score_spread":0.20514297265022347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196201121","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":[0.9197399,0.00024039067,0.07893521,0.000014945769,0.00085068407,0.00009617039,0.0000027896272,0.000018844863,0.000101090955],"genre_scores_gemma":[0.9989617,0.00001137129,0.000384916,0.000015317735,0.0005807028,0.0000019832555,0.0000010288843,0.000020116111,0.000022864384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911994,0.00006367032,0.00038381582,0.00008521758,0.00019993726,0.00014744271],"domain_scores_gemma":[0.99931055,0.00027740176,0.00008828337,0.000052996755,0.00020693212,0.0000638464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034943782,0.00010191281,0.00024523732,0.0001195911,0.000078100384,0.000021221911,0.000024696863,0.00012670713,0.000013686065],"category_scores_gemma":[0.0001430443,0.000094397736,0.00015361639,0.00018464921,0.00001450043,0.00006908862,0.000002939577,0.00030682064,5.738944e-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":[0.0002592122,0.000012231594,0.00009631238,0.000045408913,0.0000360376,0.0000059454483,0.000046653884,0.82431835,0.17266652,0.000010898658,0.00004302722,0.002459414],"study_design_scores_gemma":[0.0035968937,0.0010864822,0.0034825094,0.0002736214,0.000089101246,0.00034910825,0.00020796093,0.86620677,0.12143209,0.000023600001,0.0030661558,0.00018570942],"about_ca_topic_score_codex":0.000003838,"about_ca_topic_score_gemma":0.0000081825465,"teacher_disagreement_score":0.07922182,"about_ca_system_score_codex":0.00006255258,"about_ca_system_score_gemma":0.00003064002,"threshold_uncertainty_score":0.3849428},"labels":[],"label_agreement":null},{"id":"W3203379805","doi":"10.18280/ts.380417","title":"Detection of Various Types of Metal Surface Defects Based on Image Processing","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"People's Government of Jilin Province","keywords":"Fuse (electrical); Computer science; Artificial intelligence; Surface (topology); Image (mathematics); Computer vision; Image processing; Production (economics); Detector; Pattern recognition (psychology); Engineering; Mathematics; Electrical engineering","score_opus":0.013771204513971692,"score_gpt":0.21988175472996369,"score_spread":0.206110550215992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203379805","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":[0.9255051,0.00014383486,0.07217814,0.0000037938855,0.00025168402,0.00015249928,0.000010722993,0.00008443747,0.0016697743],"genre_scores_gemma":[0.9995254,0.0000015970885,0.00035362173,0.0000066080647,0.000067180255,0.000004841637,0.000004305064,0.000018577271,0.000017861395],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991497,0.00005653119,0.0002982349,0.00012503547,0.00025023488,0.00012029865],"domain_scores_gemma":[0.999622,0.000052578725,0.000078204204,0.000097234086,0.00011880279,0.000031196058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023424774,0.00010966667,0.0002100336,0.00007616563,0.000037071466,0.000017070122,0.00003833736,0.00007636525,0.000118465774],"category_scores_gemma":[0.000021144368,0.00010502671,0.00009214784,0.0002708577,0.000015068962,0.00007463774,0.000006268007,0.00009869818,0.0000064582223],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048958358,0.000052917767,0.000056077664,0.00017910985,0.000027869139,0.0000053319395,0.00007396511,0.11228422,0.8788196,0.0000096004715,0.00001819452,0.008424193],"study_design_scores_gemma":[0.0005858398,0.00019680637,0.0006395137,0.000095511874,0.00003765173,0.000003778209,0.000047014066,0.12157407,0.8765012,0.000009185914,0.00021675254,0.000092673516],"about_ca_topic_score_codex":0.00001991221,"about_ca_topic_score_gemma":0.000009442098,"teacher_disagreement_score":0.0740203,"about_ca_system_score_codex":0.00004228928,"about_ca_system_score_gemma":0.000037506776,"threshold_uncertainty_score":0.4282865},"labels":[],"label_agreement":null},{"id":"W3208387531","doi":"10.5281/zenodo.3406508","title":"AWARE characterization factor samples","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Factor (programming language); Characterization (materials science); Computer science; Materials science; Nanotechnology; Programming language","score_opus":0.07997840928112612,"score_gpt":0.2582508778266486,"score_spread":0.17827246854552245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208387531","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013767146,0.000039438008,0.0000057263715,0.000001034969,0.0014127272,0.00037432223,0.9979098,0.0002003251,0.00004289107],"genre_scores_gemma":[0.00025891786,0.000009329442,5.1483204e-7,0.000018340334,0.0010644728,0.000106369465,0.99835783,0.000043080247,0.00014112938],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913317,0.000022406117,0.00024536284,0.00020569396,0.00020317527,0.00019020404],"domain_scores_gemma":[0.9993423,0.00004988915,0.00009660657,0.00039444017,0.000061808045,0.000054911157],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000016353644,0.00025514848,0.0002906043,0.00014392496,0.000042333253,0.0001156295,0.00017351643,0.0006256958,0.21140161],"category_scores_gemma":[0.00013831782,0.00024740002,0.000099656434,0.00013574968,8.932643e-7,0.000116503434,0.000041422645,0.0003573769,0.023646742],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015647953,0.0000023958817,2.355295e-7,0.0005797143,0.000018160583,0.0000025997615,0.0000035386188,0.000051582963,0.00008536315,1.0988304e-8,0.998281,0.00097383076],"study_design_scores_gemma":[0.00009581009,0.000019556892,0.00011194793,0.0014362666,0.0000066615535,0.0000041400217,0.000002169293,0.00013341659,0.00024355053,1.0496173e-7,0.9976681,0.00027831085],"about_ca_topic_score_codex":0.000010043253,"about_ca_topic_score_gemma":0.000005854644,"teacher_disagreement_score":0.18775487,"about_ca_system_score_codex":0.000080500235,"about_ca_system_score_gemma":0.000030583204,"threshold_uncertainty_score":0.99999785},"labels":[],"label_agreement":null},{"id":"W3208489782","doi":"10.32920/ryerson.14637387.v1","title":"Implementation of Object Recognition Algorithm to enhance Manufacturing and Maintenance Tasks on an Aircraft","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer science; Cognitive neuroscience of visual object recognition; Convolutional neural network; Object (grammar); Object detection; Class (philosophy); 3D single-object recognition; Machine learning; Deep learning; Pattern recognition (psychology); Transfer of learning; Consistency (knowledge bases); Computer vision; Algorithm","score_opus":0.019848854568924722,"score_gpt":0.2945895539626166,"score_spread":0.2747406993936919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208489782","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":[0.89865893,0.000035511985,0.0984338,0.000015071451,0.0012390037,0.0005896041,0.00008244592,0.00023072524,0.00071491295],"genre_scores_gemma":[0.99360204,0.00006108168,0.0057759318,0.00003645582,0.00025100127,0.00008598063,0.000099374854,0.000036316138,0.00005184456],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987403,0.000060645623,0.00042556025,0.00038426023,0.00020058578,0.00018865457],"domain_scores_gemma":[0.99943036,0.00003059151,0.00009177383,0.00027578764,0.000085970736,0.00008549345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028073494,0.00022529422,0.00031821817,0.00019110709,0.00003976304,0.000084228835,0.00006538543,0.00024095607,0.000110852205],"category_scores_gemma":[0.00001151946,0.00022696078,0.00006775978,0.00007861934,0.000007139935,0.00011133634,0.000076937635,0.00032375232,0.000011432308],"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":[0.000014171325,0.000012263852,0.000009328276,0.00019645586,0.00004727945,0.000005368834,0.0004549261,0.003664487,0.007558652,0.000001783292,0.0003621519,0.9876731],"study_design_scores_gemma":[0.00024088791,0.0002182408,0.0015011791,0.0004989271,0.000021579097,0.000009624217,0.0016061119,0.0036525447,0.9914615,0.00006461195,0.00038222768,0.00034257356],"about_ca_topic_score_codex":0.0010486322,"about_ca_topic_score_gemma":0.000358071,"teacher_disagreement_score":0.98733056,"about_ca_system_score_codex":0.00011655784,"about_ca_system_score_gemma":0.000024620052,"threshold_uncertainty_score":0.92551917},"labels":[],"label_agreement":null},{"id":"W3210146046","doi":"10.23977/jemm.2021.060110","title":"Surface Defect Detection Model of Motor Commutator Based on Semantic Segmentation","year":2021,"lang":"en","type":"article","venue":"Journal of Engineering Mechanics and Machinery","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Computer science; Interpretability; Artificial intelligence; Encoder; Pattern recognition (psychology); Feature (linguistics); Scale-space segmentation; Image segmentation; Computer vision","score_opus":0.013169960170244749,"score_gpt":0.2130836102844284,"score_spread":0.19991365011418366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210146046","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":[0.4736554,0.00020034352,0.52541965,0.0000070497786,0.0006229423,0.00004703874,0.0000062713843,0.000024040128,0.000017259832],"genre_scores_gemma":[0.99711365,0.000053499498,0.0027065577,0.000010896253,0.00007859004,8.151737e-7,0.0000013032695,0.000026732096,0.000007976847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992471,0.000027290513,0.00036148098,0.0000700561,0.0001942718,0.000099830955],"domain_scores_gemma":[0.99954206,0.00007709455,0.00011199498,0.00009958898,0.00010675615,0.000062501604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035816783,0.00012051743,0.00024285099,0.00014863849,0.00002638992,0.00002398124,0.000038517588,0.000091341295,0.000003746593],"category_scores_gemma":[0.00006467094,0.0001119742,0.00013008344,0.00014435418,0.0000017281639,0.000084361534,0.000008993582,0.00024039109,5.003569e-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":[0.000018346607,0.000009940542,0.0000035269882,0.000075628326,0.000026066495,0.000004139428,0.000020695317,0.6107465,0.3882223,0.000053978965,0.000010563145,0.00080827076],"study_design_scores_gemma":[0.00046239112,0.00015718733,0.000015405856,0.000121506244,0.000029897607,0.000041063027,0.000025260837,0.84895736,0.15000805,0.00005272905,0.00004449284,0.00008465305],"about_ca_topic_score_codex":0.000004572889,"about_ca_topic_score_gemma":0.0000016233736,"teacher_disagreement_score":0.52345824,"about_ca_system_score_codex":0.000056469074,"about_ca_system_score_gemma":0.000024310199,"threshold_uncertainty_score":0.45661753},"labels":[],"label_agreement":null},{"id":"W3214236298","doi":"10.1016/j.ifacol.2021.08.043","title":"Predictive Models to Improve the Quality of a Textile Dyeing Process","year":2021,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Industrial Vision Systems and Defect Detection","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":"Université Laval","funders":"","keywords":"Quality (philosophy); Textile; Conformity; Process (computing); Computer science; Control (management); Dyeing; Operations management; Manufacturing engineering; Operations research; Engineering; Artificial intelligence","score_opus":0.03834465633923623,"score_gpt":0.2941104071781284,"score_spread":0.2557657508388922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214236298","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":[0.9601666,0.0002689666,0.031804036,0.00017096834,0.00084096903,0.0004959975,0.00017849596,0.00020736197,0.0058666416],"genre_scores_gemma":[0.9971295,0.0000070002448,0.0020448752,0.00006186331,0.00035528818,0.000047457175,0.00000854933,0.00002566248,0.00031981137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988501,0.00006309364,0.00038547776,0.0002116099,0.00028925738,0.00020046823],"domain_scores_gemma":[0.9992608,0.00010580144,0.00006508936,0.00028531448,0.00021125647,0.00007175171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036561742,0.00014046422,0.00026783004,0.000045278346,0.00007578561,0.000028079385,0.00011209405,0.00011952759,0.000043136537],"category_scores_gemma":[0.00013405878,0.00010451754,0.00010085106,0.00038200966,0.000018120949,0.00012545384,0.00003349119,0.00021118665,0.000013473427],"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":[0.00019339217,0.00011757244,0.00011369409,0.0004195128,0.00024182891,0.000011878413,0.010343509,0.5083444,0.43402994,0.00034187452,0.00006175443,0.045780644],"study_design_scores_gemma":[0.0018236004,0.00037402325,0.0010684242,0.00035030057,0.00007926139,0.000027022441,0.0142577635,0.690392,0.28912437,0.0006536619,0.0011875747,0.0006620096],"about_ca_topic_score_codex":0.00011452628,"about_ca_topic_score_gemma":0.000074383286,"teacher_disagreement_score":0.1820476,"about_ca_system_score_codex":0.000056412686,"about_ca_system_score_gemma":0.00005447186,"threshold_uncertainty_score":0.42621014},"labels":[],"label_agreement":null},{"id":"W3215343428","doi":"10.1109/rose52750.2021.9611765","title":"Detection and Location of Sheet Metal Parts for Industrial Robots","year":2021,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Clutter; Artificial intelligence; Computer science; Computer vision; Robot; Set (abstract data type); Process (computing); Object (grammar); Pattern recognition (psychology); Sheet metal; Industrial robot; Object detection; Cognitive neuroscience of visual object recognition; Engineering; Radar","score_opus":0.04041400744362248,"score_gpt":0.24006437608092981,"score_spread":0.19965036863730734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215343428","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":[0.73445696,0.0003554069,0.25970286,0.000022635286,0.0026054715,0.00047724167,0.0000069617217,0.00018564715,0.0021868413],"genre_scores_gemma":[0.99939096,0.0000054284665,0.00016516943,0.0000034361306,0.00026037288,0.000021588748,0.0000039202405,0.000009849763,0.00013926638],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995516,0.000018986471,0.0001900308,0.00009187261,0.00007040325,0.00007710785],"domain_scores_gemma":[0.9996682,0.00004489622,0.00002637923,0.00007579219,0.0001569585,0.000027797261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001518644,0.000060685117,0.00012628558,0.000048619124,0.00003211961,0.000017269173,0.000013269158,0.00013289461,0.000019075276],"category_scores_gemma":[0.00012306753,0.000057962337,0.000026764747,0.00026156075,0.000006821318,0.00008358495,0.0000065252807,0.00005637213,0.000001932874],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015392169,0.00005089414,0.00035229832,0.00034423653,0.00022533795,0.0000026148707,0.0002295026,0.0788397,0.47452924,0.0010095602,0.0023575798,0.4419051],"study_design_scores_gemma":[0.0013021226,0.00013628874,0.00040026757,0.00004377195,0.00004203532,0.000022208887,0.00019575213,0.042165853,0.94860536,0.00009146676,0.0068510966,0.00014379286],"about_ca_topic_score_codex":0.000027967404,"about_ca_topic_score_gemma":0.000048307375,"teacher_disagreement_score":0.4740761,"about_ca_system_score_codex":0.000019042798,"about_ca_system_score_gemma":0.0000189382,"threshold_uncertainty_score":0.23636356},"labels":[],"label_agreement":null},{"id":"W3215403001","doi":"10.14733/cadaps.2022.624-661","title":"A Prototype of an Automated Feature Recognition Algorithm for Aerospace Sheet Metal Parts","year":2021,"lang":"en","type":"article","venue":"Computer-Aided Design and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"CAD; Computer Aided Design; Engineering drawing; Computer-aided manufacturing; Aerospace; Computer science; Electronic design automation; Feature recognition; Domain (mathematical analysis); Feature (linguistics); Engineering; Manufacturing engineering; Feature extraction; Artificial intelligence; Mechanical engineering; Embedded system","score_opus":0.033236924856175784,"score_gpt":0.2689035612067446,"score_spread":0.23566663635056878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215403001","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":[0.004517505,0.0001380689,0.99281347,0.000022539734,0.00013457326,0.0016676119,0.0000490064,0.00061487214,0.000042329888],"genre_scores_gemma":[0.21238469,0.00003939649,0.7828974,0.00006128676,0.00080588233,0.0033020219,0.000363243,0.000065927474,0.000080130274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993717,0.000053202282,0.00018657578,0.00019884578,0.00007310786,0.00011656072],"domain_scores_gemma":[0.99943924,0.00010248591,0.000050026334,0.00017948556,0.00016094776,0.00006780801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015362228,0.00011009136,0.00018645223,0.000053511307,0.00009558274,0.000050623712,0.00004562382,0.00012795645,0.0000041728886],"category_scores_gemma":[0.000007519337,0.00011069135,0.000048723567,0.00025410054,0.000015232764,0.00009879593,0.000011787986,0.000077850884,0.000004961489],"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":[0.000043341803,0.00014094601,0.0000024164829,0.0001700601,0.00011200644,0.0000030355425,0.0001373913,0.009109418,0.02595781,0.0002346512,0.008586683,0.9555022],"study_design_scores_gemma":[0.0005741824,0.0002726304,0.00005657135,0.000042973777,0.000036296,0.00002885249,0.000025414194,0.9463428,0.041478965,0.0003796405,0.010605158,0.0001565059],"about_ca_topic_score_codex":0.0000029392825,"about_ca_topic_score_gemma":0.0000010818102,"teacher_disagreement_score":0.95534575,"about_ca_system_score_codex":0.000015371488,"about_ca_system_score_gemma":0.00002506779,"threshold_uncertainty_score":0.4513862},"labels":[],"label_agreement":null},{"id":"W3216360349","doi":"10.1111/1556-4029.14942","title":"The validation of 3D virtual comparison microscopy (VCM) in the comparison of expended cartridge cases","year":2021,"lang":"en","type":"article","venue":"Journal of Forensic Sciences","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Canadian Mounted Police","funders":"","keywords":"Cartridge; Scanner; Software; Microscope; Computer science; Sensitivity (control systems); Microscopy; Simulation; Biomedical engineering; Artificial intelligence; Engineering; Mechanical engineering; Medicine; Pathology; Operating system","score_opus":0.06934677619247065,"score_gpt":0.34622339752165454,"score_spread":0.2768766213291839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216360349","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":[0.9965459,0.0011957455,0.00046857412,0.000048543938,0.0013431183,0.00006610217,0.0000024304497,0.000003492644,0.0003261028],"genre_scores_gemma":[0.99963856,0.000021608836,0.00021075517,0.0000046960095,0.000113978465,8.194572e-7,3.647418e-7,0.000002887983,0.000006361439],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984338,0.00015079565,0.0007324628,0.0000621449,0.0005038932,0.00011688448],"domain_scores_gemma":[0.9987193,0.0005898978,0.00039067274,0.000105757,0.0001744448,0.00001994537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015060292,0.00006862075,0.00027770284,0.00010894726,0.0001172727,0.00006230834,0.000189479,0.000048251368,0.0000055066985],"category_scores_gemma":[0.000258009,0.000037456186,0.00008114313,0.00053217163,0.00016032821,0.00013988472,0.000013600205,0.00017166948,6.8917717e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033733126,0.0003639019,0.07129156,0.000110348934,0.00016161476,0.00006502133,0.0249771,0.24961607,0.51467633,0.0013290269,0.03106896,0.106002755],"study_design_scores_gemma":[0.00041734654,0.0005486593,0.0026077544,0.00014151659,0.000019873158,0.00013960015,0.019909006,0.0057231216,0.9692162,0.000063447565,0.0011481523,0.00006526933],"about_ca_topic_score_codex":0.000066191074,"about_ca_topic_score_gemma":0.00009455436,"teacher_disagreement_score":0.45453992,"about_ca_system_score_codex":0.000028802426,"about_ca_system_score_gemma":0.00008593464,"threshold_uncertainty_score":0.1527419},"labels":[],"label_agreement":null},{"id":"W3216658279","doi":"10.18280/ts.380504","title":"Color Based Object Categorization Using Histograms of Oriented Hue and Saturation","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Hue; Histogram; Pattern recognition (psychology); Luminance; Chrominance; Computer vision; Computer science; Cognitive neuroscience of visual object recognition; Support vector machine; Categorization; Invariant (physics); Color space; Object (grammar); Mathematics; Image (mathematics)","score_opus":0.017264351181495424,"score_gpt":0.21456024352174083,"score_spread":0.19729589234024542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216658279","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":[0.8209927,0.000068994996,0.1781693,0.000006033336,0.00035657224,0.00016183523,0.0000074187788,0.00006276425,0.00017439896],"genre_scores_gemma":[0.9992129,0.0000026937073,0.00060229015,0.00000942897,0.00010442607,0.0000072034995,0.000036707064,0.000012140766,0.000012208952],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935466,0.000043448366,0.00024946497,0.000108825174,0.00015117782,0.00009239894],"domain_scores_gemma":[0.9997278,0.000025706815,0.000052225983,0.00006268614,0.000099398654,0.00003219154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013456416,0.0000839677,0.00012933533,0.00007190162,0.000054730015,0.000023422335,0.000016609521,0.000073480005,0.00008445957],"category_scores_gemma":[0.000013961786,0.00008490162,0.000033480675,0.000243128,0.000011835782,0.00008423616,0.0000050067993,0.000055774653,0.0000011033842],"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":[0.000057214093,0.000039761206,0.0012050912,0.00013946171,0.000042939522,0.000006669163,0.00027597096,0.0790717,0.9124945,0.0003535092,0.000118880205,0.0061943205],"study_design_scores_gemma":[0.0013780018,0.00015415986,0.0029482716,0.000091086535,0.000052375155,0.0000120312,0.00021829912,0.63004905,0.3629417,0.000015391724,0.0019560005,0.00018365876],"about_ca_topic_score_codex":0.000044467524,"about_ca_topic_score_gemma":0.000024514458,"teacher_disagreement_score":0.55097735,"about_ca_system_score_codex":0.00007882242,"about_ca_system_score_gemma":0.000033843946,"threshold_uncertainty_score":0.34621876},"labels":[],"label_agreement":null},{"id":"W4200106304","doi":"10.1109/ictc52510.2021.9620801","title":"Robust Inspection of Micro-LED Chip Defects Using Unsupervised Anomaly Detection","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Information and Communication Technology Convergence (ICTC)","topic":"Industrial Vision Systems and Defect Detection","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":"Information Technology Research Centre; Ministry of Science and ICT, South Korea; Ministry of Trade, Industry and Energy","keywords":"Autoencoder; Anomaly detection; Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Pattern recognition (psychology); Process (computing); Unsupervised learning; Ground truth; Machine learning","score_opus":0.03552251364881482,"score_gpt":0.24223021881004,"score_spread":0.2067077051612252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200106304","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":[0.95985323,0.00019882726,0.029421816,0.00029793385,0.0010818894,0.00023735137,0.000024418694,0.00031784808,0.008566683],"genre_scores_gemma":[0.99788606,0.0010051278,0.0008713442,0.000046200676,0.000029892715,0.00003469818,0.00007268546,0.000010669832,0.0000432933],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878776,0.0000737638,0.0006179156,0.0001505858,0.0002280521,0.00014192419],"domain_scores_gemma":[0.9985335,0.00003829558,0.00023822756,0.00042816452,0.00071635726,0.00004548849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022213288,0.00017155813,0.00021722053,0.0005831415,0.00018857059,0.00008979802,0.0002640696,0.00030951406,0.00036323746],"category_scores_gemma":[0.00012417449,0.00019114038,0.000064712585,0.000647893,0.00010249168,0.0007101488,0.00009479972,0.0003559853,0.000054782377],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024679257,0.0001861796,0.009521211,0.00023276152,0.0004428503,0.000004980963,0.0018530893,0.013941551,0.7019963,0.1217199,0.00036620407,0.14948817],"study_design_scores_gemma":[0.0013952233,0.00013680622,0.0034521136,0.00029418676,0.000036586764,0.00010159014,0.0046886536,0.44777477,0.53568834,0.0013559362,0.0046404614,0.00043534368],"about_ca_topic_score_codex":0.000060134364,"about_ca_topic_score_gemma":0.00005817296,"teacher_disagreement_score":0.43383324,"about_ca_system_score_codex":0.00013908262,"about_ca_system_score_gemma":0.0000772295,"threshold_uncertainty_score":0.77944785},"labels":[],"label_agreement":null},{"id":"W4200127458","doi":"10.3390/s21248480","title":"Detecting Teeth Defects on Automotive Gears Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automotive industry; Process (computing); Scalability; Engineering; Automotive engineering; Visual inspection; Component (thermodynamics); Computer science; Artificial intelligence","score_opus":0.021659974509176082,"score_gpt":0.24000533710431635,"score_spread":0.21834536259514026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200127458","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":[0.99071944,0.000100854384,0.0016506555,0.0000035024666,0.0011725534,0.000097227916,8.885086e-7,0.00050475635,0.005750143],"genre_scores_gemma":[0.999088,0.0000064300125,0.00026585007,0.00001798401,0.00034051432,0.0000023138102,0.00000142754,0.0000535788,0.00022388961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989137,0.00014862051,0.00021068286,0.00022722298,0.00020086956,0.00029892265],"domain_scores_gemma":[0.99947494,0.0001548163,0.00004652065,0.00015567213,0.00009372364,0.00007430663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022209714,0.00016355066,0.00020232362,0.00012799942,0.00020548391,0.000066772336,0.000037265265,0.00017513605,0.000046879995],"category_scores_gemma":[0.00040590088,0.00017199964,0.00010646514,0.00039800195,0.000011244865,0.00005626864,0.000019125366,0.00049645314,0.00012129267],"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":[0.000008609458,0.0000068863033,0.00023038559,0.00002454845,0.0000362796,0.000097339056,0.0005421616,0.9346085,0.04955148,0.000015337453,0.000015029601,0.014863439],"study_design_scores_gemma":[0.0006046718,0.00012541914,0.0010093241,0.00022196947,0.0000347582,0.00017423231,0.002565534,0.656073,0.33527374,0.000029357803,0.0034355384,0.0004525102],"about_ca_topic_score_codex":0.000029942328,"about_ca_topic_score_gemma":0.000014522858,"teacher_disagreement_score":0.28572226,"about_ca_system_score_codex":0.00016076643,"about_ca_system_score_gemma":0.000017008351,"threshold_uncertainty_score":0.7013942},"labels":[],"label_agreement":null},{"id":"W4200522721","doi":"10.5121/csit.2021.112314","title":"One-Class Model for Fabric Defect Detection","year":2021,"lang":"en","type":"article","venue":"Natural Language Processing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Leverage (statistics); Autoencoder; Pattern recognition (psychology); Filter bank; Computer vision; Gabor filter; Filter (signal processing); Feature extraction; Deep learning","score_opus":0.018530193602422192,"score_gpt":0.25532403215627597,"score_spread":0.23679383855385377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200522721","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":[0.2970492,0.042984057,0.6545142,0.000036631587,0.0016871359,0.00048449248,0.000016832731,0.0011727461,0.002054727],"genre_scores_gemma":[0.99747413,0.000004663955,0.0014092247,0.000041068797,0.0004133532,0.00004074045,0.000014117402,0.0000394821,0.0005631935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919885,0.000017699955,0.00019347342,0.00019818691,0.00015847114,0.00023328855],"domain_scores_gemma":[0.9995968,0.000036565918,0.00003983333,0.00011504838,0.0001680247,0.000043752283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013031723,0.0001285912,0.00017034703,0.00008547168,0.0001492922,0.00012076802,0.00005250747,0.00016760638,0.0000066143048],"category_scores_gemma":[0.00014573967,0.00012993155,0.00012685117,0.00030525486,0.0000072641233,0.00021860344,0.000013703141,0.00026016665,0.000008195049],"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":[0.000016693557,0.000006524024,0.0000022486681,0.00021619124,0.000018221823,0.0000037566413,0.00045602222,0.011330979,0.174796,0.0000046695445,0.000055269593,0.8130934],"study_design_scores_gemma":[0.00034422215,0.000011846375,0.0000117273885,0.00006681269,0.000024771414,0.000022783059,0.00021471232,0.8630391,0.13567908,0.00004049364,0.00038249165,0.00016197082],"about_ca_topic_score_codex":0.000007983214,"about_ca_topic_score_gemma":0.00005438161,"teacher_disagreement_score":0.8517081,"about_ca_system_score_codex":0.0001427138,"about_ca_system_score_gemma":0.00004663141,"threshold_uncertainty_score":0.5298455},"labels":[],"label_agreement":null},{"id":"W4200571536","doi":"10.5539/ijsp.v11n1p40","title":"Parameters Estimation for Wear-out Failure Period of Three-Parameter Weibull Distribution","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hyperparameter; Estimator; Shape parameter; Mathematics; Weibull distribution; Statistics; Monte Carlo method; Sample size determination; Scale parameter; Hyperparameter optimization; Estimation theory; Linear regression; Applied mathematics; Algorithm; Computer science; Artificial intelligence","score_opus":0.021987838757915747,"score_gpt":0.2641721221547378,"score_spread":0.24218428339682205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200571536","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":[0.31185946,0.00006373882,0.6863107,0.00010904166,0.000898463,0.00010430847,0.0006345393,0.000006583553,0.000013157714],"genre_scores_gemma":[0.9370671,0.00001670877,0.062739424,0.0000052532623,0.00009309911,0.0000048560332,0.000060263395,0.0000068626146,0.0000064526125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990485,0.000031478554,0.0005118463,0.00008481494,0.00024584084,0.00007752491],"domain_scores_gemma":[0.99863803,0.00023904475,0.00019480372,0.00006972066,0.0008129372,0.000045451838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039279505,0.0000809196,0.0001915288,0.000040205672,0.00002741746,0.00006389201,0.00006450793,0.000073180185,0.000022210083],"category_scores_gemma":[0.00069986447,0.000072748226,0.0000735126,0.000044190103,0.00003732839,0.00011001065,0.000013868542,0.0001175368,7.5171374e-7],"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":[0.0018754391,0.000610378,0.01842353,0.00167121,0.0022689824,0.000089318295,0.0023921567,0.11449194,0.027615292,0.044550236,0.014806949,0.7712046],"study_design_scores_gemma":[0.0062169973,0.0015339727,0.02354412,0.0008197413,0.0003649094,0.0007939507,0.00045290464,0.57760185,0.064587675,0.30037782,0.022903705,0.0008023761],"about_ca_topic_score_codex":0.000007448926,"about_ca_topic_score_gemma":0.000030041698,"teacher_disagreement_score":0.7704022,"about_ca_system_score_codex":0.00008295138,"about_ca_system_score_gemma":0.000051208295,"threshold_uncertainty_score":0.29665864},"labels":[],"label_agreement":null},{"id":"W4205170610","doi":"10.18280/ts.380608","title":"Precise Inspection of Geometric Parameters for Polyvinyl Chloride Pipe Section Based on Computer Vision","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province","keywords":"Polyvinyl chloride; Roundness (object); Computer science; Computer vision; Context (archaeology); Artificial intelligence; Section (typography); Region of interest; Engineering; Materials science; Mechanical engineering; Geology","score_opus":0.017907807440070214,"score_gpt":0.2278274549380799,"score_spread":0.20991964749800968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205170610","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":[0.5424177,0.000022098142,0.45468423,0.000017667217,0.0019968688,0.00040691928,0.000022065007,0.00018831818,0.00024411092],"genre_scores_gemma":[0.9980676,0.00000464464,0.0010818451,0.000030682215,0.00066681823,0.000057255453,0.00003505851,0.00002911765,0.00002694998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878526,0.000046942198,0.00041997214,0.00023744116,0.00031819573,0.00019219944],"domain_scores_gemma":[0.99937874,0.00018683985,0.00008793693,0.00015648601,0.00012802923,0.00006199138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003359796,0.00016327176,0.0002393416,0.00043602387,0.00009011799,0.000048619164,0.000052453066,0.0001352549,0.00008186962],"category_scores_gemma":[0.000027345033,0.00016174611,0.00017517112,0.00067506084,0.0000121581625,0.000117241805,0.000008589169,0.00013084074,0.000007695836],"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":[0.0009574786,0.00033385764,0.00022263324,0.0002890613,0.00011829128,0.000007742715,0.000120341334,0.76163584,0.13042288,0.000046665937,0.00497961,0.10086562],"study_design_scores_gemma":[0.002086519,0.0013777508,0.0021488664,0.0001381912,0.000036844558,0.0000072647613,0.000034286764,0.5003784,0.48977003,0.000015632433,0.0037978492,0.00020838661],"about_ca_topic_score_codex":0.00002376095,"about_ca_topic_score_gemma":0.000006501627,"teacher_disagreement_score":0.4556499,"about_ca_system_score_codex":0.00016510407,"about_ca_system_score_gemma":0.000024224528,"threshold_uncertainty_score":0.6595815},"labels":[],"label_agreement":null},{"id":"W4206777409","doi":"10.1007/b100437","title":"Image Analysis and Recognition","year":2004,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology); Computer graphics (images)","score_opus":0.013096029702195505,"score_gpt":0.22491161204326518,"score_spread":0.21181558234106967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206777409","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":[0.0018049998,0.00017008926,0.99487364,0.000009256214,0.0010108728,0.00015885792,0.000008350709,0.0001283164,0.0018356068],"genre_scores_gemma":[0.8459101,0.000109755994,0.14969681,0.000256609,0.0031127173,0.000028045824,0.00007827548,0.000108996515,0.0006987215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887455,0.000014576236,0.00023499207,0.00038881466,0.00027211508,0.00021492783],"domain_scores_gemma":[0.9994973,0.00009194688,0.000051343504,0.00023174073,0.000066826324,0.000060852733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035285632,0.00019520416,0.0003086569,0.00096251006,0.00007326862,0.00019488919,0.00015629217,0.00026343277,0.000016943932],"category_scores_gemma":[0.00003173003,0.00018200946,0.00007654663,0.0011637291,0.00013025965,0.0001570602,0.000055121116,0.00039649662,0.000016560914],"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":[0.0000034549255,0.000005064244,0.000022820428,0.0000823847,0.00005957814,0.00002469386,0.0003878201,0.13200966,0.000595685,0.0000028667175,0.00004298202,0.866763],"study_design_scores_gemma":[0.0007044462,0.00018952381,0.00066170347,0.00076272956,0.00024289159,0.000083178675,4.867846e-7,0.95574886,0.009595984,0.029075023,0.0017637849,0.0011713692],"about_ca_topic_score_codex":0.00004102442,"about_ca_topic_score_gemma":0.00008780216,"teacher_disagreement_score":0.86559165,"about_ca_system_score_codex":0.00050243665,"about_ca_system_score_gemma":0.00018560166,"threshold_uncertainty_score":0.7422131},"labels":[],"label_agreement":null},{"id":"W4207070084","doi":"10.20380/gi2011.10","title":"Occlusion Tiling","year":2011,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Occlusion; Medicine; Cardiology","score_opus":0.020764926637078997,"score_gpt":0.21756753515084692,"score_spread":0.19680260851376793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207070084","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":[0.36201978,0.0019482067,0.3117855,0.00039753487,0.0029873638,0.00083652337,0.000053658525,0.0018865957,0.31808487],"genre_scores_gemma":[0.9899855,0.00031407503,0.00615405,0.000014172367,0.000096435775,0.000039138686,0.000120936515,0.00008325896,0.003192415],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967253,0.001602276,0.0005457652,0.00049633795,0.00032061472,0.0003096752],"domain_scores_gemma":[0.9969194,0.00037877422,0.00021604536,0.0014815867,0.0008522601,0.00015195632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003528942,0.00033138855,0.00037358276,0.00023320153,0.00024533278,0.00023292727,0.00060215883,0.0005793352,0.00022965991],"category_scores_gemma":[0.0004313552,0.00035445765,0.00022598659,0.00026158607,0.00005836801,0.00010226396,0.00067071314,0.00087559293,0.00016680562],"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":[0.00010917504,0.0011428238,0.00251269,0.002848863,0.00091159047,0.00009763225,0.061311133,0.01325853,0.09857654,0.07863153,0.026376182,0.7142233],"study_design_scores_gemma":[0.0017803019,0.0000010842964,0.0023856515,0.009547494,0.00017758702,0.00008296569,0.00035659465,0.21447006,0.66702634,0.010666614,0.09099348,0.0025118135],"about_ca_topic_score_codex":0.0009020626,"about_ca_topic_score_gemma":0.00034766458,"teacher_disagreement_score":0.71171147,"about_ca_system_score_codex":0.00014317085,"about_ca_system_score_gemma":0.000085985135,"threshold_uncertainty_score":0.99989074},"labels":[],"label_agreement":null},{"id":"W4210778605","doi":"10.1007/978-3-030-92529-1_110","title":"Machine Vision Sensor Based on Image Texture Analysis Applied to Industrial Anode Paste","year":2022,"lang":"en","type":"book-chapter","venue":"˜The œminerals, metals & materials series","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Alcoa (Canada)","funders":"","keywords":"Robustness (evolution); Anode; Artificial intelligence; Texture (cosmology); Process engineering; Materials science; Process (computing); Computer science; Computer vision; Pattern recognition (psychology); Engineering; Image (mathematics)","score_opus":0.01732938421216915,"score_gpt":0.22169019102011353,"score_spread":0.20436080680794438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210778605","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064407945,0.0017795356,0.006309265,0.0020692598,0.04380834,0.01863818,0.028916731,0.006209829,0.8278609],"genre_scores_gemma":[0.6848928,0.000108908906,0.0004831201,0.0009003738,0.0066339755,0.0007944943,0.0028827286,0.0009051394,0.30239847],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99569297,0.000307844,0.0014580134,0.000885745,0.0010596083,0.0005958245],"domain_scores_gemma":[0.9976119,0.00018082956,0.00051242433,0.0013756373,0.00011455633,0.00020469505],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014833855,0.0011660377,0.0021552334,0.0010541199,0.00044312867,0.000600468,0.0005021683,0.000882327,0.009244687],"category_scores_gemma":[0.000039652263,0.0008804627,0.0006155799,0.0005214611,0.00008123925,0.00020873192,0.00021465152,0.0008332623,0.00044246236],"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":[0.0030557748,0.000069185386,0.000001698533,0.00030445086,0.003116052,0.00013780562,0.00026517845,0.13960813,0.807525,0.0038041559,0.037969444,0.0041431254],"study_design_scores_gemma":[0.0012354541,0.00063355605,0.00001040927,0.00013745403,0.0018981409,0.000029225985,0.00006906547,0.00081622484,0.02741177,0.00022340514,0.9660618,0.0014735117],"about_ca_topic_score_codex":0.00028006293,"about_ca_topic_score_gemma":0.00009170358,"teacher_disagreement_score":0.92809236,"about_ca_system_score_codex":0.00024562096,"about_ca_system_score_gemma":0.000054576278,"threshold_uncertainty_score":0.9993646},"labels":[],"label_agreement":null},{"id":"W4210862162","doi":"10.1109/iaecst54258.2021.9695592","title":"Research on Adaptive Processing Method of Feature Data Domain of Bearing Health State of Injection Molding Machine","year":2021,"lang":"en","type":"article","venue":"2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MD Precision (Canada)","funders":"Research and Development; National Natural Science Foundation of China","keywords":"Computer science; Generalization; Feature (linguistics); Artificial intelligence; Domain (mathematical analysis); Machine learning; Bearing (navigation); Transfer of learning; Data modeling; Data mining; Mathematics","score_opus":0.18182591015535585,"score_gpt":0.43090806067296694,"score_spread":0.2490821505176111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210862162","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":[0.9081885,0.0006236718,0.082378685,0.0041017495,0.0008528523,0.00049452594,0.00022502549,0.00009680029,0.0030381654],"genre_scores_gemma":[0.9968643,0.00027120803,0.002657005,0.00003367256,0.000058004243,0.000013581821,0.000028217859,0.000009611249,0.00006437826],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99785465,0.00008244022,0.000575443,0.0004057026,0.00086545496,0.00021631834],"domain_scores_gemma":[0.9968753,0.00009974182,0.0003642877,0.00028763013,0.0023444572,0.000028588622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040047136,0.00011765577,0.00027614462,0.0018171092,0.00012821228,0.000023297023,0.00048021527,0.00022393945,0.000025673442],"category_scores_gemma":[0.0006619262,0.00011246039,0.000013118072,0.005362636,0.00030281578,0.00037418932,0.00027468608,0.000932437,0.0000011427568],"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":[0.0000524473,0.000031148895,0.00037357994,0.00010812756,0.000029526516,0.0000017970958,0.00053918315,0.00050578214,0.31447348,0.04566535,0.00026634368,0.6379532],"study_design_scores_gemma":[0.0010376755,0.000896312,0.001191146,0.0023539742,0.000008502485,0.000078753095,0.005223855,0.14205606,0.8272486,0.016265457,0.0033195158,0.00032017176],"about_ca_topic_score_codex":0.00004822047,"about_ca_topic_score_gemma":0.000014956993,"teacher_disagreement_score":0.637633,"about_ca_system_score_codex":0.00017343531,"about_ca_system_score_gemma":0.00033140377,"threshold_uncertainty_score":0.45860013},"labels":[],"label_agreement":null},{"id":"W4210913278","doi":"10.1007/978-3-030-80538-8_1","title":"Interpreting I-V Curves","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Diode; Voltage; Current (fluid); Nonlinear system; Electrical engineering; Semiconductor; Measure (data warehouse); Semiconductor device; Computer science; Mathematics; Materials science; Physics; Engineering; Nanotechnology; Data mining","score_opus":0.017089303341668655,"score_gpt":0.2028316135181874,"score_spread":0.18574231017651874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210913278","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000052276832,0.0016939858,0.00042301288,0.000013220452,0.0020968693,0.00016221746,0.000014500446,0.00057690643,0.9950141],"genre_scores_gemma":[0.007537248,0.00061703805,0.000023247212,0.00012366856,0.0004198585,0.000018905037,0.000026337253,0.00011282786,0.9911209],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993398,0.000006409893,0.00024454205,0.00013134113,0.000178204,0.00009971267],"domain_scores_gemma":[0.99970317,0.000035127516,0.000038142614,0.00017567695,0.000015231592,0.00003263643],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014087527,0.00016797564,0.00021860414,0.0001043591,0.000046928268,0.000017383181,0.00007845752,0.00016417209,0.019032931],"category_scores_gemma":[0.000009354181,0.00016441107,0.00012295222,0.000017408238,0.0000061296632,0.000040631974,0.000045282904,0.00045775497,0.00031200427],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000123179,0.0000033564097,0.0000019932422,0.0008058271,0.00025958446,0.00004623801,0.00009443562,0.0013416281,0.00020453267,0.0822104,0.8393893,0.07563039],"study_design_scores_gemma":[0.000044932218,0.000025271585,1.7497317e-7,0.0003823565,0.0000121937965,0.000014587929,0.0000059927665,0.0004012767,0.00005897377,0.00023644748,0.99862367,0.00019409534],"about_ca_topic_score_codex":0.000014486011,"about_ca_topic_score_gemma":0.0000036455358,"teacher_disagreement_score":0.15923439,"about_ca_system_score_codex":0.000099563724,"about_ca_system_score_gemma":0.0000085233405,"threshold_uncertainty_score":0.9818638},"labels":[],"label_agreement":null},{"id":"W4213216719","doi":"10.15376/biores.17.1.1187-1204","title":"Wood classification study based on thermal physical parameters with intelligent method of artificial neural networks","year":2022,"lang":"en","type":"article","venue":"BioResources","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Artificial neural network; Principal component analysis; Materials science; Transient (computer programming); Biological system; Emissivity; Thermal; Support vector machine; Artificial intelligence; Pattern recognition (psychology); Computer science; Meteorology; Optics","score_opus":0.03946090896204821,"score_gpt":0.2660719434800917,"score_spread":0.2266110345180435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213216719","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":[0.9730876,0.000009440673,0.026008124,0.000010076294,0.00027072764,0.00039139765,0.0000059294575,0.000104851846,0.00011183677],"genre_scores_gemma":[0.99957603,6.2448805e-8,0.00012637125,0.000012020761,0.00016479928,0.000088603614,0.0000043966675,0.000023077873,0.0000046459986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989089,0.0002574451,0.0002106586,0.0001717881,0.00031319723,0.00013802867],"domain_scores_gemma":[0.9995291,0.00013724228,0.000077718185,0.00020075572,0.000018564115,0.000036623864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003210828,0.00012306939,0.00018559667,0.00011944769,0.00011548308,0.000025659312,0.00010303263,0.000035487377,0.0000209891],"category_scores_gemma":[0.000008950519,0.00009605137,0.000068985464,0.000323831,0.000019496621,0.000022039232,0.000017742841,0.0002293632,0.0000017831757],"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":[0.0002142526,0.00020926389,0.00080936856,0.0000044373896,0.00002866005,0.0000021693652,0.0006223721,0.94573665,0.0024838739,0.00001026441,0.000023307608,0.049855355],"study_design_scores_gemma":[0.0002644522,0.0014215355,0.0028137213,0.000005737775,0.000027751408,0.0000015961828,0.0027661945,0.9875271,0.004809309,0.0000014622045,0.00024433143,0.00011683863],"about_ca_topic_score_codex":0.000052151237,"about_ca_topic_score_gemma":0.000004390028,"teacher_disagreement_score":0.049738515,"about_ca_system_score_codex":0.000048059337,"about_ca_system_score_gemma":0.0000061218307,"threshold_uncertainty_score":0.3916861},"labels":[],"label_agreement":null},{"id":"W4214937350","doi":"10.2139/ssrn.4047890","title":"Materials Informatics of Woven Fabric Composites: Effect of Different Dimensionality Reduction and Learning Methods","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Composite material; Woven fabric; Materials science; Dimensionality reduction; Reduction (mathematics); Curse of dimensionality; Artificial intelligence; Computer science; Mathematics; Geometry","score_opus":0.007341413685022096,"score_gpt":0.2550690205372272,"score_spread":0.2477276068522051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214937350","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":[0.98972386,0.00059428165,0.0089465035,0.0000039422084,0.0005100121,0.00013635083,0.0000024149103,0.000025478985,0.000057150697],"genre_scores_gemma":[0.9995867,0.00015118321,0.00013655689,6.4592666e-7,0.000066693945,0.0000064992837,0.0000036961476,0.000012697347,0.000035317684],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99817157,0.000684209,0.0004958873,0.000051554947,0.00021513802,0.00038165037],"domain_scores_gemma":[0.99949896,0.00012916472,0.00024782622,0.00006482437,0.000031428375,0.000027803017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038386055,0.00010333794,0.00031854436,0.00014106192,0.00018534777,0.000014279281,0.000057766887,0.000047483576,0.000023238214],"category_scores_gemma":[0.000045537265,0.00008471501,0.00006488309,0.00014138126,0.000015499741,0.00007823784,0.000041726315,0.0009850352,3.7932338e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028619487,0.000024230796,0.00048152075,0.00019972128,0.00028956545,3.3548162e-7,0.00065124914,0.024505006,0.91670215,0.0013769178,0.000017370874,0.055465735],"study_design_scores_gemma":[0.0028939222,0.005489917,0.001282689,0.00014126864,0.00022691916,0.0019238729,0.005433467,0.011100693,0.96489346,0.0056746206,0.0005324474,0.00040674102],"about_ca_topic_score_codex":0.000031406664,"about_ca_topic_score_gemma":0.0000010102376,"teacher_disagreement_score":0.055058993,"about_ca_system_score_codex":0.00032781178,"about_ca_system_score_gemma":0.00005415798,"threshold_uncertainty_score":0.42795426},"labels":[],"label_agreement":null},{"id":"W4221151971","doi":"10.48550/arxiv.2203.01902","title":"Instance Segmentation for Autonomous Log Grasping in Forestry Operations","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"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":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Computer science; Bounding overwatch; Automation; Task (project management); Segmentation; Artificial intelligence; Pixel; Artificial neural network; Data mining; Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.07797306437300797,"score_gpt":0.20632405283946617,"score_spread":0.1283509884664582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221151971","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":[0.74679804,0.000049992257,0.24961661,0.0000058020964,0.001442235,0.0007664777,0.00004532965,0.000201847,0.0010736374],"genre_scores_gemma":[0.99902743,0.00004188797,0.00019793403,0.000010403748,0.000107581356,0.000027694914,0.00009103337,0.000029851735,0.00046620754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912834,0.00004686647,0.00022737282,0.00036167642,0.000045624118,0.00019011638],"domain_scores_gemma":[0.99956,0.000041276155,0.00005465335,0.000261382,0.000038840786,0.000043832693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017814283,0.00017398577,0.00021378897,0.00028512438,0.00014964952,0.00004771221,0.00017374063,0.00022924451,0.000049754683],"category_scores_gemma":[0.000018191757,0.00023406716,0.00011098423,0.00032349897,0.000013913475,0.00015753129,0.00011607903,0.0004502578,0.000007126835],"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":[0.000020506472,0.000012763407,0.00072855985,0.00008151542,0.000024898778,0.000016715974,0.000129117,0.99564093,0.0001886253,0.0026958056,0.000094527444,0.0003660419],"study_design_scores_gemma":[0.00092756795,0.000054221247,0.00075711636,0.000086071814,0.000033058335,0.000001911879,0.0006364141,0.9938727,0.00042519224,0.0015314028,0.0013231516,0.00035117063],"about_ca_topic_score_codex":0.00023737336,"about_ca_topic_score_gemma":0.0003741725,"teacher_disagreement_score":0.25222933,"about_ca_system_score_codex":0.00074126734,"about_ca_system_score_gemma":0.00007997337,"threshold_uncertainty_score":0.9544982},"labels":[],"label_agreement":null},{"id":"W4223476417","doi":"10.1088/1361-6501/ac6663","title":"Lightweight edge-attention network for surface-defect detection of rubber seal rings","year":2022,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"MD Precision (Canada)","funders":"","keywords":"Computer science; Pyramid (geometry); Backbone network; Artificial intelligence; Convolutional neural network; Block (permutation group theory); Edge detection; Bottleneck; Feature (linguistics); Artificial neural network; Enhanced Data Rates for GSM Evolution; Natural rubber; Pattern recognition (psychology); Computer vision; Algorithm; Image processing; Image (mathematics); Embedded system; Mathematics; Materials science; Computer network","score_opus":0.021314916042489453,"score_gpt":0.21454512222892608,"score_spread":0.19323020618643663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223476417","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":[0.9890351,0.00046629494,0.007563132,0.000107886946,0.0015638012,0.0005146162,0.000003808886,0.00027895978,0.00046639232],"genre_scores_gemma":[0.9996879,0.0000071223953,0.00012036345,0.0000047763497,0.00006331438,0.00008518283,4.301627e-7,0.000010388273,0.000020510659],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986074,0.000020751862,0.00022130753,0.00022399616,0.0006562292,0.00027033742],"domain_scores_gemma":[0.9993747,0.000012873422,0.000068789646,0.00016035812,0.00035210993,0.000031172807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002512004,0.00009229299,0.00015426007,0.00030139787,0.0005276074,0.000020604659,0.00013621438,0.000079964586,0.0000074774043],"category_scores_gemma":[0.00008592705,0.000089533154,0.000043013035,0.0013692725,0.000111459674,0.00010639139,0.00006270798,0.00015826957,0.000001886741],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020417012,0.000016268808,0.0015144248,0.00003322489,0.000020243704,4.2502143e-7,0.000025216297,0.0028444764,0.9760446,0.0006951101,0.0005825109,0.018203061],"study_design_scores_gemma":[0.0014191816,0.0012065987,0.0025892665,0.00005620115,0.00006203554,0.000049436767,0.00046119004,0.019909391,0.8905815,0.0015980724,0.08166402,0.0004031196],"about_ca_topic_score_codex":0.000014531068,"about_ca_topic_score_gemma":0.000021966905,"teacher_disagreement_score":0.08546313,"about_ca_system_score_codex":0.0002946061,"about_ca_system_score_gemma":0.000040716062,"threshold_uncertainty_score":0.40579826},"labels":[],"label_agreement":null},{"id":"W4229440506","doi":"10.18280/ts.390238","title":"Deep Learning Application in Detecting Glass Defects with Color Space Conversion and Adaptive Histogram Equalization","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Histogram equalization; Artificial intelligence; HSL and HSV; Computer science; Adaptive histogram equalization; Computer vision; Preprocessor; Histogram; Color space; Process (computing); Equalization (audio); Pattern recognition (psychology); Image (mathematics); Channel (broadcasting)","score_opus":0.010937697455367792,"score_gpt":0.19840799968796316,"score_spread":0.18747030223259536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229440506","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":[0.8211626,0.00009527611,0.17767313,0.0000076229826,0.00009844588,0.0005117701,0.000001231516,0.00016025557,0.00028966964],"genre_scores_gemma":[0.99955106,0.0000045138913,0.00014211699,0.000010119633,0.00005344181,0.00018694415,0.000011983538,0.000023522185,0.000016310623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908364,0.00012605969,0.00019552805,0.00019111694,0.00023572777,0.00016793504],"domain_scores_gemma":[0.99972326,0.00007411027,0.000079729616,0.0000539371,0.000026999045,0.000041946132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041255614,0.00011902051,0.00013770032,0.00016592271,0.000244434,0.00002768221,0.000039674087,0.000049698836,0.000042417498],"category_scores_gemma":[0.000008633916,0.0001236446,0.000021892936,0.0003522668,0.000013391629,0.00010084423,0.000025405307,0.00023596005,0.00000326821],"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":[0.00028062778,0.00003205431,0.0039060812,0.00005251348,0.000023724715,0.0000075659204,0.0016652588,0.94188654,0.03130039,0.00034169605,0.000022961916,0.020480582],"study_design_scores_gemma":[0.0017488922,0.0008916593,0.0020503644,0.000030866875,0.000021796617,0.000019278263,0.0031509113,0.98249626,0.003687532,0.000011866339,0.0056556854,0.0002348625],"about_ca_topic_score_codex":0.0001360717,"about_ca_topic_score_gemma":0.000091547634,"teacher_disagreement_score":0.17838846,"about_ca_system_score_codex":0.0003264455,"about_ca_system_score_gemma":0.000010439868,"threshold_uncertainty_score":0.504208},"labels":[],"label_agreement":null},{"id":"W4229610751","doi":"10.1109/iembs.2006.4398462","title":"A computational model for reading frame maintenance","year":2006,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Industrial Vision Systems and Defect Detection","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":"Canadian Association for the Study of the Liver; North Carolina State University","keywords":"Computer science; Frame (networking); Reading (process); Computer network","score_opus":0.025576396003080214,"score_gpt":0.23305573966884496,"score_spread":0.20747934366576476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229610751","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":[0.14897,0.00001607069,0.8380881,0.000048881546,0.00022888758,0.0003024585,0.000013461949,0.00036700885,0.011965142],"genre_scores_gemma":[0.9919877,0.0000012632843,0.006988252,0.000020045318,0.00016355106,0.00008366695,0.0000065452477,0.00002023521,0.0007287493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934167,9.402105e-7,0.00020034329,0.00014790609,0.000110132605,0.00019903343],"domain_scores_gemma":[0.99962467,0.000021043317,0.00003876425,0.00003352883,0.00024804106,0.000033929144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012239671,0.000107419146,0.00013300481,0.00006920172,0.00007770043,0.00010593495,0.000069327376,0.00010495307,0.000008719223],"category_scores_gemma":[0.000029988909,0.00010532136,0.000045390352,0.00010354654,0.000016155785,0.00016633768,0.000008809189,0.00010153542,0.000014984777],"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":[0.000086007305,0.00003753832,0.0009705093,0.0004407867,0.000039950202,0.0000011284955,0.001406167,0.5499119,0.038544934,0.30820426,0.090625994,0.0097308215],"study_design_scores_gemma":[0.00029441854,0.000018976843,0.00009512528,0.000063524,0.0000047328117,0.0000055632563,0.000066441724,0.97323316,0.0009856337,0.022708487,0.0023906818,0.00013325238],"about_ca_topic_score_codex":0.000011937197,"about_ca_topic_score_gemma":0.000001279127,"teacher_disagreement_score":0.8430177,"about_ca_system_score_codex":0.000055396238,"about_ca_system_score_gemma":0.000020847321,"threshold_uncertainty_score":0.42948803},"labels":[],"label_agreement":null},{"id":"W4230301994","doi":"10.32920/ryerson.14645934.v1","title":"Spatial data analysis for laser scanning based shape error inspection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Laser scanning; Fast Fourier transform; Scanner; Computer science; Structured-light 3D scanner; Fourier transform; Discrete Fourier transform (general); Projection (relational algebra); Spatial frequency; Data processing; Artificial intelligence; Spatial analysis; Computer vision; Optics; Fourier analysis; Laser; Algorithm; Mathematics; Short-time Fourier transform; Statistics; Physics","score_opus":0.09108564960275241,"score_gpt":0.3025505371702329,"score_spread":0.2114648875674805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230301994","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":[0.101073,0.00008222665,0.891993,0.000028490342,0.0037307818,0.00056565535,0.00031242555,0.00087728444,0.0013371238],"genre_scores_gemma":[0.99242175,0.0000038001863,0.00361041,0.00002444413,0.0010999879,0.000098667966,0.002542966,0.00005384836,0.0001441449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983634,0.000051795832,0.00047998122,0.00063232076,0.00025127202,0.00022125251],"domain_scores_gemma":[0.9984636,0.000078323705,0.00010597159,0.001132372,0.00014502025,0.00007474321],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050507736,0.0002701318,0.0005391943,0.00048020986,0.00011162182,0.00027043137,0.00027606543,0.0005275664,0.0004583348],"category_scores_gemma":[0.000109823195,0.000272615,0.00033062143,0.0005257677,0.000009255429,0.00012922613,0.00024763547,0.0004298904,0.000010268812],"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":[0.00003463927,0.000021691534,0.0007581178,0.00024533801,0.001150939,0.000004744234,0.00005015114,0.98253727,0.00041630137,0.000002919674,0.0043085963,0.010469284],"study_design_scores_gemma":[0.0003552841,0.00001949116,0.0010520681,0.000079356854,0.00058328884,8.3031966e-7,0.00011041132,0.99140674,0.0026800842,0.0000034002894,0.0034020292,0.00030700627],"about_ca_topic_score_codex":0.0016877825,"about_ca_topic_score_gemma":0.002033818,"teacher_disagreement_score":0.8913487,"about_ca_system_score_codex":0.00015634148,"about_ca_system_score_gemma":0.00008998449,"threshold_uncertainty_score":0.9999726},"labels":[],"label_agreement":null},{"id":"W4231059779","doi":"10.1007/978-3-319-10602-1","title":"Computer Vision – ECCV 2014","year":2014,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":233,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Simon Fraser University; Lunds Universitet; Technische Universität Dresden; University of Toronto; Albert-Ludwigs-Universität Freiburg; Carnegie Mellon University; Institute of Science and Technology Austria; University College London; Imperial College London; Drexel University; Technische Universität Darmstadt; Tel Aviv University; Seoul National University; University of South Florida; California Institute of Technology; RWTH Aachen University; State University of New York; Microsoft Research; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; Massachusetts Institute of Technology","keywords":"Computer science; Volume (thermodynamics); Computer graphics (images); Set (abstract data type); Computer vision; Artificial intelligence; Programming language","score_opus":0.009582690174665225,"score_gpt":0.22831043900542425,"score_spread":0.21872774883075902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231059779","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":[0.00035133775,0.00021079702,0.9843767,0.000029428144,0.009827256,0.00032783966,0.000004055038,0.0003487869,0.004523761],"genre_scores_gemma":[0.8426676,0.000085989646,0.120269656,0.0017562556,0.030466635,0.000049022063,0.00006787674,0.00041943497,0.0042175646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755305,0.000052111704,0.0004888936,0.0007339187,0.0006582596,0.00051377906],"domain_scores_gemma":[0.99865407,0.00028959493,0.00011100501,0.00069800054,0.0001125762,0.00013476548],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00088527537,0.0004355761,0.00054696033,0.00067940704,0.00015121714,0.00031766677,0.00068464753,0.00060687854,0.000038462542],"category_scores_gemma":[0.000022904702,0.00038203894,0.00013299247,0.00046767897,0.00018235891,0.00019814505,0.00021795406,0.0008796251,0.0003336199],"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":[0.0000044624358,0.000006259071,0.000007326664,0.000072214985,0.0000069038597,0.000015835669,0.00008521125,0.41809377,0.00019725096,0.000032797918,0.011314362,0.5701636],"study_design_scores_gemma":[0.00030672443,0.00028008607,0.00006423814,0.0005670546,0.0000071606028,0.00006405314,3.9341707e-8,0.9170697,0.0009895725,0.002313081,0.077754885,0.00058342644],"about_ca_topic_score_codex":0.000013118916,"about_ca_topic_score_gemma":0.00003091078,"teacher_disagreement_score":0.8641071,"about_ca_system_score_codex":0.00044358082,"about_ca_system_score_gemma":0.00015784291,"threshold_uncertainty_score":0.99986315},"labels":[],"label_agreement":null},{"id":"W4232925706","doi":"10.1109/memea.2015.7145160","title":"Table of contents","year":2015,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"National Cancer Institute; Istituto Nazionale di Ricerca Metrologica; Masarykova Univerzita; University of Missouri; Xiamen University; Universität Bremen; University College London; Universitat Rovira i Virgili; Sapienza Università di Roma; Shenyang Institute of Automation; Turun Yliopisto; Vysoké Učení Technické v Brně; Chinese Academy of Sciences; ISCTE – Instituto Universitário de Lisboa; University of Sheffield; Istituto Italiano di Tecnologia; University of Tennessee, Knoxville; Politecnico di Torino; Institute of Automation, Chinese Academy of Sciences; Ottawa Hospital Research Institute; University of Chinese Academy of Sciences; Università degli Studi di Cagliari; Università degli Studi Roma Tre; Nazarbayev University; Università degli Studi di Salerno; University of Ottawa; Southeast University; Università degli Studi di Napoli Federico II; Missouri University of Science and Technology; Università della Calabria; Università degli Studi di Padova","keywords":"Table (database); Computer science; Database","score_opus":0.06158257562144078,"score_gpt":0.23740761584727385,"score_spread":0.17582504022583306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232925706","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":[0.6158923,0.00012728476,0.011146111,0.0000059992685,0.001789019,0.000114134506,0.0000027083433,0.0002405783,0.37068188],"genre_scores_gemma":[0.99864364,5.8184264e-7,0.00005420468,0.0000030314143,0.000042986438,0.0000013057263,3.012917e-7,0.0000037688799,0.0012501949],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997957,0.0000037341686,0.00007860668,0.000022229942,0.000058013677,0.000041769556],"domain_scores_gemma":[0.9998766,0.000004212386,0.00000692952,0.000049213624,0.00003457077,0.00002842696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066508896,0.000023067863,0.000055868037,0.000020405025,0.0000035158344,0.0000034565248,0.000016768294,0.000029569672,0.000034207827],"category_scores_gemma":[0.000013581658,0.000018502285,0.000010611539,0.00006282099,0.0000023854438,0.000033575627,0.000003160758,0.000020815254,0.000052702704],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098152486,0.00006790412,0.0076191975,0.00012378869,0.00015591618,0.000007720526,0.0008246808,0.046600692,0.18899895,0.0050840466,0.70565844,0.04476051],"study_design_scores_gemma":[0.0027555658,0.00026123627,0.0010349867,0.000051747196,0.000011708543,0.000015897123,0.0010770223,0.04053904,0.476666,0.00028888296,0.47701815,0.0002797469],"about_ca_topic_score_codex":0.00009560624,"about_ca_topic_score_gemma":0.000003450245,"teacher_disagreement_score":0.38275135,"about_ca_system_score_codex":0.0000073690885,"about_ca_system_score_gemma":0.000003028744,"threshold_uncertainty_score":0.07545013},"labels":[],"label_agreement":null},{"id":"W4233081149","doi":"10.1109/tcsvt.2019.2910747","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Electrical engineering; Engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233081149","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4234912296","doi":"10.1002/9781119302872","title":"Pattern Recognition","year":2018,"lang":"en","type":"book","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence","score_opus":0.028848420951577924,"score_gpt":0.20517139725244027,"score_spread":0.17632297630086236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234912296","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008641886,0.000036819925,0.003510307,0.000001539567,0.0029930843,0.0001447469,0.000024924946,0.00062779523,0.99257433],"genre_scores_gemma":[0.002796696,0.000022743954,0.000034857876,0.000043572767,0.004321351,0.000010338551,0.000113812166,0.000093994226,0.99256265],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994786,0.000008309999,0.00018451053,0.00011487287,0.00011213133,0.0001015928],"domain_scores_gemma":[0.99973464,0.000015618984,0.000028178501,0.00014304477,0.000043311007,0.00003519793],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00008381079,0.00014328584,0.00016009215,0.000098903394,0.000026593878,0.00003329227,0.000043708435,0.00046427257,0.0027605447],"category_scores_gemma":[0.0000045267216,0.00013235045,0.00007522931,0.000025298763,0.000008094562,0.00003708845,0.000008890403,0.00019209625,0.007954312],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.949136e-7,7.3038075e-7,1.2747321e-7,0.00003531873,0.000020612131,0.0000018965084,0.000010193573,0.0000021789785,0.000014789797,0.00000393057,0.81333244,0.18657689],"study_design_scores_gemma":[0.00010501139,0.000042874155,0.0000010829835,0.00014920621,0.000014159955,0.000011364434,0.0000030518272,0.0005472331,0.00019353179,0.00045034167,0.99827236,0.00020979757],"about_ca_topic_score_codex":0.000007896755,"about_ca_topic_score_gemma":0.000010276818,"teacher_disagreement_score":0.18636708,"about_ca_system_score_codex":0.00011270931,"about_ca_system_score_gemma":0.000014094149,"threshold_uncertainty_score":0.99815106},"labels":[],"label_agreement":null},{"id":"W4235192255","doi":"10.1007/978-94-017-2231-5_11","title":"Pattern Recognition and Classification Methodology for the Characterization of Material Response States","year":2000,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Identification (biology); Conjunction (astronomy); Characterization (materials science); Computer science; Artificial intelligence; Pattern recognition (psychology); Materials science; Physics; Nanotechnology; Biology","score_opus":0.13891296426016517,"score_gpt":0.27474224256557733,"score_spread":0.13582927830541217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235192255","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":[0.4002745,0.00024978942,0.54063696,0.00035360872,0.008517219,0.0067139845,0.0063946857,0.00085618277,0.036003057],"genre_scores_gemma":[0.86325395,0.0053132186,0.0015721725,0.00023529287,0.0043654824,0.0007559226,0.0071701664,0.0006138577,0.11671993],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992728,0.000072635456,0.0003646204,0.00013747299,0.000073743955,0.00007868096],"domain_scores_gemma":[0.99922705,0.00042562006,0.00012851079,0.00013393287,0.000067219684,0.00001766603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006120863,0.0001430121,0.00023068576,0.00011529048,0.000043571956,0.000027776567,0.000037801074,0.00034497352,0.0004197204],"category_scores_gemma":[0.000020564945,0.00010706327,0.000053471194,0.000015468087,0.000026247053,0.00005071223,0.000006153648,0.00009039093,0.000020332407],"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":[0.0014128304,0.0000031334293,7.5573564e-7,0.00013557833,0.000103141436,4.5200449e-7,0.00018830223,0.000023581117,0.19577971,0.00020291212,0.00032846112,0.8018211],"study_design_scores_gemma":[0.002687707,0.0013483602,0.0024377033,0.0007706161,0.0006493383,0.00010623201,0.00017754112,0.024609314,0.1395912,0.015180767,0.81094754,0.0014937032],"about_ca_topic_score_codex":0.000013998487,"about_ca_topic_score_gemma":0.000004585217,"teacher_disagreement_score":0.81061906,"about_ca_system_score_codex":0.000025069327,"about_ca_system_score_gemma":0.000008967975,"threshold_uncertainty_score":0.45956442},"labels":[],"label_agreement":null},{"id":"W4235387616","doi":"10.1007/978-981-10-6113-4_9","title":"Edge Detection","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Edge detection; Computer vision; Artificial intelligence; Morphological gradient; Pixel; Enhanced Data Rates for GSM Evolution; Zero crossing; Digital image; Image (mathematics); Computer science; Mathematics; Line (geometry); Canny edge detector; Deriche edge detector; Pattern recognition (psychology); Image processing; Physics; Geometry","score_opus":0.028941450095261535,"score_gpt":0.22021192461837205,"score_spread":0.19127047452311052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235387616","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019540063,0.00016425358,0.0037851771,0.0000019688323,0.004252321,0.00016880149,0.0000062009003,0.00054861855,0.9910531],"genre_scores_gemma":[0.08593736,0.000050515613,0.000010223796,0.00000466617,0.0014763664,0.000006286268,0.0000040046257,0.00007420832,0.91243637],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994137,0.000002496213,0.00019349836,0.00014627664,0.00013186914,0.0001121052],"domain_scores_gemma":[0.99946004,0.000011768943,0.00006107809,0.00038176824,0.00003563241,0.000049718597],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008817165,0.0002012383,0.00022687169,0.000121082965,0.00011040182,0.00007627926,0.0000868565,0.0006094653,0.00081580516],"category_scores_gemma":[0.0000081129265,0.00018682695,0.0001307992,0.0000040287828,0.000013298693,0.000067558496,0.000015859114,0.0003306576,0.0017045068],"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":[0.000016321508,0.0000023286582,5.706952e-7,0.00013071448,0.00019289312,0.000025245969,0.000029264971,0.00022878015,0.002428383,0.011027287,0.045541245,0.94037694],"study_design_scores_gemma":[0.00011691292,0.000029702403,0.0000030431136,0.000073300806,0.000018302515,0.00001753843,0.0000011074098,0.00045468615,0.002873216,0.0010129152,0.99516046,0.00023882043],"about_ca_topic_score_codex":0.000020339174,"about_ca_topic_score_gemma":0.00006133903,"teacher_disagreement_score":0.94961923,"about_ca_system_score_codex":0.00008329255,"about_ca_system_score_gemma":0.000008281837,"threshold_uncertainty_score":0.9990728},"labels":[],"label_agreement":null},{"id":"W4235452358","doi":"10.1007/978-0-387-39940-9_2154","title":"Camera Break Detection","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Computer graphics (images)","score_opus":0.012879083133347988,"score_gpt":0.20975450129537684,"score_spread":0.19687541816202886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235452358","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018974565,0.0037524076,0.0017694882,0.000002251805,0.0074421214,0.0008713193,0.00087608007,0.0004786302,0.98461795],"genre_scores_gemma":[0.13975263,0.0055215457,0.00011199505,0.000015599418,0.009141153,0.00010181253,0.0009400861,0.00048371952,0.84393144],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99773675,0.000028283208,0.0010183691,0.00039021397,0.0005413594,0.00028501722],"domain_scores_gemma":[0.9985129,0.000079288795,0.00033840715,0.0007940367,0.00013094494,0.00014441278],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038175032,0.00050518924,0.00082488655,0.00043390013,0.00006802484,0.000038119244,0.00019406878,0.00067277387,0.0001242038],"category_scores_gemma":[0.000031843934,0.00050587894,0.00022357056,0.00009223876,0.000031461972,0.00020597022,0.000035320485,0.0006166169,0.00035849278],"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":[0.00018246642,0.00007399855,0.000013287558,0.0047143837,0.001056153,0.00027533225,0.00042148505,0.005854888,0.0072644106,0.010424826,0.18676816,0.7829506],"study_design_scores_gemma":[0.0003346514,0.00012192715,0.0000059446284,0.0009613805,0.00009534869,0.0000870136,0.00002678031,0.0007485896,0.00056066,0.00004663854,0.9964775,0.0005336075],"about_ca_topic_score_codex":0.00031549114,"about_ca_topic_score_gemma":0.000056693298,"teacher_disagreement_score":0.8097093,"about_ca_system_score_codex":0.00017313423,"about_ca_system_score_gemma":0.00005034508,"threshold_uncertainty_score":0.9997393},"labels":[],"label_agreement":null},{"id":"W4237006680","doi":"10.1109/cccrv.2004.1301409","title":"Proceedings 1st Canadian Conference on Computer and Robot Vision 2004","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Robot; Computer vision; Computer graphics (images); Human–computer interaction","score_opus":0.015972218403801697,"score_gpt":0.21002495601663296,"score_spread":0.19405273761283126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237006680","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":[0.87706983,0.00006367963,0.017802736,0.00051253545,0.0016029166,0.00053539465,0.000006751582,0.00057865266,0.1018275],"genre_scores_gemma":[0.99923366,0.000005621201,0.000349649,0.00008344624,0.00017509128,0.000003640033,9.978587e-7,0.000011668083,0.00013619485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953055,0.00000205506,0.000106719905,0.00011937772,0.0000825102,0.00015879206],"domain_scores_gemma":[0.9997521,0.00000444898,0.000009653393,0.00004905261,0.00003746419,0.00014728004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068551904,0.000091555565,0.000094365976,0.00011137671,0.0000685316,0.00009477385,0.00003301486,0.00010298672,0.000042835894],"category_scores_gemma":[0.0000031934153,0.00007791042,0.00001594475,0.00009482897,0.0000094723355,0.00009462045,0.00000582369,0.00011007349,0.00009297669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013626413,0.00013551464,0.0018984809,0.00046084292,0.00019537834,0.00010940942,0.0034133005,0.27285835,0.057140496,0.14193566,0.11414752,0.40756878],"study_design_scores_gemma":[0.017178629,0.0065594944,0.064813994,0.0034321544,0.00008730397,0.0007198748,0.0015044443,0.4348973,0.12604693,0.00600104,0.33340165,0.005357192],"about_ca_topic_score_codex":0.015476675,"about_ca_topic_score_gemma":0.008447643,"teacher_disagreement_score":0.4022116,"about_ca_system_score_codex":0.00011642418,"about_ca_system_score_gemma":0.000025428422,"threshold_uncertainty_score":0.99107933},"labels":[],"label_agreement":null},{"id":"W4237738977","doi":"10.32920/ryerson.14652267.v1","title":"IC testing using thermal image based on intelligent classification methods","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Support vector machine; Pattern recognition (psychology); Feature extraction; Adaptive neuro fuzzy inference system; Computer science; Histogram; Perceptron; Fuzzy logic; Segmentation; Artificial neural network; Engineering; Image (mathematics); Fuzzy control system","score_opus":0.19285184374631936,"score_gpt":0.3724424168196926,"score_spread":0.17959057307337323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237738977","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":[0.11343198,0.00006679198,0.8646415,0.000011329451,0.002870255,0.000370855,0.0000039919946,0.0005437397,0.018059563],"genre_scores_gemma":[0.8366796,0.0000023791101,0.16261263,0.0000317255,0.0004926712,0.00003681449,0.000022552565,0.000071858216,0.000049824757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983244,0.00031359246,0.0005122296,0.00039367544,0.00023415967,0.00022195236],"domain_scores_gemma":[0.9987807,0.00031163121,0.00012311383,0.00054448936,0.00016954211,0.00007052505],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00097358314,0.00029844488,0.0003521105,0.0002317237,0.00008352706,0.00026378955,0.0001301903,0.000494241,0.0001931725],"category_scores_gemma":[0.00029597312,0.0002795837,0.0001749177,0.00028904658,0.000013737313,0.000060753053,0.00008547357,0.0008207842,0.00002493217],"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":[0.000007552112,0.000020121062,0.000033641507,0.00017566099,0.000034342997,0.000006231876,0.000071952316,0.53331596,0.35820544,0.000012454221,0.000048755206,0.10806791],"study_design_scores_gemma":[0.000088604225,0.000019900657,0.00025940654,0.0003004609,0.000027342721,0.0000032212397,0.0001630973,0.8605899,0.13805833,0.000010508351,0.00022138201,0.00025780857],"about_ca_topic_score_codex":0.00015738448,"about_ca_topic_score_gemma":0.0000031319728,"teacher_disagreement_score":0.7232476,"about_ca_system_score_codex":0.00034912443,"about_ca_system_score_gemma":0.00009767543,"threshold_uncertainty_score":0.9999656},"labels":[],"label_agreement":null},{"id":"W4238155101","doi":"10.32920/ryerson.14664126.v1","title":"Implementation of Edge & Shape Detection Techniques and their Performance Evaluation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Window (computing); Artificial intelligence; Enhanced Data Rates for GSM Evolution; Computer vision; Canny edge detector; Conveyor belt; Edge detection; Object (grammar); Key (lock); Image (mathematics); Image processing; Engineering","score_opus":0.044825775765887664,"score_gpt":0.30539750787047687,"score_spread":0.2605717321045892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238155101","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":[0.9864236,0.00039251026,0.010100674,0.0000030991082,0.000797403,0.0007090916,0.0000085390875,0.00024510187,0.0013199407],"genre_scores_gemma":[0.99892974,0.0002571745,0.00032055588,0.0000021222065,0.0002326769,0.00016268613,0.000053219566,0.000023896917,0.000017912267],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902403,0.00006955329,0.00040071592,0.00020855982,0.00019239663,0.0001047145],"domain_scores_gemma":[0.999389,0.00002120162,0.000117548385,0.00022025304,0.00022551848,0.000026493693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006686309,0.00017935417,0.0002430425,0.0001832995,0.000045971945,0.0000596228,0.000046997644,0.00030445014,0.00020041435],"category_scores_gemma":[0.000009947403,0.00016119133,0.00006718536,0.00012806572,0.000011102304,0.00012739429,0.000076822405,0.00025705853,0.000002079283],"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":[0.0000058078117,0.0000041978897,0.00020230506,0.0002542896,0.000042066375,8.186852e-8,0.00045119325,0.0009846642,0.043305654,0.0000016038533,0.000043929937,0.9547042],"study_design_scores_gemma":[0.00028663588,0.00006740205,0.0027178568,0.00020590097,0.000042174546,0.000016195938,0.0009989063,0.07835246,0.915681,0.00002841596,0.0013599893,0.00024307206],"about_ca_topic_score_codex":0.00023226139,"about_ca_topic_score_gemma":0.00034115897,"teacher_disagreement_score":0.95446116,"about_ca_system_score_codex":0.00012558396,"about_ca_system_score_gemma":0.00004251986,"threshold_uncertainty_score":0.6573192},"labels":[],"label_agreement":null},{"id":"W4238498597","doi":"10.1109/tcsvt.2019.2946106","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Electrical engineering; Engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238498597","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4239720362","doi":"10.1007/978-1-4939-7131-2_100860","title":"Pattern Recognition","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence","score_opus":0.0401481398640599,"score_gpt":0.2098775822060593,"score_spread":0.16972944234199938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239720362","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001549626,0.000039172195,0.0024071601,0.0000024829712,0.0021438461,0.00013654766,0.000024482555,0.0005320562,0.9945593],"genre_scores_gemma":[0.025164029,0.000071712864,0.000060377726,0.000074811,0.0044283713,0.000008560053,0.0000821934,0.00018081767,0.9699291],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99947214,0.0000028280285,0.00019748922,0.00012396931,0.00011272861,0.00009086356],"domain_scores_gemma":[0.9997236,0.000010577345,0.000031835316,0.00014931484,0.00004678588,0.000037876227],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000064036896,0.00016120476,0.00016313895,0.00010019938,0.000027132652,0.000030033683,0.000038904265,0.00044919224,0.014286351],"category_scores_gemma":[0.0000024479764,0.00014957295,0.00008560134,0.000009719629,0.000009669813,0.000036961133,0.000009668044,0.00018471287,0.011401625],"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":[0.0000044755957,0.0000015487902,3.2899246e-7,0.00006782798,0.00008659192,0.000009004742,0.000048464368,0.000005887177,0.0000896624,0.00019598658,0.2774901,0.7220001],"study_design_scores_gemma":[0.00012193315,0.000055804634,8.356956e-7,0.00017238868,0.000019195335,0.000017052313,0.0000048284455,0.00041310367,0.00028317724,0.0018032274,0.99683154,0.00027690033],"about_ca_topic_score_codex":0.000009351433,"about_ca_topic_score_gemma":0.000012929639,"teacher_disagreement_score":0.7217232,"about_ca_system_score_codex":0.00004989084,"about_ca_system_score_gemma":0.0000038323096,"threshold_uncertainty_score":0.98936814},"labels":[],"label_agreement":null},{"id":"W4240180750","doi":"10.1109/tcsvt.2019.2903710","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240180750","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4240253771","doi":"10.1109/tcsvt.2019.2891212","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Engineering; Electrical engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240253771","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4240584261","doi":"10.1007/978-3-662-53120-4_300742","title":"Visual Inspection","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence","score_opus":0.01460979378931326,"score_gpt":0.2122297103458652,"score_spread":0.19761991655655195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240584261","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000104945044,0.000107407555,0.0021183991,0.0000020077412,0.0039810003,0.00020527071,0.000004030964,0.0010356621,0.9924413],"genre_scores_gemma":[0.100969896,0.000038306873,0.000008141694,0.000011597072,0.001059783,0.0000023196328,0.00001134887,0.00008109377,0.8978175],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993944,0.000002629906,0.0002097214,0.00014142621,0.00015341984,0.00009841507],"domain_scores_gemma":[0.99974334,0.000014083396,0.000033021428,0.0001461827,0.000029817085,0.000033564655],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000060329967,0.00018426355,0.00022002273,0.00015670294,0.000028125101,0.000030426036,0.000036134206,0.0005200764,0.0011264175],"category_scores_gemma":[0.0000022741217,0.00017115544,0.00010875307,0.000017517465,0.000006516465,0.000049820173,0.000010962283,0.00030402665,0.0050248927],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000646708,0.00001618664,0.000014767979,0.00051493914,0.00063481834,0.000033258737,0.00011166897,0.005727094,0.003036733,0.28424662,0.4620275,0.24357173],"study_design_scores_gemma":[0.0001712029,0.000077379045,0.000006233927,0.00007989882,0.000015738548,0.000014110495,0.000005306112,0.004062286,0.0004463481,0.00030002007,0.99453443,0.0002870281],"about_ca_topic_score_codex":0.000011837838,"about_ca_topic_score_gemma":0.0000098598575,"teacher_disagreement_score":0.53250694,"about_ca_system_score_codex":0.00011453114,"about_ca_system_score_gemma":0.000014932907,"threshold_uncertainty_score":0.9997867},"labels":[],"label_agreement":null},{"id":"W4240818985","doi":"10.4095/220090","title":"Investigation of multiscale product for change eetection in difference images","year":2004,"lang":"en","type":"report","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Product (mathematics); Computer science; Mathematics; Geometry","score_opus":0.12103955073311118,"score_gpt":0.2940163038918147,"score_spread":0.17297675315870353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240818985","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":[0.8325343,0.0068424772,0.05399603,0.00014968411,0.037538454,0.020455975,0.0006167258,0.0022907685,0.045575544],"genre_scores_gemma":[0.9967414,0.00020449812,0.00032762627,0.0000018175878,0.0010277581,0.0004940996,0.000052796247,0.000047538735,0.0011024477],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987823,0.000021221702,0.00051511574,0.00023843907,0.00028464757,0.00015828773],"domain_scores_gemma":[0.99938446,0.000029043735,0.00014794212,0.00019302253,0.00021211288,0.00003339134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044994362,0.00019660134,0.00039710652,0.0003543306,0.000022430862,0.000016325934,0.00005587264,0.00034397858,0.000009701123],"category_scores_gemma":[0.00015470794,0.00017759246,0.00009576121,0.00024689132,0.000018624598,0.00009175296,0.000011182384,0.00022349425,0.000002925119],"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":[0.00013853495,0.00008353911,0.0065651317,0.019312305,0.0002525831,0.000009070463,0.0018174967,0.005191708,0.31143185,0.00006931438,0.0089255795,0.6462029],"study_design_scores_gemma":[0.0019501601,0.0004193071,0.073762506,0.0033395707,0.00008616985,0.00004663746,0.00010231327,0.0033591827,0.90002334,0.00040435896,0.015478565,0.0010278868],"about_ca_topic_score_codex":0.0031258892,"about_ca_topic_score_gemma":0.0004888655,"teacher_disagreement_score":0.645175,"about_ca_system_score_codex":0.00039007294,"about_ca_system_score_gemma":0.00012364818,"threshold_uncertainty_score":0.724201},"labels":[],"label_agreement":null},{"id":"W4241128676","doi":"10.14322/publons.r1035621","title":"10.14322/publons.r1035621","year":2000,"lang":"en","type":"dataset","venue":"Time to knit","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Chain code; Code (set theory); Pixel; Computer science; Chain (unit); Artificial intelligence; Mathematics; Programming language; Statistics; Image (mathematics); Physics","score_opus":0.007120997148260065,"score_gpt":0.17378571658024491,"score_spread":0.16666471943198485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241128676","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000016333257,0.00008319146,6.842541e-7,0.0000059154872,0.000081484584,0.00029255616,0.6877,0.00030774236,0.31152683],"genre_scores_gemma":[3.1240978e-7,0.0000012363787,0.0000034389266,0.000007720076,0.0007377329,0.000035479497,0.40965527,0.000037781763,0.589521],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99864346,0.000045643126,0.00038353028,0.00028760472,0.00029223578,0.00034752488],"domain_scores_gemma":[0.9990521,0.00004773338,0.000044943525,0.0006318654,0.000039561048,0.00018378695],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00018586835,0.00035067275,0.00044542106,0.00034095877,0.00007224717,0.00011647979,0.00027419208,0.0006042974,0.9774827],"category_scores_gemma":[0.000040421884,0.00035529022,0.00013456996,0.00033463223,0.00001089939,0.000070164664,0.00004074588,0.0004596316,0.9883048],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022505308,0.00000896647,5.229575e-10,0.00003617505,0.000037969443,0.000011069702,9.1128487e-7,0.00075376517,0.0000055536566,9.908167e-9,0.9141786,0.08494448],"study_design_scores_gemma":[0.00018787957,0.00010609066,2.1996084e-7,0.00009796108,0.00004124659,0.00002232168,2.6897087e-7,0.000100557096,0.000023861345,2.9076227e-7,0.99902004,0.00039927807],"about_ca_topic_score_codex":0.000114484494,"about_ca_topic_score_gemma":8.4079e-7,"teacher_disagreement_score":0.27804467,"about_ca_system_score_codex":0.00011363662,"about_ca_system_score_gemma":0.00003033112,"threshold_uncertainty_score":0.9998899},"labels":[],"label_agreement":null},{"id":"W4241400486","doi":"10.1007/978-3-319-10584-0","title":"Computer Vision – ECCV 2014","year":2014,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer graphics (images); Computer vision; Artificial intelligence","score_opus":0.009582690174665225,"score_gpt":0.22831043900542425,"score_spread":0.21872774883075902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241400486","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":[0.00035133775,0.00021079702,0.9843767,0.000029428144,0.009827256,0.00032783966,0.000004055038,0.0003487869,0.004523761],"genre_scores_gemma":[0.8426676,0.000085989646,0.120269656,0.0017562556,0.030466635,0.000049022063,0.00006787674,0.00041943497,0.0042175646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755305,0.000052111704,0.0004888936,0.0007339187,0.0006582596,0.00051377906],"domain_scores_gemma":[0.99865407,0.00028959493,0.00011100501,0.00069800054,0.0001125762,0.00013476548],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00088527537,0.0004355761,0.00054696033,0.00067940704,0.00015121714,0.00031766677,0.00068464753,0.00060687854,0.000038462542],"category_scores_gemma":[0.000022904702,0.00038203894,0.00013299247,0.00046767897,0.00018235891,0.00019814505,0.00021795406,0.0008796251,0.0003336199],"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":[0.0000044624358,0.000006259071,0.000007326664,0.000072214985,0.0000069038597,0.000015835669,0.00008521125,0.41809377,0.00019725096,0.000032797918,0.011314362,0.5701636],"study_design_scores_gemma":[0.00030672443,0.00028008607,0.00006423814,0.0005670546,0.0000071606028,0.00006405314,3.9341707e-8,0.9170697,0.0009895725,0.002313081,0.077754885,0.00058342644],"about_ca_topic_score_codex":0.000013118916,"about_ca_topic_score_gemma":0.00003091078,"teacher_disagreement_score":0.8641071,"about_ca_system_score_codex":0.00044358082,"about_ca_system_score_gemma":0.00015784291,"threshold_uncertainty_score":0.99986315},"labels":[],"label_agreement":null},{"id":"W4241443285","doi":"10.1109/tcsvt.2019.2928405","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Electrical engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241443285","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4241702673","doi":"10.18280/rces.020105","title":"SUPER-RESOLUTION RECONSTRUCTION METHOD INTEGRATED WITH IMAGE REGISTRATION","year":2015,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer vision; Image registration; Artificial intelligence; Computer science; Superresolution; Resolution (logic); Image (mathematics); Remote sensing; Geology","score_opus":0.03700147041843923,"score_gpt":0.2878967589850897,"score_spread":0.2508952885666505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241702673","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":[0.0052211303,0.039452747,0.9531072,0.000042610467,0.0012816502,0.000328328,0.0000033877473,0.00034630104,0.0002166479],"genre_scores_gemma":[0.2351395,0.03940446,0.72301716,0.00007450683,0.0018843856,0.0001935406,0.000041854222,0.00015515096,0.00008946346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991292,0.0000471474,0.00039927458,0.0001282154,0.00017546183,0.000120740195],"domain_scores_gemma":[0.9993272,0.00005730809,0.000073612064,0.00016049983,0.00033798133,0.00004340892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005564284,0.00016382673,0.00043074985,0.000099806435,0.000020899713,0.000014439872,0.000052602085,0.000050721916,0.0000016484086],"category_scores_gemma":[0.00009192776,0.0001254849,0.000060215963,0.00033260998,0.000017212722,0.0001753676,0.000013996848,0.00012465472,0.000005219017],"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":[0.000058871407,0.000042723976,0.000079061014,0.031470794,0.0012694707,0.000018995575,0.0010228759,0.50829303,0.005523871,0.0008254889,0.040115587,0.41127923],"study_design_scores_gemma":[0.0016714188,0.0008937753,0.0002489173,0.051194936,0.00030872406,0.0007110406,0.00040070698,0.84687865,0.011717522,0.000046138346,0.08492483,0.0010033589],"about_ca_topic_score_codex":0.000011027037,"about_ca_topic_score_gemma":0.0000016253928,"teacher_disagreement_score":0.41027588,"about_ca_system_score_codex":0.00011281435,"about_ca_system_score_gemma":0.000018859517,"threshold_uncertainty_score":0.5117126},"labels":[],"label_agreement":null},{"id":"W4242420045","doi":"10.1007/978-0-387-39940-9_2533","title":"Edge Detection","year":2009,"lang":"pt","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer vision","score_opus":0.021434317594231003,"score_gpt":0.2329948110872595,"score_spread":0.2115604934930285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242420045","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008454738,0.0076337717,0.009932846,0.000009234994,0.027179126,0.0027043975,0.0024167574,0.00053268485,0.9487457],"genre_scores_gemma":[0.5580861,0.00893846,0.00014560691,0.000017913742,0.014506897,0.00014874217,0.0011258062,0.0006445746,0.4163859],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948223,0.00012458263,0.0024000518,0.00092017796,0.0010827555,0.00065009226],"domain_scores_gemma":[0.99637234,0.00023379522,0.0010841663,0.0016137451,0.00034099625,0.0003549614],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011806759,0.0010811761,0.0017270361,0.00091763627,0.00021069092,0.00009766186,0.0004191684,0.0014631605,0.00042589533],"category_scores_gemma":[0.00016024135,0.0011251639,0.0005411212,0.0002699369,0.000075024866,0.00046401136,0.00011505378,0.0012914457,0.0012544707],"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":[0.0007347565,0.00031224353,0.00007333724,0.011368494,0.0022095367,0.0005630437,0.001195555,0.0073445477,0.017774845,0.023579393,0.04581397,0.8890303],"study_design_scores_gemma":[0.0010636991,0.0005086146,0.00003634235,0.003639144,0.0003556179,0.00016873056,0.00017282765,0.0025285895,0.0015000185,0.00003367579,0.98874164,0.0012510841],"about_ca_topic_score_codex":0.001066023,"about_ca_topic_score_gemma":0.00007927969,"teacher_disagreement_score":0.94292766,"about_ca_system_score_codex":0.00034275564,"about_ca_system_score_gemma":0.00012205599,"threshold_uncertainty_score":0.99983317},"labels":[],"label_agreement":null},{"id":"W4242725658","doi":"10.32920/ryerson.14649096","title":"Statistics Based Neural Networks Method for Industrial Image Inspection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Image (mathematics); Pixel; Pattern recognition (psychology); Set (abstract data type); Computer vision","score_opus":0.045816508315605325,"score_gpt":0.29572064492814343,"score_spread":0.2499041366125381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242725658","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":[0.0018303366,0.000065782864,0.98116016,0.000019794705,0.014102384,0.0009854723,0.00019526658,0.0008878351,0.00075296924],"genre_scores_gemma":[0.696804,0.000018092513,0.2907581,0.000106057014,0.009663811,0.00061599613,0.0014288682,0.00026678477,0.00033828517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983342,0.00015488482,0.00058586255,0.000404823,0.00021300468,0.00030718828],"domain_scores_gemma":[0.9988481,0.00032699507,0.00012571986,0.00037351772,0.00022656578,0.00009913353],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005832171,0.00034167795,0.00052136334,0.00017547113,0.00010968409,0.0003574172,0.000117957934,0.0010946994,0.00013661994],"category_scores_gemma":[0.0001827532,0.00034879637,0.00023448264,0.00019811196,0.000013069063,0.00007832149,0.00007940483,0.0011472839,0.000003292903],"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":[0.00005632908,0.000015074209,0.000011538846,0.00013593952,0.00008598874,0.0000074375334,0.00002315736,0.92548364,0.00045878897,0.00006277308,0.032123066,0.041536245],"study_design_scores_gemma":[0.00094766007,0.00006578097,0.000018980729,0.000068633984,0.00007566496,0.0000051116667,0.00006427113,0.9927485,0.0026193708,0.00004904592,0.0029790557,0.00035791437],"about_ca_topic_score_codex":0.00031245136,"about_ca_topic_score_gemma":0.00008784667,"teacher_disagreement_score":0.69497365,"about_ca_system_score_codex":0.00022399798,"about_ca_system_score_gemma":0.0000867402,"threshold_uncertainty_score":0.9998964},"labels":[],"label_agreement":null},{"id":"W4243132958","doi":"10.1088/1757-899x/646/1/011001","title":"Preface","year":2019,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Prosperity; Beijing; Engineering ethics; Computer science; Engineering; China; Library science; Engineering management; Artificial intelligence; Political science","score_opus":0.013077701443030372,"score_gpt":0.1994718937064846,"score_spread":0.18639419226345424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243132958","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":[0.9941192,0.000035504007,0.00032208252,0.000015498252,0.00235389,0.00016836416,0.0000055386695,0.00031058092,0.002669331],"genre_scores_gemma":[0.9995045,0.000032816428,0.00016484536,0.0000065808085,0.000094579955,0.000012275863,8.475513e-7,0.00001485434,0.00016871729],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990992,0.0000053453646,0.00017935615,0.00019498511,0.00022990198,0.00029120635],"domain_scores_gemma":[0.99962443,0.000010326023,0.000019276084,0.00017411305,0.000087948974,0.00008388287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004011949,0.00013758268,0.00018238933,0.00012551881,0.00007142514,0.00032607833,0.00013612547,0.00006635303,0.00017096102],"category_scores_gemma":[0.000038543894,0.0001258906,0.000011409634,0.00027180972,0.000057167268,0.00075981155,0.00004784575,0.00007043405,0.000104009625],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043868495,9.027014e-7,0.000013245517,0.00006150331,0.000002670326,7.659497e-7,0.000119239965,0.0020807427,0.9955268,0.0012486135,0.000024123525,0.0009170115],"study_design_scores_gemma":[0.00014817782,0.000064702166,0.0013804752,0.00008060035,0.0000029217895,0.000033033204,0.00015638511,0.0052183536,0.9850689,0.000026731994,0.007578893,0.00024082682],"about_ca_topic_score_codex":0.000016300944,"about_ca_topic_score_gemma":0.0000010135218,"teacher_disagreement_score":0.010457895,"about_ca_system_score_codex":0.000042634474,"about_ca_system_score_gemma":0.00003826348,"threshold_uncertainty_score":0.51336694},"labels":[],"label_agreement":null},{"id":"W4244063911","doi":"10.21611/qirt.2010.102","title":"Region of Interest Extraction based on Multi–resolution Analysis for Infrared Nondestructive Testing","year":2010,"lang":"en","type":"article","venue":"Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"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":"Natural Sciences and Engineering Research Council of Canada; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)","keywords":"Nondestructive testing; Extraction (chemistry); Resolution (logic); Infrared; Computer science; Materials science; Artificial intelligence; Optics; Physics; Chemistry","score_opus":0.13028781316021665,"score_gpt":0.3152962243231574,"score_spread":0.18500841116294076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244063911","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":[0.96017563,0.0000057214775,0.023211578,0.000060456943,0.0018688983,0.0006818098,0.00014234234,0.000101419704,0.013752127],"genre_scores_gemma":[0.99335456,0.0000024741805,0.006365702,0.000013572767,0.00007215183,0.000080004356,0.000010636706,0.00002386573,0.000077048986],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878806,0.000018820205,0.00049523456,0.00025162043,0.00029220554,0.00015406524],"domain_scores_gemma":[0.99739796,0.00031683245,0.00058475195,0.00015151632,0.0015059685,0.00004298852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035314512,0.0002124428,0.0002919927,0.00096701464,0.000096927484,0.00005796722,0.00036582994,0.00019280087,0.00003082703],"category_scores_gemma":[0.0007596097,0.00016899858,0.00034321775,0.0009868365,0.00014708278,0.00026731874,0.000023957942,0.00042973273,0.0000016588093],"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":[0.0015097406,0.00028031413,0.02083323,0.00014360486,0.001188143,4.227861e-7,0.00075185054,0.009627935,0.8067099,0.15513232,0.00042500976,0.0033975604],"study_design_scores_gemma":[0.0015425608,0.0008099259,0.083957255,0.0005030284,0.00020532415,0.0000037940213,0.0009335903,0.76495564,0.13581294,0.010687933,0.00020527863,0.00038275032],"about_ca_topic_score_codex":0.000037878697,"about_ca_topic_score_gemma":0.000017702592,"teacher_disagreement_score":0.7553277,"about_ca_system_score_codex":0.00004166258,"about_ca_system_score_gemma":0.00002653395,"threshold_uncertainty_score":0.68915623},"labels":[],"label_agreement":null},{"id":"W4244147709","doi":"10.1109/tcsvt.2019.2931602","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Electrical engineering; Engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244147709","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4244729059","doi":"10.32920/ryerson.14660640","title":"Analysis of Characteristics of the Zinc Oxide Material in Nanoscale","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Zinc; High-resolution transmission electron microscopy; Oxide; Aerospace; Materials science; Fourier transform infrared spectroscopy; Nanoscopic scale; X-ray photoelectron spectroscopy; Selected area diffraction; Computer science; Chemical engineering; Nanotechnology; Engineering; Metallurgy; Transmission electron microscopy; Aerospace engineering","score_opus":0.013689926746174847,"score_gpt":0.2210254014831417,"score_spread":0.20733547473696687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244729059","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":[0.99626964,0.000016347974,0.0006493202,0.0000027527985,0.0019883434,0.00014279451,0.000111016794,0.000024892412,0.00079487293],"genre_scores_gemma":[0.99972606,0.000013771341,0.00004873486,0.0000022604167,0.00006547683,0.0000089304995,0.00003158637,0.000012324578,0.00009085607],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893916,0.000062569445,0.00061690435,0.00012983664,0.00016467915,0.00008687291],"domain_scores_gemma":[0.9993411,0.00002577993,0.0001554399,0.0003991467,0.00006327039,0.000015289572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020343947,0.00011781792,0.0006016232,0.0002301264,0.000008375714,0.000021502863,0.00013166729,0.0003144173,0.00018186086],"category_scores_gemma":[0.000034459652,0.000088394256,0.00027143265,0.00050038396,0.000014013827,0.000016495795,0.00016102377,0.00020462552,6.8845753e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100387,0.00014069569,0.06528191,0.0018618391,0.0035832298,0.0000138056785,0.0012203781,0.1592505,0.76071,0.000117939235,0.00029751114,0.0074218125],"study_design_scores_gemma":[0.00029944783,0.000017005863,0.42522976,0.00054612564,0.0008108463,0.0000017795574,0.00027545384,0.032169335,0.53996074,0.000013277551,0.0003381625,0.00033803927],"about_ca_topic_score_codex":0.0010193748,"about_ca_topic_score_gemma":0.00025388532,"teacher_disagreement_score":0.35994786,"about_ca_system_score_codex":0.0000502132,"about_ca_system_score_gemma":0.000028576284,"threshold_uncertainty_score":0.3604613},"labels":[],"label_agreement":null},{"id":"W4244977724","doi":"10.7287/peerj-cs.187v0.1/reviews/1","title":"Peer Review #1 of \"Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk (v0.1)\"","year":2019,"lang":"en","type":"peer-review","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Sciences and Engineering Research Council","funders":"","keywords":"Fault (geology); Inverse; Test (biology); Reliability engineering; Computer science; Seismology; Engineering; Mathematics; Geology","score_opus":0.04862664376956864,"score_gpt":0.37697522430039065,"score_spread":0.328348580530822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244977724","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029587247,0.24323305,0.34969422,0.020097353,0.10726178,0.08007516,0.013001855,0.0062112133,0.17746666],"genre_scores_gemma":[0.0014160111,0.18087666,0.030832725,0.016089214,0.009692555,0.0033503529,0.0033547003,0.0015770404,0.7528107],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9943477,0.0008631683,0.0015823687,0.0008843176,0.0018541561,0.00046832574],"domain_scores_gemma":[0.99542004,0.00045921156,0.0004763733,0.0014667602,0.0017739729,0.00040364717],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007042694,0.0008098216,0.002318708,0.0005692566,0.00007855693,0.000082399245,0.0004213788,0.00068999425,0.00078430405],"category_scores_gemma":[0.004739537,0.0006056179,0.00067014206,0.0014308562,0.000015042978,0.00021262457,0.00008235977,0.00112518,0.0011139275],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000398668,0.00005372667,0.0000062837953,0.028842328,0.00016505938,0.0000029550413,0.000034708835,0.0013322978,0.00015477769,0.0000020311052,0.8865955,0.082770444],"study_design_scores_gemma":[0.0004819994,0.0018884011,0.000034699,0.09104137,0.0007537896,0.000020730069,0.000013012978,0.00050453725,0.0005740088,0.0000021448402,0.90408957,0.00059572863],"about_ca_topic_score_codex":0.0005944616,"about_ca_topic_score_gemma":0.00020715139,"teacher_disagreement_score":0.5753441,"about_ca_system_score_codex":0.0003627053,"about_ca_system_score_gemma":0.0001465843,"threshold_uncertainty_score":0.99966383},"labels":[],"label_agreement":null},{"id":"W4246733687","doi":"10.32920/ryerson.14652267","title":"IC testing using thermal image based on intelligent classification methods","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Support vector machine; Feature extraction; Computer science; Adaptive neuro fuzzy inference system; Histogram; Perceptron; Fuzzy logic; Segmentation; Artificial neural network; Image (mathematics); Fuzzy control system","score_opus":0.19285184374631936,"score_gpt":0.3724424168196926,"score_spread":0.17959057307337323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246733687","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":[0.11343198,0.00006679198,0.8646415,0.000011329451,0.002870255,0.000370855,0.0000039919946,0.0005437397,0.018059563],"genre_scores_gemma":[0.8366796,0.0000023791101,0.16261263,0.0000317255,0.0004926712,0.00003681449,0.000022552565,0.000071858216,0.000049824757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983244,0.00031359246,0.0005122296,0.00039367544,0.00023415967,0.00022195236],"domain_scores_gemma":[0.9987807,0.00031163121,0.00012311383,0.00054448936,0.00016954211,0.00007052505],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00097358314,0.00029844488,0.0003521105,0.0002317237,0.00008352706,0.00026378955,0.0001301903,0.000494241,0.0001931725],"category_scores_gemma":[0.00029597312,0.0002795837,0.0001749177,0.00028904658,0.000013737313,0.000060753053,0.00008547357,0.0008207842,0.00002493217],"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":[0.000007552112,0.000020121062,0.000033641507,0.00017566099,0.000034342997,0.000006231876,0.000071952316,0.53331596,0.35820544,0.000012454221,0.000048755206,0.10806791],"study_design_scores_gemma":[0.000088604225,0.000019900657,0.00025940654,0.0003004609,0.000027342721,0.0000032212397,0.0001630973,0.8605899,0.13805833,0.000010508351,0.00022138201,0.00025780857],"about_ca_topic_score_codex":0.00015738448,"about_ca_topic_score_gemma":0.0000031319728,"teacher_disagreement_score":0.7232476,"about_ca_system_score_codex":0.00034912443,"about_ca_system_score_gemma":0.00009767543,"threshold_uncertainty_score":0.9999656},"labels":[],"label_agreement":null},{"id":"W4246913635","doi":"10.1109/tcsvt.2019.2912856","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Engineering; Electrical engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246913635","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4247194616","doi":"10.32920/ryerson.14660640.v1","title":"Analysis of Characteristics of the Zinc Oxide Material in Nanoscale","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Zinc; High-resolution transmission electron microscopy; Oxide; Aerospace; Fourier transform infrared spectroscopy; Materials science; Nanoscopic scale; Computer science; Nanotechnology; Chemical engineering; Engineering; Metallurgy; Transmission electron microscopy; Aerospace engineering","score_opus":0.013689926746174847,"score_gpt":0.2210254014831417,"score_spread":0.20733547473696687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247194616","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":[0.99626964,0.000016347974,0.0006493202,0.0000027527985,0.0019883434,0.00014279451,0.000111016794,0.000024892412,0.00079487293],"genre_scores_gemma":[0.99972606,0.000013771341,0.00004873486,0.0000022604167,0.00006547683,0.0000089304995,0.00003158637,0.000012324578,0.00009085607],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893916,0.000062569445,0.00061690435,0.00012983664,0.00016467915,0.00008687291],"domain_scores_gemma":[0.9993411,0.00002577993,0.0001554399,0.0003991467,0.00006327039,0.000015289572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020343947,0.00011781792,0.0006016232,0.0002301264,0.000008375714,0.000021502863,0.00013166729,0.0003144173,0.00018186086],"category_scores_gemma":[0.000034459652,0.000088394256,0.00027143265,0.00050038396,0.000014013827,0.000016495795,0.00016102377,0.00020462552,6.8845753e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100387,0.00014069569,0.06528191,0.0018618391,0.0035832298,0.0000138056785,0.0012203781,0.1592505,0.76071,0.000117939235,0.00029751114,0.0074218125],"study_design_scores_gemma":[0.00029944783,0.000017005863,0.42522976,0.00054612564,0.0008108463,0.0000017795574,0.00027545384,0.032169335,0.53996074,0.000013277551,0.0003381625,0.00033803927],"about_ca_topic_score_codex":0.0010193748,"about_ca_topic_score_gemma":0.00025388532,"teacher_disagreement_score":0.35994786,"about_ca_system_score_codex":0.0000502132,"about_ca_system_score_gemma":0.000028576284,"threshold_uncertainty_score":0.3604613},"labels":[],"label_agreement":null},{"id":"W4247936228","doi":"10.14322/publons.r1037817","title":"10.14322/publons.r1037817","year":2000,"lang":"en","type":"dataset","venue":"Time to knit","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Metrology; Sampling (signal processing); Computer science; Data mining; Statistics; Mathematics; Computer vision","score_opus":0.007231978702022455,"score_gpt":0.17475786489824716,"score_spread":0.1675258861962247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247936228","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000015970849,0.00008090682,5.256412e-7,0.0000069234125,0.00008197215,0.00029346682,0.6888849,0.0003145031,0.3103352],"genre_scores_gemma":[2.6555267e-7,0.0000012198841,0.0000032043176,0.0000108661525,0.000748239,0.00003486047,0.40773028,0.000037676993,0.5914334],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986495,0.000044675187,0.00038069245,0.00028679008,0.0002913279,0.0003470494],"domain_scores_gemma":[0.99903095,0.00004694599,0.0000458236,0.0006419412,0.000050976287,0.00018334475],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001866417,0.0003508152,0.00044459727,0.00034095795,0.000074880816,0.00011847004,0.00028169015,0.00060327834,0.9765352],"category_scores_gemma":[0.00005142272,0.00035530012,0.00013432006,0.0003197522,0.00001105073,0.00007075593,0.00004099495,0.00046001526,0.9884233],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023301409,0.000008993532,4.3008294e-10,0.00003680655,0.000038534694,0.000011083609,9.222287e-7,0.0007577441,0.000006262493,9.30987e-9,0.9311339,0.067982405],"study_design_scores_gemma":[0.00018703418,0.00010662224,1.8624014e-7,0.00009756457,0.000041373187,0.000021309686,2.9313975e-7,0.000085608575,0.000027574682,2.8938908e-7,0.99903315,0.00039901942],"about_ca_topic_score_codex":0.000117886724,"about_ca_topic_score_gemma":8.415582e-7,"teacher_disagreement_score":0.28115463,"about_ca_system_score_codex":0.000113217655,"about_ca_system_score_gemma":0.000030032053,"threshold_uncertainty_score":0.9998899},"labels":[],"label_agreement":null},{"id":"W4248781873","doi":"10.1007/978-3-662-53120-4_300763","title":"Shape Distortion","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Distortion (music); Computer science; Materials science; Telecommunications","score_opus":0.019187313374522678,"score_gpt":0.19564994593116178,"score_spread":0.1764626325566391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248781873","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009181084,0.00018133604,0.001462001,0.0000024615997,0.0030545818,0.00019132716,0.00000886858,0.0004262263,0.9945814],"genre_scores_gemma":[0.051400006,0.000031985488,0.000010321798,0.000011035656,0.00063079956,0.0000024304215,0.000019722844,0.00006749697,0.9478262],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994357,0.0000019797849,0.00020504126,0.00012726465,0.00014125035,0.00008876175],"domain_scores_gemma":[0.9997066,0.000012237532,0.00003402181,0.00019112452,0.000023149889,0.00003281962],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000055015797,0.00016451383,0.00020277651,0.00009169446,0.000018456756,0.000021607177,0.00004372077,0.00043351486,0.0033072561],"category_scores_gemma":[0.000002215483,0.00014886154,0.00010970285,0.0000105216695,0.0000050409267,0.000041060815,0.000009619831,0.00022610567,0.0045280373],"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":[0.000031777767,0.000006518123,0.000010541653,0.00046175052,0.0003244601,0.000021449727,0.0000698993,0.0024164887,0.0010948824,0.14754507,0.36782548,0.4801917],"study_design_scores_gemma":[0.00009748725,0.000027528295,0.00000654956,0.00007361447,0.000013783256,0.0000054719044,0.0000015826818,0.0019130174,0.000134967,0.00028930858,0.99721944,0.0002172375],"about_ca_topic_score_codex":0.000005272838,"about_ca_topic_score_gemma":0.0000051054544,"teacher_disagreement_score":0.629394,"about_ca_system_score_codex":0.000105060906,"about_ca_system_score_gemma":0.000011089777,"threshold_uncertainty_score":0.99760383},"labels":[],"label_agreement":null},{"id":"W4249237664","doi":"10.1109/tcsvt.2019.2943268","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Electrical engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249237664","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4249501470","doi":"10.1109/cccrv.2004.1301411","title":"Proceedings 1st Canadian Conference on Computer and Robot Vision 2004 Table of Contents","year":2004,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Table (database); Computer science; Table of contents; Robot; Artificial intelligence; Computer graphics (images); Computer vision; Robot vision; Human–computer interaction; Multimedia; Mobile robot; World Wide Web; Database","score_opus":0.021664721949718198,"score_gpt":0.2132091482164519,"score_spread":0.1915444262667337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249501470","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":[0.95257217,0.00006249653,0.007495512,0.00013002373,0.00086138607,0.0004411508,0.000013069234,0.00016250511,0.038261693],"genre_scores_gemma":[0.99949855,0.0000061891624,0.00023403847,0.000034967223,0.00006276941,0.0000029009293,0.0000012099542,0.000009510125,0.00014989523],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99951386,0.0000022555973,0.00014741533,0.00010249841,0.00009226599,0.00014169625],"domain_scores_gemma":[0.999738,0.000005084127,0.000019899817,0.000049488364,0.00007670324,0.000110846006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000067566405,0.00008301884,0.00013250254,0.00010533383,0.000041211777,0.000040640763,0.00003564936,0.000091763424,0.000032978063],"category_scores_gemma":[0.0000050607214,0.00007075534,0.000015690388,0.0001047392,0.000012553658,0.00009544831,0.0000065077793,0.000081431586,0.000019759225],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034249132,0.0003081928,0.0075985375,0.0011592932,0.00040326986,0.000058965485,0.0033507263,0.27339113,0.3720748,0.09384215,0.092509605,0.15496084],"study_design_scores_gemma":[0.02332271,0.0073149563,0.075129434,0.005422861,0.000106023166,0.0002755297,0.0019975132,0.29698223,0.49345425,0.0023388832,0.09018384,0.0034717754],"about_ca_topic_score_codex":0.033358324,"about_ca_topic_score_gemma":0.005584992,"teacher_disagreement_score":0.15148906,"about_ca_system_score_codex":0.00008012177,"about_ca_system_score_gemma":0.000023027884,"threshold_uncertainty_score":0.9730786},"labels":[],"label_agreement":null},{"id":"W4249594181","doi":"10.1007/978-3-319-10590-1","title":"Computer Vision – ECCV 2014","year":2014,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":168,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer graphics (images); Computer vision; Artificial intelligence","score_opus":0.009582690174665225,"score_gpt":0.22831043900542425,"score_spread":0.21872774883075902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249594181","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":[0.00035133775,0.00021079702,0.9843767,0.000029428144,0.009827256,0.00032783966,0.000004055038,0.0003487869,0.004523761],"genre_scores_gemma":[0.8426676,0.000085989646,0.120269656,0.0017562556,0.030466635,0.000049022063,0.00006787674,0.00041943497,0.0042175646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755305,0.000052111704,0.0004888936,0.0007339187,0.0006582596,0.00051377906],"domain_scores_gemma":[0.99865407,0.00028959493,0.00011100501,0.00069800054,0.0001125762,0.00013476548],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00088527537,0.0004355761,0.00054696033,0.00067940704,0.00015121714,0.00031766677,0.00068464753,0.00060687854,0.000038462542],"category_scores_gemma":[0.000022904702,0.00038203894,0.00013299247,0.00046767897,0.00018235891,0.00019814505,0.00021795406,0.0008796251,0.0003336199],"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":[0.0000044624358,0.000006259071,0.000007326664,0.000072214985,0.0000069038597,0.000015835669,0.00008521125,0.41809377,0.00019725096,0.000032797918,0.011314362,0.5701636],"study_design_scores_gemma":[0.00030672443,0.00028008607,0.00006423814,0.0005670546,0.0000071606028,0.00006405314,3.9341707e-8,0.9170697,0.0009895725,0.002313081,0.077754885,0.00058342644],"about_ca_topic_score_codex":0.000013118916,"about_ca_topic_score_gemma":0.00003091078,"teacher_disagreement_score":0.8641071,"about_ca_system_score_codex":0.00044358082,"about_ca_system_score_gemma":0.00015784291,"threshold_uncertainty_score":0.99986315},"labels":[],"label_agreement":null},{"id":"W4249892566","doi":"10.32920/ryerson.14645934","title":"Spatial data analysis for laser scanning based shape error inspection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Laser scanning; Fast Fourier transform; Scanner; Structured-light 3D scanner; Computer science; Fourier transform; Discrete Fourier transform (general); Projection (relational algebra); Spatial frequency; Data processing; Artificial intelligence; Computer vision; Optics; Laser; Fourier analysis; Algorithm; Mathematics; Short-time Fourier transform; Physics","score_opus":0.09108564960275241,"score_gpt":0.3025505371702329,"score_spread":0.2114648875674805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249892566","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":[0.101073,0.00008222665,0.891993,0.000028490342,0.0037307818,0.00056565535,0.00031242555,0.00087728444,0.0013371238],"genre_scores_gemma":[0.99242175,0.0000038001863,0.00361041,0.00002444413,0.0010999879,0.000098667966,0.002542966,0.00005384836,0.0001441449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983634,0.000051795832,0.00047998122,0.00063232076,0.00025127202,0.00022125251],"domain_scores_gemma":[0.9984636,0.000078323705,0.00010597159,0.001132372,0.00014502025,0.00007474321],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050507736,0.0002701318,0.0005391943,0.00048020986,0.00011162182,0.00027043137,0.00027606543,0.0005275664,0.0004583348],"category_scores_gemma":[0.000109823195,0.000272615,0.00033062143,0.0005257677,0.000009255429,0.00012922613,0.00024763547,0.0004298904,0.000010268812],"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":[0.00003463927,0.000021691534,0.0007581178,0.00024533801,0.001150939,0.000004744234,0.00005015114,0.98253727,0.00041630137,0.000002919674,0.0043085963,0.010469284],"study_design_scores_gemma":[0.0003552841,0.00001949116,0.0010520681,0.000079356854,0.00058328884,8.3031966e-7,0.00011041132,0.99140674,0.0026800842,0.0000034002894,0.0034020292,0.00030700627],"about_ca_topic_score_codex":0.0016877825,"about_ca_topic_score_gemma":0.002033818,"teacher_disagreement_score":0.8913487,"about_ca_system_score_codex":0.00015634148,"about_ca_system_score_gemma":0.00008998449,"threshold_uncertainty_score":0.9999726},"labels":[],"label_agreement":null},{"id":"W4250063739","doi":"10.1149/ma2016-02/33/2149","title":"(Invited) Transparent Top Gate Oxide TFT with ITO/Ag/ITO Low Resistance Electrode for the Application to the High Speed Operation Fingerprint Sensor Array in the Touch Panel","year":2016,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Thin-film transistor; Materials science; Transparency (behavior); Optoelectronics; Capacitance; Transistor; Electrode; Fingerprint (computing); Sheet resistance; Indium tin oxide; Computer science; Capacitive sensing; Fingerprint recognition; Electrical engineering; Layer (electronics); Nanotechnology; Engineering; Artificial intelligence; Voltage","score_opus":0.021882470621306934,"score_gpt":0.2334581741444746,"score_spread":0.21157570352316765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250063739","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":[0.96071804,0.000060614555,0.029960683,0.006415396,0.0003773167,0.0016898158,0.0000134053325,0.00011329465,0.0006514328],"genre_scores_gemma":[0.9983715,0.000014844931,0.0003846066,0.00021396123,0.00059396727,0.00028428558,0.000003861246,0.00003362109,0.000099386205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986026,0.00008222914,0.0004133766,0.00027196092,0.00029929585,0.00033048747],"domain_scores_gemma":[0.99867916,0.0006709535,0.000104973886,0.0004266697,0.00007437244,0.000043885426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011760141,0.00018925262,0.00016750825,0.00005376308,0.00025732137,0.00012778655,0.00023376626,0.000101333346,0.0000020492566],"category_scores_gemma":[0.00017126635,0.00008606146,0.00005265096,0.00023206764,0.000016797836,0.00006946052,0.000005962219,0.00021792884,0.000020169045],"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":[0.00013232236,0.000014722553,0.00005342493,0.000027159324,0.000018124534,0.0000012564229,0.000758297,0.52357495,0.47255695,0.000015110646,0.00094048533,0.0019071744],"study_design_scores_gemma":[0.0010070733,0.00011341766,0.015624533,0.00052497495,0.000041606552,0.0000120822215,0.0006731669,0.0056260456,0.9283885,0.00006646495,0.047551595,0.00037054787],"about_ca_topic_score_codex":0.00035512898,"about_ca_topic_score_gemma":0.0010841687,"teacher_disagreement_score":0.5179489,"about_ca_system_score_codex":0.00012974712,"about_ca_system_score_gemma":0.000019567264,"threshold_uncertainty_score":0.35094845},"labels":[],"label_agreement":null},{"id":"W4251294750","doi":"10.24908/iqurcp.8601","title":"Detection of Visually Cued Faults in High Speed Automation","year":2018,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Automation; Machine vision; Process (computing); Computer science; Fault (geology); Quality (philosophy); Fault detection and isolation; Artificial intelligence; Real-time computing; Engineering; Reliability engineering; Operating system","score_opus":0.07612586890899167,"score_gpt":0.35117785861721096,"score_spread":0.2750519897082193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251294750","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":[0.9884919,0.000013275621,0.0055963984,0.00026144623,0.0006455626,0.0006606042,0.0000030897857,0.0003268719,0.0040008617],"genre_scores_gemma":[0.9991125,0.00005614571,0.000102932136,0.0000045489737,0.0005012169,0.000043540535,0.0000038632834,0.000040832838,0.00013443883],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99737704,0.00009262081,0.00065495295,0.00037479858,0.0009167289,0.00058387406],"domain_scores_gemma":[0.99792916,0.00010911506,0.000115257375,0.00016610634,0.0015504817,0.00012990594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018566104,0.00021489208,0.00034978165,0.0010826098,0.00014637675,0.00013453582,0.00025323583,0.0003122791,0.000025071633],"category_scores_gemma":[0.00040955568,0.00021396847,0.0000564999,0.0016830442,0.00030173876,0.00062638556,0.000077270866,0.0005975732,0.000121699835],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045052575,0.00011935422,0.002251923,0.00059216557,0.00008756569,0.00000763046,0.0057589356,0.00018707638,0.9134353,0.01123922,0.0015024138,0.06436792],"study_design_scores_gemma":[0.0026145421,0.0018110175,0.01167904,0.0010400263,0.000015990125,0.000019001933,0.0035808135,0.14453043,0.80877507,0.02321537,0.0020201472,0.0006985239],"about_ca_topic_score_codex":0.0009981069,"about_ca_topic_score_gemma":0.0001549356,"teacher_disagreement_score":0.14434336,"about_ca_system_score_codex":0.00034215758,"about_ca_system_score_gemma":0.00011399923,"threshold_uncertainty_score":0.8725381},"labels":[],"label_agreement":null},{"id":"W4252117927","doi":"10.1109/tcsvt.2019.2918716","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Information retrieval; Electrical engineering; Engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252117927","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4252308724","doi":"10.25071/10315/35247","title":"Comparative Analysis Of Optitrack Motion Capture Systems","year":2018,"lang":"en","type":"article","venue":"Progress in Canadian Mechanical Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute for Christian Studies; University of Toronto","funders":"","keywords":"Computer science; Motion capture; Motion analysis; Motion (physics); Artificial intelligence; Computer vision","score_opus":0.019399867418451486,"score_gpt":0.2543730031196259,"score_spread":0.23497313570117442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252308724","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":[0.93590784,0.002508426,0.052898478,0.00003273032,0.0045958306,0.0009944609,0.00013035563,0.00045477026,0.0024770885],"genre_scores_gemma":[0.9994937,0.0000031235058,0.00028189796,0.0000032350272,0.00015110291,0.000034856188,0.000009398098,0.000016271591,0.0000064105143],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989285,0.000023452558,0.00036805714,0.00016574579,0.00016075469,0.00035350822],"domain_scores_gemma":[0.99943644,0.000031768825,0.00003627501,0.00018086498,0.00008232781,0.00023234438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003164598,0.00014380763,0.00041956865,0.0008679078,0.000026405678,0.000033425706,0.00011029744,0.00021274888,0.00003284379],"category_scores_gemma":[0.00002546079,0.00014994014,0.000081723025,0.0014332964,0.000019212886,0.00008217661,0.000008629185,0.00020922645,0.0000101710575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001603123,0.000021584594,0.0025607033,0.00018563008,0.00085110625,0.00002776929,0.0007729288,0.9795359,0.0017023138,0.010112134,0.00015508327,0.0040588113],"study_design_scores_gemma":[0.00014567073,0.00003165796,0.0017084088,0.00007696022,0.000074346,0.0000033438876,0.000075976044,0.9936191,0.0023215213,0.00000360784,0.0017760214,0.00016339215],"about_ca_topic_score_codex":0.019495007,"about_ca_topic_score_gemma":0.047101617,"teacher_disagreement_score":0.06358585,"about_ca_system_score_codex":0.00030879502,"about_ca_system_score_gemma":0.000034348708,"threshold_uncertainty_score":0.98703426},"labels":[],"label_agreement":null},{"id":"W4252399243","doi":"10.1109/tcsvt.2018.2833172","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Electrical engineering; Engineering","score_opus":0.02458374953077947,"score_gpt":0.2433630229134183,"score_spread":0.21877927338263883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252399243","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":[0.039514862,0.00052696705,0.94737124,0.00025707393,0.006175086,0.0036693842,0.00047749333,0.0016957775,0.00031214155],"genre_scores_gemma":[0.9960022,0.00013855386,0.000042249696,0.000060781298,0.00024668232,0.0031710446,0.000011001635,0.000086542634,0.00024095067],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972544,0.00005991043,0.0011395116,0.0005943961,0.00028990803,0.0006618764],"domain_scores_gemma":[0.9979339,0.00027456883,0.00030127022,0.00063797674,0.00069115934,0.00016112979],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006340877,0.00048610952,0.0007360112,0.0024870522,0.0008997737,0.00030402557,0.00024258115,0.001203983,0.000009295963],"category_scores_gemma":[0.000052881725,0.000485349,0.00015802846,0.0012027189,0.0002302004,0.00072696444,0.000001133538,0.00056256365,0.00005637182],"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":[0.0002913434,0.0003581362,0.00003752075,0.003327865,0.0012106173,0.0000049101855,0.0011825594,0.030499391,0.052117113,0.012067328,0.0074344105,0.8914688],"study_design_scores_gemma":[0.010946438,0.007890646,0.000033152057,0.0017885469,0.00066924264,0.0016960078,0.0052720318,0.3753352,0.19342504,0.001276177,0.39885175,0.0028157542],"about_ca_topic_score_codex":0.00011141717,"about_ca_topic_score_gemma":0.00005895678,"teacher_disagreement_score":0.95648736,"about_ca_system_score_codex":0.00028423293,"about_ca_system_score_gemma":0.00005878732,"threshold_uncertainty_score":0.9997598},"labels":[],"label_agreement":null},{"id":"W4252905448","doi":"10.1109/tcsvt.2019.2900782","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Electrical engineering; Engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252905448","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4253540109","doi":"10.1149/ma2011-02/16/990","title":"Diagnostic Tools for Stack Performance and Degradation","year":2011,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Automotive Fuel Cell Cooperation (Canada)","funders":"","keywords":"Stack (abstract data type); Degradation (telecommunications); Computer science; Operating system; Telecommunications","score_opus":0.05034264146843936,"score_gpt":0.22997142246993155,"score_spread":0.17962878100149218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253540109","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":[0.96694434,0.00006630852,0.00018011697,0.0000028211957,0.00059434405,0.00023104982,0.0000042308975,0.00015500512,0.031821758],"genre_scores_gemma":[0.9987648,0.000027048523,0.00090080174,0.0000047271014,0.00018481961,0.000030204785,0.000004471747,0.000018588351,0.00006453897],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994665,0.000008199348,0.00020521651,0.00009983397,0.00007092011,0.00014935505],"domain_scores_gemma":[0.99937963,0.00041263766,0.00004662988,0.00008028012,0.000034253324,0.000046567442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031719948,0.00008489072,0.000092670256,0.000043110656,0.00008228755,0.000053947202,0.00003217704,0.000081179984,0.0000061444066],"category_scores_gemma":[0.00067723426,0.00008235843,0.000020575788,0.000050798622,0.000007772897,0.00023287823,0.0000061381015,0.00008086031,0.000022095352],"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":[0.0002864032,0.00015540243,0.022025468,0.0021297613,0.00021580304,0.000027571548,0.005754042,0.19456823,0.121510155,0.00011740939,0.013438729,0.63977104],"study_design_scores_gemma":[0.0016198892,0.00045380183,0.212705,0.0007156678,0.0000635494,0.000043673565,0.00043733316,0.029564677,0.72017914,0.00018505614,0.03327303,0.00075916864],"about_ca_topic_score_codex":0.000034067794,"about_ca_topic_score_gemma":0.000004467918,"teacher_disagreement_score":0.63901186,"about_ca_system_score_codex":0.000020444757,"about_ca_system_score_gemma":0.0000060791012,"threshold_uncertainty_score":0.3358479},"labels":[],"label_agreement":null},{"id":"W4256242627","doi":"10.32920/ryerson.14637387","title":"Implementation of Object Recognition Algorithm to enhance Manufacturing and Maintenance Tasks on an Aircraft","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Cognitive neuroscience of visual object recognition; Object (grammar); Object detection; Class (philosophy); 3D single-object recognition; Machine learning; Deep learning; Pattern recognition (psychology); Transfer of learning; Computer vision; Algorithm","score_opus":0.019848854568924722,"score_gpt":0.2945895539626166,"score_spread":0.2747406993936919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256242627","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":[0.89865893,0.000035511985,0.0984338,0.000015071451,0.0012390037,0.0005896041,0.00008244592,0.00023072524,0.00071491295],"genre_scores_gemma":[0.99360204,0.00006108168,0.0057759318,0.00003645582,0.00025100127,0.00008598063,0.000099374854,0.000036316138,0.00005184456],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987403,0.000060645623,0.00042556025,0.00038426023,0.00020058578,0.00018865457],"domain_scores_gemma":[0.99943036,0.00003059151,0.00009177383,0.00027578764,0.000085970736,0.00008549345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028073494,0.00022529422,0.00031821817,0.00019110709,0.00003976304,0.000084228835,0.00006538543,0.00024095607,0.000110852205],"category_scores_gemma":[0.00001151946,0.00022696078,0.00006775978,0.00007861934,0.000007139935,0.00011133634,0.000076937635,0.00032375232,0.000011432308],"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":[0.000014171325,0.000012263852,0.000009328276,0.00019645586,0.00004727945,0.000005368834,0.0004549261,0.003664487,0.007558652,0.000001783292,0.0003621519,0.9876731],"study_design_scores_gemma":[0.00024088791,0.0002182408,0.0015011791,0.0004989271,0.000021579097,0.000009624217,0.0016061119,0.0036525447,0.9914615,0.00006461195,0.00038222768,0.00034257356],"about_ca_topic_score_codex":0.0010486322,"about_ca_topic_score_gemma":0.000358071,"teacher_disagreement_score":0.98733056,"about_ca_system_score_codex":0.00011655784,"about_ca_system_score_gemma":0.000024620052,"threshold_uncertainty_score":0.92551917},"labels":[],"label_agreement":null},{"id":"W4256292777","doi":"10.1109/tcsvt.2019.2940862","title":"IEEE Transactions on Circuits and Systems for Video Technology publication information","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Electronic circuit; Multimedia; Engineering; Electrical engineering","score_opus":0.019515755355761107,"score_gpt":0.23109048353986253,"score_spread":0.21157472818410142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256292777","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":[0.08397668,0.0006846691,0.9011177,0.00023457046,0.006481295,0.0050733723,0.00049216487,0.0016099338,0.00032962023],"genre_scores_gemma":[0.996108,0.00016212459,0.0000274109,0.00005179451,0.000110307694,0.003050315,0.000013927403,0.000088296285,0.0003878121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727285,0.000057461442,0.0011269047,0.0006032223,0.00030111906,0.0006384592],"domain_scores_gemma":[0.99808675,0.00031974283,0.0003050865,0.00066919846,0.00047297723,0.00014623495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006014105,0.00048789108,0.0008067459,0.002443294,0.00054308074,0.0002884631,0.00023752468,0.0012139336,0.000011216403],"category_scores_gemma":[0.000035392164,0.00048820625,0.00017678073,0.0010788372,0.00010634377,0.0007686005,0.000001088168,0.00064068363,0.00008106973],"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":[0.0003080527,0.0004077613,0.000107322056,0.0062623923,0.0014257524,0.0000052624337,0.0009948597,0.16909997,0.07308687,0.015364211,0.004497158,0.7284404],"study_design_scores_gemma":[0.014496962,0.0064824987,0.000049732356,0.0022263764,0.0006557428,0.001640336,0.006639794,0.50055665,0.123616114,0.0010343125,0.33936805,0.0032334677],"about_ca_topic_score_codex":0.000098119985,"about_ca_topic_score_gemma":0.000027659305,"teacher_disagreement_score":0.9121313,"about_ca_system_score_codex":0.0002959078,"about_ca_system_score_gemma":0.00005714316,"threshold_uncertainty_score":0.99975693},"labels":[],"label_agreement":null},{"id":"W4256534299","doi":"10.48009/2_iis_2016_193-200","title":"DETERMINING RSA PRIVATE KEY USING MICROSOFT EXCEL SOLVER","year":2016,"lang":"en","type":"article","venue":"Issues in Information Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kwantlen Polytechnic University","funders":"","keywords":"Microsoft excel; Computer science; Key (lock); Solver; Operating system; Programming language; Software engineering","score_opus":0.020634599159324963,"score_gpt":0.24990338098636922,"score_spread":0.22926878182704424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256534299","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":[0.9185532,0.0002360212,0.07005605,0.000010761607,0.0046374737,0.0005339825,0.000018918512,0.0004525958,0.0055010375],"genre_scores_gemma":[0.99918044,0.000021648404,0.00028831523,0.000011395032,0.00032610234,0.000029183273,0.0000032451167,0.000019721909,0.00011992621],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985366,0.000043981992,0.00081045873,0.00009351848,0.00024665176,0.00026880272],"domain_scores_gemma":[0.9994366,0.000059414495,0.0001444597,0.00023325214,0.00007158052,0.00005469609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049734215,0.00016660366,0.0002551904,0.00032786382,0.00006633136,0.00017393084,0.00011381781,0.0002033346,0.000022850885],"category_scores_gemma":[0.000063116146,0.00012685519,0.000048590868,0.00023753342,0.000017256438,0.001642216,0.000027788205,0.00010154427,0.00041400164],"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":[0.00021171826,0.0000624333,0.050013315,0.0030006412,0.00031025903,0.00005534749,0.038806006,0.24752754,0.38629246,0.00468016,0.016294928,0.25274518],"study_design_scores_gemma":[0.002714948,0.00008104133,0.0014547331,0.002507834,0.000013601929,0.00015817127,0.0013596629,0.10923222,0.033935297,0.000037792677,0.8476192,0.0008855166],"about_ca_topic_score_codex":0.00010306682,"about_ca_topic_score_gemma":0.0000033985125,"teacher_disagreement_score":0.8313243,"about_ca_system_score_codex":0.00023920725,"about_ca_system_score_gemma":0.000014262389,"threshold_uncertainty_score":0.53212917},"labels":[],"label_agreement":null},{"id":"W4280545349","doi":"10.18280/isi.270220","title":"Computer Big Data Analysis and Predictive Maintenance Based on Deep Learning","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Big data; Artificial intelligence; Deep learning; Preprocessor; Machine learning; Data pre-processing; Convolutional neural network; Small data; Data mining","score_opus":0.019074882978418323,"score_gpt":0.21054699834172202,"score_spread":0.1914721153633037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280545349","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":[0.089150116,0.00003600631,0.9063557,0.0000067554997,0.0008796487,0.00024693683,0.00011663997,0.00029647854,0.002911714],"genre_scores_gemma":[0.998879,0.0000054291154,0.00041154088,0.000054341082,0.00013285104,0.00004460547,0.0004471357,0.000010834617,0.000014226845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899286,0.00009317099,0.00034660316,0.00013107902,0.0002658063,0.00017046704],"domain_scores_gemma":[0.9994153,0.00007509879,0.00011674104,0.00028906344,0.000056875968,0.000046931724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058442255,0.00012334868,0.00018941521,0.00050226954,0.00038470852,0.00013772752,0.00013966847,0.00005737735,0.000032340977],"category_scores_gemma":[0.00006641344,0.00012314455,0.000045438966,0.0008204974,0.000025085512,0.0006612333,0.00010453226,0.00026923817,0.000012229372],"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":[0.00004086293,0.0000042340234,0.0011471421,0.00004555352,0.000092873546,0.0000012473638,0.00076209556,0.82771504,0.000010860393,0.000019077184,0.00031333306,0.16984768],"study_design_scores_gemma":[0.0003345529,0.00017379377,0.0070818574,0.000024227153,0.000055564058,0.000008290093,0.00053656666,0.98129904,0.000043329306,0.0000209901,0.010288558,0.00013323985],"about_ca_topic_score_codex":0.000051413153,"about_ca_topic_score_gemma":0.000006104022,"teacher_disagreement_score":0.90972894,"about_ca_system_score_codex":0.0002353394,"about_ca_system_score_gemma":0.000013949005,"threshold_uncertainty_score":0.5021689},"labels":[],"label_agreement":null},{"id":"W4280546078","doi":"10.1109/syscon53536.2022.9773846","title":"Analyzing and Predicting Overall Equipment Effectiveness in Manufacturing Industries using Machine Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Systems Conference (SysCon)","topic":"Industrial Vision Systems and Defect Detection","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":"Queen's University","funders":"","keywords":"Metric (unit); Overall equipment effectiveness; Factory (object-oriented programming); Computer science; Context (archaeology); Machine learning; Process (computing); Artificial intelligence; Production (economics); Product (mathematics); Manufacturing; Industrial engineering; Data mining; Engineering; Mathematics","score_opus":0.036078038746132066,"score_gpt":0.26184676857929523,"score_spread":0.22576872983316315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280546078","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":[0.9892526,0.00034512024,0.0048855324,0.000010114437,0.0042544072,0.00044249263,0.000045560082,0.00016362524,0.0006005142],"genre_scores_gemma":[0.999296,0.000023158676,0.000011995946,0.0000041460526,0.0002764088,0.00013643426,0.000025006924,0.00003573849,0.000191075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796355,0.0003656648,0.0005551828,0.00034692252,0.00047394476,0.00029476648],"domain_scores_gemma":[0.99939716,0.00018537458,0.00017083589,0.00012350708,0.00005291359,0.00007019392],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011643291,0.0002359208,0.00035920643,0.00046799245,0.00029181255,0.00023321432,0.00019509926,0.00010728835,0.00013403391],"category_scores_gemma":[0.00005178674,0.00026270308,0.00005243474,0.00018758683,0.000020243871,0.00032761498,0.00015354442,0.00083766505,0.0000039632855],"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":[0.00008345319,0.000025215515,0.10911882,0.00017888655,0.00020139379,0.00006477799,0.00059928506,0.8466044,0.038393475,0.00038451396,0.000018102772,0.004327644],"study_design_scores_gemma":[0.0014309699,0.00010907742,0.006579809,0.000548935,0.000022677314,0.00019118538,0.0015695977,0.97329074,0.012018761,0.000038096074,0.0037278028,0.0004723227],"about_ca_topic_score_codex":0.0028551696,"about_ca_topic_score_gemma":0.0000726129,"teacher_disagreement_score":0.12668633,"about_ca_system_score_codex":0.00081272837,"about_ca_system_score_gemma":0.00004782376,"threshold_uncertainty_score":0.99998254},"labels":[],"label_agreement":null},{"id":"W4280562762","doi":"10.1088/2058-8585/ac6ea6","title":"Electromagnetic characteristic estimation on spiral antennas through AOI, ML, and AI","year":2022,"lang":"en","type":"article","venue":"Flexible and Printed Electronics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cybernet Systems Corporation (Canada)","funders":"Ministry of Science and Technology, Taiwan","keywords":"Spiral (railway); Computer science; Microelectronics; Artificial intelligence; Line (geometry); Mathematics; Engineering; Geometry; Mathematical analysis; Electrical engineering","score_opus":0.012731178288733437,"score_gpt":0.23875824417802535,"score_spread":0.22602706588929192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280562762","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":[0.989054,0.0019593905,0.005874407,0.00013818714,0.0004221854,0.0003700041,0.000011072467,0.00039716647,0.0017735704],"genre_scores_gemma":[0.99894375,0.00023798067,0.00007332882,0.00019370909,0.00008997036,0.000057435474,0.000022043878,0.00003154769,0.00035023686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990111,0.00005304657,0.0002177019,0.00022142392,0.0001628049,0.00033394905],"domain_scores_gemma":[0.99970394,0.000035270885,0.000040533047,0.00015024954,0.000021765862,0.00004824992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017566944,0.0001655785,0.00018848824,0.00010810477,0.0002566802,0.00006992241,0.000059529568,0.000074729156,0.00008846435],"category_scores_gemma":[0.000019230767,0.00017071769,0.000033355325,0.0002577595,0.00001683791,0.00011281573,0.000037628342,0.00053982687,0.00001355155],"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":[0.002049672,0.0003853534,0.00081642525,0.0008382695,0.00061280804,0.00008638219,0.0031817567,0.043530952,0.409659,0.04841321,0.0123890145,0.47803718],"study_design_scores_gemma":[0.0037690867,0.010820488,0.004656009,0.00016101495,0.00017687755,0.0008485962,0.00025915608,0.55935556,0.06999783,0.0091080675,0.33928618,0.0015611624],"about_ca_topic_score_codex":0.000025221734,"about_ca_topic_score_gemma":0.0000040393597,"teacher_disagreement_score":0.5158246,"about_ca_system_score_codex":0.00011830571,"about_ca_system_score_gemma":0.000032205116,"threshold_uncertainty_score":0.6961665},"labels":[],"label_agreement":null},{"id":"W4280580087","doi":"10.31449/inf.v46i1.3015","title":"Automatic Fabric Inspection using GLCM-based Jensen-Shannon Divergence","year":2022,"lang":"en","type":"article","venue":"Informatica","topic":"Industrial Vision Systems and Defect Detection","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":"Horizon College and Seminary","funders":"Hong Kong Baptist University","keywords":"Divergence (linguistics); Computer science; Cluster analysis; Mutual information; Computation; Block (permutation group theory); Artificial intelligence; Kullback–Leibler divergence; Entropy (arrow of time); Matrix (chemical analysis); Hierarchical clustering; Pattern recognition (psychology); Algorithm; Computer vision; Mathematics; Combinatorics","score_opus":0.021370997830154538,"score_gpt":0.21997217324385276,"score_spread":0.19860117541369823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280580087","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":[0.9584519,0.000035897538,0.031790268,0.000010176862,0.0022296663,0.0003412615,0.00001744233,0.0011393538,0.0059840195],"genre_scores_gemma":[0.9993298,8.4388256e-7,0.00045102256,0.00004652249,0.00008341524,0.000040443243,0.0000075190405,0.000016709379,0.000023698574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892735,0.00003921748,0.00041054504,0.00007151782,0.0003463624,0.00020502295],"domain_scores_gemma":[0.9996117,0.00003745021,0.00007995928,0.0001884471,0.00002947366,0.000052993633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026901776,0.00012034823,0.00015227184,0.00023730371,0.00039656626,0.000054815395,0.0001014434,0.00005387364,0.0004855168],"category_scores_gemma":[0.00002823459,0.00012445293,0.00006716653,0.00055274105,0.000012588073,0.000264056,0.00004489516,0.0002288217,0.00014681436],"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":[0.000021964757,0.000033189383,0.0003013834,0.00029286736,0.000046854548,0.000010950216,0.0016973108,0.95426106,0.0036213633,0.00014359287,0.0047840974,0.03478537],"study_design_scores_gemma":[0.00030410732,0.000071613445,0.00022850333,0.000025209249,0.000011220439,0.000039324324,0.00043916775,0.98781216,0.0014621224,0.000023018878,0.009423124,0.00016039827],"about_ca_topic_score_codex":0.00006321318,"about_ca_topic_score_gemma":0.0000017240656,"teacher_disagreement_score":0.04087791,"about_ca_system_score_codex":0.00030707679,"about_ca_system_score_gemma":0.000034164044,"threshold_uncertainty_score":0.53160685},"labels":[],"label_agreement":null},{"id":"W4280582977","doi":"10.1109/icai55435.2022.9773637","title":"Transferable Learning Architecture for Scalable Visual Quality Inspection","year":2022,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Inference; Convolutional neural network; Pipeline (software); Scalability; Artificial intelligence; Deep learning; Object detection; Transfer of learning; Automation; Software deployment; Machine learning; Inference engine; Real-time computing; Segmentation; Database; Software engineering","score_opus":0.02047797796938107,"score_gpt":0.2680452073163127,"score_spread":0.24756722934693165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280582977","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":[0.6217824,0.0000657508,0.35961446,0.000050275357,0.001569668,0.00054390996,0.000011720167,0.0015374057,0.014824348],"genre_scores_gemma":[0.99760413,0.000001528398,0.00015158337,0.000019859646,0.00021351234,0.00016306332,0.000012139882,0.000025315141,0.0018088865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992235,0.00008425013,0.00020933225,0.00013861715,0.00016445578,0.00017987016],"domain_scores_gemma":[0.99979186,0.00006571584,0.000016377318,0.000068000736,0.000021723246,0.000036299363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005246263,0.000088998946,0.00013964817,0.0000976036,0.00049499265,0.00003358483,0.000043086366,0.00005526824,0.00031535167],"category_scores_gemma":[0.00002172984,0.00008886001,0.000089266316,0.00023510674,0.000005425752,0.000057238663,0.000010554482,0.00033813386,0.000009312572],"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":[0.00013576406,0.00003352708,0.0002023794,0.00007654334,0.000038584756,6.7811413e-7,0.0004689984,0.90658015,0.05300027,0.00074316474,0.004006251,0.034713667],"study_design_scores_gemma":[0.0023484726,0.0010343088,0.00043189674,0.000012104391,0.00002116895,0.000036526166,0.0024621377,0.22475287,0.053800985,0.00050602126,0.7140319,0.00056162605],"about_ca_topic_score_codex":0.00016319701,"about_ca_topic_score_gemma":0.00003085314,"teacher_disagreement_score":0.7100256,"about_ca_system_score_codex":0.00011525951,"about_ca_system_score_gemma":0.000011158431,"threshold_uncertainty_score":0.3807133},"labels":[],"label_agreement":null},{"id":"W4281716694","doi":"10.1088/1742-6596/2278/1/011001","title":"Preface","year":2022,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"China; Library science; Postponement; Political science; Engineering; Computer science; Law","score_opus":0.02483030253314729,"score_gpt":0.21749810712255774,"score_spread":0.19266780458941046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281716694","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":[0.9194738,0.00030578044,0.061276477,0.00014386479,0.0061620795,0.00016875206,0.000025073456,0.0001302787,0.012313855],"genre_scores_gemma":[0.9993081,0.000013015906,0.00007847432,0.0000071191357,0.000337146,0.0000028835886,4.784896e-7,0.0000091528655,0.00024363337],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994306,0.00003351915,0.00020508376,0.000035054385,0.00021521878,0.000080531005],"domain_scores_gemma":[0.9996965,0.0000134992615,0.0000985362,0.000068457666,0.000092140486,0.00003089723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015527142,0.000061085935,0.00013997372,0.00003640971,0.000089547895,0.00003499564,0.000095582414,0.000018133358,0.0001478361],"category_scores_gemma":[0.000007991401,0.000056162422,0.000062941326,0.00012566896,0.000011312402,0.00025277372,0.000023572167,0.00026920316,0.000005146746],"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":[0.00029477946,0.00010413997,0.00035267882,0.00011144503,0.00028796034,0.00006817086,0.005354239,0.20321943,0.22196317,0.03259013,0.020658571,0.5149953],"study_design_scores_gemma":[0.0012403707,0.001612456,0.0005831638,0.00007853204,0.000053431235,0.0005645904,0.0065402114,0.0025298211,0.66313994,0.014142397,0.30901626,0.0004988221],"about_ca_topic_score_codex":0.0000026917037,"about_ca_topic_score_gemma":5.966859e-7,"teacher_disagreement_score":0.51449645,"about_ca_system_score_codex":0.000046930407,"about_ca_system_score_gemma":0.000045766566,"threshold_uncertainty_score":0.22902371},"labels":[],"label_agreement":null},{"id":"W4281976694","doi":"10.3390/app12115697","title":"An Intelligent Manufacturing Approach Based on a Novel Deep Learning Method for Automatic Machine and Working Status Recognition","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":16,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Machine learning; Robot; Working environment; Machine tool; Manufacturing; Engineering","score_opus":0.051095232141295674,"score_gpt":0.27694408410360016,"score_spread":0.22584885196230448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281976694","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":[0.1764384,0.0000344777,0.8199295,0.0000057258417,0.00025753558,0.0004989041,0.000005497295,0.00024763567,0.002582312],"genre_scores_gemma":[0.94056493,0.0000010143999,0.059055164,0.000039028793,0.000057141773,0.00024728046,0.000016492253,0.0000149688285,0.000003966536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990116,0.000049631286,0.0001876693,0.00026506835,0.00025089562,0.00023513405],"domain_scores_gemma":[0.9996031,0.00019652121,0.000057174722,0.00007695464,0.000008093252,0.00005816039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014419806,0.00011077962,0.0001344954,0.00020770886,0.00067982124,0.00011632984,0.0000862114,0.0000371099,0.000025918747],"category_scores_gemma":[0.000020471694,0.00010126384,0.000028316286,0.00024004545,0.000020277093,0.00005686541,0.000020523956,0.00021344514,0.0000013872306],"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":[0.000014388134,0.000016776423,0.000011302752,0.000022234632,0.000004116376,1.15942605e-7,0.00034363274,0.57666564,0.004195568,0.00006795139,0.0000027527392,0.41865554],"study_design_scores_gemma":[0.000299393,0.000200687,0.00006416949,0.000010504673,0.000008184809,0.0000040107966,0.0019257555,0.98555833,0.010355729,0.00014293777,0.0012898372,0.0001404882],"about_ca_topic_score_codex":0.000030628602,"about_ca_topic_score_gemma":0.0000031969737,"teacher_disagreement_score":0.76412654,"about_ca_system_score_codex":0.0000755622,"about_ca_system_score_gemma":0.000010603001,"threshold_uncertainty_score":0.5228704},"labels":[],"label_agreement":null},{"id":"W4283750660","doi":"10.1109/codit55151.2022.9803939","title":"Enhancing the quality inspection process in the food manufacturing industry through automation","year":2022,"lang":"en","type":"article","venue":"2022 8th International Conference on Control, Decision and Information Technologies (CoDIT)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"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":"Queen's University; Queen's University Belfast","keywords":"Automation; Manufacturing engineering; Production line; Quality (philosophy); Process (computing); Production (economics); Product (mathematics); Automated optical inspection; Manufacturing; Manufacturing process; Computer science; Engineering; Engineering drawing; Business; Mechanical engineering","score_opus":0.0356083557824236,"score_gpt":0.2959791908692148,"score_spread":0.2603708350867912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283750660","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":[0.9548786,0.000071960036,0.026978549,0.0031506224,0.0021640463,0.0009828671,0.00010492623,0.0010663215,0.010602088],"genre_scores_gemma":[0.9989806,0.00008479634,0.000032603293,0.00041257043,0.000058610065,0.00038636074,0.00002534633,0.000007900057,0.000011231471],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979173,0.000121856705,0.00077182276,0.00017205167,0.0008291969,0.00018775529],"domain_scores_gemma":[0.9990258,0.00028867356,0.00026617953,0.00028415435,0.00011958883,0.000015554439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013890524,0.00018250894,0.00019093508,0.000373293,0.00053111545,0.00031342875,0.0005340306,0.00022562269,0.00014688383],"category_scores_gemma":[0.000438463,0.00012408005,0.000057478108,0.00039047794,0.000051901836,0.001333631,0.000102261096,0.0012520675,0.000020867157],"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":[0.0009076856,0.0001539025,0.00073660974,0.000113627844,0.00020315456,0.0000072751222,0.008726247,0.10268939,0.0013823208,0.26303563,0.0068640886,0.6151801],"study_design_scores_gemma":[0.009586016,0.0016990058,0.02561185,0.00036684613,0.00004412258,0.0002249019,0.20810787,0.5162741,0.031001603,0.06395148,0.14163187,0.0015003409],"about_ca_topic_score_codex":0.000025827665,"about_ca_topic_score_gemma":0.000037641727,"teacher_disagreement_score":0.6136797,"about_ca_system_score_codex":0.0002570471,"about_ca_system_score_gemma":0.00004072232,"threshold_uncertainty_score":0.543968},"labels":[],"label_agreement":null},{"id":"W4285142210","doi":"10.2139/ssrn.4127721","title":"Detection Method of Table Grape Ear and Stem Based on Far-Close-Range Combined Vision System and Hand-Eye-Coordinated Picking Test","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Table (database); Test (biology); Range (aeronautics); Computer science; Artificial intelligence; Computer vision; Engineering; Data mining; Biology; Botany","score_opus":0.007036996550589256,"score_gpt":0.2206280341671498,"score_spread":0.21359103761656054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285142210","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":[0.910778,0.0012869859,0.08634203,0.000025735626,0.0007676502,0.00041425996,0.000013472434,0.00016158816,0.00021030664],"genre_scores_gemma":[0.9996017,0.00007105838,0.000046664594,0.0000072502103,0.000088195724,0.000020316962,0.0000018034016,0.000044285745,0.00011873129],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797505,0.00028660859,0.00042653133,0.00021880667,0.00034297106,0.000750026],"domain_scores_gemma":[0.99934685,0.00016972338,0.00018964845,0.00014396568,0.0000748922,0.00007492328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002681205,0.00019798591,0.00034785623,0.0003712124,0.00060795294,0.00008785288,0.00008873825,0.00011511198,0.000008316553],"category_scores_gemma":[0.000031800144,0.00018631932,0.00007501169,0.0004983822,0.000018869443,0.00013300935,0.000032182626,0.0013429598,0.0000012054862],"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":[0.0021479605,0.00032780782,0.010544778,0.0008366181,0.00068300765,0.000044245746,0.0010743137,0.06811426,0.5051488,0.004086631,0.00012178979,0.40686974],"study_design_scores_gemma":[0.0153275905,0.015857967,0.0048840735,0.00069556665,0.00035312522,0.0024715671,0.0119980695,0.87277585,0.0699363,0.0011841978,0.0032378882,0.001277831],"about_ca_topic_score_codex":0.00014625258,"about_ca_topic_score_gemma":0.00006849246,"teacher_disagreement_score":0.8046616,"about_ca_system_score_codex":0.0009430553,"about_ca_system_score_gemma":0.00013004916,"threshold_uncertainty_score":0.7597881},"labels":[],"label_agreement":null},{"id":"W4286560905","doi":"10.1038/s41598-022-16302-3","title":"End-to-end deep learning framework for printed circuit board manufacturing defect classification","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":115,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Rework; Bottleneck; Computer science; Convolutional neural network; Deep learning; Printed circuit board; Benchmark (surveying); Artificial intelligence; Reliability (semiconductor); Throughput; Object detection; Production line; Pattern recognition (psychology); Real-time computing; Reliability engineering; Embedded system; Engineering; Telecommunications","score_opus":0.03187696537167703,"score_gpt":0.25583609371932076,"score_spread":0.22395912834764373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286560905","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":[0.7103263,0.00008616755,0.2601359,0.00003146132,0.025054388,0.0011225111,0.000004595126,0.00071868417,0.0025200224],"genre_scores_gemma":[0.99733454,5.706794e-7,0.0006022242,0.000012293732,0.00024247993,0.0003851031,0.00004184857,0.000047275364,0.0013336863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99781424,0.00007880951,0.00054582633,0.0006209235,0.00056609994,0.00037408847],"domain_scores_gemma":[0.9989359,0.000113499445,0.00018777917,0.00056566787,0.00007215515,0.00012504397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002074529,0.00016232608,0.00020846396,0.00036803307,0.0009857598,0.00031234772,0.00012598731,0.00010505026,0.0004298858],"category_scores_gemma":[0.00030031137,0.00017571692,0.00019805036,0.0005330441,0.00002534131,0.00011516151,0.00007315493,0.00045325488,0.000037718957],"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":[0.00007024731,0.00007574352,0.0018734342,0.00021141973,0.00017617087,0.00016663458,0.0026071037,0.55076635,0.17413057,0.001359026,0.011532438,0.25703084],"study_design_scores_gemma":[0.00021978548,0.00012862342,0.0019491068,0.000051760446,0.000040774976,0.0002294305,0.0009630854,0.0134193115,0.089041375,0.0054188794,0.8880363,0.00050154165],"about_ca_topic_score_codex":0.000018845816,"about_ca_topic_score_gemma":0.000008140496,"teacher_disagreement_score":0.8765039,"about_ca_system_score_codex":0.00037427645,"about_ca_system_score_gemma":0.000041802145,"threshold_uncertainty_score":0.7581767},"labels":[],"label_agreement":null},{"id":"W4287271956","doi":"10.12454/foo.bar","title":"How To Fix HP Printer Error Code 0xc19a0022?","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Code (set theory); Computer science; Programming language; Arithmetic; Mathematics; Set (abstract data type)","score_opus":0.04894517206607636,"score_gpt":0.2430934094270137,"score_spread":0.19414823736093734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287271956","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25988635,0.00034697854,0.17341973,0.005041116,0.003360579,0.0019748616,0.0007475213,0.00861577,0.54660714],"genre_scores_gemma":[0.9917395,0.000013236808,0.00025427478,0.00010036306,0.00036959557,8.165227e-8,0.00021569243,0.0010879412,0.0062193284],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988351,0.00014605663,0.00018414335,0.00028169632,0.00026613136,0.00028686365],"domain_scores_gemma":[0.99902076,0.000013204859,0.000028443816,0.00037700613,0.0003811563,0.00017940695],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00031249566,0.00012271597,0.00013983458,0.00015613821,0.00064608373,0.0009099962,0.00031986684,0.000083730316,0.0048716343],"category_scores_gemma":[0.00043742874,0.00013113712,0.000057650577,0.0005431138,0.000021267808,0.00017898456,0.0003863832,0.00024047402,0.006469352],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056153127,0.00006772101,0.0000036874485,0.00012480012,0.00009422254,0.00007421491,0.0013423732,0.0022447782,0.099809036,0.0014917487,0.734073,0.16061826],"study_design_scores_gemma":[0.0002867255,0.00008458308,0.00011797559,0.000034478944,0.0000056346516,0.00010976784,0.00034425707,0.0009731657,0.0133341625,0.00002082972,0.9845362,0.00015227473],"about_ca_topic_score_codex":0.0000040511322,"about_ca_topic_score_gemma":8.67416e-7,"teacher_disagreement_score":0.7318531,"about_ca_system_score_codex":0.00014240372,"about_ca_system_score_gemma":0.0000023202376,"threshold_uncertainty_score":0.996038},"labels":[],"label_agreement":null},{"id":"W4287811697","doi":"10.5281/zenodo.3795366","title":"TraceSanitizer – Eliminating the Effects of Non-determinism on Error Propagation Analysis","year":2020,"lang":"en","type":"paratext","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","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 British Columbia","funders":"European Commission","keywords":"Computer science; Error analysis; Determinism; Mathematics; Epistemology; Philosophy; Applied mathematics","score_opus":0.026009088004202093,"score_gpt":0.24812941629795562,"score_spread":0.22212032829375353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287811697","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036651812,0.0005128655,0.037690084,0.00035956263,0.0046670823,0.0050670216,0.0010112601,0.0018029959,0.9122373],"genre_scores_gemma":[0.9934779,0.00008354166,0.000028831962,0.000045580164,0.0006330563,4.261781e-7,0.0013085916,0.001258898,0.0031632],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979882,0.00038546082,0.00047795242,0.000366345,0.0005122082,0.0002697946],"domain_scores_gemma":[0.998749,0.000114750364,0.0002542653,0.00044613727,0.00033683114,0.00009897859],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00047561334,0.0002611263,0.00042113193,0.0004841658,0.00092340336,0.0004119838,0.0006248424,0.00023791008,0.0033267348],"category_scores_gemma":[0.00050163735,0.0002177919,0.00020627657,0.0012974265,0.00006925563,0.00010216437,0.00021792925,0.0006550423,0.009240159],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010516814,0.00007646765,1.8346832e-7,0.0018036915,0.0010167301,0.000018206565,0.0030834416,0.0093965875,0.026424518,0.000089808775,0.80142355,0.15656163],"study_design_scores_gemma":[0.0005533062,0.0007692433,0.00008132882,0.0003233339,0.0003387547,0.000016129525,0.00027683313,0.016497428,0.014700006,0.0000041990675,0.966092,0.00034746988],"about_ca_topic_score_codex":0.000011483254,"about_ca_topic_score_gemma":1.7400951e-7,"teacher_disagreement_score":0.9568261,"about_ca_system_score_codex":0.00014255258,"about_ca_system_score_gemma":0.000004290154,"threshold_uncertainty_score":0.99758434},"labels":[],"label_agreement":null},{"id":"W4288075324","doi":"10.18280/ts.390315","title":"An Improved BM3D-Canny-Zernike Algorithm for Micro-Size Detection of Electronic Connectors","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Zernike polynomials; Canny edge detector; Subpixel rendering; Algorithm; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Pixel; Computer science; Edge detection; Mathematics; Mean squared error; Computer vision; Pattern recognition (psychology); Image processing; Image (mathematics); Statistics; Optics","score_opus":0.00851818156788188,"score_gpt":0.21314850521615758,"score_spread":0.2046303236482757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288075324","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":[0.8076791,0.00012381608,0.18981548,0.000006515852,0.00096978503,0.0009688143,0.00014666586,0.00023125076,0.000058552105],"genre_scores_gemma":[0.9988213,0.0000023445284,0.0003277564,0.000018521974,0.00035419792,0.00035587096,0.000025300804,0.000044767137,0.000049907838],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99872327,0.00007770134,0.00041137953,0.00022020546,0.00022173568,0.00034569076],"domain_scores_gemma":[0.9995372,0.0000852084,0.000103865736,0.00014692264,0.00006159781,0.00006520425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000552679,0.0001702635,0.00023766984,0.00012598213,0.00020551178,0.000026213076,0.00012650213,0.000075571916,0.00033261586],"category_scores_gemma":[0.000009064133,0.00018339389,0.00014201985,0.00022290797,0.000013247658,0.00010564881,0.000015396661,0.00021398351,0.0000022624329],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107814245,0.00006377629,0.0000132255855,0.00003015286,0.00009039009,8.7907205e-7,0.0001852822,0.008721215,0.889754,0.00002218107,0.00015277581,0.100858316],"study_design_scores_gemma":[0.00294245,0.0031235064,0.0002499884,0.000010336286,0.0000692875,0.000028698138,0.0006213714,0.3957618,0.5748589,0.00009134297,0.021860411,0.0003819337],"about_ca_topic_score_codex":0.00009747465,"about_ca_topic_score_gemma":0.000023398547,"teacher_disagreement_score":0.3870406,"about_ca_system_score_codex":0.00029690829,"about_ca_system_score_gemma":0.00004246251,"threshold_uncertainty_score":0.7478586},"labels":[],"label_agreement":null},{"id":"W4289452547","doi":"","title":"Hardness of Minimum Activation Path","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Path (computing); Computer science; Computer network","score_opus":0.01649999086932364,"score_gpt":0.21593524332541755,"score_spread":0.19943525245609392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289452547","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":[0.599047,0.00046819128,0.30861422,0.0004571267,0.0024085634,0.0009433517,0.00009756023,0.0005520897,0.0874119],"genre_scores_gemma":[0.9942719,0.00011073599,0.0022246602,0.000008727146,0.000052642714,0.00004395573,0.00017648855,0.000057525896,0.003053401],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99702704,0.0013996268,0.0005888596,0.00039328475,0.00037232088,0.0002188802],"domain_scores_gemma":[0.9965004,0.0005008279,0.00036926105,0.0013879049,0.001160675,0.00008092911],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002571363,0.00027087715,0.00044504058,0.00022512797,0.000087086744,0.00013414433,0.000479097,0.0004932509,0.000072851995],"category_scores_gemma":[0.0005021188,0.00029129858,0.00020578408,0.000290013,0.00005167769,0.00011744442,0.00031703163,0.0005440073,0.000051781208],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018311113,0.0015088641,0.0046944534,0.007953744,0.0012714682,0.000007768602,0.039418284,0.054123655,0.45717695,0.04553008,0.049249228,0.3388824],"study_design_scores_gemma":[0.0012683318,0.0000012032903,0.0031946162,0.006945338,0.000074295094,0.0000072317766,0.00029322668,0.12209003,0.8207082,0.0009946618,0.043478765,0.0009440615],"about_ca_topic_score_codex":0.00053259946,"about_ca_topic_score_gemma":0.00006193173,"teacher_disagreement_score":0.39522487,"about_ca_system_score_codex":0.00012636447,"about_ca_system_score_gemma":0.00011971932,"threshold_uncertainty_score":0.9999539},"labels":[],"label_agreement":null},{"id":"W4289667648","doi":"10.1208/s12249-022-02360-3","title":"Environmental Monitoring for Closed Robotic Workcells Used in Aseptic Processing: Data to Support Advanced Environmental Monitoring Strategies","year":2022,"lang":"en","type":"article","venue":"AAPS PharmSciTech","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Emergent BioSolutions (Canada)","funders":"","keywords":"Aseptic processing; Process (computing); Process engineering; Computer science; Engineering; Waste management; Manufacturing engineering; Operations management; Mechanical engineering; Operating system","score_opus":0.06535537314037256,"score_gpt":0.30128603037127394,"score_spread":0.2359306572309014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289667648","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":[0.99312395,0.0002939123,0.002936168,0.000013140588,0.0020108803,0.0011753894,0.00014667478,0.00022517944,0.00007473093],"genre_scores_gemma":[0.9977562,0.000018924507,0.0009731543,0.000007966911,0.0004495509,0.0004625591,0.00009799424,0.0000923696,0.00014130032],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979661,0.000047375317,0.00048215874,0.0005369828,0.0004545714,0.0005128116],"domain_scores_gemma":[0.9992391,0.00006720223,0.00007773252,0.00048356754,0.0000033602548,0.00012907476],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045372045,0.00028274485,0.00031055967,0.00025746736,0.00026769578,0.00012709013,0.00047550298,0.00008992538,0.00011142732],"category_scores_gemma":[0.000010203253,0.00033209226,0.000063999934,0.00031717942,0.000018397677,0.0006121411,0.00026611067,0.0004109708,0.000035276553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009516861,0.00010946397,0.00467934,0.00006577655,0.000028391074,0.000028947508,0.00070204755,0.24440981,0.67998797,0.000001068235,0.0001675929,0.069724455],"study_design_scores_gemma":[0.014765039,0.002558712,0.023216214,0.00054261484,0.00024425008,0.0001490131,0.04639149,0.09578476,0.7431134,0.00015510542,0.06904563,0.004033756],"about_ca_topic_score_codex":0.000009954629,"about_ca_topic_score_gemma":0.000001291092,"teacher_disagreement_score":0.14862505,"about_ca_system_score_codex":0.00060209935,"about_ca_system_score_gemma":0.00003439508,"threshold_uncertainty_score":0.9999131},"labels":[],"label_agreement":null},{"id":"W4289783984","doi":"10.6084/m9.figshare.1057731","title":"Amorçage inter-sensoriel : implication des fonctions exécutives dans la sélection des traces","year":2014,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Humanities; Sociology; Philosophy","score_opus":0.021324699360288604,"score_gpt":0.24197054773258175,"score_spread":0.22064584837229315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289783984","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":[0.32231137,0.00065892056,0.5859934,0.0006594626,0.0009998275,0.00058789377,0.00005969566,0.00056958944,0.08815983],"genre_scores_gemma":[0.96214914,0.0008961786,0.01846932,0.000010236155,0.00022801556,0.0002628673,0.0002161561,0.00013619948,0.017631901],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98901916,0.0077430494,0.0011307646,0.0010393597,0.00043314457,0.00063454476],"domain_scores_gemma":[0.9928461,0.0022063043,0.00065884297,0.0018271765,0.0021402151,0.0003213815],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0059393626,0.00068014243,0.00066740904,0.00042306236,0.0013275092,0.00083918514,0.00074969436,0.00097367127,0.00023743477],"category_scores_gemma":[0.0017771848,0.0007706555,0.0004905241,0.0008729968,0.00082281796,0.0004841826,0.0004248006,0.001278572,0.00020093798],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008027663,0.0010562009,0.0054070456,0.001231504,0.0005910714,0.00000968122,0.06453849,0.008431768,0.1491698,0.15191656,0.0019288964,0.61563873],"study_design_scores_gemma":[0.0024453686,0.000012425422,0.10437058,0.009290885,0.00055085897,0.0004891801,0.005221305,0.10435059,0.5663701,0.024368828,0.17987125,0.0026585788],"about_ca_topic_score_codex":0.007937698,"about_ca_topic_score_gemma":0.0057230694,"teacher_disagreement_score":0.63983774,"about_ca_system_score_codex":0.00089014554,"about_ca_system_score_gemma":0.00018033593,"threshold_uncertainty_score":0.99997264},"labels":[],"label_agreement":null},{"id":"W4290669654","doi":"10.1007/11867661","title":"Image Analysis and Recognition","year":2006,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Computer vision","score_opus":0.011866757002449236,"score_gpt":0.2204167698162916,"score_spread":0.20855001281384236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290669654","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":[0.0021344114,0.00012968956,0.9922703,0.0000062983668,0.0008913312,0.00014722772,0.000011190458,0.00012892712,0.0042806566],"genre_scores_gemma":[0.7961513,0.00007270517,0.19458818,0.00028111305,0.0059783417,0.000043743665,0.00021205559,0.00016486808,0.002507686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988684,0.000017912416,0.00024839022,0.00038131778,0.00027002496,0.00021394242],"domain_scores_gemma":[0.99948984,0.000116442345,0.000054617078,0.00022511443,0.00006783133,0.000046156594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035929692,0.00019337144,0.0003080848,0.0009928582,0.00007238952,0.00020384352,0.0001469154,0.00025896757,0.00001179944],"category_scores_gemma":[0.00002252507,0.0001808193,0.000075592645,0.0011521475,0.000113893315,0.00014459113,0.0000511738,0.00035923757,0.000016507616],"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":[0.0000028755992,0.0000051752204,0.00006490658,0.00006763079,0.000046338202,0.000020585354,0.00011236835,0.065522686,0.0006488331,0.0000016791337,0.00065000844,0.9328569],"study_design_scores_gemma":[0.00027642646,0.00007587149,0.0007972361,0.00023314005,0.00013140525,0.00003066239,1.2896338e-7,0.9844598,0.003225215,0.0075740153,0.0025641154,0.0006319741],"about_ca_topic_score_codex":0.00007049453,"about_ca_topic_score_gemma":0.00013362772,"teacher_disagreement_score":0.9322249,"about_ca_system_score_codex":0.00019730355,"about_ca_system_score_gemma":0.000054027714,"threshold_uncertainty_score":0.73735976},"labels":[],"label_agreement":null},{"id":"W4292375879","doi":"10.1007/978-3-031-14615-2_33","title":"Defect Detection in Additive Manufacturing Using Computer Vision Monitoring","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Setpoint; Computer science; Process (computing); Computer vision; Pixel; Artificial intelligence; Segmentation; Software; Enhanced Data Rates for GSM Evolution; Layer (electronics); Code (set theory); Raspberry pi; Computer graphics (images); Embedded system; Materials science","score_opus":0.01448232785681428,"score_gpt":0.22470087159481597,"score_spread":0.2102185437380017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292375879","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":[0.039538268,0.0009621447,0.9439441,0.000010377683,0.0113661,0.0012703367,0.00004202111,0.0011004665,0.0017661586],"genre_scores_gemma":[0.996291,0.000105934916,0.001999377,0.00001617172,0.0011997932,0.000047785885,0.000016865692,0.0002852362,0.00003784265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754447,0.000043533964,0.0007706306,0.0006219506,0.00047371213,0.000545725],"domain_scores_gemma":[0.9990224,0.0003953361,0.000107789594,0.0003468999,0.000023523202,0.000104053],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00044322544,0.00066841533,0.000770011,0.0010000047,0.00008677615,0.00007110351,0.00019853275,0.00087632384,0.00018549825],"category_scores_gemma":[0.00006916329,0.0007644438,0.00031667808,0.00020751431,0.000009205153,0.00018197855,0.0001512325,0.0027748158,0.000014269447],"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":[0.000030926487,0.0000063866037,7.910759e-7,0.00010043415,0.000049234022,0.00013412259,0.000042058182,0.87836444,0.0145763885,0.00010125073,0.0000024447863,0.10659151],"study_design_scores_gemma":[0.00090448617,0.0002720381,0.000028542478,0.0017121396,0.000052278774,0.00014820397,0.0000037088023,0.82816046,0.15208028,0.0007144573,0.014593445,0.0013299547],"about_ca_topic_score_codex":0.000075818,"about_ca_topic_score_gemma":0.00005049872,"teacher_disagreement_score":0.9567527,"about_ca_system_score_codex":0.0015907639,"about_ca_system_score_gemma":0.00001914272,"threshold_uncertainty_score":0.99952585},"labels":[],"label_agreement":null},{"id":"W4292722430","doi":"10.1007/s10845-022-01994-1","title":"Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review","year":2022,"lang":"en","type":"review","venue":"Journal of Intelligent Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":false,"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":"Artificial intelligence; Deep learning; Computer science; Machine learning; Convolutional neural network; Scalability; Data pre-processing; Artificial neural network; Preprocessor; Feature extraction; Engineering; Pattern recognition (psychology)","score_opus":0.06881587220163982,"score_gpt":0.3147513517397384,"score_spread":0.24593547953809858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292722430","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":[0.000031710977,0.9946455,0.0037381642,0.0000072520425,0.0003527437,0.00064551743,0.000004165338,0.000046761448,0.0005282126],"genre_scores_gemma":[0.00048532127,0.9988718,0.000112083755,0.0000064175883,0.00026330963,0.00012692738,0.000034019507,0.000055240835,0.00004486338],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99770606,0.00032343357,0.0012601274,0.00023595951,0.00027860992,0.0001957995],"domain_scores_gemma":[0.99874455,0.00028500624,0.00071783114,0.00010355534,0.000051478764,0.00009757652],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012192298,0.00032737778,0.0011385859,0.00055224437,0.00014437933,0.00007530824,0.00011675908,0.00017705293,0.00020848702],"category_scores_gemma":[0.00013730626,0.000276266,0.00028446628,0.00026252648,0.000018013086,0.00023829065,0.00005897013,0.0018354808,0.000019429115],"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":[0.00000498333,0.000007310798,0.000004252588,0.017203141,0.00005164869,0.000009179916,0.000043002947,0.00027691564,7.603816e-7,0.0000012881695,0.000009134497,0.9823884],"study_design_scores_gemma":[0.00010331066,0.000081163766,0.0000029042124,0.016604263,0.0002737398,0.0005019397,0.000094056326,0.00017712743,0.000026134456,0.000024601208,0.98185676,0.0002540074],"about_ca_topic_score_codex":0.0000066691164,"about_ca_topic_score_gemma":0.000007172759,"teacher_disagreement_score":0.9821344,"about_ca_system_score_codex":0.00031028356,"about_ca_system_score_gemma":0.000033963996,"threshold_uncertainty_score":0.99996895},"labels":[],"label_agreement":null},{"id":"W4293223364","doi":"10.11159/mhci22.110","title":"Visual Task Classification using Classic Machine Learning and CNNs","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Task (project management); Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.012881799957649693,"score_gpt":0.22305099865668962,"score_spread":0.21016919869903994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293223364","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":[0.99615043,0.0007211693,0.0011767524,0.000024459665,0.001461622,0.00021521575,0.0000017520142,0.00011578213,0.0001328329],"genre_scores_gemma":[0.99965274,0.000016884893,0.0000641862,0.0000059930294,0.00011978809,0.000014867781,1.3203342e-7,0.0000125444985,0.00011284187],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990025,0.000010781631,0.000221061,0.00024003015,0.0003142524,0.00021135593],"domain_scores_gemma":[0.99968565,0.000053523723,0.00008159732,0.00004469627,0.000055940338,0.000078621255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048170914,0.0001298704,0.00019576917,0.00030241386,0.0004295768,0.00017879749,0.00013033934,0.000029809107,5.241672e-7],"category_scores_gemma":[0.000022202994,0.00010246126,0.000023886061,0.00088854076,0.00006324625,0.00012268637,0.000121522986,0.00035133574,9.3788046e-8],"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":[0.00012002323,0.00010962892,0.019182652,0.00093553093,0.00013719244,0.000004298424,0.0007280241,0.30764064,0.54315203,0.047093913,0.00062272884,0.08027332],"study_design_scores_gemma":[0.00017580287,0.00012912047,0.00107418,0.000076451346,0.0000080449545,0.00006232266,0.000025152536,0.9945063,0.00095059257,0.0000045641805,0.002865566,0.0001219225],"about_ca_topic_score_codex":0.000019022544,"about_ca_topic_score_gemma":3.0803224e-7,"teacher_disagreement_score":0.6868656,"about_ca_system_score_codex":0.000075571705,"about_ca_system_score_gemma":0.000012453695,"threshold_uncertainty_score":0.4178249},"labels":[],"label_agreement":null},{"id":"W4293491576","doi":"10.1016/j.array.2022.100247","title":"Semi-supervised learning approach for localization and pose estimation of texture-less objects in cluttered scenes","year":2022,"lang":"en","type":"article","venue":"Array","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Artificial intelligence; Computer vision; Pose; Computer science; Segmentation; 3D pose estimation; Pattern recognition (psychology); Image warping; Histogram; RGB color model; Object (grammar); Image (mathematics)","score_opus":0.017460047891365484,"score_gpt":0.22171263011908304,"score_spread":0.20425258222771756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293491576","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":[0.55717784,0.00013386816,0.4414625,0.0000044182852,0.00024259693,0.00040178036,0.0000055955657,0.00007354333,0.0004979041],"genre_scores_gemma":[0.999219,0.0000032624455,0.00056381576,0.0000075269904,0.000046237838,0.00007501604,0.000038693244,0.000016670216,0.000029793366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994604,0.000058066027,0.00018469364,0.00010674425,0.00009812543,0.00009199245],"domain_scores_gemma":[0.99982536,0.00003631551,0.000038468763,0.00006364517,0.000018711195,0.000017528906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027216593,0.00006757455,0.00012892888,0.00011936141,0.00008884088,0.00001413749,0.00003166743,0.00005288116,0.0000063548955],"category_scores_gemma":[0.00003824012,0.00007054461,0.000023008508,0.0002115898,0.0000065614913,0.000071056886,0.000010764338,0.00010577822,2.7780226e-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":[0.000028529379,0.000011689113,0.0008707775,0.00011822321,0.000006448855,2.0505824e-7,0.0011182553,0.9697082,0.016426163,0.000023129856,0.000049611615,0.011638783],"study_design_scores_gemma":[0.00072135136,0.00007818626,0.0003191633,0.000022641945,0.0000060366483,0.0000047554627,0.0012260235,0.98570156,0.011460682,0.0000398708,0.00032980344,0.00008995295],"about_ca_topic_score_codex":0.00004871447,"about_ca_topic_score_gemma":0.000004977579,"teacher_disagreement_score":0.44204116,"about_ca_system_score_codex":0.00004374782,"about_ca_system_score_gemma":0.0000080308855,"threshold_uncertainty_score":0.28767255},"labels":[],"label_agreement":null},{"id":"W4296086392","doi":"10.1520/ssms20220003","title":"A Modular Smart Vision System for Industrial Inspection and Control of Conformity","year":2022,"lang":"en","type":"article","venue":"Smart and Sustainable Manufacturing Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PricewaterhouseCoopers (Canada)","funders":"","keywords":"Machine vision; Modular design; Process (computing); Machining; Factory (object-oriented programming); Manufacturing engineering; Production line; Robot; Computer science; Machine tool; Automation; Engineering; Artificial intelligence; Mechanical engineering","score_opus":0.0090458979936327,"score_gpt":0.1955459107963835,"score_spread":0.1865000128027508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296086392","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":[0.9827837,0.00065657473,0.011121755,0.000011121009,0.0023259681,0.0020420693,0.000055605757,0.00033958422,0.0006636587],"genre_scores_gemma":[0.9988524,0.0000052390174,0.000005138608,0.0000029161265,0.0002796312,0.00032068673,0.000010357299,0.00003469078,0.0004889923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984167,0.00012945589,0.0005288034,0.00025924106,0.00026528968,0.0004004707],"domain_scores_gemma":[0.9993671,0.000106968335,0.00015021529,0.00019635612,0.00008509981,0.00009422092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014615807,0.00021031551,0.0005119293,0.00029092544,0.0006753753,0.00012041629,0.0000742952,0.00016850352,0.000003301153],"category_scores_gemma":[0.000028810733,0.00020597989,0.000079844314,0.00012530426,0.000032411972,0.00020207553,0.00007497815,0.00025416093,5.1590246e-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":[0.008869042,0.00044136643,0.017036946,0.062116943,0.0030305488,0.0003105961,0.010273586,0.75128514,0.050409593,0.045080714,0.012191471,0.03895404],"study_design_scores_gemma":[0.02240466,0.003321922,0.0039388137,0.0006028974,0.0003542904,0.0007249733,0.09551063,0.46806753,0.031956404,0.0002344464,0.37114102,0.0017424162],"about_ca_topic_score_codex":0.0010573452,"about_ca_topic_score_gemma":0.0000048705,"teacher_disagreement_score":0.35894954,"about_ca_system_score_codex":0.00031843688,"about_ca_system_score_gemma":0.000033891505,"threshold_uncertainty_score":0.8399616},"labels":[],"label_agreement":null},{"id":"W4296297626","doi":"10.21203/rs.3.rs-2040065/v1","title":"Online Prediction of Automotive Tempered Glass Quality using Machine Learning","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Industrial Vision Systems and Defect Detection","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":"Université du Québec à Rimouski","funders":"","keywords":"Machine learning; Artificial intelligence; Computer science; Automotive industry; Process (computing); Quality (philosophy); Regression; Mean squared error; Engineering; Statistics; Mathematics","score_opus":0.1855955536919763,"score_gpt":0.42103417572994756,"score_spread":0.23543862203797125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296297626","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":[0.99133205,0.00085395225,0.0015145638,0.000016967726,0.0015284455,0.0011167319,0.001956945,0.00050665886,0.0011736545],"genre_scores_gemma":[0.99799174,0.00013532901,0.00017597068,0.0000011104873,0.0005690489,0.00007317476,0.00060280366,0.00008243341,0.00036838566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995086,0.0016924847,0.00079488463,0.00045602908,0.0015189745,0.00045160158],"domain_scores_gemma":[0.99815714,0.00035715319,0.00018346887,0.0005332712,0.0006471734,0.00012181255],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0039066356,0.0002612248,0.0005760229,0.0007542766,0.00033677105,0.0000726569,0.00026991984,0.0004988533,0.000538453],"category_scores_gemma":[0.0009275842,0.00027105928,0.0002448781,0.00075236324,0.00006271644,0.000085815635,0.00070363283,0.004070174,0.0000072859903],"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":[0.00017145355,0.00023432303,0.011535163,0.0037412662,0.00031301437,0.000022414375,0.0014839134,0.95592815,0.022566982,0.000050610102,0.0004181694,0.003534557],"study_design_scores_gemma":[0.0013460078,0.0007309133,0.022513734,0.0016623088,0.000046553203,0.000014473766,0.0046043075,0.9454732,0.009034866,0.00015372672,0.013841222,0.0005787187],"about_ca_topic_score_codex":0.0038372877,"about_ca_topic_score_gemma":0.00008827228,"teacher_disagreement_score":0.013532115,"about_ca_system_score_codex":0.0010498781,"about_ca_system_score_gemma":0.00022146033,"threshold_uncertainty_score":0.99997413},"labels":[],"label_agreement":null},{"id":"W4296337593","doi":"10.1007/978-3-031-11898-2_206","title":"Digital Particle Size Distribution for Fabric Quantification Using X-ray μ-CT Imaging","year":2022,"lang":"en","type":"book-chapter","venue":"Geotechnical, geological and earthquake engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Silt; Materials science; Digital imaging; Particle-size distribution; Particle (ecology); Sieve (category theory); Digital image processing; Image processing; Digital image; Particle size; Computer science; Geology; Artificial intelligence; Mathematics; Image (mathematics); Geomorphology","score_opus":0.0233517719207651,"score_gpt":0.2112440665038614,"score_spread":0.1878922945830963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296337593","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":[0.6481181,0.0021966805,0.33922482,0.0001371903,0.0017701115,0.0017801063,0.0007271114,0.0025635406,0.0034823488],"genre_scores_gemma":[0.99654543,0.000087476976,0.00018418943,0.000010169444,0.00030429385,0.00006882438,0.00017385298,0.00007138635,0.0025544025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983004,0.000010231279,0.00056766364,0.00045710165,0.0002371115,0.00042753076],"domain_scores_gemma":[0.99912393,0.0003116017,0.00010321191,0.0002743656,0.000047751124,0.00013912437],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032204186,0.00039188284,0.00047167388,0.000056095723,0.00022915326,0.000120257806,0.00012137918,0.00025188926,0.00015394362],"category_scores_gemma":[0.00022988844,0.00038442394,0.00021915359,0.000100505866,0.000053967553,0.0001888104,0.00009975109,0.0006167367,0.00001834361],"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":[0.000022643355,0.000011622347,0.00001781694,0.0001044097,0.00004518908,0.000020333056,0.00000512919,0.97650075,0.0010893586,0.0048739617,0.00020973835,0.017099036],"study_design_scores_gemma":[0.00033070002,0.00010100245,0.0004401863,0.00008332392,0.000056821744,0.00009824661,0.000008270624,0.5526125,0.000108245615,0.00027547675,0.445332,0.0005532217],"about_ca_topic_score_codex":0.0000069373914,"about_ca_topic_score_gemma":3.9102733e-7,"teacher_disagreement_score":0.44512227,"about_ca_system_score_codex":0.00011541831,"about_ca_system_score_gemma":0.000013003816,"threshold_uncertainty_score":0.99986076},"labels":[],"label_agreement":null},{"id":"W4297769468","doi":"10.5267/j.dsl.2022.6.001","title":"Exploring quality inspection and grade judgment of distortion defects in the fabrication of spectacle lenses","year":2022,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Distortion (music); Lens (geology); Artificial intelligence; Curvature; Image quality; Computer vision; Computer science; Optics; Mathematics; Image (mathematics); Physics; Geometry; Telecommunications","score_opus":0.1323323609173162,"score_gpt":0.297137851160647,"score_spread":0.1648054902433308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297769468","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":[0.98979473,0.000035367222,0.009159992,0.000071157025,0.00067130296,0.00017831712,0.000002495111,0.000027903288,0.00005871955],"genre_scores_gemma":[0.9998076,0.000012504523,0.00007281736,0.000034554247,0.000030783627,0.00003652774,5.8102927e-7,0.0000041855774,4.527439e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9985871,0.00009278825,0.00035678255,0.00016220384,0.0006916617,0.00010950241],"domain_scores_gemma":[0.99948615,0.00016972367,0.000108027554,0.00018923984,0.000027846378,0.000018989824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019458611,0.000060365004,0.00012487323,0.00035175984,0.0001789454,0.00002202376,0.00014640336,0.000012784181,0.000003949793],"category_scores_gemma":[0.00015154808,0.000049219252,0.000033910204,0.0012087817,0.000084608124,0.0003182247,0.000042073323,0.000120831624,6.9596365e-7],"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":[0.000048142116,0.000050564242,0.013522387,0.000026250267,0.0000032956805,0.0000016538663,0.004479957,0.030302834,0.9155357,0.00047399403,0.0002598216,0.035295438],"study_design_scores_gemma":[0.0005826032,0.00017827259,0.83805066,0.000048048383,0.000006864799,0.000015076452,0.0048453417,0.0039829006,0.15127596,0.00020647819,0.0006403571,0.0001674506],"about_ca_topic_score_codex":0.00016753463,"about_ca_topic_score_gemma":0.00001108266,"teacher_disagreement_score":0.8245283,"about_ca_system_score_codex":0.0001708388,"about_ca_system_score_gemma":0.000009048227,"threshold_uncertainty_score":0.2007103},"labels":[],"label_agreement":null},{"id":"W4300076287","doi":"10.1007/978-3-642-21596-4","title":"Image Analysis and Recognition","year":2011,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision","score_opus":0.019773685077724954,"score_gpt":0.22430361378792615,"score_spread":0.2045299287102012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300076287","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":[0.0019165343,0.0001613081,0.99228454,0.0000032362568,0.0011147666,0.00014722103,0.0000075929256,0.00012274251,0.0042420877],"genre_scores_gemma":[0.82568115,0.00017431886,0.16951005,0.00023344207,0.0031821928,0.00003615358,0.00006785034,0.00012680177,0.0009880256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989411,0.000018773278,0.00023085027,0.00037899512,0.0002212095,0.00020902394],"domain_scores_gemma":[0.9994704,0.00009182969,0.00005522704,0.00025014995,0.00007122326,0.00006116124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039510857,0.00019040513,0.00030462837,0.0009639035,0.000066140135,0.00013893841,0.00017213712,0.00026300506,0.000033741715],"category_scores_gemma":[0.00002934818,0.00017434706,0.00007333018,0.0009720517,0.00012443494,0.00015947306,0.000063787345,0.00037317115,0.000029463206],"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":[0.0000036766583,0.000004053412,0.000037810612,0.0000520114,0.00005381809,0.000014191197,0.00035254427,0.0051258407,0.00049786904,0.000002508659,0.00009618819,0.9937595],"study_design_scores_gemma":[0.00050974754,0.00022131723,0.0011526876,0.0005203372,0.0003041746,0.00007165158,5.699424e-7,0.95608693,0.013365231,0.023965634,0.0024975096,0.0013042301],"about_ca_topic_score_codex":0.000047193014,"about_ca_topic_score_gemma":0.00007824795,"teacher_disagreement_score":0.99245524,"about_ca_system_score_codex":0.00014307405,"about_ca_system_score_gemma":0.000058762642,"threshold_uncertainty_score":0.7109667},"labels":[],"label_agreement":null},{"id":"W4302138820","doi":"10.1007/978-3-031-01647-9","title":"Analysis of Oriented Texture","year":2011,"lang":"en","type":"book","venue":"Synthesis lectures on biomedical engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Texture (cosmology); Computer science; Task (project management); Artificial intelligence; Computer vision; Information retrieval; Image (mathematics); Engineering","score_opus":0.009862820558029322,"score_gpt":0.19730546170051358,"score_spread":0.18744264114248427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302138820","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057351966,0.00800129,0.23795201,0.00006784557,0.020682082,0.002874071,0.0024832515,0.0070919087,0.7151123],"genre_scores_gemma":[0.9573733,0.00028127746,0.00046300824,0.000060790197,0.002672315,0.00018616633,0.00041797888,0.000630361,0.037914783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99787027,0.000027630156,0.00070532574,0.00037979212,0.0006429774,0.0003740341],"domain_scores_gemma":[0.9986058,0.00045102765,0.00013602983,0.0005338609,0.000060978095,0.00021228974],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00027122357,0.00053340953,0.0012159108,0.0022415838,0.000034256926,0.000017050814,0.00026810376,0.0013235137,0.0007971333],"category_scores_gemma":[0.00034336382,0.0004538324,0.0006504387,0.0010389019,0.00005665618,0.000029966988,0.000030598145,0.0008120541,0.00004898385],"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":[0.00043340673,0.00040313057,0.000022206892,0.0047839265,0.058369953,0.0002913436,0.0013443106,0.39350364,0.029309507,0.006152419,0.17575455,0.32963163],"study_design_scores_gemma":[0.00031232886,0.00022804675,0.00015593687,0.001375887,0.0033157445,0.000010376645,0.00000860257,0.0746313,0.013099192,0.00004651171,0.90558803,0.0012280591],"about_ca_topic_score_codex":0.000014231629,"about_ca_topic_score_gemma":0.0000038469516,"teacher_disagreement_score":0.9516381,"about_ca_system_score_codex":0.00024902093,"about_ca_system_score_gemma":0.00006276884,"threshold_uncertainty_score":0.999973},"labels":[],"label_agreement":null},{"id":"W4302556148","doi":"10.1007/978-3-319-41501-7","title":"Image Analysis and Recognition","year":2016,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Image (mathematics); Artificial intelligence; Library science; Operations research; Engineering","score_opus":0.013607163514049987,"score_gpt":0.2267060538329625,"score_spread":0.21309889031891252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302556148","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":[0.0013324457,0.00013315427,0.9945317,0.000013761537,0.0011519585,0.00013919374,0.000013223571,0.00011849911,0.002566067],"genre_scores_gemma":[0.8621639,0.00029461333,0.12738432,0.00036776043,0.0071288245,0.000051386625,0.00006366469,0.00017519544,0.002370338],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988978,0.00001936088,0.0002261002,0.00038393014,0.00025574936,0.00021706724],"domain_scores_gemma":[0.9994024,0.00016560714,0.000053475225,0.00024596284,0.00007224491,0.000060313432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040274378,0.00018544117,0.0002968888,0.00092029205,0.00006670164,0.00015453513,0.00016066698,0.0002471578,0.00002804505],"category_scores_gemma":[0.000039248025,0.00014473997,0.00007225277,0.0008090228,0.00012920522,0.00016927131,0.000061592014,0.00027019277,0.00003628853],"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":[0.0000023398563,0.0000017633199,0.0000229191,0.000030644886,0.00003472694,0.000008613278,0.0000879741,0.0023678532,0.0009831714,0.0000014851895,0.00011466652,0.99634385],"study_design_scores_gemma":[0.001087359,0.00028112627,0.0010983855,0.0014433207,0.00034692476,0.00010171565,4.840592e-7,0.92741907,0.021046996,0.038045816,0.007123129,0.0020056665],"about_ca_topic_score_codex":0.000013721688,"about_ca_topic_score_gemma":0.000039868206,"teacher_disagreement_score":0.99433815,"about_ca_system_score_codex":0.00020233075,"about_ca_system_score_gemma":0.00006331219,"threshold_uncertainty_score":0.5902325},"labels":[],"label_agreement":null},{"id":"W4306844463","doi":"10.1155/2022/4017071","title":"Research on the Method of Detecting the Spreading Rate of the Simultaneous Crushed Stone Sealing Layer Based on Machine Vision","year":2022,"lang":"en","type":"article","venue":"Advances in Civil Engineering","topic":"Industrial Vision Systems and Defect Detection","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":"Transport Canada","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation","keywords":"Wavelet; Computer science; Artificial intelligence; Pixel; Noise (video); Mean squared error; Image (mathematics); SIGNAL (programming language); Peak signal-to-noise ratio; Noise reduction; Pattern recognition (psychology); Computer vision; Algorithm; Mathematics; Statistics","score_opus":0.02952353749256186,"score_gpt":0.33079802976180106,"score_spread":0.3012744922692392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306844463","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":[0.8562087,0.0019430413,0.13184355,0.000267631,0.003670825,0.0018340793,0.000046243473,0.00027450803,0.003911408],"genre_scores_gemma":[0.99949706,0.000020162937,0.0002369857,0.00002075862,0.00008547374,0.000074570766,6.406855e-7,0.00004655867,0.000017811595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793446,0.0005614864,0.0004319178,0.00020545506,0.0005560896,0.00031060277],"domain_scores_gemma":[0.9929641,0.006363738,0.00010135733,0.0004905235,0.000055887795,0.000024427627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004674732,0.00016938332,0.00024632126,0.00027590315,0.00031734566,0.000020317024,0.0003896281,0.000059481066,0.000035565612],"category_scores_gemma":[0.00088869984,0.00010086577,0.00010162231,0.001341264,0.000028494103,0.00007518729,0.000114665774,0.0011941132,0.0000011544439],"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":[0.00005808988,0.000013107624,0.000026926818,0.00006082761,0.000009721812,0.0000032966805,0.00022573431,0.9534245,0.03784038,0.0002165409,0.0000074579225,0.008113409],"study_design_scores_gemma":[0.00028750292,0.00014676976,0.00006921969,0.00028071855,0.000005228886,0.0000044139847,0.00044556684,0.9122941,0.083940044,0.000043438547,0.002381646,0.000101357306],"about_ca_topic_score_codex":0.00009769454,"about_ca_topic_score_gemma":0.00009168858,"teacher_disagreement_score":0.14328833,"about_ca_system_score_codex":0.00020484053,"about_ca_system_score_gemma":0.000017752573,"threshold_uncertainty_score":0.5187894},"labels":[],"label_agreement":null},{"id":"W4307714734","doi":"","title":"MP-SIM: Maintenance Policy Simulations Tool","year":2022,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Computer science","score_opus":0.016213722483660025,"score_gpt":0.23663294898143822,"score_spread":0.2204192264977782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307714734","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":[0.3265439,0.0014169978,0.38457066,0.005203867,0.0038584333,0.0022556926,0.0009595065,0.0030498086,0.27214113],"genre_scores_gemma":[0.9831751,0.00018292756,0.0031275228,0.00005381201,0.00015752534,0.0001343173,0.00038999433,0.0000937313,0.012685069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99582547,0.0021086829,0.000653506,0.00055587204,0.00045909194,0.0003973769],"domain_scores_gemma":[0.99616015,0.0008058139,0.0002622509,0.0018129604,0.000827831,0.00013098762],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027207972,0.00035189383,0.00039596195,0.00039896392,0.00045725948,0.00034251437,0.00074068765,0.00036007774,0.00063548214],"category_scores_gemma":[0.0013095066,0.00040100556,0.0002504868,0.00071138726,0.00007067623,0.00012640208,0.0008082898,0.001090645,0.000066502354],"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":[0.00006137624,0.0007992123,0.0021604758,0.0011426128,0.000674773,0.000037817204,0.019194828,0.47663563,0.014307976,0.19151011,0.043953676,0.24952151],"study_design_scores_gemma":[0.0013873939,0.0000011249396,0.0023868438,0.0017784341,0.00008004466,0.000028766892,0.0002891696,0.4088337,0.019061534,0.006630882,0.55798906,0.0015330339],"about_ca_topic_score_codex":0.0015054247,"about_ca_topic_score_gemma":0.00046460272,"teacher_disagreement_score":0.6566312,"about_ca_system_score_codex":0.00048736387,"about_ca_system_score_gemma":0.00026222423,"threshold_uncertainty_score":0.9998442},"labels":[],"label_agreement":null},{"id":"W4308444644","doi":"10.1155/2022/8220453","title":"A Hybrid Improved Neural Networks Algorithm Based on L2 and Dropout Regularization","year":2022,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Overfitting; MNIST database; Computer science; Autoencoder; Artificial neural network; Regularization (linguistics); Artificial intelligence; Machine learning; Dropout (neural networks); Data set; Algorithm; Pattern recognition (psychology)","score_opus":0.007389142467747076,"score_gpt":0.18605188185314134,"score_spread":0.17866273938539426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308444644","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":[0.010936138,0.000070450136,0.98735887,0.00001775374,0.0004688265,0.00047483807,0.0000053157646,0.00035628967,0.00031152176],"genre_scores_gemma":[0.9979684,0.0000013459183,0.0016308718,0.000017064922,0.00009451598,0.00019257831,0.000008277577,0.00005497716,0.000032015883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989773,0.000032835887,0.00034677764,0.00018775175,0.0001927315,0.00026260153],"domain_scores_gemma":[0.9996194,0.000104445935,0.000029079598,0.00017268909,0.000010728261,0.00006361361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004344251,0.00017328227,0.00022801443,0.00019828383,0.0000738018,0.00004850968,0.000075866585,0.000063257976,0.000042173793],"category_scores_gemma":[0.00004722513,0.00017764544,0.000043066277,0.00024514695,0.0000090233925,0.00006616353,0.00004400576,0.00045355147,0.0000019059052],"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":[0.0000031424736,0.000020314288,0.000006286919,0.000118790995,0.0000054535035,0.0000055692526,0.000043584463,0.9922305,0.00056963245,0.00020548228,0.00003223069,0.006759067],"study_design_scores_gemma":[0.0004773705,0.00009699654,0.000008463787,0.00007056243,0.000005291585,0.00002606792,0.000013695155,0.9982739,0.00019407379,0.00042105108,0.0002247024,0.0001878],"about_ca_topic_score_codex":0.00000398236,"about_ca_topic_score_gemma":2.1984098e-7,"teacher_disagreement_score":0.98703223,"about_ca_system_score_codex":0.00013441013,"about_ca_system_score_gemma":0.0000045054794,"threshold_uncertainty_score":0.7244171},"labels":[],"label_agreement":null},{"id":"W4311041400","doi":"10.21203/rs.3.rs-2277713/v1","title":"Effect of Dataset Size and Auxiliary Data in Bayesian Learning of Advanced Manufacturing: A Composite Autoclave Processing Diagnostic Study","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Industrial Vision Systems and Defect Detection","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 British Columbia","funders":"","keywords":"Overfitting; Computer science; Machine learning; Bayesian probability; Bayesian network; Data-driven; Proxy (statistics); Context (archaeology); Artificial intelligence; Data mining; Artificial neural network","score_opus":0.0407180326383974,"score_gpt":0.38769481111573684,"score_spread":0.34697677847733943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311041400","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":[0.99415463,0.0011576838,0.00014117238,0.000005450304,0.00016725293,0.0028811914,0.0013174476,0.00007536351,0.000099798075],"genre_scores_gemma":[0.99832827,0.000107007014,0.00004618914,3.830441e-7,0.000061257815,0.00025961618,0.001132085,0.00005352768,0.000011649145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957374,0.0016890444,0.0006283867,0.0006152183,0.0009568207,0.00037316224],"domain_scores_gemma":[0.9950233,0.0036760916,0.00015268473,0.0009943472,0.000062729014,0.00009083904],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.005070979,0.00026490848,0.0007247387,0.0005564077,0.00014482028,0.000075607764,0.00055701856,0.00020090351,0.000050669012],"category_scores_gemma":[0.0020165227,0.0002549389,0.000044117252,0.0003837397,0.000060037044,0.00016361175,0.0023602962,0.0024499244,0.0000013155029],"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":[0.0017545094,0.00072283583,0.084772244,0.04942327,0.00040325598,0.0005986324,0.0051104706,0.58913445,0.006764799,0.000002750319,0.00057617854,0.2607366],"study_design_scores_gemma":[0.018477853,0.018069044,0.3629576,0.026886066,0.00043502174,0.00006976547,0.017895259,0.47775266,0.0646564,0.00013949968,0.009617436,0.0030434],"about_ca_topic_score_codex":0.00081343553,"about_ca_topic_score_gemma":0.000061843646,"teacher_disagreement_score":0.27818537,"about_ca_system_score_codex":0.00018728903,"about_ca_system_score_gemma":0.00009780236,"threshold_uncertainty_score":0.9999903},"labels":[],"label_agreement":null},{"id":"W4311153899","doi":"10.3390/foods11244009","title":"Online Detection of Impurities in Corn Deep-Bed Drying Process Utilizing Machine Vision","year":2022,"lang":"en","type":"article","venue":"Foods","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Process (computing); Hue; Computer science; Color space; Computer vision; Process engineering; Engineering; Image (mathematics)","score_opus":0.018042988042123252,"score_gpt":0.26417407913639934,"score_spread":0.24613109109427608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311153899","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":[0.9958955,0.00044569196,0.0018908576,0.0000047561566,0.00090976735,0.00020539005,0.000020996053,0.00016335881,0.00046367085],"genre_scores_gemma":[0.9997597,0.0000067382375,0.00003929982,0.000005554846,0.00009662353,0.000036218018,0.000012414197,0.0000270827,0.000016379963],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906695,0.00006402027,0.000349023,0.00013484628,0.00022194175,0.0001631965],"domain_scores_gemma":[0.99972403,0.0000370199,0.00006335788,0.00012116698,0.000027593302,0.000026857117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003037713,0.00010709315,0.00019631596,0.00026753376,0.00010713434,0.00001589617,0.000079136626,0.00006834401,0.000048859853],"category_scores_gemma":[0.0000287606,0.00011521276,0.000053361335,0.000499496,0.000009481269,0.00011536617,0.00004115055,0.0003082458,0.0000014257142],"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":[0.000489565,0.00035833634,0.0042229295,0.0009087496,0.00008436825,0.000029279145,0.0049612415,0.24380958,0.46289608,0.000084771396,0.00006175119,0.28209332],"study_design_scores_gemma":[0.0028524692,0.0013934992,0.0060686343,0.00022591022,0.000026967904,0.000082613435,0.008008369,0.74094427,0.23640132,0.00032757598,0.0030873101,0.0005810625],"about_ca_topic_score_codex":0.00016213201,"about_ca_topic_score_gemma":0.00018904048,"teacher_disagreement_score":0.4971347,"about_ca_system_score_codex":0.000118217955,"about_ca_system_score_gemma":0.000011886953,"threshold_uncertainty_score":0.46982402},"labels":[],"label_agreement":null},{"id":"W4311165016","doi":"10.18280/ts.390536","title":"Threshold Values of Different Classical Edge Detection Algorithms","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Prewitt operator; Sobel operator; Digital image; Edge detection; Canny edge detector; Algorithm; Threshold limit value; Image (mathematics); Mathematics; Enhanced Data Rates for GSM Evolution; Range (aeronautics); Computer science; Noise (video); Artificial intelligence; Image processing; Pattern recognition (psychology)","score_opus":0.022402424237702567,"score_gpt":0.22548549548822774,"score_spread":0.2030830712505252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311165016","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":[0.9743926,0.00008889161,0.022067863,0.000017199503,0.0015563099,0.00033118547,0.000026771557,0.0002252832,0.0012939164],"genre_scores_gemma":[0.999355,0.000003300044,0.000020877811,0.00001036402,0.00039469285,0.00009413442,0.0000077010145,0.000022775235,0.00009115223],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884015,0.00006198399,0.0003491172,0.00015037051,0.0004177365,0.00018061984],"domain_scores_gemma":[0.99971676,0.000035368743,0.000054160224,0.00011555859,0.00002391819,0.000054223743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025062647,0.00013545937,0.00020489126,0.00013143917,0.00015165395,0.000018590486,0.00009417472,0.0000554617,0.0006389506],"category_scores_gemma":[0.0000032277817,0.00012511449,0.0001212513,0.00017779834,0.000017881994,0.000057347548,0.00003976974,0.00023950366,0.000007773977],"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":[0.0003138434,0.00051625574,0.0009386296,0.00016371263,0.00033599272,0.000020027617,0.0012797833,0.10300771,0.6677554,0.000782409,0.0071705137,0.21771568],"study_design_scores_gemma":[0.0033047309,0.002132947,0.006272436,0.00004904253,0.000105988816,0.000049106104,0.00096665806,0.50541455,0.45944646,0.00062361005,0.020967519,0.0006669433],"about_ca_topic_score_codex":0.000010763009,"about_ca_topic_score_gemma":0.0000033405177,"teacher_disagreement_score":0.40240684,"about_ca_system_score_codex":0.000150915,"about_ca_system_score_gemma":0.000007931409,"threshold_uncertainty_score":0.6996061},"labels":[],"label_agreement":null},{"id":"W4311412360","doi":"10.1109/sensors52175.2022.9967124","title":"Machine learning based optimization of a ceramic bushing manufacturing process","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Steinbach Bible College","funders":"","keywords":"Bushing; Process (computing); Ceramic; Computer science; Position (finance); Machine learning; Artificial intelligence; Mechanical engineering; Process engineering; Engineering; Materials science","score_opus":0.011391661109722367,"score_gpt":0.21372205824161294,"score_spread":0.20233039713189058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311412360","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":[0.98485124,0.000071496,0.01252708,0.00001057268,0.0010233121,0.00022491427,0.000019320645,0.00032148225,0.0009505659],"genre_scores_gemma":[0.9995283,0.000004647915,0.00010208503,0.0000086591,0.00008523975,0.000023866669,0.000019555484,0.000043809265,0.00018382462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990055,0.00010635707,0.00026890563,0.00015201254,0.00030023765,0.00016700367],"domain_scores_gemma":[0.99968576,0.000049184237,0.000089537294,0.0001191359,0.000022494438,0.000033894976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030063733,0.00011915516,0.00017655508,0.00021971947,0.00021745492,0.00002048477,0.00008117668,0.000051406612,0.00042651375],"category_scores_gemma":[0.000023988136,0.00013264194,0.00006788744,0.00028867985,0.0000072636017,0.000073926916,0.000015702268,0.00042408667,0.000004426163],"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":[0.00002282055,0.000008233268,0.00007774146,0.0000589441,0.000014296078,0.00000500016,0.0002509322,0.9945623,0.004073849,9.880494e-7,0.00004618152,0.000878697],"study_design_scores_gemma":[0.00037281538,0.000063981315,0.000025462541,0.000016265736,0.00001051384,0.000011277803,0.00039308073,0.9333748,0.06434662,0.0000031469149,0.0012471289,0.000134913],"about_ca_topic_score_codex":0.00007390158,"about_ca_topic_score_gemma":0.0000023956989,"teacher_disagreement_score":0.06118752,"about_ca_system_score_codex":0.00011389896,"about_ca_system_score_gemma":0.000013521901,"threshold_uncertainty_score":0.54089814},"labels":[],"label_agreement":null},{"id":"W4312123790","doi":"10.1007/978-1-4842-8710-1_8","title":"Basic Filters and Smart Filters","year":2022,"lang":"en","type":"book-chapter","venue":"Apress eBooks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Delta-Q Technologies (Canada)","funders":"","keywords":"Focus (optics); Distortion (music); Filter (signal processing); Computer science; Computer vision; Artificial intelligence; Optics; Physics; Telecommunications","score_opus":0.02201976901032654,"score_gpt":0.1921292520527213,"score_spread":0.17010948304239476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312123790","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00090434484,0.00043964863,0.00008307363,0.0000053059184,0.0022888477,0.00037880635,0.00009659953,0.00035609145,0.9954473],"genre_scores_gemma":[0.08217962,0.0000833205,0.000055948327,0.000113156806,0.00076937396,0.000117330324,0.0000404531,0.0002711834,0.9163696],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896693,0.000016991447,0.00028839346,0.00028321336,0.00024141176,0.0002030357],"domain_scores_gemma":[0.99942404,0.00006465105,0.00006833645,0.00033453357,0.000017049733,0.00009140993],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001233264,0.00030739533,0.00033992864,0.00017608557,0.00012316229,0.000060222574,0.000115754156,0.00029963977,0.0007139825],"category_scores_gemma":[0.0000051295124,0.00032606578,0.00012468785,0.0000082156375,0.000038265694,0.000040874995,0.00009079643,0.00054206565,0.00004857428],"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":[0.00025015807,0.000014094718,0.00001923403,0.0014484414,0.0016509253,0.00043202288,0.0021136533,0.00229327,0.005654297,0.0695975,0.22659871,0.6899277],"study_design_scores_gemma":[0.00028025097,0.00006029037,0.0000037252364,0.000106208456,0.000045703466,0.000031802458,0.0000125410115,0.0002430294,0.00047500792,0.00036106937,0.99801797,0.00036243064],"about_ca_topic_score_codex":0.000060345563,"about_ca_topic_score_gemma":0.000009052776,"teacher_disagreement_score":0.7714192,"about_ca_system_score_codex":0.00007804821,"about_ca_system_score_gemma":0.000013139539,"threshold_uncertainty_score":0.9999191},"labels":[],"label_agreement":null},{"id":"W4312123793","doi":"10.1007/978-1-4842-8710-1_1","title":"Introduction to Warps","year":2022,"lang":"en","type":"book-chapter","venue":"Apress eBooks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Delta-Q Technologies (Canada)","funders":"","keywords":"Distortion (music); Set (abstract data type); Workspace; Object (grammar); Computer science; Computer vision; Computer graphics (images); Pixel; Artificial intelligence; Telecommunications","score_opus":0.01878948816818089,"score_gpt":0.20421829678147924,"score_spread":0.18542880861329836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312123793","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011555284,0.0001132537,0.0005148125,0.000033894587,0.00618877,0.00045996794,0.000035892,0.0005434886,0.9919944],"genre_scores_gemma":[0.009541475,0.000009227757,0.000046100595,0.000047142697,0.007215823,0.00010264196,0.000026103357,0.0001584121,0.98285306],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989638,0.000012049127,0.0002868023,0.00028964537,0.00027390014,0.00017383529],"domain_scores_gemma":[0.9993931,0.000019374535,0.000048434067,0.00042109517,0.000033602177,0.000084414365],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013636451,0.00024575074,0.00027997873,0.00022108774,0.00010318309,0.000039994382,0.00012401065,0.0002677572,0.0016919186],"category_scores_gemma":[0.000008680505,0.0002675041,0.000116264586,0.000013376142,0.00001037545,0.000030301699,0.000072450166,0.0005113783,0.000476543],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008722875,0.0000052148416,2.3810505e-7,0.00018714405,0.00030135128,0.000038906604,0.00055772974,0.011056024,0.006246708,0.10650026,0.6337962,0.24122296],"study_design_scores_gemma":[0.00010264407,0.00006127355,3.8967298e-7,0.000029455347,0.000024671415,0.0000173543,0.0000057885472,0.00005151403,0.0018254152,0.00047470754,0.99712783,0.00027897433],"about_ca_topic_score_codex":0.000030417359,"about_ca_topic_score_gemma":0.0000064162655,"teacher_disagreement_score":0.3633316,"about_ca_system_score_codex":0.00016291256,"about_ca_system_score_gemma":0.00001238053,"threshold_uncertainty_score":0.9999777},"labels":[],"label_agreement":null},{"id":"W4312281621","doi":"10.1115/msec2022-85334","title":"A Deterministic Inspection of Surface Preparation for Metalization","year":2022,"lang":"en","type":"article","venue":"Volume 1: Additive Manufacturing; Biomanufacturing; Life Cycle Engineering; Manufacturing Equipment and Automation; Nano/Micro/Meso Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Histogram; Thresholding; Coating; Computer science; Composite number; Materials science; Artificial intelligence; Computer vision; Composite material; Image (mathematics)","score_opus":0.010017976172617057,"score_gpt":0.21746160937067263,"score_spread":0.2074436331980556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312281621","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":[0.95923316,0.00044939134,0.028897984,0.00007433199,0.0040984196,0.0031313938,0.0009887806,0.0027638397,0.00036270768],"genre_scores_gemma":[0.993926,0.0001267564,0.002710156,0.000067484885,0.00072595954,0.0008354536,0.000757849,0.00039658093,0.00045374842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9927987,0.00024111867,0.0023887379,0.0016413071,0.0013100145,0.0016201333],"domain_scores_gemma":[0.9967062,0.00050657993,0.0010618727,0.0010126497,0.00013523194,0.00057749613],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0011519287,0.0016094585,0.0015986691,0.0015523119,0.0015749591,0.0004760187,0.0007664325,0.00052794896,0.00043263595],"category_scores_gemma":[0.00012564371,0.001817578,0.00067116733,0.0003527213,0.00017951046,0.001252295,0.0006027161,0.00095457013,0.000041911568],"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":[0.0006197747,0.00031954484,0.00009594365,0.0022722434,0.0012124741,0.000037627546,0.0018416076,0.897513,0.070550874,0.00011877645,0.0025756557,0.02284248],"study_design_scores_gemma":[0.0027587446,0.00059709675,0.006366679,0.00020317307,0.00028023083,0.00013351074,0.0004095426,0.070702136,0.88739043,0.00021729973,0.029134028,0.0018071443],"about_ca_topic_score_codex":0.00016138225,"about_ca_topic_score_gemma":0.000014995264,"teacher_disagreement_score":0.8268109,"about_ca_system_score_codex":0.0011924369,"about_ca_system_score_gemma":0.00013601538,"threshold_uncertainty_score":0.99972486},"labels":[],"label_agreement":null},{"id":"W4312307445","doi":"10.33965/ijcsis_2019140202","title":"HETEROGENEOUS DESIGN AND EFFICIENT CPU-GPU IMPLEMENTATION OF COLLISION DETECTION","year":2019,"lang":"en","type":"article","venue":"IADIS INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Computer science; Collision; Parallel computing; Central processing unit; Computer hardware; Computer security","score_opus":0.010539892222620995,"score_gpt":0.24565559021198785,"score_spread":0.23511569798936685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312307445","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":[0.7082118,0.000029880393,0.2868015,0.00001151323,0.0045274664,0.00027175187,0.000004494483,0.000026483933,0.00011515947],"genre_scores_gemma":[0.9995184,0.000036992493,0.00022590299,0.00004078978,0.00016447954,0.000004971378,0.0000016486506,0.000004252662,0.0000025816878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984769,0.00003765478,0.0005018694,0.00009307209,0.00077102025,0.000119441676],"domain_scores_gemma":[0.9990392,0.00004378645,0.0002165888,0.0000780834,0.00054383883,0.00007854039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011086857,0.00009541915,0.00012862982,0.00056926225,0.000116904914,0.00048137264,0.0001384945,0.0000497012,0.0000057254465],"category_scores_gemma":[0.0000099167655,0.00007927153,0.000027331695,0.00022813116,0.00003376424,0.0012797562,0.000033113207,0.00010070055,0.00002581787],"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":[0.00005293428,0.000008982786,0.00018816904,0.000032599288,0.00003760226,0.000001484033,0.0009958812,0.78618836,0.005015499,0.0004044722,0.00014214337,0.20693189],"study_design_scores_gemma":[0.000807838,0.0003927636,0.0013994782,0.000106695166,0.0000037584875,0.0004263801,0.00030520116,0.9747494,0.015222969,0.000006253554,0.006461781,0.00011752224],"about_ca_topic_score_codex":0.000018303052,"about_ca_topic_score_gemma":3.9865873e-7,"teacher_disagreement_score":0.2913066,"about_ca_system_score_codex":0.00017704687,"about_ca_system_score_gemma":0.00004423941,"threshold_uncertainty_score":0.46418875},"labels":[],"label_agreement":null},{"id":"W4312378154","doi":"10.1109/tcad.2022.3222287","title":"Knowledge-Intensive Diagnostics Using Case-Based Reasoning and Synthetic Case Generation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Bottleneck; Computer science; Printed circuit board; Volume (thermodynamics); Production (economics); Knowledge base; Recommender system; Reliability engineering; Industrial engineering; Data mining; Artificial intelligence; Machine learning; Engineering; Embedded system","score_opus":0.067575852722435,"score_gpt":0.2511200386228634,"score_spread":0.18354418590042842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312378154","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":[0.302131,0.00061815116,0.6942326,0.0000019012544,0.0022917404,0.00051224756,0.00008114022,0.00011991231,0.000011344011],"genre_scores_gemma":[0.9991978,0.000028715383,0.0004734841,0.000012427214,0.00013132059,0.00008706904,0.00000416475,0.00004984307,0.000015167246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998136,0.0005139431,0.0006002698,0.00033470927,0.00018860462,0.00022649155],"domain_scores_gemma":[0.9986768,0.0005077712,0.00013385761,0.00022418935,0.000339774,0.00011765142],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005756729,0.00028193498,0.00045370133,0.00045301273,0.00059719296,0.0001272837,0.000055904293,0.00015161771,0.0000121430685],"category_scores_gemma":[0.000019699135,0.0002750106,0.00007819844,0.0004372908,0.00004539563,0.00009730327,0.0000021157896,0.00041754197,0.000001377434],"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":[0.000019248393,0.000048108617,0.0000025014226,0.000096740885,0.00009970558,0.0011856587,0.00055178336,0.9620363,0.011566673,0.000023729024,0.00016044278,0.024209116],"study_design_scores_gemma":[0.0006613147,0.00054588856,5.6502915e-7,0.00028741435,0.00009865996,0.025251878,0.0015509808,0.96261036,0.008623523,0.0000017112591,0.000106556115,0.00026116404],"about_ca_topic_score_codex":0.0008395137,"about_ca_topic_score_gemma":0.000023429951,"teacher_disagreement_score":0.6970668,"about_ca_system_score_codex":0.00023693137,"about_ca_system_score_gemma":0.00008120289,"threshold_uncertainty_score":0.9999702},"labels":[],"label_agreement":null},{"id":"W4312405046","doi":"10.23977/acss.2022.060601","title":"Classification and identification of glass artifacts based on high-dimensional clustering and XGBoost algorithm","year":2022,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Identification (biology); Artifact (error); k-means clustering; Barium; Pattern recognition (psychology); Computer science; Algorithm; Feature (linguistics); Artificial intelligence; Materials science; Metallurgy","score_opus":0.016897003894567893,"score_gpt":0.2373563600426986,"score_spread":0.22045935614813073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312405046","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":[0.6714453,0.0025742026,0.32343602,0.000021360778,0.0019444306,0.00044403723,0.000034033437,0.000059580383,0.000041022326],"genre_scores_gemma":[0.99933976,0.000049500293,0.00042516243,0.000012102824,0.00010060064,0.00004634994,0.000008347942,0.000011096866,0.0000070626193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989789,0.00011154938,0.0004015595,0.00020877316,0.00019831478,0.00010086082],"domain_scores_gemma":[0.9995725,0.00014084356,0.000112982496,0.000108418426,0.000028442915,0.000036789494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000498972,0.00010368629,0.00020680527,0.00016113947,0.00010099054,0.000049437374,0.00003732457,0.00004415253,0.000002410158],"category_scores_gemma":[0.000004636742,0.00010169969,0.000014475862,0.00013235942,0.000020700165,0.00014862383,0.000032906337,0.00010874189,5.188351e-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":[0.000026122518,0.000019641884,0.0005930819,0.00016555656,0.000008494827,0.0000031080085,0.000107603344,0.8496007,0.006907795,0.00011945123,0.000027703043,0.14242075],"study_design_scores_gemma":[0.00044417812,0.0001473743,0.0039769043,0.00009967961,0.0000036256158,0.000016123799,0.00006264176,0.9939689,0.00046707777,0.00003918563,0.0006704098,0.00010391939],"about_ca_topic_score_codex":0.000034070683,"about_ca_topic_score_gemma":0.0000037075627,"teacher_disagreement_score":0.32789448,"about_ca_system_score_codex":0.000037957834,"about_ca_system_score_gemma":0.0000059219024,"threshold_uncertainty_score":0.41471928},"labels":[],"label_agreement":null},{"id":"W4312689237","doi":"10.1115/detc2022-89921","title":"Normalization and Dimension Reduction for Machine Learning in Advanced Manufacturing","year":2022,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Normalization (sociology); Computer science; Leverage (statistics); Pharmaceutical manufacturing; Principal component analysis; Cellular manufacturing; Analytics; Database normalization; Data analysis; Artificial intelligence; Data mining; Machine learning; Manufacturing engineering; Pattern recognition (psychology); Engineering","score_opus":0.009365726087279784,"score_gpt":0.20869324234157174,"score_spread":0.19932751625429196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312689237","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":[0.98933685,0.00010545963,0.009153072,0.000012423277,0.0005468445,0.00026176477,0.0000016075062,0.0001731992,0.00040878463],"genre_scores_gemma":[0.9993611,0.000011844072,0.00019136025,0.0000037547063,0.000039100592,0.000060020466,0.0000151637505,0.000013286026,0.00030434373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999614,0.000023070728,0.00012593894,0.000089114255,0.00006403372,0.00008383982],"domain_scores_gemma":[0.9999119,0.000012418441,0.000018571473,0.000036458518,0.0000061880746,0.000014465223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018263605,0.000052725547,0.000069402704,0.00011415772,0.00013727524,0.00001110365,0.000013374252,0.000025352087,0.000032998345],"category_scores_gemma":[0.000008401188,0.000054825672,0.00001394432,0.000079960744,0.00000189632,0.000095342395,0.000016588558,0.000114839255,6.9171716e-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":[0.00005420106,0.000004810063,0.0002653482,0.000027251455,0.0000035414093,5.401884e-7,0.00019171154,0.9074655,0.04051258,0.000058101134,0.000069902366,0.051346503],"study_design_scores_gemma":[0.0020347864,0.00029948773,0.0014752999,0.000023165121,0.000007190397,0.00006039833,0.001087893,0.80597,0.1419797,0.00011395042,0.04667223,0.00027587157],"about_ca_topic_score_codex":0.000046344547,"about_ca_topic_score_gemma":0.000009916341,"teacher_disagreement_score":0.10149547,"about_ca_system_score_codex":0.00007009298,"about_ca_system_score_gemma":0.0000020757998,"threshold_uncertainty_score":0.2235726},"labels":[],"label_agreement":null},{"id":"W4312790200","doi":"10.1109/jiot.2022.3228869","title":"Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Rotor (electric); Edge computing; Enhanced Data Rates for GSM Evolution; Microcontroller; Deep learning; Real-time computing; Embedded system; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.034635096000673056,"score_gpt":0.2879129144679047,"score_spread":0.25327781846723163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312790200","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":[0.963315,0.00006548957,0.02882412,0.0000024066173,0.005677531,0.0008330872,0.000017765471,0.00015314292,0.0011114473],"genre_scores_gemma":[0.99915236,0.0000071280506,0.00021983133,0.0000033122676,0.00041431107,0.000049158447,0.0000010145161,0.000035444482,0.000117464435],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978251,0.00036255424,0.0009727466,0.00014779263,0.00049895595,0.00019285694],"domain_scores_gemma":[0.9988062,0.00011277053,0.00062740926,0.00016392517,0.00020392191,0.00008581785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018259112,0.00016810825,0.00046022228,0.00035312038,0.00012626026,0.00004377403,0.00021331687,0.000104780585,0.00010570356],"category_scores_gemma":[0.000060588976,0.00016471535,0.00026913415,0.00022959145,0.000028541705,0.00024629434,0.000055623543,0.00063150807,0.000008937736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005206095,0.00007652734,0.00021947773,0.00030842327,0.0003168704,0.000018886112,0.003804283,0.041441925,0.9447789,0.00009671321,0.0011651694,0.0072521903],"study_design_scores_gemma":[0.0011777822,0.0011636628,0.00036360783,0.0008010109,0.00009185761,0.001221737,0.0031536499,0.29861617,0.69087094,0.00003795167,0.002109062,0.00039254184],"about_ca_topic_score_codex":0.0010335895,"about_ca_topic_score_gemma":0.000001498886,"teacher_disagreement_score":0.25717425,"about_ca_system_score_codex":0.0012933945,"about_ca_system_score_gemma":0.00003737489,"threshold_uncertainty_score":0.6716897},"labels":[],"label_agreement":null},{"id":"W4313038448","doi":"10.1016/j.ifacol.2022.09.594","title":"Predicting the Wood Mean Moisture Content in a Conventional Kiln-based Drying Process: A Data-driven Approach","year":2022,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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 du Québec à Rimouski; FPInnovations; École de Technologie Supérieure","funders":"","keywords":"Kiln; Water content; Wood drying; Process (computing); Reliability (semiconductor); Process engineering; Humidity; Environmental science; Pulp and paper industry; Equilibrium moisture content; Moisture; Quality (philosophy); Computer science; Mathematics; Agricultural engineering; Machine learning; Waste management; Engineering; Materials science; Composite material; Meteorology; Chemistry; Geotechnical engineering","score_opus":0.06323083612911994,"score_gpt":0.25864392300586403,"score_spread":0.19541308687674408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313038448","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":[0.99084944,0.00052293687,0.0033377905,0.0003733193,0.0011072467,0.0011871391,0.00053341314,0.00043664666,0.0016520718],"genre_scores_gemma":[0.99641204,0.000002156143,0.002236988,0.00013122834,0.00040632713,0.00022034228,0.00043259922,0.00004070864,0.00011760083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828887,0.0001393407,0.00041192886,0.000355794,0.0005155903,0.00028848991],"domain_scores_gemma":[0.9992972,0.00010087837,0.00009926153,0.00039788656,0.000046974692,0.000057769903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074956106,0.00019562544,0.0002516403,0.00012354308,0.00031940473,0.00006377311,0.00040669675,0.00009035391,0.00010344315],"category_scores_gemma":[0.00008145653,0.00015587916,0.00008490125,0.0004388223,0.000027488946,0.00011968223,0.00010656385,0.0007277605,0.0000055285554],"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":[0.00020431237,0.0003171055,0.006683389,0.0002909052,0.00019255013,0.00003700534,0.0038133657,0.9785351,0.005139809,0.000089365014,0.00015516054,0.004541921],"study_design_scores_gemma":[0.0019224823,0.00009069662,0.000920156,0.0000737208,0.00003442237,0.00003715869,0.0084576765,0.9859526,0.00015712797,0.000007784013,0.002108928,0.00023722782],"about_ca_topic_score_codex":0.0003029963,"about_ca_topic_score_gemma":0.0001599298,"teacher_disagreement_score":0.007417508,"about_ca_system_score_codex":0.0001828438,"about_ca_system_score_gemma":0.00007802589,"threshold_uncertainty_score":0.6356568},"labels":[],"label_agreement":null},{"id":"W4313230099","doi":"10.1007/978-1-4842-8829-0_5","title":"Working with Shape Warp (Liquify) Tools","year":2022,"lang":"en","type":"book-chapter","venue":"Apress eBooks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Delta-Q Technologies (Canada)","funders":"","keywords":"Computer science; Filter (signal processing); Computer graphics (images); Engineering drawing; Warp drive; Computer vision; Engineering; Mathematics","score_opus":0.06299431701081135,"score_gpt":0.2089336341835995,"score_spread":0.14593931717278816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313230099","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034476368,0.0005498528,0.00033239293,0.000002580411,0.0018850281,0.0005088712,0.00002317274,0.00064709847,0.99570626],"genre_scores_gemma":[0.15735275,0.000042720374,0.00012196975,0.00006627825,0.0016832491,0.00020300406,0.00004535911,0.0004259184,0.84005874],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985857,0.000017121287,0.0003576111,0.00033734879,0.00041947278,0.00028274086],"domain_scores_gemma":[0.9992265,0.00008274058,0.00011454362,0.00045641197,0.000033894517,0.00008591954],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013979025,0.00040034187,0.00044730288,0.00018437272,0.00016186119,0.00012125797,0.0002069136,0.00040911615,0.001116346],"category_scores_gemma":[0.000004881978,0.00036997822,0.00014943448,0.000017525015,0.000030786763,0.000057518362,0.00009011819,0.0008967283,0.00008672939],"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":[0.0004204585,0.000012842258,0.000008696201,0.00043512532,0.0013251958,0.0005787878,0.0010192153,0.007802537,0.0021263969,0.10389523,0.021112865,0.8612626],"study_design_scores_gemma":[0.00031822134,0.00010878799,0.0000010828873,0.00040209875,0.00006034168,0.000042295393,0.000011505138,0.00033797554,0.0006620386,0.0002491932,0.99731445,0.0004919919],"about_ca_topic_score_codex":0.000022058512,"about_ca_topic_score_gemma":0.0000147967985,"teacher_disagreement_score":0.9762016,"about_ca_system_score_codex":0.00013746384,"about_ca_system_score_gemma":0.000030874016,"threshold_uncertainty_score":0.9998752},"labels":[],"label_agreement":null},{"id":"W4313404444","doi":"10.3390/machines10121233","title":"Machine Learning in CNC Machining: Best Practices","year":2022,"lang":"en","type":"article","venue":"Machines","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Machining; Leverage (statistics); Software deployment; Computer science; Software; Numerical control; Machine tool; Focus (optics); Machine learning; Artificial intelligence; Industrial engineering; Software engineering; Engineering; Mechanical engineering; Operating system","score_opus":0.041540482209630344,"score_gpt":0.28085792078506355,"score_spread":0.2393174385754332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313404444","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":[0.9550091,0.0019066659,0.00025973844,0.00014936348,0.002125956,0.00026239417,0.000019466112,0.00061472534,0.039652605],"genre_scores_gemma":[0.9976396,0.000023205861,0.000056114597,0.000026609507,0.00021109071,0.00006413199,0.000018287072,0.000040205803,0.0019207778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989058,0.000188923,0.0002740856,0.00017918304,0.00023886182,0.00021315095],"domain_scores_gemma":[0.99957615,0.0001117778,0.00012100808,0.00013785498,0.000011119018,0.00004207068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006197664,0.00014635094,0.00019059489,0.0002509148,0.00025621473,0.00005462725,0.00012965892,0.000051915227,0.00052130764],"category_scores_gemma":[0.0001597599,0.00014461574,0.000053667965,0.00042055268,0.000007648505,0.00014162382,0.00008462926,0.0008084614,0.000056832927],"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":[0.00012316713,0.00011994457,0.15299536,0.00009006187,0.000057294645,0.00012631253,0.0012896183,0.6754419,0.006900403,0.00030270213,0.0011637058,0.16138949],"study_design_scores_gemma":[0.0009748609,0.00024739676,0.0024596013,0.000021908741,0.0000148714425,0.00013386735,0.0005529173,0.5293273,0.00022292796,0.00008723575,0.46558526,0.00037188342],"about_ca_topic_score_codex":0.002047222,"about_ca_topic_score_gemma":0.00023347318,"teacher_disagreement_score":0.46442154,"about_ca_system_score_codex":0.00009210316,"about_ca_system_score_gemma":0.000012631204,"threshold_uncertainty_score":0.58972585},"labels":[],"label_agreement":null},{"id":"W4313485777","doi":"10.1007/978-981-19-7218-8_7","title":"Smart Inspection; Conceptual Framework, Industrial Scenarios, and Sustainability Perspectives","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec à Rimouski","funders":"","keywords":"Field (mathematics); Factory (object-oriented programming); Computer science; Computer-aided; Quality (philosophy); Point (geometry); Sustainability; Industry 4.0; Systems engineering; Manufacturing engineering; Data science; Industrial engineering; Engineering; Data mining","score_opus":0.03707582057538012,"score_gpt":0.24425573378663956,"score_spread":0.20717991321125945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313485777","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052010585,0.0013443513,0.004237304,0.0002603932,0.009754492,0.0024152973,0.00013536269,0.006935982,0.9697158],"genre_scores_gemma":[0.48897496,0.00036919033,0.000053315027,0.000020315323,0.0040665804,0.000079376994,0.000021874812,0.00027629128,0.5061381],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982873,0.00003249296,0.0004953081,0.00052614603,0.00034077585,0.00031795565],"domain_scores_gemma":[0.99888,0.00025626682,0.000088745284,0.00037273637,0.0002454812,0.00015673139],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003548237,0.0004545191,0.0005737776,0.00033233818,0.00018932109,0.00012717614,0.000099745215,0.0017567604,0.0005837131],"category_scores_gemma":[0.00034196305,0.0004413988,0.00017576398,0.00012241247,0.00025948422,0.00013095798,0.00008151008,0.0015925901,0.00017692904],"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":[0.00014339572,0.000023682764,0.00020096952,0.0001931238,0.00060486374,0.00006888589,0.0039638816,0.0012008656,0.000012660686,0.9087501,0.064731374,0.020106196],"study_design_scores_gemma":[0.001981482,0.00062553247,0.00017209667,0.0006628295,0.00019687334,0.000073416384,0.01819459,0.0019390685,0.000085867956,0.055726014,0.9181358,0.0022064361],"about_ca_topic_score_codex":0.00017986554,"about_ca_topic_score_gemma":0.00006900103,"teacher_disagreement_score":0.8534044,"about_ca_system_score_codex":0.0006099222,"about_ca_system_score_gemma":0.00012292582,"threshold_uncertainty_score":0.9998038},"labels":[],"label_agreement":null},{"id":"W4315486958","doi":"10.1007/s00170-022-10649-7","title":"Online prediction of automotive tempered glass quality using machine learning","year":2023,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Machine learning; Quality assurance; Automotive industry; Artificial intelligence; Quality (philosophy); Support vector machine; Mean squared error; Regression; Computer science; Regression analysis; Field (mathematics); Process (computing); Industrial engineering; Engineering; Mathematics; Statistics","score_opus":0.03302414998139032,"score_gpt":0.29662822832193053,"score_spread":0.2636040783405402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315486958","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":[0.9937004,0.000092144415,0.0036253529,0.0002641032,0.0018759698,0.00008029743,0.00003320699,0.00028340507,0.00004515798],"genre_scores_gemma":[0.9988316,0.00013365036,0.00072875706,0.0000066136395,0.00022697094,0.0000015122081,0.0000061567716,0.000018892046,0.000045790017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878883,0.00004366769,0.000604327,0.00008482196,0.00034470102,0.00013362849],"domain_scores_gemma":[0.99908257,0.00012014941,0.00039711277,0.00012293088,0.0002554202,0.000021848853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046475526,0.00010874352,0.00023352048,0.00059899135,0.000058739814,0.000013460755,0.00035355194,0.00012680813,0.000014292467],"category_scores_gemma":[0.00028218047,0.0000818143,0.000095902906,0.0002344899,0.0000486823,0.00014567596,0.00008243703,0.00053290377,0.0000034002883],"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":[0.000113181486,0.000025923957,0.00052491325,0.000018930981,0.00024232168,0.000020483183,0.00014252534,0.63090324,0.34806967,0.00008600138,0.000046221583,0.019806597],"study_design_scores_gemma":[0.000987694,0.00016258204,0.0040174136,0.00017250192,0.000021736787,0.00023337231,0.0008485504,0.02727313,0.9617882,0.0013097944,0.003086191,0.00009885922],"about_ca_topic_score_codex":0.000028183611,"about_ca_topic_score_gemma":0.000009891824,"teacher_disagreement_score":0.6137185,"about_ca_system_score_codex":0.0001709584,"about_ca_system_score_gemma":0.000016938971,"threshold_uncertainty_score":0.333629},"labels":[],"label_agreement":null},{"id":"W4316371256","doi":"10.18280/ts.390613","title":"An Industrial Application Towards Classification and Optimization of Multi-Class Tile Surface Defects Based on Geometric and Wavelet Features","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Artificial intelligence; Wavelet; Computer science; Tile; Pattern recognition (psychology); Preprocessor; Software; Wavelet transform; Support vector machine; Computer vision","score_opus":0.03145785943447099,"score_gpt":0.23899657812471403,"score_spread":0.20753871869024304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316371256","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":[0.8868912,0.000074392556,0.11158103,0.000029957446,0.00022117565,0.00082196156,0.000085011496,0.00013321737,0.00016207082],"genre_scores_gemma":[0.9990872,0.000006329761,0.0005769087,0.000021995256,0.00009444536,0.00007723704,0.0001042163,0.000022434442,0.000009212718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989322,0.0001231016,0.00027045244,0.00022694441,0.00031844943,0.00012885117],"domain_scores_gemma":[0.9995764,0.00006367151,0.00010410442,0.00013525355,0.0000410697,0.00007949158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004959133,0.00013574566,0.00016937146,0.0002744335,0.00015223368,0.000040563893,0.00006419563,0.00010971278,0.00007962429],"category_scores_gemma":[0.000021860098,0.0001398546,0.000029798575,0.00049206056,0.000019147225,0.000097614924,0.000014668113,0.00019487759,6.326107e-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":[0.000105123014,0.00009375103,0.00039751237,0.00002663527,0.000012947644,4.110644e-7,0.00008808759,0.9475553,0.021191027,0.000039237624,0.00023926959,0.030250667],"study_design_scores_gemma":[0.001800728,0.0005112165,0.005971028,0.000010695841,0.000022403123,0.0000027780231,0.00015578039,0.9824361,0.008200003,0.0000021103274,0.00074220024,0.00014491855],"about_ca_topic_score_codex":0.000051362484,"about_ca_topic_score_gemma":0.0000029020819,"teacher_disagreement_score":0.112196036,"about_ca_system_score_codex":0.00010890134,"about_ca_system_score_gemma":0.000027928889,"threshold_uncertainty_score":0.57031053},"labels":[],"label_agreement":null},{"id":"W4316467284","doi":"10.18280/ts.390618","title":"Research on Industrial Production Defect Detection Method Based on Machine Vision Technology in Industrial Internet of Things","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Machine vision; Artificial intelligence; Computer science; Realization (probability); Computer vision; Product (mathematics); Pooling; Industrial production; Production (economics); Internet of Things; Machine learning; Embedded system","score_opus":0.06767190001072824,"score_gpt":0.3199821137062007,"score_spread":0.25231021369547246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316467284","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":[0.9850887,0.000026800837,0.0092124,0.00024885868,0.0025797097,0.0016065898,0.000018002102,0.00029683614,0.00092210644],"genre_scores_gemma":[0.9990443,0.0000010808504,0.000088262954,0.000020044974,0.00045588668,0.00028286458,0.000017585346,0.000043703396,0.000046254005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964033,0.0010228211,0.00069523725,0.00046854833,0.001030331,0.0003797614],"domain_scores_gemma":[0.9991505,0.0002947614,0.00013991029,0.00027994675,0.000078747864,0.00005613232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005357047,0.00023162182,0.00035811056,0.00252883,0.00019213406,0.000033951736,0.00022643685,0.00036240162,0.00037626602],"category_scores_gemma":[0.00018809632,0.0002294004,0.00012979373,0.002174106,0.000041972427,0.00013130273,0.000075054486,0.0021440503,0.000012823302],"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":[0.0042073815,0.0005648616,0.00057296146,0.00004553037,0.00007300531,0.000020173255,0.00038801954,0.5581116,0.09739791,0.00029946104,0.0023404968,0.3359786],"study_design_scores_gemma":[0.008380682,0.011436528,0.00028673015,0.00036173794,0.000037067402,0.000027244603,0.0008334982,0.48320517,0.48063672,0.00028116943,0.0139991,0.0005143635],"about_ca_topic_score_codex":0.00033578285,"about_ca_topic_score_gemma":0.000021534564,"teacher_disagreement_score":0.3832388,"about_ca_system_score_codex":0.0008009402,"about_ca_system_score_gemma":0.000059266506,"threshold_uncertainty_score":0.93546766},"labels":[],"label_agreement":null},{"id":"W4318211862","doi":"10.1109/tip.2022.3232009","title":"IEEE Transactions on Image Processing publication information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Industrial Vision Systems and Defect Detection","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":"College of Engineering, Michigan State University; Centre National de la Recherche Scientifique; Chinese Academy of Sciences; Academia Sinica; École Polytechnique Fédérale de Lausanne; Politecnico di Torino; Institut national de recherche en informatique et en automatique (INRIA); Universidad Nacional de Colombia; Université Catholique de Louvain; Google; Arizona State University; McMaster University; Johns Hopkins University","keywords":"Computer science; Image processing; Document image processing; Transaction processing; Computer vision; Information retrieval; Information processing; Image (mathematics); Computer graphics (images); Artificial intelligence; Image segmentation; Database; Database transaction","score_opus":0.01560890400085701,"score_gpt":0.24061921004082235,"score_spread":0.22501030603996533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318211862","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":[0.006229141,0.00006181811,0.98609775,0.00017634708,0.0016693531,0.0006161029,0.000112948335,0.0012943739,0.003742192],"genre_scores_gemma":[0.9967356,0.000016219827,0.0017587506,0.00019867143,0.00012410949,0.00065021106,0.000018591449,0.00008783126,0.00041006636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757797,0.000104775085,0.00076253165,0.00035819784,0.00072788843,0.0004686356],"domain_scores_gemma":[0.9989462,0.00005418896,0.0002094194,0.00032818297,0.00031526666,0.00014678088],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00050234364,0.0003719025,0.00030003118,0.00090084807,0.0018643658,0.000712404,0.00023019973,0.00017466211,0.0004276241],"category_scores_gemma":[0.000008592433,0.0004108905,0.00018698255,0.0014549411,0.000056965437,0.0033167358,7.9415116e-7,0.0011651865,0.00022140548],"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":[0.00013225722,0.00017085954,2.9303408e-7,0.0002811236,0.000033211745,0.0000033643396,0.0014971875,0.18041109,0.024609169,0.000002103026,0.00080782373,0.79205155],"study_design_scores_gemma":[0.0021783733,0.00045272152,0.000015690028,0.00026922388,0.00014368334,0.00013648503,0.0025138524,0.6064163,0.36729383,0.000056534802,0.019401526,0.001121785],"about_ca_topic_score_codex":0.00003713405,"about_ca_topic_score_gemma":0.0000069105045,"teacher_disagreement_score":0.9905064,"about_ca_system_score_codex":0.00059751264,"about_ca_system_score_gemma":0.00015676049,"threshold_uncertainty_score":0.9998343},"labels":[],"label_agreement":null},{"id":"W4318481268","doi":"10.23977/acss.2023.070105","title":"Transmission Defects Localization Network: Towards Wrong Assembly in the Transmission Assembly Process","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transmission (telecommunications); Component (thermodynamics); Computer science; Process (computing); Artificial intelligence; Telecommunications","score_opus":0.022282896088687533,"score_gpt":0.2760007758179664,"score_spread":0.25371787972927884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318481268","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":[0.21000615,0.008864587,0.77805156,0.00002735815,0.0014265494,0.0008181997,0.0000020194811,0.00025998187,0.0005435957],"genre_scores_gemma":[0.9981371,0.0011485736,0.00007239514,0.00002625254,0.00047721516,0.00009033248,0.000009455844,0.000027843887,0.00001078932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981405,0.00028686025,0.0005573101,0.00029152844,0.00034958057,0.00037420163],"domain_scores_gemma":[0.9994927,0.00019599483,0.000065779655,0.00014376025,0.00003920756,0.00006252961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013403243,0.00021707521,0.00035267227,0.0002030549,0.0001253087,0.00014929453,0.00015631894,0.00018797867,0.0000018064621],"category_scores_gemma":[0.000005710457,0.00015385423,0.00005465979,0.001102339,0.000013885183,0.00041586495,0.00001163914,0.0002301791,0.000005536445],"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":[0.00001488251,0.000010513274,0.00040630382,0.00036408106,0.00000483503,0.000020412786,0.0010982628,0.8517698,0.00026953107,0.000039144994,0.00025034513,0.1457519],"study_design_scores_gemma":[0.00056866964,0.00011567642,0.00080637855,0.0010998629,0.0000057233706,0.000022209524,0.00028864754,0.97987616,0.000309209,0.00017521979,0.016513294,0.00021892061],"about_ca_topic_score_codex":0.00004006428,"about_ca_topic_score_gemma":0.000018012246,"teacher_disagreement_score":0.788131,"about_ca_system_score_codex":0.00003809024,"about_ca_system_score_gemma":0.000018287714,"threshold_uncertainty_score":0.6273994},"labels":[],"label_agreement":null},{"id":"W4318715726","doi":"10.2139/ssrn.4329704","title":"Automatic Detection of Surface Defects Based on Deep Random Chains","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Fuse (electrical); Variety (cybernetics); Generalization; Sample (material); Pattern recognition (psychology); Machine learning; Pixel; Scale (ratio); Focus (optics); Data mining; Mathematics; Engineering; Cartography","score_opus":0.008137303335409216,"score_gpt":0.21445887766074168,"score_spread":0.20632157432533246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318715726","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":[0.93008095,0.00024023307,0.06769957,0.000015234017,0.0009513332,0.00020344676,0.0000012844904,0.00037937003,0.0004285926],"genre_scores_gemma":[0.9994492,0.0001621202,0.0000100207735,0.0000064041797,0.00021177373,0.000006052561,0.000001584682,0.000039973747,0.00011289409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806046,0.00011939195,0.0003546874,0.00011462089,0.00031603395,0.0010348173],"domain_scores_gemma":[0.9994776,0.00015268591,0.00010500951,0.00015236613,0.000052034888,0.000060287264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018418168,0.00015239173,0.00025198256,0.00030241816,0.00013448314,0.000028605094,0.00009571109,0.00014052489,0.000017255661],"category_scores_gemma":[0.000103577506,0.00013556881,0.00017187961,0.000668672,0.000011544911,0.000079042096,0.000006252484,0.0009842223,0.00009097172],"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":[0.00024585065,0.000039374987,0.00020822171,0.00009779201,0.00019989008,0.000009007733,0.00016969247,0.79528934,0.0810419,0.00080297067,0.00009108977,0.12180489],"study_design_scores_gemma":[0.0029757281,0.0006314639,0.00039823155,0.00009218564,0.00003444926,0.00007949714,0.00036732675,0.96624064,0.027124228,0.0016108908,0.00024182806,0.00020355452],"about_ca_topic_score_codex":0.000021301144,"about_ca_topic_score_gemma":0.00015151166,"teacher_disagreement_score":0.1709513,"about_ca_system_score_codex":0.0005635609,"about_ca_system_score_gemma":0.00020220094,"threshold_uncertainty_score":0.55283356},"labels":[],"label_agreement":null},{"id":"W4319597842","doi":"10.3390/a16020095","title":"Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review","year":2023,"lang":"en","type":"review","venue":"Algorithms","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":284,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Artificial intelligence; Digitization; Machine learning; Sample (material); Identification (biology); Task (project management); Pattern recognition (psychology); Computer vision; Engineering; Systems engineering","score_opus":0.2189905129289432,"score_gpt":0.43321507070519505,"score_spread":0.21422455777625185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319597842","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":[1.5200096e-7,0.78962606,0.20090078,0.00000212871,0.0034797434,0.0044032945,0.000013469315,0.0015192215,0.00005517781],"genre_scores_gemma":[1.1890242e-7,0.9731887,0.021658089,0.000010784591,0.0028914441,0.001650755,0.0000666171,0.00035581616,0.00017770054],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99646366,0.00076139724,0.001304422,0.00067723804,0.0002509517,0.00054233824],"domain_scores_gemma":[0.9981771,0.00059463846,0.00046527557,0.00047736595,0.00018063065,0.000104993574],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0037712539,0.0006811779,0.0024561118,0.00057882356,0.00024650255,0.0001012171,0.00023036155,0.0010699593,0.000009545088],"category_scores_gemma":[0.0015723478,0.00060745026,0.0010405314,0.0020231076,0.000020939086,0.00014449752,0.000065936976,0.0014120977,0.00006425621],"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":[0.0000018831765,0.0000038207377,1.1891091e-8,0.03032058,0.00022578327,0.0000034404407,0.0000069906587,0.000028554268,0.00003179273,6.144908e-7,0.0003377058,0.96903884],"study_design_scores_gemma":[0.00014636651,0.00012819309,4.9781517e-9,0.047456685,0.0012728898,0.00017504684,0.0000068160384,0.0039798976,0.00030158742,0.0000068065883,0.94593686,0.0005888545],"about_ca_topic_score_codex":0.000036813504,"about_ca_topic_score_gemma":0.000002214599,"teacher_disagreement_score":0.96844995,"about_ca_system_score_codex":0.0005292433,"about_ca_system_score_gemma":0.00012753712,"threshold_uncertainty_score":0.99963766},"labels":[],"label_agreement":null},{"id":"W4319990837","doi":"10.18280/ts.390614","title":"Deep Learning Based Semantic Segmentation Technique for Anomaly Detection on Metal Surfaces Using High Calibre U- Shaped Network","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Convolutional neural network; Deep learning; Process (computing); Task (project management); Computer vision; Artificial neural network; Pattern recognition (psychology); Anomaly detection; Engineering","score_opus":0.02183040768939513,"score_gpt":0.23206237463529697,"score_spread":0.21023196694590185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319990837","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":[0.4427984,0.000026121244,0.555587,0.0000054183183,0.00048057735,0.0008538621,0.0000103839875,0.00022007966,0.000018141534],"genre_scores_gemma":[0.99766934,7.591582e-7,0.0013191783,0.000028159195,0.00036425682,0.00049198925,0.00005540184,0.00005405841,0.000016875449],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859333,0.00019493747,0.00036621245,0.0002369667,0.0003244804,0.00028408915],"domain_scores_gemma":[0.999606,0.000114440656,0.00011381198,0.00008330327,0.000035280886,0.000047148096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071781146,0.00019419559,0.00021847394,0.00016620342,0.00058366894,0.000059109905,0.00007108171,0.00008263095,0.00028424803],"category_scores_gemma":[0.000008567028,0.00020925063,0.000120906734,0.0003376533,0.000008950559,0.000119023054,0.000016701639,0.00026278137,0.000003052837],"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":[0.00011494637,0.000018113818,0.00009172883,0.000033130003,0.000037208207,0.0000020691398,0.000040005103,0.69803786,0.2992006,0.000008393964,0.000022049762,0.002393924],"study_design_scores_gemma":[0.00094121567,0.00073714054,0.00023927179,0.000027019594,0.00006131323,0.000008656501,0.00016183988,0.8494631,0.1475149,0.000021198928,0.0006026844,0.00022166313],"about_ca_topic_score_codex":0.00010774705,"about_ca_topic_score_gemma":0.000015166697,"teacher_disagreement_score":0.5548709,"about_ca_system_score_codex":0.00030525436,"about_ca_system_score_gemma":0.000016802367,"threshold_uncertainty_score":0.8532993},"labels":[],"label_agreement":null},{"id":"W4321613578","doi":"10.21203/rs.3.rs-2586790/v1","title":"Artificial Intelligence for Product Quality Inspection in Manufacturing Industry - Online Detection of Edge Defects on Inorganic Solid Material","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Industrial Vision Systems and Defect Detection","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":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Process (computing); Quality (philosophy); Object detection; Domain (mathematical analysis); Artificial intelligence; Product (mathematics); Machine vision; Enhanced Data Rates for GSM Evolution; Field (mathematics); Set (abstract data type); Industrial engineering; Machine learning; Engineering; Pattern recognition (psychology)","score_opus":0.21267894648401553,"score_gpt":0.43029325407119107,"score_spread":0.21761430758717554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321613578","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":[0.98990715,0.00003737252,0.0021756836,0.000028912085,0.004498061,0.0024643214,0.0003482143,0.0004963558,0.000043943466],"genre_scores_gemma":[0.996802,0.00004471007,0.000028360406,0.0000012468896,0.0024623175,0.00038152363,0.00013633342,0.00010898414,0.000034539065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99630773,0.000491276,0.0011243598,0.0006901513,0.0007829415,0.00060355966],"domain_scores_gemma":[0.99840206,0.00038908198,0.00018119099,0.0006206112,0.00030661564,0.00010044011],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0033168155,0.00032797482,0.0005889873,0.0014374186,0.00016562264,0.00012876959,0.00026107358,0.0012424504,0.000020156496],"category_scores_gemma":[0.0011136184,0.00034433193,0.00017739655,0.0007231044,0.000062349885,0.00010108086,0.00025620378,0.0031204505,0.00003680204],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002568895,0.00066662213,0.0005999655,0.014412213,0.00021252573,0.000028102431,0.0015111195,0.283768,0.5421411,0.00022489043,0.0003690578,0.15349756],"study_design_scores_gemma":[0.00014064371,0.00040724018,0.00799682,0.0012139078,0.000008462657,0.00000225463,0.000650193,0.0054303724,0.9816775,0.0020624534,0.00009494759,0.00031521768],"about_ca_topic_score_codex":0.001237461,"about_ca_topic_score_gemma":0.0016072299,"teacher_disagreement_score":0.43953642,"about_ca_system_score_codex":0.0009485966,"about_ca_system_score_gemma":0.0001494629,"threshold_uncertainty_score":0.9999009},"labels":[],"label_agreement":null},{"id":"W4322492289","doi":"10.4028/p-23rx59","title":"Crack Detection of Pharmaceutical Vials Using Agglomerative Clustering Technique","year":2023,"lang":"en","type":"article","venue":"Advances in science and technology","topic":"Industrial Vision Systems and Defect Detection","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":"Horizon College and Seminary","funders":"","keywords":"Vial; Cluster analysis; Overhead (engineering); Materials science; Hierarchical clustering; Quality (philosophy); Data mining; Process engineering; Computer science; Engineering; Artificial intelligence; Chromatography; Operating system","score_opus":0.02808182455572576,"score_gpt":0.3439686362277974,"score_spread":0.3158868116720716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322492289","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":[0.9560065,0.0005497345,0.04183734,0.000019476627,0.0005650538,0.000277244,0.0000014581675,0.0003591657,0.00038399617],"genre_scores_gemma":[0.99905944,0.0003463154,0.0005322675,0.0000026184664,0.000021342517,0.000029635177,8.264213e-8,0.0000058850187,0.0000024298351],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992797,0.000011288969,0.00020316802,0.00016387893,0.00014174455,0.00020022232],"domain_scores_gemma":[0.9997402,0.000035198755,0.00003543317,0.00009695213,0.000070878166,0.000021381316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005432843,0.00006918863,0.00014177656,0.0009227373,0.000085352236,0.000011283283,0.000094103096,0.0001114862,0.0000016466438],"category_scores_gemma":[0.00015910066,0.00006537242,0.0000096252,0.0033930924,0.00033496093,0.0003685281,0.00006085977,0.00015095227,0.000002718164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034217444,0.0000020723783,0.00020795949,0.00002179192,9.904066e-7,0.0000026833652,0.000034406614,0.0035909635,0.88698155,0.0000993474,6.602018e-7,0.109054156],"study_design_scores_gemma":[0.00014049489,0.000053855892,0.00007655439,0.00005951673,0.0000017936579,0.000032010375,0.00025095267,0.0782346,0.9188476,0.00087527663,0.0013461469,0.00008118801],"about_ca_topic_score_codex":0.000007233798,"about_ca_topic_score_gemma":0.000016587679,"teacher_disagreement_score":0.10897297,"about_ca_system_score_codex":0.00006591939,"about_ca_system_score_gemma":0.000014562303,"threshold_uncertainty_score":0.266581},"labels":[],"label_agreement":null},{"id":"W4322716928","doi":"10.48550/arxiv.2302.13317","title":"TransferD2: Automated Defect Detection Approach in Smart Manufacturing using Transfer Learning Techniques","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Industrial Vision Systems and Defect Detection","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":"New Brunswick Innovation Foundation","keywords":"Computer science; Classifier (UML); Transfer of learning; Artificial intelligence; Bounding overwatch; Machine learning; Labeled data; Pattern recognition (psychology); Data mining","score_opus":0.08780876397727855,"score_gpt":0.19678893832163705,"score_spread":0.1089801743443585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322716928","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":[0.7096268,0.000019303503,0.28443515,6.833898e-7,0.0006050228,0.00054268696,0.000007992943,0.004182772,0.0005795717],"genre_scores_gemma":[0.99940276,0.00007908854,0.00011535021,0.0000018812731,0.00013325727,0.000007621152,0.000022251625,0.00012470913,0.000113060545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980663,0.00024009703,0.00039869527,0.0007342582,0.00011161364,0.00044901756],"domain_scores_gemma":[0.9993999,0.000064450505,0.000048387188,0.00034987682,0.000040631832,0.00009673973],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005395284,0.0004486905,0.0005268134,0.0010174516,0.0001625242,0.00008530683,0.0002343429,0.00097650266,0.000010879767],"category_scores_gemma":[0.000012115121,0.00055347173,0.00036888622,0.00070204446,0.00003250031,0.00020860026,0.00008298644,0.0016191982,0.000025363566],"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":[0.000049919334,0.000017393331,0.00036618576,0.00033083622,0.00009751418,0.000082871884,0.00017997209,0.99131423,0.00637096,0.000023288861,0.000004477048,0.0011623466],"study_design_scores_gemma":[0.0005190919,0.000051390474,0.00080323545,0.00034438245,0.000109238725,0.000015028271,0.00024306786,0.93536544,0.061644457,0.00014978823,0.00013407292,0.0006208367],"about_ca_topic_score_codex":0.0009815444,"about_ca_topic_score_gemma":0.00019787501,"teacher_disagreement_score":0.28977597,"about_ca_system_score_codex":0.0006786184,"about_ca_system_score_gemma":0.000038080394,"threshold_uncertainty_score":0.99969167},"labels":[],"label_agreement":null},{"id":"W4323241324","doi":"10.5220/0011748100003414","title":"Skin Tone via Device-Independent Colour Space","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; Toronto Metropolitan University","funders":"","keywords":"Tone (literature); Computer science; Space (punctuation); Art; Operating system","score_opus":0.020418726459785836,"score_gpt":0.2653378059835399,"score_spread":0.24491907952375405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323241324","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":[0.9249358,0.000044167202,0.014762608,0.00011167459,0.0029291434,0.00033202616,0.0000035542462,0.0022776735,0.054603305],"genre_scores_gemma":[0.9932918,0.0000072124753,0.000052627125,0.000017522048,0.00027773736,0.00001766872,0.0000028350198,0.00002370086,0.006308904],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993255,0.00001751606,0.00016100609,0.000115137336,0.00017848189,0.0002023564],"domain_scores_gemma":[0.99971974,0.00003405582,0.000014675889,0.00014385777,0.000024295181,0.00006338684],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022726614,0.00009399277,0.00012435707,0.000122058635,0.00005190339,0.00003461349,0.000056008117,0.00012797985,0.00029726385],"category_scores_gemma":[0.000012227653,0.00008406822,0.000052152936,0.00043192136,0.0000039910924,0.00006178141,0.00002183293,0.00012051991,0.0032670738],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008636864,0.00007187162,0.0018110375,0.00029568005,0.00036791785,0.00025859717,0.0014745459,0.12831461,0.2335466,0.0030122085,0.4651577,0.16560286],"study_design_scores_gemma":[0.002031932,0.0002408639,0.013954331,0.000082653874,0.000033715536,0.000094603216,0.0014601978,0.1948906,0.26634267,0.0003193467,0.51958746,0.00096165185],"about_ca_topic_score_codex":0.00017821438,"about_ca_topic_score_gemma":0.00010622007,"teacher_disagreement_score":0.16464122,"about_ca_system_score_codex":0.000059456775,"about_ca_system_score_gemma":0.0000073212304,"threshold_uncertainty_score":0.997509},"labels":[],"label_agreement":null},{"id":"W4324122516","doi":"10.23977/acss.2023.070203","title":"Lightweight Steel Bar Detection Network Based on YOLOv5","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pyramid (geometry); Feature (linguistics); Bar (unit); Volume (thermodynamics); Computer science; Artificial intelligence; Function (biology); Set (abstract data type); Image (mathematics); Pattern recognition (psychology); Data set; Computer vision; Layer (electronics); Mathematics; Materials science; Geology; Geometry; Physics","score_opus":0.015827944691510932,"score_gpt":0.23433702011746899,"score_spread":0.21850907542595804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324122516","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":[0.2759499,0.00807857,0.6831072,0.000034884666,0.025623912,0.0014101792,0.000016240803,0.001755185,0.0040239454],"genre_scores_gemma":[0.9981292,0.00018297584,0.00008724001,0.000025780488,0.0014372009,0.000060852086,0.0000035542969,0.000026692987,0.000046531994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987453,0.00011920769,0.0003835567,0.00025611135,0.00020324264,0.0002925745],"domain_scores_gemma":[0.9994347,0.00024128256,0.000054522556,0.00017844609,0.000026843527,0.000064183376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054535037,0.00017871689,0.00029932946,0.00020861051,0.000098968005,0.00009412396,0.00007425044,0.00014215591,0.000005036567],"category_scores_gemma":[0.000006020152,0.00015463865,0.000049466023,0.0005274003,0.000011980014,0.00017949031,0.000016957794,0.00017057093,0.00006824625],"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":[0.000016084738,0.0000049185023,0.00028718615,0.00009163417,0.000007267037,0.000013729771,0.000035971418,0.9511772,0.00028431232,0.00006326271,0.00061662355,0.047401804],"study_design_scores_gemma":[0.00042773274,0.00019241156,0.00049752195,0.00034670986,0.0000030058154,0.0000069015477,0.00002078975,0.9528709,0.00042931573,0.00006444937,0.044951677,0.00018856631],"about_ca_topic_score_codex":0.00001437,"about_ca_topic_score_gemma":0.000012574081,"teacher_disagreement_score":0.7221793,"about_ca_system_score_codex":0.000045780213,"about_ca_system_score_gemma":0.0000055879614,"threshold_uncertainty_score":0.6305981},"labels":[],"label_agreement":null},{"id":"W4327512437","doi":"10.21611/qirt.2022.3014","title":"Defect Detection Enhancement, A Survey","year":2022,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Segmentation; Conditional random field; Artificial intelligence; Image segmentation; Prior probability; Surface (topology); Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.023058558197394323,"score_gpt":0.21840005077367977,"score_spread":0.19534149257628544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327512437","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":[0.9165091,0.00007351127,0.06266408,0.000004468463,0.0028166338,0.00021818496,0.00000994306,0.000500258,0.017203823],"genre_scores_gemma":[0.99908847,0.0000026322439,0.000011523186,0.000036453435,0.00004851562,0.00007917289,0.000006050562,0.000015138037,0.0007120223],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938434,0.0001015912,0.00013551943,0.000099842175,0.00015482193,0.00012386349],"domain_scores_gemma":[0.99979246,0.00003740409,0.000015518768,0.000112965754,0.000014687846,0.00002693937],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00052127615,0.000066572095,0.00007923874,0.000084724394,0.00016973594,0.000019147956,0.000045076347,0.000023264183,0.0011064723],"category_scores_gemma":[0.000016357164,0.00006774133,0.000054882814,0.00031014613,0.000002795782,0.000044757082,0.000024645451,0.00014921786,0.000096605014],"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":[0.00024550853,0.00014437854,0.0034502444,0.000045089975,0.0002721809,0.000013499377,0.00035809647,0.069315284,0.55835617,0.0001308123,0.1161438,0.25152496],"study_design_scores_gemma":[0.0013001226,0.0008849726,0.005950168,0.000005698239,0.000016739908,0.000066824156,0.00028329153,0.057775736,0.24386157,0.000052319632,0.6891654,0.0006371249],"about_ca_topic_score_codex":0.00031698451,"about_ca_topic_score_gemma":0.00014729306,"teacher_disagreement_score":0.57302165,"about_ca_system_score_codex":0.00011914682,"about_ca_system_score_gemma":0.0000053260846,"threshold_uncertainty_score":0.99980664},"labels":[],"label_agreement":null},{"id":"W4327790973","doi":"10.5267/j.msl.2023.1.001","title":"Quality assessment of fresh meat cuts as a performance indicator of knives specifically adapted for robot-assisted operations","year":2023,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Quality (philosophy); Bespoke; Robot; Operations management; Simulation; Artificial intelligence; Business; Engineering; Advertising","score_opus":0.05522106676000587,"score_gpt":0.3118295030110082,"score_spread":0.2566084362510023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327790973","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":[0.9462096,0.000002656349,0.04938963,0.00028618635,0.0004356323,0.00065099,0.000013138195,0.0001370764,0.0028751043],"genre_scores_gemma":[0.9971016,0.0000074655127,0.0025903843,0.000056703553,0.000037692327,0.000059817572,0.000007444619,0.000009806814,0.00012905285],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9985885,0.000027536042,0.00040719047,0.00022343514,0.0005274745,0.00022584123],"domain_scores_gemma":[0.99954075,0.000041069412,0.00007593935,0.0002594906,0.00003470166,0.00004806784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009321969,0.00009851545,0.00018702558,0.00046479818,0.00015577805,0.00004775868,0.0002609228,0.00003469017,0.000024069515],"category_scores_gemma":[0.000025313373,0.00009141584,0.00006474571,0.0011442422,0.00011833946,0.00022943268,0.00005873157,0.000067352834,0.000014806579],"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":[0.000023389586,0.000043819,0.0030848372,0.00037025823,0.00007758582,0.0000025506529,0.00030125753,0.19164684,0.7832211,0.004551633,0.003109162,0.013567522],"study_design_scores_gemma":[0.001112433,0.00017951166,0.77067506,0.00016219605,0.00003063793,0.0000011926702,0.001035944,0.15155794,0.071509026,0.000014802751,0.0034085936,0.00031267773],"about_ca_topic_score_codex":0.0000250695,"about_ca_topic_score_gemma":0.000005812939,"teacher_disagreement_score":0.7675902,"about_ca_system_score_codex":0.00007867066,"about_ca_system_score_gemma":0.000013680663,"threshold_uncertainty_score":0.37278298},"labels":[],"label_agreement":null},{"id":"W4360989175","doi":"10.18280/ria.370108","title":"Efficient Method Using Attention Based Convolutional Neural Networks for Ceramic Tiles Defect Classification","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Ceramic tiles; Ceramic; Materials science; Metallurgy","score_opus":0.09965376628126603,"score_gpt":0.32079644299394594,"score_spread":0.2211426767126799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360989175","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":[0.20388512,0.00008029262,0.793488,0.000028666598,0.0015308915,0.00048682676,0.0000147050805,0.00037778504,0.000107680324],"genre_scores_gemma":[0.9981906,0.000004268494,0.0011117272,0.00001222812,0.0003664466,0.000089293026,0.000060936076,0.000039463444,0.00012505864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866134,0.000084689564,0.00046269654,0.0002984348,0.00015092012,0.00034194143],"domain_scores_gemma":[0.99914557,0.0003473858,0.000086218686,0.0002359623,0.000121295096,0.00006354602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008118549,0.00016384156,0.00019521567,0.00023691104,0.0002491636,0.00006613919,0.00010296632,0.00017093871,0.000037082864],"category_scores_gemma":[0.00008496747,0.00017179325,0.00023653783,0.0007556251,0.00002729997,0.00004801075,0.0000148723875,0.00016108417,0.000104071696],"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":[0.000019403285,0.000013793565,0.00006547629,0.00005119015,0.0000109316825,8.0705337e-7,0.000031964355,0.9618315,0.02460933,0.0003490308,0.00024689417,0.012769654],"study_design_scores_gemma":[0.00008540298,0.000045572822,0.00023217704,0.000062615836,0.000025750807,0.000008111324,0.00020820275,0.9923639,0.005979251,0.000054108044,0.00075169344,0.00018317676],"about_ca_topic_score_codex":0.000018489758,"about_ca_topic_score_gemma":0.0000035568303,"teacher_disagreement_score":0.79430544,"about_ca_system_score_codex":0.000152368,"about_ca_system_score_gemma":0.000018132181,"threshold_uncertainty_score":0.7005525},"labels":[],"label_agreement":null},{"id":"W4361219341","doi":"10.1007/s00107-023-01949-0","title":"Transitioning to data-driven quality control in industrial veneer drying: a case study","year":2023,"lang":"en","type":"article","venue":"European Journal of Wood and Wood Products","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Mitacs","keywords":"Veneer; Quality (philosophy); Green wood; Data quality; Process engineering; Computer science; Water content; Sorting; Softwood; Process (computing); Environmental science; Wood drying; Engineering; Agricultural engineering; Manufacturing engineering; Pulp and paper industry; Operations management; Materials science; Composite material; Algorithm","score_opus":0.11889414318187028,"score_gpt":0.3025281709440159,"score_spread":0.18363402776214563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361219341","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":[0.99751973,0.00010274945,0.00022648482,0.00022562979,0.00131207,0.0003665544,0.00001959957,0.00006567719,0.00016152025],"genre_scores_gemma":[0.9984444,0.000005340375,0.000041437954,0.000020815161,0.0014336528,0.0000015403796,0.0000025887734,0.0000310948,0.00001917102],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980264,0.0006575131,0.00067620963,0.00021028928,0.00023116497,0.00019844827],"domain_scores_gemma":[0.99933916,0.000073496805,0.00011161056,0.00026388728,0.000088760535,0.00012310126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00398759,0.00013481051,0.00032005753,0.00032928612,0.000081419064,0.00009345359,0.0001465125,0.000033978456,0.0000037877887],"category_scores_gemma":[0.0003349275,0.00011928031,0.000031129293,0.00064854446,0.000009039732,0.00023872193,0.00004134835,0.0004183532,0.000018732517],"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":[0.0024350462,0.0011808177,0.01067158,0.00050006364,0.001819701,0.09351882,0.09565669,0.16302463,0.12561055,0.000026172052,0.070468485,0.43508744],"study_design_scores_gemma":[0.2287226,0.03618502,0.09655245,0.008321431,0.0023475662,0.05761795,0.23174861,0.035479505,0.023978446,0.00008649932,0.2693872,0.009572739],"about_ca_topic_score_codex":0.000027549591,"about_ca_topic_score_gemma":0.00001582558,"teacher_disagreement_score":0.4255147,"about_ca_system_score_codex":0.00002721043,"about_ca_system_score_gemma":0.000032202617,"threshold_uncertainty_score":0.48641098},"labels":[],"label_agreement":null},{"id":"W4361829816","doi":"10.1109/iccsse55346.2022.10079777","title":"Defect Detection for Printed Circuit Board Assembly Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Ontario Tech University","funders":"","keywords":"Deep learning; Printed circuit board; Artificial intelligence; Computer science; Task (project management); Pattern recognition (psychology); Sampling (signal processing); Layer (electronics); Computer vision; Machine learning; Engineering; Materials science","score_opus":0.03385775727346523,"score_gpt":0.24470802887392085,"score_spread":0.2108502716004556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361829816","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":[0.48333073,0.00004633232,0.5126027,0.0000021597384,0.001308758,0.00034271568,0.0000013500782,0.0005532196,0.0018120564],"genre_scores_gemma":[0.9992196,0.000001230494,0.00016697054,0.000022271965,0.00016533476,0.00012871643,0.0000033024362,0.000048777776,0.00024377696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999076,0.00007892244,0.00023704539,0.00018413599,0.00018635114,0.00023752633],"domain_scores_gemma":[0.9996682,0.00007958803,0.000047662743,0.00011613708,0.000042557058,0.000045851248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004644314,0.00012220626,0.0001590275,0.00017770375,0.00046498797,0.00005195677,0.00006232336,0.00006856128,0.00012973162],"category_scores_gemma":[0.00006316035,0.00013285894,0.00017001032,0.00031416543,0.0000042322254,0.00009242419,0.000032324613,0.00031940744,0.000012279826],"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":[0.000044218214,0.000011840327,0.0001751427,0.000036514586,0.00007550256,0.0000023742448,0.00015035972,0.5445784,0.376582,0.0001060088,0.00017935582,0.07805834],"study_design_scores_gemma":[0.00078155624,0.0003018659,0.00016268638,0.00000964218,0.000034266533,0.000061540006,0.0006853995,0.845085,0.046691835,0.00005623403,0.10582373,0.000306226],"about_ca_topic_score_codex":0.00009778238,"about_ca_topic_score_gemma":0.00002669965,"teacher_disagreement_score":0.5158889,"about_ca_system_score_codex":0.0003227325,"about_ca_system_score_gemma":0.0000105050185,"threshold_uncertainty_score":0.54178303},"labels":[],"label_agreement":null},{"id":"W4362600044","doi":"10.1111/mice.13001","title":"Unsupervised defect segmentation with pose priors","year":2023,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":12,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Leverage (statistics); Segmentation; Outlier; Minimum bounding box; False alarm; Bounding overwatch; Computer vision; Pattern recognition (psychology); Machine learning; Image (mathematics)","score_opus":0.004417954318021914,"score_gpt":0.1704507330625819,"score_spread":0.16603277874456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362600044","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":[0.8546883,0.00011400789,0.1422721,0.0000074385216,0.0012024612,0.0002329898,0.000006276456,0.0012798341,0.0001966283],"genre_scores_gemma":[0.99724233,0.000036143672,0.002007896,0.000017215973,0.0005840588,0.000020236139,0.000022141856,0.000056830853,0.000013167297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990456,0.000015723936,0.00023320572,0.00023077882,0.00017540132,0.00029925525],"domain_scores_gemma":[0.9996017,0.000055650362,0.00002790654,0.00016851719,0.000032160904,0.00011402744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000115937124,0.0002557933,0.0002395874,0.00028023572,0.000084739135,0.0001058522,0.000078993,0.00013425769,0.000020179057],"category_scores_gemma":[0.000008709493,0.00021881184,0.00005866036,0.00059032335,0.000012959899,0.00021075387,0.000036920268,0.00023639646,0.000016593427],"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":[0.000013010771,0.0000024664712,0.00062795024,0.00018782157,0.000095457835,0.000021280946,0.00050597667,0.9368017,0.028965192,0.0000747324,0.0017921458,0.030912299],"study_design_scores_gemma":[0.001431368,0.00026150275,0.04964737,0.00024071423,0.000038520615,0.00013828899,0.000114084745,0.9350919,0.006288802,0.000053290045,0.0060890517,0.00060507294],"about_ca_topic_score_codex":0.0000057213497,"about_ca_topic_score_gemma":0.0000027938693,"teacher_disagreement_score":0.14255404,"about_ca_system_score_codex":0.000054114986,"about_ca_system_score_gemma":0.000010857201,"threshold_uncertainty_score":0.8922888},"labels":[],"label_agreement":null},{"id":"W4365515432","doi":"10.3389/fcomp.2023.1192413","title":"Editorial: Deep neural computing for advanced automotive system applications","year":2023,"lang":"en","type":"editorial","venue":"Frontiers in Computer Science","topic":"Industrial Vision Systems and Defect Detection","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":"","keywords":"Automotive industry; Computer science; Artificial neural network; Artificial intelligence; Engineering; Aerospace engineering","score_opus":0.0069835238607770115,"score_gpt":0.24479776197914474,"score_spread":0.23781423811836772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365515432","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012307114,0.000060697173,0.48723215,0.0000024038136,0.51137817,0.0006573114,0.000028524404,0.00061101944,0.000017411327],"genre_scores_gemma":[0.002099346,0.000006827211,0.026737561,0.0000021294395,0.97074264,0.00027374644,0.00004379121,0.0000797366,0.000014202932],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969413,0.00004610249,0.00062669296,0.0007775482,0.00094825553,0.00066010014],"domain_scores_gemma":[0.99802876,0.0006026172,0.00018871606,0.00047303608,0.00057264493,0.0001342364],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014003526,0.0003471933,0.00060428394,0.0007049853,0.00034761665,0.00030999098,0.00087236444,0.00063838565,1.6974047e-7],"category_scores_gemma":[0.00024984425,0.00036802847,0.0001349646,0.0016242373,0.000121069155,0.00028414314,0.00016159327,0.0007289937,0.000014551971],"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":[0.0000075011562,0.000004150863,0.0000048183706,0.00016465886,0.000010691579,0.0000011163573,0.00008527481,0.15928008,0.0000069062294,0.000004468171,0.8056787,0.034751646],"study_design_scores_gemma":[0.00038923713,0.000051037172,0.00000391251,0.00016885814,0.000008022127,5.176226e-7,0.00004802708,0.60702777,0.000024719267,0.000017046626,0.3920106,0.0002502267],"about_ca_topic_score_codex":0.00001785147,"about_ca_topic_score_gemma":0.0000052950318,"teacher_disagreement_score":0.4604946,"about_ca_system_score_codex":0.0010909322,"about_ca_system_score_gemma":0.00018438106,"threshold_uncertainty_score":0.99987715},"labels":[],"label_agreement":null},{"id":"W4366492340","doi":"10.11159/iceptp23.126","title":"Rapid Classification of Microplastics by Using the Application of a Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Civil, Structural, and Environmental Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Prince of Songkla University","keywords":"Microplastics; Convolutional neural network; Computer science; Artificial intelligence; Artificial neural network; Biology; Ecology","score_opus":0.011253240400253271,"score_gpt":0.1986664581194176,"score_spread":0.18741321771916433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366492340","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":[0.9986224,0.00021681914,0.00019231743,0.000016728183,0.0005791758,0.00023579464,0.00004557701,0.000041142957,0.000049999784],"genre_scores_gemma":[0.9997543,0.00004070761,0.00005900579,0.0000028760307,0.00007513978,0.000010956089,0.000006705147,0.000019684207,0.000030634124],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992384,0.000004502572,0.00030121184,0.00011760951,0.00019521102,0.00014306512],"domain_scores_gemma":[0.9996496,0.000065069726,0.00016108723,0.00008418569,0.000014095704,0.000025950698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011433842,0.00013210175,0.00017442598,0.00007361037,0.00008046261,0.000012569735,0.00012803666,0.000055213175,0.000004699128],"category_scores_gemma":[0.000011458478,0.0000944757,0.000058255308,0.00028687128,0.00009187095,0.0000734698,0.00004737442,0.00013962536,3.590046e-7],"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":[0.000022675815,0.0000038473163,0.0024427809,0.00014061834,0.00004447912,3.2120468e-8,0.000047907797,0.23193511,0.76246166,0.00060089305,0.00063806033,0.00166195],"study_design_scores_gemma":[0.00027334466,0.000030301515,0.017396545,0.0001175602,0.000034374898,0.000007065335,0.00013919812,0.90308255,0.07778602,0.000044421387,0.00095806224,0.00013058359],"about_ca_topic_score_codex":0.000007709794,"about_ca_topic_score_gemma":0.0000015428111,"teacher_disagreement_score":0.68467563,"about_ca_system_score_codex":0.000043031447,"about_ca_system_score_gemma":0.0000021947392,"threshold_uncertainty_score":0.38526073},"labels":[],"label_agreement":null},{"id":"W4367728153","doi":"10.35483/acsa.am.108.50","title":"Digitizing Wood | Analyzing Wood Grain in 2x4s using Facial Recognition Software Strategies","year":2020,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Solver; Veneer; Process (computing); Usability; Computer science; Software; Artificial intelligence; Engineering drawing; Engineering; Human–computer interaction; Materials science; Programming language","score_opus":0.05973393632193033,"score_gpt":0.2526465754033884,"score_spread":0.1929126390814581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367728153","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":[0.9259701,0.00006858945,0.0700102,0.000018490027,0.00044406077,0.00019900317,0.000014977407,0.0005991323,0.0026754704],"genre_scores_gemma":[0.99832475,0.0000064620526,0.0012045924,0.00003116698,0.00037636753,0.0000068153895,0.00001255751,0.000029382185,0.000007908508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990307,0.000042371765,0.0003496296,0.00019406207,0.00014053048,0.00024272464],"domain_scores_gemma":[0.9997381,0.00003763143,0.00003816429,0.000073575706,0.00003210878,0.000080394806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014829545,0.00015665707,0.00022183941,0.00016049702,0.00007042729,0.00019902598,0.000055451535,0.00017135656,0.000072288385],"category_scores_gemma":[0.00009146032,0.0001610202,0.00007264909,0.0005357512,0.000008739001,0.00050564326,0.000018856343,0.00027353468,0.00004684308],"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":[0.00016359435,0.00006014372,0.0025428378,0.00056506204,0.00020308256,0.00015983942,0.0048012948,0.15829135,0.16732846,0.0001724155,0.0011033862,0.66460854],"study_design_scores_gemma":[0.013023264,0.0012057562,0.0036330323,0.0024446973,0.00025673676,0.0002036084,0.051728424,0.70381445,0.18794255,0.0048089945,0.024481677,0.0064567984],"about_ca_topic_score_codex":0.00015450035,"about_ca_topic_score_gemma":0.000079893995,"teacher_disagreement_score":0.65815175,"about_ca_system_score_codex":0.00008922938,"about_ca_system_score_gemma":0.000030306594,"threshold_uncertainty_score":0.65662134},"labels":[],"label_agreement":null},{"id":"W4375858707","doi":"10.32473/flairs.36.133373","title":"Using Knowledge Graph Embedding for Fault Detection","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ... International Florida Artificial Intelligence Research Society Conference","topic":"Industrial Vision Systems and Defect Detection","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 Windsor","funders":"","keywords":"Automotive industry; Fault detection and isolation; Automotive engineering; Fault (geology); Electric vehicle; Computer science; Engineering; Business; Artificial intelligence; Power (physics); Actuator","score_opus":0.2955318834200215,"score_gpt":0.42575329626852526,"score_spread":0.13022141284850375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375858707","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":[0.90626514,0.00006854981,0.08279327,0.00023713532,0.005656752,0.0010717249,0.000042405587,0.00036540578,0.0034995931],"genre_scores_gemma":[0.9981376,0.00012752738,0.0004807806,0.000004162642,0.0008834288,0.00011627794,0.0000023496293,0.00003029789,0.00021756015],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980057,0.000015702539,0.0004997854,0.00029304915,0.0007454865,0.00044029177],"domain_scores_gemma":[0.99736,0.0002802955,0.00009633737,0.00011052049,0.0020810496,0.00007183096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021645434,0.00015846339,0.00018755971,0.00026467402,0.00044504576,0.00024991037,0.0006769999,0.00018408078,0.000025913347],"category_scores_gemma":[0.0005017546,0.000135776,0.00031055292,0.0014785086,0.00016674548,0.0003232768,0.0001946564,0.00047499404,0.00003809201],"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":[0.000057758738,0.000022201724,0.000039856313,0.00016156332,0.00010313982,1.2328336e-7,0.0019942021,0.015546507,0.9253557,0.018314693,0.0034000145,0.035004277],"study_design_scores_gemma":[0.000031676154,0.000029882596,0.00000983718,0.00012194362,0.000004722878,0.0000019374918,0.002874973,0.509641,0.47232255,0.012633668,0.0022320016,0.000095833224],"about_ca_topic_score_codex":0.000058435817,"about_ca_topic_score_gemma":0.000012018395,"teacher_disagreement_score":0.4940945,"about_ca_system_score_codex":0.00028003837,"about_ca_system_score_gemma":0.000069043825,"threshold_uncertainty_score":0.5536785},"labels":[],"label_agreement":null},{"id":"W4376126001","doi":"10.5430/jms.v14n1p1","title":"Exploring the Common Failures and Routine Maintenance of Jeans Overlocking Machine","year":2023,"lang":"en","type":"article","venue":"Journal of Management and Strategy","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Troubleshooting; Production line; Production (economics); Overall equipment effectiveness; Total productive maintenance; Rework; Originality; Preventive maintenance; Corrective maintenance; Computer science; Machine tool; Reliability engineering; Manufacturing engineering; Engineering; Industrial engineering; Artificial intelligence; Mechanical engineering; Embedded system","score_opus":0.05864426652531165,"score_gpt":0.2394161618455132,"score_spread":0.18077189532020155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376126001","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":[0.99676204,0.00041711377,0.00045560338,0.000099633035,0.00031282718,0.00007647571,0.0000014354015,0.000021928567,0.0018529433],"genre_scores_gemma":[0.9987291,0.0009860769,0.000018290988,0.0000037307457,0.00009637278,0.0000015765688,2.812742e-7,0.000006898167,0.0001576582],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994517,0.000021667547,0.00026345626,0.00004246605,0.0001276546,0.000093043105],"domain_scores_gemma":[0.99977565,0.000039452014,0.000084574735,0.000057376878,0.000019427454,0.000023495688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045856583,0.00006529246,0.00014291696,0.000117525946,0.00005125591,0.000040902094,0.00005062688,0.000017461494,0.000003243695],"category_scores_gemma":[0.0000054357906,0.000041563155,0.00003362167,0.00015582495,0.000013626752,0.00013515766,0.000024346611,0.00011904653,6.7061615e-7],"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":[0.00038703321,0.000045831555,0.014901939,0.0013876285,0.0011345863,0.0003908445,0.0026389714,0.14529029,0.0054795356,0.021735962,0.010031911,0.7965755],"study_design_scores_gemma":[0.009196784,0.002033516,0.6794992,0.0027537716,0.0005360052,0.0004484086,0.034036767,0.13897134,0.006955295,0.004571219,0.119910285,0.0010874125],"about_ca_topic_score_codex":0.00003378126,"about_ca_topic_score_gemma":0.000017476359,"teacher_disagreement_score":0.79548806,"about_ca_system_score_codex":0.000008888229,"about_ca_system_score_gemma":0.0000014491918,"threshold_uncertainty_score":0.16948962},"labels":[],"label_agreement":null},{"id":"W4377031088","doi":"10.1016/j.eswa.2023.120472","title":"Automatic detection of surface defects based on deep random chains","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Fuse (electrical); Variety (cybernetics); Generalization; Pattern recognition (psychology); Sample (material); Machine learning; Focus (optics); Pixel; Scale (ratio); Mathematics","score_opus":0.012574117385895619,"score_gpt":0.23054086774900756,"score_spread":0.21796675036311194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377031088","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":[0.30468065,0.00036456075,0.685433,0.000022385457,0.0008448137,0.003182513,0.000020987785,0.0024908858,0.0029602654],"genre_scores_gemma":[0.99805933,0.000008383504,0.00010376219,0.000008007873,0.00019055748,0.0014654062,0.00001453105,0.000052421237,0.00009758862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988281,0.000071791794,0.00037494357,0.00021025923,0.0003010263,0.00021387232],"domain_scores_gemma":[0.99906534,0.00022432534,0.00010310371,0.00044770612,0.00007907797,0.000080417616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030555314,0.00017599248,0.00029822075,0.0002315882,0.00014811044,0.000034113087,0.00009812151,0.00013872242,0.000009368039],"category_scores_gemma":[0.000019505747,0.00014552193,0.000072823,0.0010685573,0.00002164042,0.000056438043,0.0000066391162,0.00011535004,0.000191398],"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":[0.00006588812,0.000049542417,0.000107325635,0.0003378804,0.000067203604,0.000002385536,0.00044609763,0.9068202,0.08290012,0.00025997803,0.00081567053,0.008127715],"study_design_scores_gemma":[0.0012522386,0.00011764747,0.0002093198,0.0001553271,0.000011654761,0.00000700509,0.00035041786,0.967392,0.022451932,0.000003692019,0.007855684,0.00019306921],"about_ca_topic_score_codex":0.00010667344,"about_ca_topic_score_gemma":0.00002105028,"teacher_disagreement_score":0.6933787,"about_ca_system_score_codex":0.0000992527,"about_ca_system_score_gemma":0.000021386742,"threshold_uncertainty_score":0.5934212},"labels":[],"label_agreement":null},{"id":"W4377832614","doi":"10.18280/ts.400230","title":"Mechanical Structure Defect Detection after High Temperature Based on Image Processing","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Image processing; Computer vision; Artificial intelligence; Computer science; Image (mathematics); Materials science; Pattern recognition (psychology)","score_opus":0.00784584772671346,"score_gpt":0.20831165081618408,"score_spread":0.20046580308947062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377832614","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":[0.9911308,0.000018313312,0.006340373,0.00003628299,0.00087508507,0.00041453313,0.000049828086,0.0010002543,0.0001345296],"genre_scores_gemma":[0.9989131,9.995088e-7,0.00010643018,0.00007422156,0.00071092,0.00008436296,0.000028356311,0.000054052965,0.000027537946],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869287,0.000065600776,0.00027766728,0.0002729626,0.0003872399,0.00030364137],"domain_scores_gemma":[0.99964035,0.000042642645,0.000037405134,0.00014529533,0.00005089816,0.00008343558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025132022,0.0002352645,0.00019867416,0.00024359974,0.0001419542,0.00014108939,0.000073863135,0.00024481697,0.00040874706],"category_scores_gemma":[0.000015243796,0.00020065451,0.00012991216,0.0005572202,0.000011918161,0.00014312039,0.000010984426,0.00035589014,0.00009943584],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032537954,0.000027347294,0.000030391548,0.00018052316,0.000042576336,0.00005664053,0.000094839255,0.05244801,0.9115218,0.000017221611,0.001287212,0.0339681],"study_design_scores_gemma":[0.002508774,0.00067870243,0.00472123,0.0002958293,0.000076814,0.000018773493,0.000102503,0.3140958,0.6739097,0.00016512163,0.0027203816,0.0007063416],"about_ca_topic_score_codex":0.000008187538,"about_ca_topic_score_gemma":0.000014348871,"teacher_disagreement_score":0.2616478,"about_ca_system_score_codex":0.00011491932,"about_ca_system_score_gemma":0.000017954966,"threshold_uncertainty_score":0.81824535},"labels":[],"label_agreement":null},{"id":"W4377832618","doi":"10.18280/ts.400241","title":"Comparison of Object Region Segmentation Algorithms of PCB Defect Detection","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Zhengzhou University","keywords":"Segmentation; Artificial intelligence; Computer science; Object (grammar); Computer vision; Object based; Pattern recognition (psychology); Algorithm","score_opus":0.051248280656287046,"score_gpt":0.2945213323109555,"score_spread":0.24327305165466845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377832618","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":[0.92175233,0.000059625076,0.07651335,0.000003760703,0.0006313543,0.00037001335,0.00000868214,0.00026608593,0.00039481823],"genre_scores_gemma":[0.9996529,0.0000110191895,0.000083254505,0.0000018169643,0.00015748123,0.000031889263,0.000017698007,0.000020820482,0.000023101753],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988173,0.00006489268,0.00052738696,0.00013046963,0.0003042297,0.00015571054],"domain_scores_gemma":[0.99958533,0.00007029325,0.0001405609,0.00010491069,0.00006370015,0.00003519279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034315704,0.00012240675,0.00026302857,0.00028199045,0.00005090182,0.000011746685,0.000056368,0.000101316735,0.000041912008],"category_scores_gemma":[0.000011711198,0.00012121432,0.00013862763,0.00056115544,0.000018233386,0.00010128479,0.000010367186,0.000098080905,0.000022321661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011064759,0.000067459536,0.0029058668,0.00027926106,0.00015311374,0.0000032090645,0.001374575,0.09515494,0.72803825,0.000030786934,0.001266336,0.17061555],"study_design_scores_gemma":[0.0009869057,0.0005292364,0.0065564513,0.00008852211,0.00005080572,0.000005363091,0.00083703984,0.14221533,0.8480429,0.000030233115,0.0004986085,0.00015862357],"about_ca_topic_score_codex":0.00006059867,"about_ca_topic_score_gemma":0.0000140926095,"teacher_disagreement_score":0.17045693,"about_ca_system_score_codex":0.00006804857,"about_ca_system_score_gemma":0.000009425171,"threshold_uncertainty_score":0.49429765},"labels":[],"label_agreement":null},{"id":"W4378469676","doi":"10.18280/ria.370214","title":"Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Naive Bayes classifier; Random forest; Artificial intelligence; Computer science; Machine learning; Support vector machine","score_opus":0.09474997039247944,"score_gpt":0.35686136140033703,"score_spread":0.2621113910078576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378469676","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":[0.5921201,0.00014450787,0.4069281,0.000007629635,0.00033875648,0.00017400426,0.0000035092264,0.00010830358,0.00017507208],"genre_scores_gemma":[0.99829227,0.00007695576,0.0013105981,0.0000031274342,0.00008646234,0.000014585584,0.000005124794,0.000027997316,0.00018290529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882317,0.00010026995,0.0005121357,0.00020189083,0.00012280591,0.0002397108],"domain_scores_gemma":[0.99931306,0.00030074595,0.00008501985,0.00016796509,0.000057573197,0.00007561526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007119088,0.00014544025,0.0003553868,0.0002607281,0.0001431981,0.000038278788,0.0000921247,0.00010032468,0.000044914363],"category_scores_gemma":[0.0001518916,0.00014015599,0.000063052576,0.0006770747,0.000022221811,0.00009023197,0.000054549066,0.00024169144,0.00006455179],"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":[0.00002768773,0.000007884596,0.0260837,0.00016188387,0.000015851312,0.0000011314027,0.0015262519,0.86737996,0.027671946,0.000022216145,0.000036386344,0.07706509],"study_design_scores_gemma":[0.00006752474,0.00011203084,0.0009535243,0.00018044736,0.000011164777,0.0000062584036,0.0011494821,0.8492055,0.146819,0.000012375822,0.0013529353,0.00012974087],"about_ca_topic_score_codex":0.000103154634,"about_ca_topic_score_gemma":0.000018440012,"teacher_disagreement_score":0.40617213,"about_ca_system_score_codex":0.000038398364,"about_ca_system_score_gemma":0.00000729825,"threshold_uncertainty_score":0.5715395},"labels":[],"label_agreement":null},{"id":"W4379386378","doi":"10.22541/au.168594494.44560791/v1","title":"PCBDet: An Efficient Deep Neural Network Object Detection Architecture for Automatic PCB Component Detection on the Edge","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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 Waterloo","funders":"University of Waterloo","keywords":"Computer science; Deep learning; Benchmark (surveying); Component (thermodynamics); Artificial intelligence; Object detection; Inference; Artificial neural network; Throughput; Enhanced Data Rates for GSM Evolution; Task (project management); Real-time computing; Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.03828739123054936,"score_gpt":0.2518618756264546,"score_spread":0.21357448439590526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379386378","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":[0.52136225,0.00006348793,0.45604333,0.0000725435,0.015645046,0.003474223,0.0000272725,0.003032714,0.00027914616],"genre_scores_gemma":[0.99507374,0.0000047186727,0.00020487812,0.000053894033,0.0031782638,0.0011602738,0.00004344267,0.0001988725,0.00008189325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699813,0.00033851247,0.0008033511,0.0007056374,0.0004978478,0.0006565453],"domain_scores_gemma":[0.99797726,0.00061779557,0.00023127356,0.000920476,0.000106437736,0.00014677868],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012517484,0.00064384774,0.0005935814,0.00035774766,0.0005857243,0.00031437623,0.0003402203,0.0007996393,0.000033140215],"category_scores_gemma":[0.00012558904,0.00046509076,0.00048607468,0.00046738712,0.0000347572,0.00004048668,0.000147184,0.0014778039,0.00009935021],"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":[0.00006929265,0.000021866816,0.0000018337756,0.00018308806,0.00009104178,0.0000017565771,0.00020472353,0.890052,0.0019018872,0.00001964004,0.0002343949,0.10721848],"study_design_scores_gemma":[0.00040951208,0.0004033537,0.0005681309,0.00018612904,0.00007977046,0.000021147289,0.00014310931,0.98322994,0.013003722,0.00043603044,0.0009998394,0.0005193189],"about_ca_topic_score_codex":0.00023271478,"about_ca_topic_score_gemma":0.0010657406,"teacher_disagreement_score":0.47371152,"about_ca_system_score_codex":0.00042412934,"about_ca_system_score_gemma":0.000023447834,"threshold_uncertainty_score":0.99978006},"labels":[],"label_agreement":null},{"id":"W4379408297","doi":"10.2139/ssrn.4469912","title":"Commit-Time Defect Prediction Using One-Class Classification","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Commit; Class (philosophy); Computer science; Artificial intelligence; Machine learning; Database","score_opus":0.05915975244909197,"score_gpt":0.2634481417183164,"score_spread":0.2042883892692244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379408297","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":[0.7414471,0.0029062484,0.23505163,0.00017879282,0.013469066,0.0013888059,0.00011580012,0.0026632324,0.0027793392],"genre_scores_gemma":[0.9939714,0.0016747888,0.00007867212,0.0000060531756,0.003001598,0.00003189387,0.00006912104,0.00018133204,0.0009851066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962255,0.00023663796,0.00082059123,0.00038355438,0.000551774,0.0017819705],"domain_scores_gemma":[0.9988472,0.000074809745,0.00034307264,0.00044966603,0.00016501665,0.00012024928],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0029060252,0.00039607735,0.00050661596,0.0005440968,0.0003211358,0.00025229497,0.00031299583,0.0008840191,0.000026757794],"category_scores_gemma":[0.000087616136,0.00042247926,0.00045587015,0.0003611317,0.000025816686,0.0001855198,0.000104183135,0.006528223,0.00031502463],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033910127,0.00023280553,0.0021345136,0.0007401988,0.006753357,0.000026640202,0.00070348615,0.79640865,0.089766234,0.009210077,0.008798528,0.08488643],"study_design_scores_gemma":[0.0019895416,0.000546453,0.0019049088,0.001339843,0.00087520754,0.0009847009,0.0010478902,0.90635,0.001219467,0.07587791,0.0062895115,0.0015745392],"about_ca_topic_score_codex":0.00013862911,"about_ca_topic_score_gemma":0.0001368254,"teacher_disagreement_score":0.25252435,"about_ca_system_score_codex":0.0045904103,"about_ca_system_score_gemma":0.001142183,"threshold_uncertainty_score":0.9998227},"labels":[],"label_agreement":null},{"id":"W4379536998","doi":"10.23977/jemm.2023.080206","title":"Method and Device for Effectively Utilizing OpenCV to Detect PCB Board Size","year":2023,"lang":"en","type":"article","venue":"Journal of Engineering Mechanics and Machinery","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Printed circuit board; Automated optical inspection; Process (computing); Automation; Measure (data warehouse); On board; Computer science; Artificial intelligence; Computer vision; Engineering drawing; Engineering; Mechanical engineering","score_opus":0.021900545116987268,"score_gpt":0.2770533986129326,"score_spread":0.25515285349594535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379536998","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":[0.2566951,0.0004648604,0.7410054,0.0000372085,0.0013377672,0.00029001516,0.000008695418,0.00012402509,0.000036923022],"genre_scores_gemma":[0.9762287,0.000119256714,0.023170535,0.000027141392,0.00035996668,0.00001446744,3.7715006e-7,0.00005446793,0.000025095476],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991801,0.00002494373,0.00033049387,0.00012063084,0.0001339222,0.00020995615],"domain_scores_gemma":[0.999088,0.00054930715,0.00006250791,0.000074127056,0.00007120082,0.00015486355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013228849,0.00016199409,0.0003368683,0.00027610833,0.000054900334,0.000070021204,0.00006832493,0.00010663141,0.0000017811612],"category_scores_gemma":[0.00040750057,0.00014206195,0.00008652816,0.00029439782,0.0000012267222,0.00011688827,0.000039918486,0.00022705931,0.0000016992542],"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":[0.00015150146,0.0000066657676,0.000014548584,0.0006355918,0.00026363152,0.000035649053,0.000337583,0.28533846,0.580749,0.0013880869,0.0013746757,0.12970462],"study_design_scores_gemma":[0.0018094503,0.0008926346,0.00074317236,0.00048582908,0.0001234993,0.00031006895,0.00014113507,0.9300919,0.030732397,0.0012554046,0.032891024,0.0005234517],"about_ca_topic_score_codex":0.000009254568,"about_ca_topic_score_gemma":0.0000023716088,"teacher_disagreement_score":0.71953356,"about_ca_system_score_codex":0.000038593076,"about_ca_system_score_gemma":0.000010614264,"threshold_uncertainty_score":0.5793118},"labels":[],"label_agreement":null},{"id":"W4379795519","doi":"10.23977/jaip.2023.060309","title":"Applications and challenges of hybrid artificial intelligence in chip age testing: a comprehensive review","year":2023,"lang":"en","type":"review","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Computer science; Artificial intelligence; Adaptation (eye); Artificial neural network; Genetic algorithm; Convolutional neural network; Machine learning; Generalization; Deep learning; Stability (learning theory); Evolutionary algorithm; Reliability engineering; Engineering","score_opus":0.3448067366208292,"score_gpt":0.4146184699396857,"score_spread":0.06981173331885648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379795519","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":[0.0000069773528,0.9844905,0.013056929,0.00011725474,0.00079767796,0.0011502713,0.000015922997,0.000052172734,0.00031229883],"genre_scores_gemma":[0.00026922606,0.9977272,0.0011470295,0.000016461492,0.00069246354,0.00007228415,0.0000028234333,0.00006876034,0.0000037331472],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99496776,0.00048711197,0.0034433182,0.0003135667,0.00049201556,0.00029625543],"domain_scores_gemma":[0.99293715,0.003895907,0.0020291412,0.00034831295,0.00065070216,0.00013880896],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025151516,0.00041301455,0.0020666954,0.0007418632,0.00006996606,0.00008184454,0.0003702334,0.00023823329,0.000012661292],"category_scores_gemma":[0.0035007976,0.0003650933,0.0003430714,0.0014067222,0.000102706654,0.0003701953,0.000076604585,0.0013694139,0.00009557355],"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":[0.000017720684,0.00007002299,3.2779035e-8,0.025432695,0.0001192678,0.00013297415,0.00010161105,0.00042491214,0.0000133099575,0.0012152753,0.000035317094,0.97243685],"study_design_scores_gemma":[0.000016567654,0.0003421377,3.5288517e-7,0.06988478,0.0009076899,0.001575835,0.0013096674,0.0010522346,0.00024936302,0.0034899742,0.92066675,0.00050462864],"about_ca_topic_score_codex":0.000039759554,"about_ca_topic_score_gemma":0.000017201071,"teacher_disagreement_score":0.97193223,"about_ca_system_score_codex":0.00013765878,"about_ca_system_score_gemma":0.00019383115,"threshold_uncertainty_score":0.9998801},"labels":[],"label_agreement":null},{"id":"W4380358040","doi":"10.3390/s23125488","title":"DAssd-Net: A Lightweight Steel Surface Defect Detection Model Based on Multi-Branch Dilated Convolution Aggregation and Multi-Domain Perception Detection Head","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":19,"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":"Shanghai Municipal Education Commission; Science and Technology Commission of Shanghai Municipality; Natural Science Foundation of Shanghai","keywords":"Convolution (computer science); Computer science; Feature (linguistics); Artificial intelligence; Redundancy (engineering); Channel (broadcasting); Pattern recognition (psychology); Computer vision; Artificial neural network","score_opus":0.027130865474849646,"score_gpt":0.24987660969703468,"score_spread":0.22274574422218504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380358040","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":[0.8320222,0.00002891508,0.16442564,0.000019433515,0.0013292759,0.0007454539,0.00002444499,0.0013310256,0.000073620904],"genre_scores_gemma":[0.99877495,0.0000383512,0.00054038304,0.000017032617,0.00018806806,0.000047276084,0.000038866878,0.000098603145,0.00025647716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793327,0.0002516951,0.0004820464,0.0005238949,0.00037408722,0.00043501816],"domain_scores_gemma":[0.9992441,0.000108111446,0.00012001007,0.00027467354,0.000115523835,0.00013757352],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006182864,0.00037690575,0.0003277919,0.0004948617,0.0003896392,0.000093758434,0.000055063952,0.00053348945,0.000009014038],"category_scores_gemma":[0.00006941178,0.00038348406,0.00016941075,0.00091741193,0.000039517556,0.0002354519,0.000012462605,0.00042523415,0.00021610521],"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":[0.000122065845,0.000025670535,0.00012914116,0.000054179836,0.000024067633,0.0000029483567,0.00029114968,0.6182585,0.36086473,0.0000011826614,0.00003319842,0.020193184],"study_design_scores_gemma":[0.0021924921,0.00019723794,0.0048078625,0.000119880904,0.000032332286,0.000010011649,0.00015373986,0.91987413,0.072007574,0.000016427352,0.00020730357,0.0003810308],"about_ca_topic_score_codex":0.00020222992,"about_ca_topic_score_gemma":0.00040159156,"teacher_disagreement_score":0.30161563,"about_ca_system_score_codex":0.00043154892,"about_ca_system_score_gemma":0.000018770988,"threshold_uncertainty_score":0.9998617},"labels":[],"label_agreement":null},{"id":"W4381163795","doi":"10.1007/s00170-023-11705-6","title":"Bayesian diagnostic learning for a costly composite manufacturing: critical role of dataset size and auxiliary in situ measurements","year":2023,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Machine learning; Bayesian network; Process (computing); Bayesian probability; Data-driven; Artificial intelligence; Industrial engineering; Data mining; Reliability engineering; Manufacturing engineering; Engineering","score_opus":0.01670760785632352,"score_gpt":0.2798966111586815,"score_spread":0.26318900330235795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381163795","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":[0.99625856,0.00020170404,0.0020448654,0.00051633804,0.0006468869,0.00017759058,0.000031859876,0.00008087457,0.000041341857],"genre_scores_gemma":[0.9991075,0.000117050964,0.000621331,0.000014591382,0.00009166892,0.000013712418,0.000010107372,0.000018557932,0.000005481574],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989478,0.00002996288,0.00046201298,0.000110632274,0.00026804997,0.00018155457],"domain_scores_gemma":[0.9985618,0.0010626966,0.00014889511,0.000113007234,0.000080027094,0.000033552904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043533975,0.00011848423,0.00023660349,0.00043997559,0.000050937713,0.00002444275,0.0003257698,0.00010844358,0.000005037186],"category_scores_gemma":[0.0009049883,0.00009730044,0.00005068657,0.000086335225,0.00005809876,0.00015037667,0.00009409009,0.00039121808,0.0000023736693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005286441,0.000056150333,0.0012149806,0.00011327531,0.00028715996,0.00009386458,0.00025001616,0.2031312,0.6976781,0.00015923019,0.0003966359,0.09609073],"study_design_scores_gemma":[0.0011452798,0.00015527917,0.0042187227,0.00024449296,0.000021159605,0.00015688232,0.00038907758,0.0011545311,0.98223776,0.0060982252,0.004066087,0.00011252682],"about_ca_topic_score_codex":0.000008333058,"about_ca_topic_score_gemma":0.0000139759695,"teacher_disagreement_score":0.2845596,"about_ca_system_score_codex":0.00009145186,"about_ca_system_score_gemma":0.000010516717,"threshold_uncertainty_score":0.3967797},"labels":[],"label_agreement":null},{"id":"W4381193990","doi":"10.11159/cdsr23.003","title":"3D Perception for Autonomous Driving","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Control, Dynamic systems, and Robotics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Perception; Computer science; Computer vision; Human–computer interaction; Psychology","score_opus":0.022584787514486906,"score_gpt":0.2446044562234803,"score_spread":0.22201966870899337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381193990","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":[0.94841033,0.00012044292,0.040007144,0.00042548904,0.0056892796,0.0011891066,0.0000941165,0.0002652961,0.003798781],"genre_scores_gemma":[0.9990476,0.00004884761,0.00020763894,0.000003921241,0.00013622936,0.000029830433,0.0000034998027,0.000015885866,0.00050659326],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991083,0.00000503049,0.0004160362,0.00011632417,0.00023115278,0.0001231347],"domain_scores_gemma":[0.99912155,0.000078984376,0.00020182165,0.00005437443,0.00051646825,0.000026806061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038234892,0.00010789139,0.00025052903,0.00013637831,0.000059393104,0.00006991149,0.00020258022,0.0001027317,0.0000032696757],"category_scores_gemma":[0.00011937044,0.00008364575,0.00007638823,0.00010171728,0.000037887672,0.00012972123,0.00003278334,0.00008197163,0.0000018850427],"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":[0.0001993949,0.000051780466,0.012429954,0.0021977818,0.00079463515,5.3549064e-7,0.0015618566,0.39965224,0.49907324,0.06682981,0.0024809935,0.014727806],"study_design_scores_gemma":[0.00059626676,0.000048614387,0.0028248266,0.0003222885,0.000034289897,0.000009633013,0.00067229505,0.99452376,0.00017771166,0.0003104361,0.00038757647,0.000092307375],"about_ca_topic_score_codex":0.000025876383,"about_ca_topic_score_gemma":0.000005083962,"teacher_disagreement_score":0.5948715,"about_ca_system_score_codex":0.00006256676,"about_ca_system_score_gemma":0.000021820204,"threshold_uncertainty_score":0.34109747},"labels":[],"label_agreement":null},{"id":"W4382317845","doi":"10.1609/aaai.v37i13.26869","title":"High-Throughput, High-Performance Deep Learning-Driven Light Guide Plate Surface Visual Quality Inspection Tailored for Real-World Manufacturing Environments","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Industrial Vision Systems and Defect Detection","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":"University of Waterloo","funders":"","keywords":"Workflow; Visual inspection; Throughput; Deep learning; Artificial intelligence; Computer science; Quality (philosophy); Consistency (knowledge bases); Computer vision; Database","score_opus":0.05683405684072034,"score_gpt":0.30154922528395306,"score_spread":0.2447151684432327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382317845","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":[0.99227816,0.000005053472,0.0033966363,0.00016791472,0.001462437,0.0007118034,0.000009912171,0.0005144612,0.0014536396],"genre_scores_gemma":[0.99818844,0.00018551198,0.00021917188,0.000010169931,0.0002760886,0.00006204437,0.000008730068,0.000052620522,0.0009972076],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976995,0.000027431754,0.00089482265,0.0004448771,0.00047790646,0.00045548595],"domain_scores_gemma":[0.99910426,0.00012855451,0.0003727361,0.00017948402,0.00013455046,0.00008042414],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00073426677,0.00031314493,0.0004120307,0.00020247407,0.00040406603,0.0001290261,0.00038650603,0.0001899114,0.00004948436],"category_scores_gemma":[0.00014878654,0.00026743513,0.0001275451,0.00055267476,0.00007601501,0.00033976097,0.000111620626,0.0004363109,0.00026520572],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005947146,0.000098749726,0.0010744883,0.00036121364,0.00013211137,8.1337345e-7,0.0013642393,0.34259135,0.58878875,0.018672727,0.0007934646,0.0455274],"study_design_scores_gemma":[0.00008729779,0.00022962574,0.0018010081,0.00017566812,0.000019358811,7.8831783e-7,0.0004637313,0.14368276,0.850832,0.0015179866,0.00091337593,0.00027638534],"about_ca_topic_score_codex":0.0004087283,"about_ca_topic_score_gemma":0.00006920483,"teacher_disagreement_score":0.26204327,"about_ca_system_score_codex":0.00022522495,"about_ca_system_score_gemma":0.000018576253,"threshold_uncertainty_score":0.99997777},"labels":[],"label_agreement":null},{"id":"W4382395297","doi":"10.18280/ts.400347","title":"Enhanced Canny Algorithm for Image Edge Detection in Print Quality Assessment","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Canny edge detector; Artificial intelligence; Computer science; Deriche edge detector; Computer vision; Enhanced Data Rates for GSM Evolution; Edge detection; Image gradient; Image (mathematics); Pattern recognition (psychology); Algorithm; Image processing","score_opus":0.038118319962033215,"score_gpt":0.30932045591449275,"score_spread":0.27120213595245957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382395297","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":[0.37791908,0.0000065212434,0.61945736,0.000015867572,0.00085890247,0.00075410766,0.000031651416,0.00036417937,0.0005923527],"genre_scores_gemma":[0.99771655,0.0000051183265,0.0012453896,0.00000973591,0.00038829414,0.0004967653,0.000022767803,0.000027988499,0.00008739227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871624,0.000066757886,0.0004681215,0.00021634021,0.00023128385,0.00030126225],"domain_scores_gemma":[0.9996328,0.00008205463,0.000053537467,0.000117392665,0.000054541597,0.000059684466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010204471,0.000149782,0.00021613626,0.00020692327,0.00007890718,0.000052011754,0.000066327884,0.00010587945,0.00006897264],"category_scores_gemma":[0.000014633547,0.00015167535,0.00009544928,0.00037280796,0.000010505731,0.00012373211,0.000015595911,0.00015558976,0.000036843714],"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":[0.000025219832,0.00003746594,0.000023101557,0.00008722588,0.00003662769,0.0000041288563,0.0002778653,0.005320953,0.39154494,0.00004880607,0.00039094666,0.6022027],"study_design_scores_gemma":[0.003017955,0.00030640204,0.018760785,0.000071794486,0.000016526707,0.0000028747286,0.00055318174,0.56947386,0.40137506,0.0001913027,0.005793077,0.00043717452],"about_ca_topic_score_codex":0.00012661386,"about_ca_topic_score_gemma":0.00012806221,"teacher_disagreement_score":0.61979747,"about_ca_system_score_codex":0.0003045407,"about_ca_system_score_gemma":0.000025340416,"threshold_uncertainty_score":0.6185141},"labels":[],"label_agreement":null},{"id":"W4383876103","doi":"10.1139/tcsme-2022-0143","title":"A doubt–confirmation-based visual detection method for foreign object debris aided by assembly models","year":2023,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Object (grammar); Feature (linguistics); Product (mathematics); Histogram; Object detection; Image (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.021274579663049985,"score_gpt":0.24848504956914558,"score_spread":0.22721046990609559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383876103","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":[0.0054656584,0.000026128753,0.9918035,0.00009349572,0.0008371687,0.0010514937,0.0003186318,0.0003801387,0.000023764367],"genre_scores_gemma":[0.9901219,0.000003550464,0.009018647,0.000044747943,0.00008694562,0.00054696953,0.000025824334,0.00008018434,0.00007121859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988171,0.000016371547,0.00038135546,0.00019071698,0.000186079,0.0004084095],"domain_scores_gemma":[0.9991574,0.0003168818,0.000050870392,0.00019780264,0.00010202033,0.00017499333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005643894,0.00019523167,0.00026370672,0.00014632478,0.000338456,0.00004333285,0.0001546201,0.00034503033,0.0000071608965],"category_scores_gemma":[0.000046759335,0.00019016721,0.0008107504,0.000675293,0.000009608819,0.00014154254,0.0000024735502,0.00022971752,0.0000017399673],"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":[0.000012405695,0.000004913004,6.0007395e-8,0.00016080104,0.0001325581,5.1201802e-8,0.00008862203,0.91483516,0.0787996,0.00026322767,0.00071314495,0.004989487],"study_design_scores_gemma":[0.0005953389,0.00006282937,8.3413966e-7,0.000035599573,0.00007160102,0.0000017689624,0.00010438505,0.78491086,0.21122676,0.00015604618,0.0026749938,0.00015900611],"about_ca_topic_score_codex":0.005379075,"about_ca_topic_score_gemma":0.004550459,"teacher_disagreement_score":0.9846563,"about_ca_system_score_codex":0.0004526801,"about_ca_system_score_gemma":0.000120301665,"threshold_uncertainty_score":0.81315887},"labels":[],"label_agreement":null},{"id":"W4384305065","doi":"10.1109/icse48619.2023.00008","title":"Artifact Evaluation","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; University of Calgary; University of Victoria","funders":"","keywords":"Artifact (error); Computer science; Artificial intelligence","score_opus":0.05296498364968949,"score_gpt":0.27296351848452444,"score_spread":0.21999853483483495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384305065","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":[0.8906011,0.000013570177,0.0016586928,0.000019045112,0.0014676604,0.00016756653,6.6795536e-7,0.0015430397,0.10452868],"genre_scores_gemma":[0.99913615,0.000002050565,0.000008571068,0.0000043282744,0.000121856545,0.000015764408,0.0000033206547,0.00000664591,0.0007012959],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968255,0.000011289857,0.00007096898,0.000040741972,0.0001254657,0.00006899037],"domain_scores_gemma":[0.9998844,0.000013278095,0.0000039037195,0.000062939806,0.000018850506,0.000016614767],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031588977,0.000029969893,0.000035038236,0.00006784827,0.000020962525,0.000014267535,0.000014427301,0.000036010268,0.00038980797],"category_scores_gemma":[0.000019849664,0.000025575275,0.000017988772,0.00024862,0.0000014140593,0.00004259722,0.000002923469,0.00003102934,0.0029571084],"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":[0.0000048142692,0.0000040672276,0.0003082504,0.000019148694,0.000029994455,0.0000027135238,0.00020055419,0.23012693,0.04184973,0.0004451103,0.18177924,0.54522943],"study_design_scores_gemma":[0.0002723767,0.00001877041,0.0045349933,0.0000075121766,0.0000061678215,0.00000239512,0.0001350456,0.9246384,0.024605166,0.00035192893,0.045322426,0.00010485784],"about_ca_topic_score_codex":0.0000076222123,"about_ca_topic_score_gemma":0.00000436182,"teacher_disagreement_score":0.6945114,"about_ca_system_score_codex":0.00002230982,"about_ca_system_score_gemma":0.0000036282966,"threshold_uncertainty_score":0.9978192},"labels":[],"label_agreement":null},{"id":"W4384560547","doi":"10.1007/s11668-023-01695-8","title":"An Autoencoder with Convolutional Neural Network for Surface Defect Detection on Cast Components","year":2023,"lang":"en","type":"article","venue":"Journal of Failure Analysis and Prevention","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Autoencoder; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Computer science; Retraining; Cluster analysis; Identification (biology); Convolution (computer science); Artificial neural network; Automation; Machine learning; Engineering","score_opus":0.021630826485476682,"score_gpt":0.25967449215329663,"score_spread":0.23804366566781995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384560547","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":[0.82378095,0.00005220769,0.17569073,0.000023775516,0.00026978424,0.00012224188,0.0000066141542,0.000042237174,0.000011480931],"genre_scores_gemma":[0.99876463,0.000013771634,0.0007494026,0.0000051782317,0.00037765646,0.0000038227467,0.000025129713,0.00001303077,0.00004736002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907833,0.00010271442,0.00033956216,0.00011009417,0.0002243432,0.00014496653],"domain_scores_gemma":[0.9994514,0.00007138181,0.000189462,0.00008244366,0.00013354451,0.00007174038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007118987,0.000110281515,0.00027698124,0.000279935,0.00014237869,0.00006658838,0.00004162637,0.00009226901,0.0000120615505],"category_scores_gemma":[0.00000804324,0.000085090236,0.00027372307,0.00076470297,0.0000099070685,0.00021666684,0.0000035589233,0.00015136536,0.0000024357316],"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":[0.00016719116,0.000013784813,0.002831974,0.000017014352,0.0007556435,0.0000032288067,0.000035279078,0.985398,0.0039886893,0.000011522373,0.00053678476,0.006240867],"study_design_scores_gemma":[0.0013670999,0.0015828406,0.06812903,0.00009504699,0.0012994005,0.00006905156,0.00013131475,0.9236403,0.0009940658,0.00019899957,0.0022823596,0.00021045463],"about_ca_topic_score_codex":0.000019096879,"about_ca_topic_score_gemma":0.00023592761,"teacher_disagreement_score":0.17498371,"about_ca_system_score_codex":0.000042443888,"about_ca_system_score_gemma":0.000009811874,"threshold_uncertainty_score":0.3469879},"labels":[],"label_agreement":null},{"id":"W4385350211","doi":"10.1117/12.2689921","title":"Object detection model-based quality inspection using a deep CNN","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Université de Moncton","funders":"","keywords":"Computer science; Object detection; Deep learning; Task (project management); Artificial intelligence; Process (computing); Quality (philosophy); Scratch; Control (management); Convolutional neural network; Fault detection and isolation; Pattern recognition (psychology); Engineering; Operating system","score_opus":0.06943656504915496,"score_gpt":0.30220980385325974,"score_spread":0.23277323880410478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385350211","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":[0.51937383,0.0000079940055,0.4753001,0.0000037073867,0.00089092937,0.00013858856,0.0000015212009,0.0023561819,0.0019271234],"genre_scores_gemma":[0.9992443,0.0000021088072,0.00033622852,0.000015376594,0.0002449216,0.00002078105,0.0000038088153,0.00003429135,0.00009823638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990258,0.00005613308,0.00030640638,0.00018071929,0.00020891218,0.00022200834],"domain_scores_gemma":[0.9996228,0.00003990937,0.00003774887,0.0001894512,0.000053467982,0.000056573528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047054156,0.00013357968,0.00016155526,0.0003113814,0.00017564262,0.000053878248,0.00004235533,0.00018598625,0.000023687955],"category_scores_gemma":[0.000044242308,0.00013124272,0.00009994471,0.00087825744,0.000010492696,0.00014936227,0.000009994397,0.00016655964,0.00016880165],"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":[0.000017286675,0.000004903789,0.000055343844,0.000029200022,0.000010989003,0.0000010528081,0.00006352238,0.90008116,0.091772206,0.000027078646,0.000116047595,0.007821194],"study_design_scores_gemma":[0.0003197747,0.000026252577,0.00041762248,0.000011988422,0.000007494367,0.000002868606,0.00011991524,0.9248143,0.07380925,0.00009222344,0.00022183996,0.0001564583],"about_ca_topic_score_codex":0.00041075004,"about_ca_topic_score_gemma":0.0001879496,"teacher_disagreement_score":0.47987038,"about_ca_system_score_codex":0.00023316526,"about_ca_system_score_gemma":0.000019820161,"threshold_uncertainty_score":0.53519225},"labels":[],"label_agreement":null},{"id":"W4385767366","doi":"10.1007/s40747-023-01180-7","title":"A deep learning model for steel surface defect detection","year":2023,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Bottleneck; Feature extraction; Computer science; Normalization (sociology); Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Convolution (computer science); Artificial neural network; Deep learning; Embedded system","score_opus":0.08834418513735037,"score_gpt":0.28283869304720877,"score_spread":0.1944945079098584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385767366","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":[0.1646437,0.00040565876,0.8278242,0.0000036172698,0.0031025184,0.0011522197,0.00001153573,0.0018818886,0.00097469566],"genre_scores_gemma":[0.99705046,0.000037634778,0.000097749464,0.0000040745663,0.00045846277,0.00019240957,0.000034621615,0.00009749373,0.0020270743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982424,0.00010259118,0.000584095,0.00032189008,0.0002724187,0.0004766214],"domain_scores_gemma":[0.99915415,0.0002247857,0.00009714126,0.0002718616,0.0001374485,0.00011459075],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007882377,0.00026508118,0.00039067297,0.0002347273,0.00028779553,0.00014826293,0.00014972636,0.00021960046,0.000015106708],"category_scores_gemma":[0.000092801216,0.0002658732,0.00028719747,0.00055676984,0.000015958294,0.00010820186,0.000032117372,0.0002569258,0.00056999375],"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":[0.000025053252,0.000005658722,0.000044094413,0.00021061944,0.00006839627,0.0000013870995,0.0003535128,0.96458805,0.026766164,0.00009494306,0.0014825036,0.0063596247],"study_design_scores_gemma":[0.00025498183,0.00010245087,0.000045912715,0.00007474655,0.000023459448,0.000015791285,0.0006129414,0.96631736,0.0041553862,0.000049434442,0.028068174,0.00027939165],"about_ca_topic_score_codex":0.00007584719,"about_ca_topic_score_gemma":0.000067902554,"teacher_disagreement_score":0.83240676,"about_ca_system_score_codex":0.00022828724,"about_ca_system_score_gemma":0.000012270439,"threshold_uncertainty_score":0.9999794},"labels":[],"label_agreement":null},{"id":"W4385801338","doi":"10.1109/cvprw59228.2023.00012","title":"MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"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 Waterloo","funders":"National Research Council Canada","keywords":"Computer science; Artificial intelligence; Redundancy (engineering); Machine learning; Object detection; Segmentation; Data mining","score_opus":0.016097426977943443,"score_gpt":0.2560690792511625,"score_spread":0.23997165227321907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385801338","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":[0.5504896,0.000015426274,0.4462956,0.000009342867,0.0011561179,0.001324517,0.00001642953,0.00063993456,0.000053046642],"genre_scores_gemma":[0.9943555,0.0000041779404,0.004515961,0.0000063539874,0.00038769533,0.00050459366,0.000022767294,0.000049759296,0.00015320975],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887085,0.000028877252,0.00036874344,0.0003155386,0.00012109422,0.00029490743],"domain_scores_gemma":[0.9993854,0.00025425636,0.00006111247,0.00014866886,0.00008947836,0.00006106663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066986174,0.0001712745,0.00019875566,0.00019588016,0.0002541047,0.00007920229,0.000040610612,0.00019941722,0.00000642476],"category_scores_gemma":[0.00018635548,0.000166155,0.0001026913,0.0002685465,0.000014333147,0.00024701346,0.000017699296,0.00010866108,0.000008775211],"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":[0.0001299291,0.000011544383,0.00033462956,0.00038204604,0.000028933875,3.7657483e-7,0.00038606205,0.021910232,0.58512634,0.000009297868,0.00012383366,0.3915568],"study_design_scores_gemma":[0.0015502416,0.0002070035,0.0019797927,0.000021972879,0.000019232093,0.0000039296683,0.0002760538,0.8293902,0.1657324,0.00011983362,0.00049619656,0.00020313819],"about_ca_topic_score_codex":0.0004814528,"about_ca_topic_score_gemma":0.00013938098,"teacher_disagreement_score":0.80748,"about_ca_system_score_codex":0.00015013013,"about_ca_system_score_gemma":0.00001257475,"threshold_uncertainty_score":0.67756045},"labels":[],"label_agreement":null},{"id":"W4385803321","doi":"10.20944/preprints202308.0979.v1","title":"Indirect Solution Modeling of Melting Behavior of SiO2 Based on the Image Processing Technology","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Industrial Vision Systems and Defect Detection","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":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Slag (welding); Pixel; Centroid; Process (computing); Computer science; Crucible (geodemography); Function (biology); Image processing; Image (mathematics); Ground granulated blast-furnace slag; MATLAB; Materials science; Algorithm; Mechanical engineering; Process engineering; Artificial intelligence; Metallurgy; Engineering; Chemistry","score_opus":0.16437007021173974,"score_gpt":0.33364701727757956,"score_spread":0.16927694706583982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385803321","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":[0.9816956,0.000060304326,0.0146296965,0.00005554097,0.00076846086,0.0008756766,0.000021588645,0.00069215236,0.0012009926],"genre_scores_gemma":[0.99922717,0.000009438663,0.00025231417,0.0000028480022,0.000102538055,0.0002871428,0.000009322401,0.00007882137,0.00003043626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979473,0.000100030906,0.0008401425,0.00045328485,0.00038910663,0.00027014283],"domain_scores_gemma":[0.9984105,0.00009836408,0.00039357485,0.00082900224,0.00023450062,0.00003401781],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001442504,0.000285025,0.00052875595,0.00061130006,0.000113583745,0.000015301574,0.00038615384,0.00066818617,0.000040525178],"category_scores_gemma":[0.00050374976,0.00024592658,0.00020483464,0.00051480683,0.00006256792,0.000054582477,0.0004077931,0.0010961695,0.00006509322],"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":[0.00004209687,0.00006785434,0.015406042,0.00088209764,0.0000744746,0.000004850538,0.0004606552,0.69949,0.27834958,0.000020712629,0.000007875614,0.005193767],"study_design_scores_gemma":[0.00017826006,0.000019691995,0.002283182,0.001153842,0.000071637194,0.0000015717103,0.00020746131,0.6698489,0.32586837,0.0001543046,0.000012523628,0.00020022754],"about_ca_topic_score_codex":0.00017467949,"about_ca_topic_score_gemma":0.0000070974897,"teacher_disagreement_score":0.047518782,"about_ca_system_score_codex":0.00014873399,"about_ca_system_score_gemma":0.000098356744,"threshold_uncertainty_score":0.9999993},"labels":[],"label_agreement":null},{"id":"W4385880456","doi":"10.1016/j.measurement.2023.113462","title":"On detailed deviation zone evaluation of scanned surfaces for automatic detection of defected regions","year":2023,"lang":"en","type":"article","venue":"Measurement","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Metrology; Point cloud; Grayscale; Computer vision; Artificial intelligence; Computer science; Feature (linguistics); Image processing; Point (geometry); Dimensional metrology; Surface (topology); Surface metrology; Image (mathematics); Engineering; Optics; Mathematics; Surface finish; Profilometer; Geometry","score_opus":0.10693884358187573,"score_gpt":0.2812831155220593,"score_spread":0.1743442719401836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385880456","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":[0.95863104,0.00007615262,0.039030816,0.000010109138,0.0006965885,0.0011663494,0.0000072861053,0.00024881188,0.00013287105],"genre_scores_gemma":[0.99967504,0.000003630315,0.0000487823,0.000001161823,0.00003978963,0.00019335242,0.000009313709,0.000019820955,0.000009128632],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983115,0.00013776582,0.00042405108,0.000119315824,0.00087678496,0.00013054557],"domain_scores_gemma":[0.99888515,0.00008701348,0.00014954605,0.00017131116,0.00068064686,0.000026327561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00285305,0.000102083766,0.0002041835,0.00028411808,0.000060905324,0.0000087176495,0.000042394746,0.000094687,0.000010893517],"category_scores_gemma":[0.000561498,0.00009716667,0.00009100319,0.0005743476,0.000008143426,0.000054021617,0.0000043958803,0.000049449936,0.000014309711],"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":[0.00006490444,0.00003791159,0.00008317625,0.00029265045,0.00012684098,8.381573e-8,0.00024536377,0.23554881,0.7123923,0.000050828363,0.00044051107,0.050716665],"study_design_scores_gemma":[0.0013028376,0.00026471345,0.010131809,0.00021476507,0.00011927329,4.1669585e-7,0.00012251403,0.55027187,0.4371441,0.0002500799,0.00007734729,0.00010029424],"about_ca_topic_score_codex":0.000030364725,"about_ca_topic_score_gemma":0.00020263884,"teacher_disagreement_score":0.31472304,"about_ca_system_score_codex":0.00028523043,"about_ca_system_score_gemma":0.00003966293,"threshold_uncertainty_score":0.39623415},"labels":[],"label_agreement":null},{"id":"W4386065424","doi":"10.1109/cvpr52729.2023.00005","title":"Message from the 2023 General and Program Chairs","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Exhibition; Computer science; Artificial intelligence; Robotics; Multimedia; Robot; Visual arts","score_opus":0.021526347491052165,"score_gpt":0.24983706868751357,"score_spread":0.2283107211964614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386065424","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":[0.9823983,0.00014163007,0.00018776474,0.000109801345,0.0010243683,0.0002541281,0.000012999671,0.0010909585,0.014780108],"genre_scores_gemma":[0.9956392,0.000041850544,0.000051658222,0.00001947514,0.00049057865,0.000047189802,0.000007861552,0.000013519189,0.0036886516],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99964565,0.000014592684,0.00008295826,0.00007519367,0.000075030905,0.000106586245],"domain_scores_gemma":[0.9998148,0.000048826434,0.000006336691,0.000097228345,0.000006653988,0.000026172102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012229818,0.000056090634,0.000063454936,0.000021422262,0.00005758531,0.000052746513,0.00003559384,0.00005813231,0.000092062975],"category_scores_gemma":[0.000010579033,0.000034510835,0.00002267215,0.00017635275,0.000008309502,0.000026134885,0.00001786272,0.0000743008,0.00015964625],"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":[0.000010567054,0.000009306382,0.0012887999,0.000017419365,0.00009442463,0.000015809415,0.00050004566,0.0031726994,0.013006868,0.0006514012,0.44001964,0.54121304],"study_design_scores_gemma":[0.0009485843,0.00011821879,0.043196633,0.000058474412,0.000022680179,0.0000082855695,0.0010538812,0.17871228,0.0077098915,0.00036303798,0.76740736,0.00040066883],"about_ca_topic_score_codex":0.00036252278,"about_ca_topic_score_gemma":0.00007402949,"teacher_disagreement_score":0.5408124,"about_ca_system_score_codex":0.000007127105,"about_ca_system_score_gemma":0.0000021084618,"threshold_uncertainty_score":0.20519829},"labels":[],"label_agreement":null},{"id":"W4386074519","doi":"10.11159/mvml23.107","title":"An Artificial Intelligence Based Defect Detection System for Transparent Substrate","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Substrate (aquarium); Artificial intelligence; Materials science; Biology","score_opus":0.02598993857470151,"score_gpt":0.23843364754370378,"score_spread":0.21244370896900228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386074519","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":[0.9361346,0.00009407043,0.058981918,0.000014228242,0.003652675,0.00057885074,0.0000051275706,0.0004973697,0.000041176874],"genre_scores_gemma":[0.99960655,0.0000032678895,0.0000823775,0.0000023862592,0.00021926596,0.00006070992,2.0810768e-7,0.000015158626,0.00001007888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998871,0.000006406343,0.00029973907,0.00027727132,0.00025947858,0.00028609327],"domain_scores_gemma":[0.99953735,0.00009209388,0.00006080004,0.00008691332,0.000118079486,0.00010476202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006779529,0.0001529696,0.00022528195,0.00039022902,0.00021034485,0.00023239851,0.00019736112,0.000056845958,8.616616e-8],"category_scores_gemma":[0.000019027777,0.00011413596,0.00005914443,0.0013439604,0.000051456875,0.0001395235,0.0000135811,0.00014147974,5.140606e-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":[0.000090921414,0.00002789853,0.00018902283,0.0012335654,0.0000384649,0.0000011008577,0.00013565442,0.7323105,0.19577539,0.0203366,0.00005980251,0.04980111],"study_design_scores_gemma":[0.00008101875,0.00018905048,0.0003385104,0.0002294653,0.000010120812,0.000008386994,0.000024622184,0.89038277,0.10850721,0.000012720352,0.00009386152,0.00012226411],"about_ca_topic_score_codex":0.00001093187,"about_ca_topic_score_gemma":0.0000022384731,"teacher_disagreement_score":0.15807231,"about_ca_system_score_codex":0.000056687488,"about_ca_system_score_gemma":0.000012996968,"threshold_uncertainty_score":0.46543294},"labels":[],"label_agreement":null},{"id":"W4386178513","doi":"10.2316/j.2023.201-0382","title":"APPLICATION OF ANT COLONY ALGORITHM IN LASER CUTTING PATH OPTIMISATION OF SHEET METAL PARTS, 159-165.","year":2023,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ant colony optimization algorithms; Sheet metal; Path (computing); ANT; Tool path; Laser cutting; Materials science; Computer science; Algorithm; Laser; Composite material; Metallurgy; Machining; Optics; Physics","score_opus":0.008262392676548696,"score_gpt":0.20962572609833288,"score_spread":0.2013633334217842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386178513","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":[0.80243945,0.0023922997,0.19130312,0.000022748201,0.0012977376,0.0018764284,0.00010409597,0.00026316478,0.0003009655],"genre_scores_gemma":[0.999509,0.000045583107,0.000049053026,0.00000193074,0.00014906909,0.00017779865,0.000012028293,0.000019628695,0.00003591802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876845,0.00008576989,0.0005773756,0.00016749291,0.00019737797,0.000203548],"domain_scores_gemma":[0.99948734,0.000087996326,0.00016699715,0.00016423852,0.000055728724,0.000037707076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092275086,0.00012556885,0.0004187492,0.00018525969,0.00003360078,0.000017051214,0.00005343225,0.0001465898,0.0000033639315],"category_scores_gemma":[0.000022791546,0.00011538176,0.00006353402,0.00030283615,0.000012097958,0.00008763377,0.000011732393,0.00009904521,0.000006086402],"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":[0.00017336488,0.00011210311,0.003912308,0.0015868788,0.00050967035,0.000015875214,0.0015097337,0.55778056,0.13206969,0.0042051836,0.00048595504,0.29763865],"study_design_scores_gemma":[0.001864037,0.00012506243,0.0006039336,0.00013913402,0.000037785867,0.0000076149677,0.0009000392,0.9900574,0.0047356407,0.0000428657,0.0013516146,0.00013484502],"about_ca_topic_score_codex":0.0006596087,"about_ca_topic_score_gemma":0.00001795196,"teacher_disagreement_score":0.43227684,"about_ca_system_score_codex":0.00006555062,"about_ca_system_score_gemma":0.000021091095,"threshold_uncertainty_score":0.47051314},"labels":[],"label_agreement":null},{"id":"W4386178648","doi":"10.1109/tim.2023.3308248","title":"Feature Clustering for Open-Set Recognition in LCD Manufacturing","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Industrial Vision Systems and Defect Detection","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":"Artificial Intelligence in Medicine (Canada)","funders":"Innovation and Technology Commission","keywords":"Cluster analysis; Classifier (UML); Artificial intelligence; Computer science; Pattern recognition (psychology); Feature extraction; Machine learning; Deep learning; Feature (linguistics); Contextual image classification; Image (mathematics)","score_opus":0.11238625908823302,"score_gpt":0.2898048954988298,"score_spread":0.1774186364105968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386178648","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":[0.6465559,0.000025861493,0.34536085,0.00030248615,0.004038334,0.0023972562,0.00010436648,0.00048550035,0.00072948134],"genre_scores_gemma":[0.9990275,0.00006287735,0.0002717669,0.00003966023,0.000045587545,0.00041380848,0.000014806231,0.000022644033,0.00010133093],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923295,0.000028964932,0.00019458462,0.00017536979,0.0002002728,0.00016788264],"domain_scores_gemma":[0.9997877,0.00002189571,0.000028624117,0.000079316735,0.000032375105,0.00005010046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004422913,0.00012171792,0.0001272864,0.00028462347,0.00014511103,0.00010216368,0.000048925565,0.0000919145,0.000017893803],"category_scores_gemma":[0.0000029759683,0.00012728693,0.000038581024,0.00018790827,0.000006489262,0.00022008545,0.0000010789793,0.00013332117,0.000024784127],"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":[0.0002394588,0.00003690571,0.000015280653,0.00020436177,0.0000663141,0.0000021073456,0.00077393383,0.06618813,0.02156911,0.000003863661,0.0014913002,0.9094092],"study_design_scores_gemma":[0.008952556,0.00046119688,0.0018954123,0.0007119547,0.00006070229,0.000019929761,0.0030232796,0.068072475,0.9027698,0.00020555935,0.013147357,0.00067977863],"about_ca_topic_score_codex":0.00004328699,"about_ca_topic_score_gemma":0.00037712097,"teacher_disagreement_score":0.90872943,"about_ca_system_score_codex":0.00020627659,"about_ca_system_score_gemma":0.000011512025,"threshold_uncertainty_score":0.519061},"labels":[],"label_agreement":null},{"id":"W4386325263","doi":"10.18280/ts.400441","title":"Enhanced Tool Detection in Industry 4.0 via Deep Learning-Augmented Human Intent Recognition: Introducing the Industry-RetinaNet Model","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Machine learning","score_opus":0.024891801195043545,"score_gpt":0.23810828131760364,"score_spread":0.2132164801225601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386325263","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":[0.96001595,0.0000142703975,0.037595004,0.00006522949,0.0005677859,0.00061217666,0.0000053614444,0.00058063364,0.00054360053],"genre_scores_gemma":[0.9986925,0.000007636442,0.000019769454,0.00003866335,0.0006821923,0.00025620728,0.00004703008,0.000050478644,0.00020552007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799114,0.00016553835,0.00066150253,0.0003483159,0.00038308106,0.00045042194],"domain_scores_gemma":[0.9994743,0.000068066314,0.0001131257,0.00019377391,0.00007980686,0.000070951945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010656378,0.00026831878,0.00025366838,0.00034963526,0.00029210286,0.00009899458,0.00014857347,0.00061713246,0.00038470316],"category_scores_gemma":[0.000056759323,0.0002362254,0.000090539565,0.0009069651,0.00003293254,0.00019295758,0.000047439647,0.002030925,0.00013376605],"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":[0.00006788477,0.00003697942,0.00015465276,0.000042373456,0.00005627808,0.000009530502,0.0009914164,0.6413298,0.22140098,0.000010970678,0.00038897354,0.13551013],"study_design_scores_gemma":[0.0017079767,0.00031244295,0.002699274,0.00020977599,0.00003752488,0.0000182365,0.00145212,0.86220074,0.12992787,0.00017204993,0.0007738416,0.0004881775],"about_ca_topic_score_codex":0.0000848864,"about_ca_topic_score_gemma":0.00008609294,"teacher_disagreement_score":0.22087088,"about_ca_system_score_codex":0.00028392265,"about_ca_system_score_gemma":0.000015710142,"threshold_uncertainty_score":0.9632992},"labels":[],"label_agreement":null},{"id":"W4386742638","doi":"10.36227/techrxiv.24123501","title":"An Autoencoder with Convolutional Neural Network for Surface Defect Detection on Cast Components","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Convolutional neural network; Retraining; Artificial intelligence; Computer science; Convolution (computer science); Pattern recognition (psychology); Cluster analysis; Identification (biology); Artificial neural network; Automation; Production (economics); Image (mathematics); Deep learning; Machine learning; Engineering","score_opus":0.06313484726997855,"score_gpt":0.26854049951175846,"score_spread":0.20540565224177992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386742638","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":[0.7031869,0.00003539127,0.2842236,0.000016660098,0.008439799,0.0015416888,0.0001447403,0.0020179416,0.00039323387],"genre_scores_gemma":[0.99666137,0.0000034557086,0.0010568236,0.000018675955,0.0014009913,0.00015873088,0.00022020668,0.00013155035,0.0003482193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827266,0.00009214086,0.0003813146,0.0005045117,0.00033222415,0.00041715987],"domain_scores_gemma":[0.9990893,0.00016421427,0.00010121097,0.00039866305,0.00012295142,0.00012363284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003991689,0.00038953614,0.0004153691,0.00011882304,0.00021296155,0.0001347895,0.00014095237,0.00052653946,0.000022885826],"category_scores_gemma":[0.00001182814,0.00033610594,0.00021217465,0.00019289905,0.00002209339,0.000085161344,0.000044072814,0.0006474225,0.000071308525],"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":[0.00019430254,0.0000149811885,0.00022535912,0.000088635206,0.000120343604,0.000003610528,0.000022851229,0.995848,0.00065781904,0.00003466297,0.0019095045,0.00087995356],"study_design_scores_gemma":[0.00072808954,0.00047245243,0.003671598,0.00016555832,0.000051984913,0.000019028499,0.00002694643,0.992031,0.00093744264,0.000116935815,0.0013304368,0.0004485243],"about_ca_topic_score_codex":0.00056287384,"about_ca_topic_score_gemma":0.0005955922,"teacher_disagreement_score":0.29347438,"about_ca_system_score_codex":0.0002563631,"about_ca_system_score_gemma":0.000034823614,"threshold_uncertainty_score":0.9999091},"labels":[],"label_agreement":null},{"id":"W4386743085","doi":"10.36227/techrxiv.24123501.v1","title":"An Autoencoder with Convolutional Neural Network for Surface Defect Detection on Cast Components","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Convolutional neural network; Artificial intelligence; Retraining; Computer science; Pattern recognition (psychology); Convolution (computer science); Cluster analysis; Identification (biology); Artificial neural network; Automation; Image (mathematics); Deep learning; Production (economics); Machine learning; Engineering","score_opus":0.06313484726997855,"score_gpt":0.26854049951175846,"score_spread":0.20540565224177992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386743085","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":[0.7031869,0.00003539127,0.2842236,0.000016660098,0.008439799,0.0015416888,0.0001447403,0.0020179416,0.00039323387],"genre_scores_gemma":[0.99666137,0.0000034557086,0.0010568236,0.000018675955,0.0014009913,0.00015873088,0.00022020668,0.00013155035,0.0003482193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827266,0.00009214086,0.0003813146,0.0005045117,0.00033222415,0.00041715987],"domain_scores_gemma":[0.9990893,0.00016421427,0.00010121097,0.00039866305,0.00012295142,0.00012363284],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003991689,0.00038953614,0.0004153691,0.00011882304,0.00021296155,0.0001347895,0.00014095237,0.00052653946,0.000022885826],"category_scores_gemma":[0.00001182814,0.00033610594,0.00021217465,0.00019289905,0.00002209339,0.000085161344,0.000044072814,0.0006474225,0.000071308525],"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":[0.00019430254,0.0000149811885,0.00022535912,0.000088635206,0.000120343604,0.000003610528,0.000022851229,0.995848,0.00065781904,0.00003466297,0.0019095045,0.00087995356],"study_design_scores_gemma":[0.00072808954,0.00047245243,0.003671598,0.00016555832,0.000051984913,0.000019028499,0.00002694643,0.992031,0.00093744264,0.000116935815,0.0013304368,0.0004485243],"about_ca_topic_score_codex":0.00056287384,"about_ca_topic_score_gemma":0.0005955922,"teacher_disagreement_score":0.29347438,"about_ca_system_score_codex":0.0002563631,"about_ca_system_score_gemma":0.000034823614,"threshold_uncertainty_score":0.9999091},"labels":[],"label_agreement":null},{"id":"W4386743161","doi":"10.3390/math11183898","title":"Enhanced Non-Maximum Suppression for the Detection of Steel Surface Defects","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Industrial Vision Systems and Defect Detection","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":"Queen's University","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Pukyong National University; National Research Foundation","keywords":"Computer science; Surface (topology); Regularization (linguistics); Object detection; Materials science; Pattern recognition (psychology); Artificial intelligence; Mathematics","score_opus":0.02374194208132006,"score_gpt":0.2524540569841363,"score_spread":0.22871211490281626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386743161","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":[0.66637033,0.000039286086,0.33126158,0.000004907802,0.0010526024,0.00054313784,0.000008500911,0.0002199736,0.00049967924],"genre_scores_gemma":[0.9992393,0.000015312682,0.00032787392,0.0000015988878,0.000097254895,0.000047399506,0.0000018376633,0.000030422645,0.00023897916],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935156,0.000011443169,0.00025726183,0.00007907275,0.00015047766,0.00015019419],"domain_scores_gemma":[0.99917185,0.00046386535,0.00006689521,0.00022222572,0.00005322476,0.000021937421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037390037,0.0000959733,0.0001676667,0.000052072985,0.00008917766,0.000019518595,0.00008124094,0.00010591428,0.0000072455978],"category_scores_gemma":[0.000107623564,0.000066129476,0.00008809142,0.0002991764,0.0000110226165,0.000044074644,0.000018274468,0.00008099531,0.000051056573],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011903001,0.000010937247,0.0000019979389,0.00043970146,0.00002812356,2.2968585e-7,0.0008264943,0.037607886,0.9538649,0.000035521512,0.0008768339,0.006295497],"study_design_scores_gemma":[0.0002862385,0.000052794276,0.000060744653,0.00009472121,0.000020535044,0.0000013648745,0.00050203205,0.2631611,0.7342612,0.0004932938,0.0009875755,0.000078408244],"about_ca_topic_score_codex":0.000004544915,"about_ca_topic_score_gemma":0.000004964223,"teacher_disagreement_score":0.332869,"about_ca_system_score_codex":0.000022589327,"about_ca_system_score_gemma":0.000005897365,"threshold_uncertainty_score":0.2696682},"labels":[],"label_agreement":null},{"id":"W4386858912","doi":"10.1109/cosite60233.2023.10249552","title":"Utilizing Different Predictive Methods to Optimize Hyperledger Fabric Instance","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reinforcement learning; Computer science; Throughput; Benchmark (surveying); Machine learning; Artificial intelligence; Baseline (sea); Host (biology); Operating system","score_opus":0.04339646075899071,"score_gpt":0.3133276018663977,"score_spread":0.26993114110740696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386858912","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":[0.32050875,0.00010121394,0.62384415,0.000034855315,0.003377319,0.00049948593,0.000007732975,0.002354805,0.049271703],"genre_scores_gemma":[0.9923957,0.000019135334,0.0054905456,0.000031401156,0.00024882282,0.00008974082,0.0000023257137,0.000038559116,0.001683767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991202,0.000062924824,0.00022761543,0.00018891162,0.00014585159,0.00025451492],"domain_scores_gemma":[0.9995245,0.00012603028,0.000014858923,0.00019078409,0.00003613157,0.00010774374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030402548,0.00014182857,0.0002159144,0.00021550384,0.000068468726,0.000041435906,0.00007930475,0.000106529704,0.00009105945],"category_scores_gemma":[0.00007650183,0.000115149756,0.000069207745,0.000713539,0.000006213759,0.00007854702,0.00004150453,0.00014484194,0.00034186622],"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":[0.0001265575,0.00003495076,0.00047222793,0.0001194896,0.0002221202,0.000021259046,0.0022063267,0.27682647,0.13296987,0.0008724082,0.052442446,0.53368586],"study_design_scores_gemma":[0.0013330712,0.00029043073,0.009575251,0.00018332792,0.00004052458,0.000015160251,0.0023924496,0.7084837,0.16367508,0.00021536904,0.11294308,0.00085253135],"about_ca_topic_score_codex":0.00001357002,"about_ca_topic_score_gemma":0.0000038320295,"teacher_disagreement_score":0.671887,"about_ca_system_score_codex":0.00009340431,"about_ca_system_score_gemma":0.0000057964685,"threshold_uncertainty_score":0.4695671},"labels":[],"label_agreement":null},{"id":"W4387088058","doi":"10.1109/wrcsara60131.2023.10261859","title":"Deep Learning based Defect Detection Algorithm for Solar Panels","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"University of Alberta","funders":"","keywords":"Photovoltaic system; Computer science; Algorithm; Production (economics); Quality (philosophy); Product (mathematics); Production line; Data mining; Artificial intelligence; Pattern recognition (psychology); Engineering; Electrical engineering; Mathematics","score_opus":0.022312956496878342,"score_gpt":0.23702400782315322,"score_spread":0.21471105132627488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387088058","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":[0.015941722,0.000018594817,0.9796398,0.0000043755495,0.0012832627,0.00033721622,0.0000022710776,0.0020297437,0.0007429673],"genre_scores_gemma":[0.9977781,0.0000048013576,0.0010315921,0.000010407475,0.00040805122,0.00012726783,0.000013129271,0.000050117982,0.00057651935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923563,0.00003699644,0.00018913909,0.00015537883,0.0001250381,0.0002578461],"domain_scores_gemma":[0.99961257,0.00016185398,0.000023355771,0.000103183804,0.000045445027,0.00005361876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043142278,0.00011987297,0.00014351073,0.00020551185,0.0001716082,0.000053533193,0.00004161401,0.00016343265,0.000048110407],"category_scores_gemma":[0.000070947935,0.00011375361,0.0001609952,0.00043876283,0.000005106914,0.0000845585,0.0000074856903,0.00016273829,0.00027510722],"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":[0.0000066393245,0.0000027548106,0.000016224314,0.000029909008,0.000024828338,0.0000018964585,0.000037225473,0.15118201,0.010734497,0.0000025833467,0.00022489711,0.83773655],"study_design_scores_gemma":[0.0004550857,0.00011302482,0.00013792125,0.000010468129,0.000011825737,0.0000032641483,0.0001098683,0.89680105,0.08023138,0.00002033467,0.021966558,0.00013918767],"about_ca_topic_score_codex":0.000028887802,"about_ca_topic_score_gemma":0.000011678468,"teacher_disagreement_score":0.9818364,"about_ca_system_score_codex":0.000057650155,"about_ca_system_score_gemma":0.00000613558,"threshold_uncertainty_score":0.46387377},"labels":[],"label_agreement":null},{"id":"W4387653858","doi":"10.1002/aaai.12129","title":"SolderNet: Towards trustworthy visual inspection of solder joints in electronics manufacturing using explainable artificial intelligence","year":2023,"lang":"en","type":"article","venue":"AI Magazine","topic":"Industrial Vision Systems and Defect Detection","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":"Regional Municipality of Waterloo; University of Waterloo","funders":"","keywords":"Soldering; Printed circuit board; Electronics; Visual inspection; Joint (building); Transparency (behavior); Automated optical inspection; Manufacturing engineering; Engineering; Computer science; Trustworthiness; Engineering drawing; Artificial intelligence; Electrical engineering; Computer security; Architectural engineering; Materials science","score_opus":0.03762574234437778,"score_gpt":0.2885290436264102,"score_spread":0.2509033012820324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387653858","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":[0.97701937,0.00009070901,0.020620776,0.000020978016,0.00093301496,0.00025257515,0.0000027949966,0.00041035953,0.0006494142],"genre_scores_gemma":[0.99946576,0.00004486065,0.000053948574,0.000009542078,0.00027262705,0.000013460623,0.000007333995,0.000041788153,0.00009065759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861926,0.000038174654,0.00051951146,0.00021198948,0.00020450713,0.0004065475],"domain_scores_gemma":[0.99966556,0.000026422069,0.000066826986,0.00015075934,0.000043786036,0.000046653295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042556334,0.00017192804,0.00027608487,0.00048626593,0.0000659007,0.00003754963,0.00007401834,0.00016897735,0.000059889146],"category_scores_gemma":[0.000032638953,0.00018332504,0.00007440741,0.00074194744,0.000020403579,0.00022126579,0.00003727362,0.00030917407,0.00012001957],"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":[0.00014288607,0.0000928425,0.00036776447,0.00033222305,0.0000753651,0.00005238815,0.0009313894,0.57270706,0.1382092,0.0004772483,0.0008288892,0.28578272],"study_design_scores_gemma":[0.00022509065,0.0001826286,0.0017676144,0.00013670155,0.000013483929,0.000019864205,0.00028053488,0.5740605,0.42041153,0.0010132077,0.0016067803,0.00028207368],"about_ca_topic_score_codex":0.00015083257,"about_ca_topic_score_gemma":0.00017912588,"teacher_disagreement_score":0.28550065,"about_ca_system_score_codex":0.00021289024,"about_ca_system_score_gemma":0.00004007264,"threshold_uncertainty_score":0.74757785},"labels":[],"label_agreement":null},{"id":"W4387738484","doi":"10.2139/ssrn.4605931","title":"Application of Self-Supervised Contrastive Learning for Mixed-Type Wafer Map Defect Recognition and Classification","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Wafer; Computer science; Pattern recognition (psychology); Natural language processing; Machine learning; Materials science; Nanotechnology","score_opus":0.03414157279409939,"score_gpt":0.25317269658419944,"score_spread":0.21903112379010004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387738484","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":[0.45397598,0.0019085354,0.5402885,0.000048117123,0.0018662963,0.0013746427,0.000027918375,0.00042954847,0.00008049173],"genre_scores_gemma":[0.9965006,0.0021837195,0.0002131679,0.000001993894,0.00063478854,0.00013415854,0.0001656728,0.0000788516,0.0000870325],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981135,0.00013091022,0.0005457138,0.0002797954,0.00019720425,0.0007328355],"domain_scores_gemma":[0.9988751,0.00017759754,0.00032017243,0.0001391567,0.00043136496,0.000056648678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018934241,0.00023341503,0.00037398713,0.0002684991,0.00014208819,0.00006244585,0.00010090049,0.0004717284,0.0000022971772],"category_scores_gemma":[0.00015558665,0.00023732087,0.00017304986,0.00020136613,0.000016055617,0.00008992664,0.000031685035,0.0021619594,0.00002991197],"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":[0.0011195157,0.00014182834,0.0025636153,0.0023663782,0.0040765423,0.0000017702472,0.0014823681,0.048917163,0.062998325,0.00430015,0.0009100637,0.8711223],"study_design_scores_gemma":[0.0055711917,0.0021848246,0.004908224,0.0010401187,0.0014082628,0.0002185717,0.004868794,0.8037998,0.006114298,0.16268317,0.0055155186,0.0016872099],"about_ca_topic_score_codex":0.000054816937,"about_ca_topic_score_gemma":0.00010139971,"teacher_disagreement_score":0.8694351,"about_ca_system_score_codex":0.0007224118,"about_ca_system_score_gemma":0.00041633012,"threshold_uncertainty_score":0.96776646},"labels":[],"label_agreement":null},{"id":"W4387743566","doi":"10.3390/s23208541","title":"A Real-Time Inspection System for Industrial Helical Gears","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual inspection; Automotive industry; Automated optical inspection; Process (computing); Inspection time; Automated X-ray inspection; Computer science; Engineering; Machine vision; Work (physics); Engineering drawing; Automotive engineering; Artificial intelligence; Mechanical engineering; Image processing","score_opus":0.033038486348292186,"score_gpt":0.24369892958241404,"score_spread":0.21066044323412186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387743566","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":[0.9877716,0.000005098924,0.00008898196,0.00002050132,0.0030592498,0.00051138515,0.000029599352,0.004993079,0.0035205497],"genre_scores_gemma":[0.9972924,0.000008216969,0.000026658878,0.0000015858928,0.0017964662,0.00005639475,0.000017486092,0.000050037816,0.000750774],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908745,0.00004218918,0.00027008666,0.00017190221,0.00016643936,0.00026193075],"domain_scores_gemma":[0.9995959,0.00009929317,0.000033325185,0.00015581738,0.000041467552,0.000074221534],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00034147003,0.00012375353,0.0002063334,0.00021305768,0.00011958227,0.00004306245,0.00004772446,0.00028825234,0.000007472919],"category_scores_gemma":[0.000070682574,0.0001212402,0.00010736883,0.0005196911,0.000011194329,0.000047052916,0.000010508409,0.00014614018,0.00092337694],"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":[0.0006561703,0.000039768966,0.00022531039,0.0005637832,0.0003781007,0.000092187496,0.0012909856,0.20949188,0.4005857,0.0019272655,0.35960567,0.025143167],"study_design_scores_gemma":[0.0035648567,0.00038312643,0.00049510907,0.0002620786,0.000060798084,0.00008737983,0.0015748754,0.8795712,0.050444815,0.000039354785,0.06283253,0.0006839054],"about_ca_topic_score_codex":0.00007702636,"about_ca_topic_score_gemma":0.0000033607985,"teacher_disagreement_score":0.6700793,"about_ca_system_score_codex":0.00016824013,"about_ca_system_score_gemma":0.000013969692,"threshold_uncertainty_score":0.9998545},"labels":[],"label_agreement":null},{"id":"W4387769392","doi":"10.1117/12.2684914","title":"Automatic quality monitoring of two-photon printed devices based on deep learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"University of Saskatchewan","funders":"","keywords":"Photoresist; Convolutional neural network; Computer science; Deep learning; Process (computing); Laser; Materials science; Quality (philosophy); Artificial neural network; Absorption (acoustics); Photopolymer; Artificial intelligence; Process engineering; Polymer; Nanotechnology; Optics; Engineering; Physics","score_opus":0.042818069305187396,"score_gpt":0.31327280441445976,"score_spread":0.27045473510927237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387769392","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":[0.98954666,0.000007634978,0.0031756551,0.0000039136344,0.0006789804,0.00014660528,5.670233e-7,0.0012596373,0.0051803337],"genre_scores_gemma":[0.99950236,0.0000012816531,0.00024262731,0.0000023046093,0.000095306386,0.00001624591,0.0000020340192,0.000020717089,0.00011712506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908787,0.000082724415,0.00033207287,0.00011333449,0.00022824123,0.00015574865],"domain_scores_gemma":[0.9994922,0.00022271869,0.000058258287,0.00014951639,0.00003781675,0.000039436913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005959111,0.000101008634,0.00018919779,0.00019318057,0.000053315456,0.000023177368,0.000057470446,0.00007678425,0.000092061615],"category_scores_gemma":[0.000112598646,0.00008916052,0.000066921464,0.0004743046,0.000005975711,0.00005308819,0.000011255161,0.00016641898,0.00012850002],"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":[0.000024928979,0.000024337069,0.035074685,0.00057641586,0.000056785757,0.000005377143,0.0003862333,0.8221448,0.06589004,0.00012741794,0.00005482652,0.07563416],"study_design_scores_gemma":[0.00041527854,0.000060968545,0.027081184,0.00016137122,0.0000054696384,4.186254e-7,0.00037935883,0.844525,0.12695614,0.000008986785,0.0002883609,0.00011751967],"about_ca_topic_score_codex":0.0001535159,"about_ca_topic_score_gemma":0.000009268806,"teacher_disagreement_score":0.07551664,"about_ca_system_score_codex":0.000051118328,"about_ca_system_score_gemma":0.000006897405,"threshold_uncertainty_score":0.36358607},"labels":[],"label_agreement":null},{"id":"W4387855642","doi":"10.23977/acss.2023.070812","title":"Multi-modal Feature Fusion 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Point cloud; Feature (linguistics); Artificial intelligence; Computer science; Feature extraction; Computer vision; Pattern recognition (psychology); Object detection; Modal; Feature detection (computer vision); Image fusion; Object (grammar); Point (geometry); Projection (relational algebra); Image (mathematics); Image processing; Mathematics; Algorithm","score_opus":0.018528973876841763,"score_gpt":0.2560136693022198,"score_spread":0.23748469542537803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387855642","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":[0.5240607,0.010371422,0.4514573,0.000011019394,0.011980174,0.0007801647,0.000013188552,0.0010086014,0.00031742718],"genre_scores_gemma":[0.9981151,0.00061693374,0.0003705468,0.0000072619328,0.00072826265,0.000045486806,0.000004428909,0.00002418662,0.000087769244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989708,0.00008530032,0.00028903762,0.00024429214,0.00017049842,0.00024005651],"domain_scores_gemma":[0.99962735,0.0000981727,0.000048557242,0.000138064,0.000032859924,0.00005496554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003176182,0.0001667782,0.0002731123,0.00024532972,0.00009571466,0.00011484658,0.00006543378,0.00017400099,0.0000020240354],"category_scores_gemma":[0.000007702247,0.00014548877,0.000043577107,0.00049082545,0.00001213408,0.0002751854,0.000031981912,0.00019822188,0.00003332295],"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":[0.000023079781,0.000012375749,0.00060451217,0.00031016307,0.000020082887,0.000036841597,0.00039275276,0.6454822,0.020802245,0.0000123665095,0.0004651096,0.3318383],"study_design_scores_gemma":[0.00058328983,0.00009795977,0.0012301739,0.00024202897,0.000003265543,0.000038392573,0.00008663491,0.97397745,0.0007743828,0.000012759081,0.022750154,0.00020348938],"about_ca_topic_score_codex":0.000049098402,"about_ca_topic_score_gemma":0.000038238097,"teacher_disagreement_score":0.47405443,"about_ca_system_score_codex":0.000044732795,"about_ca_system_score_gemma":0.0000047019817,"threshold_uncertainty_score":0.593286},"labels":[],"label_agreement":null},{"id":"W4388021801","doi":"10.18280/ts.400511","title":"Enhanced Identification of Internal Casting Defects in Vehicle Wheels Using YOLO Object Detection and X-Ray Inspection","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Identification (biology); Object (grammar); Computer vision; Artificial intelligence; Casting; Object detection; Computer science; Pattern recognition (psychology); Automotive engineering; Engineering; Materials science; Metallurgy","score_opus":0.024824550732060215,"score_gpt":0.24792848099402745,"score_spread":0.22310393026196723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388021801","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":[0.9518886,0.000058100315,0.046878178,0.0000012962804,0.0006232769,0.00025406934,0.0000031852157,0.00022491901,0.00006838255],"genre_scores_gemma":[0.9997414,0.000013803591,0.000019982255,0.0000016452643,0.00017584043,0.000021246256,0.0000020357602,0.000019425965,0.000004665738],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989265,0.000066560424,0.00047955877,0.00017380301,0.00018257827,0.00017095907],"domain_scores_gemma":[0.9996888,0.000055153734,0.00010795487,0.000071564056,0.000044089873,0.00003241107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006125642,0.00011241102,0.0001596637,0.00040176813,0.00007725512,0.000040636463,0.000038852984,0.000081001606,0.000009582404],"category_scores_gemma":[0.000036378085,0.00012231956,0.000042420525,0.00057117373,0.000016947204,0.0002083285,0.000015135603,0.00014934014,0.000009099037],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003317565,0.000009955327,0.0006499597,0.00006446813,0.000012084594,0.0000016227683,0.0005771568,0.06322493,0.9143296,0.0000048291945,0.0000045711786,0.02108766],"study_design_scores_gemma":[0.000679774,0.00008304061,0.044101365,0.00014193945,0.000011167963,0.000005771853,0.00030656578,0.44388744,0.5106041,0.000028613038,0.000028608436,0.00012164496],"about_ca_topic_score_codex":0.00015621628,"about_ca_topic_score_gemma":0.000090734065,"teacher_disagreement_score":0.40372553,"about_ca_system_score_codex":0.0001406958,"about_ca_system_score_gemma":0.000009664887,"threshold_uncertainty_score":0.4988047},"labels":[],"label_agreement":null},{"id":"W4388104418","doi":"10.18280/ts.400516","title":"Utilizing Deep Learning-Based Fusion of Laser Point Cloud Data and Imagery for Digital Measurement in Steel Truss Member Applications","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Point cloud; Artificial intelligence; Computer science; Sensor fusion; Cloud computing; Deep learning; Fusion; Point (geometry); Computer vision; Truss; Data mining; Engineering; Structural engineering; Mathematics; Geometry","score_opus":0.06111003171975341,"score_gpt":0.2621650348111626,"score_spread":0.2010550030914092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388104418","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":[0.74779624,0.0002215454,0.2473151,0.0000908834,0.0004105558,0.002473978,0.00026549952,0.00046371281,0.0009624646],"genre_scores_gemma":[0.9994295,0.000007085027,0.000092538394,0.000007756672,0.0001420576,0.00015648238,0.00012481854,0.000024611643,0.000015129595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988952,0.000028951252,0.00039019846,0.0002172843,0.00028253757,0.00018579251],"domain_scores_gemma":[0.9995402,0.000107934764,0.00006106867,0.0001839609,0.000058639453,0.000048171547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088617305,0.0001210984,0.00018320073,0.00017350097,0.0000656089,0.000050083803,0.00009712998,0.00006552572,0.000033210064],"category_scores_gemma":[0.00003903307,0.00011714925,0.00004433135,0.00026540327,0.00001649906,0.00016167054,0.000043720178,0.000104436134,0.000008755061],"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":[0.00069685903,0.00042249137,0.0055042943,0.0018622364,0.0002774362,0.000013542405,0.0013341455,0.48100552,0.16807932,0.00020062971,0.007816067,0.33278745],"study_design_scores_gemma":[0.0037326934,0.00023459255,0.0034170959,0.0002418259,0.00004940183,0.000002820893,0.0012265608,0.92425996,0.02370283,0.00008825376,0.04266637,0.00037757546],"about_ca_topic_score_codex":0.000011199601,"about_ca_topic_score_gemma":0.000022934466,"teacher_disagreement_score":0.44325444,"about_ca_system_score_codex":0.00005953257,"about_ca_system_score_gemma":0.00001721013,"threshold_uncertainty_score":0.47772077},"labels":[],"label_agreement":null},{"id":"W4388920321","doi":"10.5267/j.msl.2023.10.002","title":"A lean six sigma approach to glue problem in a furniture manufacturing company","year":2023,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"GLUE; Six Sigma; Manufacturing engineering; Computer science; Process (computing); Lean Six Sigma; Operations management; Business; Lean manufacturing; Process management; Engineering; Mechanical engineering","score_opus":0.017543185421873136,"score_gpt":0.22101468823595127,"score_spread":0.20347150281407814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388920321","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":[0.9655004,0.0000032210733,0.013469524,0.00067043776,0.00065990695,0.000888124,0.0000012795474,0.0006225822,0.01818453],"genre_scores_gemma":[0.99825275,0.0000012469245,0.0008825539,0.00045059167,0.00008764051,0.00009154145,0.0000018525245,0.000017569822,0.00021422698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847084,0.0000144847145,0.00021719756,0.00035576266,0.00044348,0.000498206],"domain_scores_gemma":[0.99964017,0.000009821102,0.000021846858,0.00023839208,0.0000042712186,0.00008548154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007842585,0.00014185705,0.00014041497,0.00094905775,0.00014040011,0.0001638861,0.00033272782,0.000036722126,0.000005737843],"category_scores_gemma":[0.000005364614,0.00013387827,0.000036047004,0.0018981559,0.00003885277,0.00022041697,0.00012687015,0.00016926584,0.00020604511],"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":[0.000008618523,0.000017446584,0.00055819063,0.00018188567,0.000014714467,0.00004696364,0.0011245178,0.9429982,0.01962463,0.00045899802,0.021323778,0.0136421],"study_design_scores_gemma":[0.0046587894,0.00022189974,0.23905544,0.0010920325,0.000058928388,0.000042702708,0.01033648,0.46302643,0.05512082,0.00044000344,0.22251539,0.0034310918],"about_ca_topic_score_codex":0.000039785074,"about_ca_topic_score_gemma":0.000006778291,"teacher_disagreement_score":0.47997174,"about_ca_system_score_codex":0.00018380488,"about_ca_system_score_gemma":0.000002035418,"threshold_uncertainty_score":0.54593974},"labels":[],"label_agreement":null},{"id":"W4389187911","doi":"10.3390/buildings13122990","title":"Vision-Based Guiding System for Autonomous Robotic Corner Cleaning of Window Frames","year":2023,"lang":"en","type":"article","venue":"Buildings","topic":"Industrial Vision Systems and Defect Detection","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Robot; Machine vision; Artificial intelligence; Robot welding; Computer vision; Process (computing); Engineering; Automation; Computer science; Image processing; Mechanical engineering","score_opus":0.02798517670547752,"score_gpt":0.2503789445637304,"score_spread":0.22239376785825288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389187911","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":[0.8122926,0.00010526152,0.18095514,0.000031623666,0.0030127142,0.00071886147,0.000013888632,0.0019235593,0.0009463977],"genre_scores_gemma":[0.99818337,0.0000012759328,0.0013466121,0.000009067408,0.00024332598,0.00004753779,0.000006272933,0.000060319402,0.000102246304],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989004,0.0000199195,0.00042442174,0.00019436979,0.00018029651,0.00028060697],"domain_scores_gemma":[0.99930567,0.00025959182,0.000098577824,0.00019420566,0.00008242894,0.00005950401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005359164,0.00015214621,0.0003188818,0.00027842468,0.00011321346,0.000052353364,0.00011396238,0.0001774847,0.000008379555],"category_scores_gemma":[0.00012150754,0.00014756469,0.00014970213,0.00047234044,0.000014765439,0.00008570244,0.000020005762,0.00008348122,0.00004040784],"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":[0.000049051323,0.000011201551,0.0003489264,0.0009922973,0.000069519505,0.0000075023345,0.00024065125,0.79907787,0.16973427,0.001326074,0.008715619,0.019427007],"study_design_scores_gemma":[0.0011564048,0.00021043792,0.00047812262,0.00096664164,0.000046383135,0.000009042964,0.0005220657,0.8398607,0.14516185,0.000041927964,0.011203413,0.0003430557],"about_ca_topic_score_codex":0.000045549532,"about_ca_topic_score_gemma":8.910277e-7,"teacher_disagreement_score":0.18589078,"about_ca_system_score_codex":0.00012588911,"about_ca_system_score_gemma":0.000024604406,"threshold_uncertainty_score":0.6017513},"labels":[],"label_agreement":null},{"id":"W4389264937","doi":"10.1016/j.eswa.2023.122749","title":"Computer vision defect detection on unseen backgrounds for manufacturing inspection","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Deep learning; Visual inspection; Task (project management); Machine learning; Variety (cybernetics); Object detection; Parameterized complexity; Pattern recognition (psychology); Computer vision; Engineering","score_opus":0.01970219987755033,"score_gpt":0.2603581894886739,"score_spread":0.24065598961112356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389264937","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":[0.075902835,0.000088476714,0.9138957,0.000032005737,0.0018354696,0.002977596,0.000023711284,0.003634957,0.0016092103],"genre_scores_gemma":[0.9934527,0.000015865733,0.00023056385,0.000016344386,0.0017633465,0.0041226773,0.000051527848,0.00008212948,0.0002647922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99874085,0.000038810023,0.0003358381,0.00035816978,0.00023869827,0.00028765344],"domain_scores_gemma":[0.9992373,0.00014578644,0.00007718971,0.00038208667,0.00006793626,0.000089726585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002604883,0.00022573865,0.00023427112,0.00034844843,0.00044831893,0.00014350594,0.000098277196,0.0001863233,0.0000031902991],"category_scores_gemma":[0.0000034713912,0.00019177636,0.00010980752,0.000518533,0.000017786098,0.00015293605,0.0000125349425,0.00015305537,0.00032106473],"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":[0.0004910002,0.00015449898,0.00006588841,0.0007209014,0.0004750851,0.00000788314,0.0011280901,0.6429979,0.05385203,0.0021819146,0.05147882,0.24644597],"study_design_scores_gemma":[0.0017642243,0.0011041493,0.0010166207,0.00028946018,0.000031968382,0.00008850598,0.00062564266,0.3427418,0.050937906,0.000051898493,0.6005939,0.00075391144],"about_ca_topic_score_codex":0.00009662503,"about_ca_topic_score_gemma":0.00002791478,"teacher_disagreement_score":0.9175499,"about_ca_system_score_codex":0.0002395592,"about_ca_system_score_gemma":0.000010154444,"threshold_uncertainty_score":0.78204125},"labels":[],"label_agreement":null},{"id":"W4389584953","doi":"10.17118/11143/21051","title":"A novel setup for in-process geometric inspection of 3D printed parts viastructured-light 3D scanning","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Process (computing); 3d scanning; Computer science; 3d printed; Structured light; 3D printing; Solid modeling; Engineering drawing; Computer graphics (images); Computer vision; Artificial intelligence; Engineering; Mechanical engineering; Manufacturing engineering","score_opus":0.025698035851086105,"score_gpt":0.2666646300403979,"score_spread":0.24096659418931182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389584953","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":[0.9065377,0.000029299328,0.08777778,0.000012444319,0.0016385316,0.0005631521,0.000015798252,0.00079437566,0.0026309227],"genre_scores_gemma":[0.99914515,0.0000026655284,0.00044423086,0.0000040973596,0.00016771308,0.000062532745,0.000008813069,0.000029230694,0.00013554336],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904525,0.000008392873,0.00037373602,0.00017448691,0.00017052882,0.0002276087],"domain_scores_gemma":[0.9996268,0.000056683337,0.00006117429,0.00012641803,0.0000896288,0.000039271763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028342457,0.0001247442,0.00022791309,0.0009979599,0.000050623283,0.000022279459,0.00006258372,0.00015181775,0.000025985451],"category_scores_gemma":[0.00012482595,0.000113580805,0.000053412114,0.0025747444,0.000007417285,0.00012080675,0.000015283184,0.00012752968,0.000013276659],"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":[0.00023977901,0.00008075254,0.0049207974,0.0013103095,0.00019423493,0.000007782235,0.0021593291,0.6339891,0.26486447,0.00033135357,0.004915403,0.086986706],"study_design_scores_gemma":[0.0047103283,0.00036257488,0.029438863,0.000408205,0.00004040331,0.00004719407,0.0014223615,0.50966644,0.4429715,0.00016512116,0.010012754,0.0007542609],"about_ca_topic_score_codex":0.00009910363,"about_ca_topic_score_gemma":0.000051999046,"teacher_disagreement_score":0.17810702,"about_ca_system_score_codex":0.00007989704,"about_ca_system_score_gemma":0.000015652595,"threshold_uncertainty_score":0.46316907},"labels":[],"label_agreement":null},{"id":"W4390489077","doi":"10.1109/jsen.2023.3346470","title":"CGTD-Net: Channel-Wise Global Transformer-Based Dual-Branch Network for Industrial Strip Steel Surface Defect Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Transformer; Dual (grammatical number); Electronic engineering; Electrical engineering; Materials science; Engineering; Voltage","score_opus":0.031334856563013616,"score_gpt":0.2544444653960638,"score_spread":0.2231096088330502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390489077","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":[0.80476105,0.0010866786,0.16413483,0.000073978,0.027809622,0.0008889717,0.00016650332,0.0006125652,0.00046578707],"genre_scores_gemma":[0.99024516,0.000044593904,0.000093406234,0.000019769703,0.009325669,0.00001936398,0.000007476295,0.00009363479,0.00015094226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974663,0.00018001442,0.000773648,0.0003374175,0.0004781919,0.0007643838],"domain_scores_gemma":[0.99905974,0.00023812144,0.000102545084,0.00018323517,0.00013392164,0.0002824482],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011947697,0.0004096782,0.0004648548,0.00017527316,0.0004001141,0.0004560562,0.00011241014,0.00063490716,0.0000486049],"category_scores_gemma":[0.00005536135,0.00037820567,0.0006472678,0.00086262054,0.0000298601,0.00030849612,0.0000034441557,0.0009956213,0.00005741738],"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":[0.00041769617,0.000020365233,0.00003179196,0.000099427736,0.00026351752,0.000053170334,0.00008558966,0.9376052,0.01211882,0.000007064257,0.017961502,0.031335868],"study_design_scores_gemma":[0.009400775,0.0019920303,0.00020181588,0.0013190544,0.00057486416,0.0020061152,0.00023905469,0.6949339,0.10858077,0.00071608997,0.17834997,0.0016855528],"about_ca_topic_score_codex":0.00004962231,"about_ca_topic_score_gemma":0.00007538767,"teacher_disagreement_score":0.24267128,"about_ca_system_score_codex":0.000513007,"about_ca_system_score_gemma":0.00014878646,"threshold_uncertainty_score":0.99986696},"labels":[],"label_agreement":null},{"id":"W4390842165","doi":"10.1049/ell2.13042","title":"PCBDet: An efficient deep neural network object detection architecture for automatic PCB component detection on the edge","year":2024,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Regional Municipality of Waterloo; University of Waterloo","funders":"","keywords":"Computer science; Benchmark (surveying); Deep learning; Component (thermodynamics); Object detection; Artificial intelligence; Artificial neural network; Inference; Throughput; Enhanced Data Rates for GSM Evolution; Task (project management); Real-time computing; Pattern recognition (psychology); Engineering","score_opus":0.009973610875854641,"score_gpt":0.21418128167797484,"score_spread":0.2042076708021202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390842165","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":[0.6880485,0.0005366402,0.30570957,0.0003636149,0.0034310087,0.00091769575,0.0000036759632,0.0009437037,0.00004561255],"genre_scores_gemma":[0.9976113,0.0000052026357,0.00005234205,0.00031434512,0.0016536586,0.00025394006,0.000009040882,0.00009230999,0.000007867547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982311,0.00016164067,0.0003413675,0.00035200664,0.00027767007,0.0006362203],"domain_scores_gemma":[0.99922955,0.00031498226,0.000049995724,0.00031028676,0.00002323181,0.00007198198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063028705,0.00029818658,0.00021762465,0.00018807525,0.00038919525,0.00024070834,0.00015014307,0.00016300444,0.000012037537],"category_scores_gemma":[0.000027659627,0.00022461184,0.00021458173,0.0004911347,0.000023706038,0.000069088,0.000012966928,0.0007607491,0.00003528642],"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":[0.000045600515,0.000010371593,5.384122e-7,0.000058606107,0.00006671114,0.0000027328663,0.00020528886,0.7471893,0.10271584,0.00013319493,0.00041072213,0.14916113],"study_design_scores_gemma":[0.00024204238,0.00046285594,0.000052598436,0.00005638275,0.000044324323,0.00005114727,0.000025582769,0.93603224,0.051250134,0.00009790623,0.011426554,0.00025823235],"about_ca_topic_score_codex":0.000015411471,"about_ca_topic_score_gemma":0.00015634357,"teacher_disagreement_score":0.30956283,"about_ca_system_score_codex":0.0005109964,"about_ca_system_score_gemma":0.000016639015,"threshold_uncertainty_score":0.91594046},"labels":[],"label_agreement":null},{"id":"W4390843766","doi":"10.1016/j.eswa.2024.123199","title":"A real-time anchor-free defect detector with global and local feature enhancement for surface defect detection","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Feature (linguistics); Detector; Computer science; Artificial intelligence; Noise (video); Pattern recognition (psychology); Field (mathematics); Feature extraction; Mathematics","score_opus":0.007278602425634316,"score_gpt":0.23387733784913706,"score_spread":0.22659873542350273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390843766","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":[0.052319694,0.005448883,0.93559265,0.000037424845,0.0004544711,0.0036627017,0.00012236851,0.0011863931,0.0011754135],"genre_scores_gemma":[0.9924309,0.00006438503,0.0025287347,0.00000589789,0.0005752746,0.0039457623,0.00002222399,0.00008284218,0.0003439485],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99853545,0.000048019952,0.00028362693,0.000520067,0.00026730643,0.00034554672],"domain_scores_gemma":[0.99904215,0.00012940736,0.00005662757,0.0005100892,0.00010744563,0.00015430414],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024160223,0.0003288817,0.00033835907,0.00008166702,0.00024955726,0.00024006389,0.00012321932,0.00023377217,0.0000063155303],"category_scores_gemma":[0.000007376117,0.00024584332,0.0001082763,0.0006035276,0.000050681392,0.00016210965,0.000020592202,0.00016433433,0.000044548513],"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":[0.0020241542,0.00024741117,0.00035483547,0.005471687,0.0038242866,0.00005197485,0.002361882,0.061981846,0.6407058,0.0034165224,0.06301775,0.21654186],"study_design_scores_gemma":[0.0048838104,0.0029967923,0.00024584107,0.0020306408,0.0004965025,0.0012817786,0.0015459093,0.40418345,0.074288234,0.0001225604,0.5055648,0.0023596736],"about_ca_topic_score_codex":0.0003425804,"about_ca_topic_score_gemma":0.00017038675,"teacher_disagreement_score":0.9401112,"about_ca_system_score_codex":0.00041745737,"about_ca_system_score_gemma":0.000054411892,"threshold_uncertainty_score":0.9999994},"labels":[],"label_agreement":null},{"id":"W4390890801","doi":"10.23977/acss.2023.071113","title":"Research Methods for Classification and Identification of Ancient Glass Types","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Barium; Weathering; Lead glass; Mineralogy; Impurity; Materials science; Potassium; Potassium iodate; Analytical Chemistry (journal); Chemistry; Geology; Metallurgy; Environmental chemistry; Geochemistry","score_opus":0.12216758542735442,"score_gpt":0.4321024841421936,"score_spread":0.30993489871483915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390890801","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":[0.23608956,0.010679672,0.7505417,0.0000130747085,0.0018438544,0.0006604851,0.000009142441,0.000081464306,0.00008105886],"genre_scores_gemma":[0.99797696,0.0005442006,0.001181295,0.0000014859374,0.00015838286,0.00010086765,0.0000036968606,0.000009168745,0.000023943432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999076,0.00016384685,0.00035835567,0.00015975657,0.00011204738,0.0001300011],"domain_scores_gemma":[0.99920833,0.00049607636,0.00005600852,0.000104673796,0.0001087916,0.000026114587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002789878,0.00005932856,0.000168599,0.0002470911,0.00005439927,0.000051979703,0.00004710382,0.00006679779,2.8557886e-7],"category_scores_gemma":[0.00003102328,0.000052931253,0.00001641861,0.00041122115,0.000026113574,0.00014068095,0.000017356737,0.00006484699,0.0000018595958],"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":[0.00003259665,0.000016384227,0.0012010506,0.0013989024,0.000026719723,8.0678456e-7,0.00081113196,0.11339269,0.18784592,0.0036421942,0.0007200233,0.6909116],"study_design_scores_gemma":[0.00019880722,0.00007063289,0.0032428384,0.00012028934,0.000002240013,0.0000024622336,0.00022004145,0.9786673,0.0027864652,0.00033025982,0.014294308,0.000064331565],"about_ca_topic_score_codex":0.00001378162,"about_ca_topic_score_gemma":0.0000029775688,"teacher_disagreement_score":0.8652746,"about_ca_system_score_codex":0.000021011134,"about_ca_system_score_gemma":0.000005584165,"threshold_uncertainty_score":0.21584739},"labels":[],"label_agreement":null},{"id":"W4391021318","doi":"10.1109/smarttechcon57526.2023.10391613","title":"Study on Multi-Tone Piano Transcription Algorithm Based on BPNN","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Adaptability; Piano; Generalization; Computer science; Tone (literature); Machine learning; Transcription (linguistics); Artificial intelligence; Algorithm; Mathematics","score_opus":0.05828487883985544,"score_gpt":0.29509149939700813,"score_spread":0.23680662055715268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391021318","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":[0.82114065,0.000008897704,0.15023962,0.000104106955,0.0063605346,0.0020272536,0.000034729852,0.0050534178,0.01503079],"genre_scores_gemma":[0.9980081,9.12612e-7,0.00014340464,0.000039601015,0.00027391964,0.0000638247,0.0000088573415,0.000035786168,0.0014256359],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909496,0.00005358081,0.00020975423,0.00018903459,0.00026380984,0.0001888798],"domain_scores_gemma":[0.99965155,0.00004655363,0.000014030776,0.00020969141,0.0000216292,0.000056558547],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030567503,0.000143679,0.00015425491,0.00027826164,0.00007642418,0.000037452002,0.00005211096,0.00010971683,0.00008725545],"category_scores_gemma":[0.000012859452,0.00012245658,0.000071143615,0.00047075868,0.0000043090577,0.000051456278,0.0000032552107,0.00017126367,0.0012581215],"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":[0.00023109904,0.0013273766,0.0005226736,0.00007887668,0.00019022457,0.00021511469,0.0022062014,0.45599207,0.060407076,0.000077714016,0.031753037,0.44699854],"study_design_scores_gemma":[0.0040579354,0.001416695,0.011768062,0.000059331593,0.00001820451,0.0000018139023,0.0013268816,0.9533205,0.019092066,0.0000042546526,0.008578096,0.0003561671],"about_ca_topic_score_codex":0.0000607435,"about_ca_topic_score_gemma":0.00001223966,"teacher_disagreement_score":0.49732843,"about_ca_system_score_codex":0.0000653909,"about_ca_system_score_gemma":0.000005897507,"threshold_uncertainty_score":0.9995195},"labels":[],"label_agreement":null},{"id":"W4391115305","doi":"10.2139/ssrn.4703226","title":"Multilinear Principal Component Analysis-Based Tensor Decomposition for Real-Time Fabric Weave Pattern Recognition from High-Dimensional Streaming Data","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Principal component analysis; Computer science; Computer vision; Multilinear map; Local binary patterns; Dimensionality reduction; Pattern recognition (psychology); Tensor (intrinsic definition); Image (mathematics); Mathematics","score_opus":0.02809649889942218,"score_gpt":0.28210818396388165,"score_spread":0.2540116850644595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391115305","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":[0.7777759,0.0007186315,0.21550837,0.00007086169,0.0020264836,0.0007926865,0.0027206705,0.0003641702,0.000022249724],"genre_scores_gemma":[0.98445976,0.00026446182,0.0015967475,0.000012123401,0.0024485562,0.000066323846,0.010946505,0.00013993026,0.00006559634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99583966,0.00021308928,0.001014487,0.00086939585,0.0005988588,0.0014645413],"domain_scores_gemma":[0.998146,0.0002772716,0.000412011,0.0007533541,0.00025204456,0.0001593014],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0018527424,0.0005709576,0.00086953345,0.0008301627,0.00023668828,0.00030783215,0.00046540974,0.00063564634,0.00007787967],"category_scores_gemma":[0.00004899325,0.0005489003,0.0005737229,0.00035199054,0.000021811355,0.00013313882,0.00029379595,0.0036593976,0.000113222864],"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":[0.00075868325,0.00032993645,0.00025252812,0.00035331174,0.01357383,0.000060613693,0.00012206606,0.5988044,0.0183686,0.000031809286,0.0007727282,0.36657146],"study_design_scores_gemma":[0.0013787126,0.00019373,0.0004793238,0.000497096,0.002565411,0.0000490844,0.00007246824,0.98887545,0.0017861543,0.0033663632,0.000103017046,0.00063320954],"about_ca_topic_score_codex":0.0019012678,"about_ca_topic_score_gemma":0.0008638452,"teacher_disagreement_score":0.390071,"about_ca_system_score_codex":0.0020725229,"about_ca_system_score_gemma":0.00086609443,"threshold_uncertainty_score":0.99969625},"labels":[],"label_agreement":null},{"id":"W4391129606","doi":"10.1109/ictc58733.2023.10393276","title":"PIDDNet: RGB-Depth Fusion Network for Real-time Semantic Segmentation","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Nexen (Canada)","funders":"","keywords":"RGB color model; Artificial intelligence; Computer science; Segmentation; Computer vision; Leverage (statistics); Context (archaeology); Feature (linguistics); Image segmentation; Geography","score_opus":0.02403404458853708,"score_gpt":0.25710091629852183,"score_spread":0.23306687170998475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391129606","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":[0.83873725,0.000058776623,0.09564192,0.000086865264,0.007020253,0.0031806014,0.000023939083,0.0065908604,0.04865955],"genre_scores_gemma":[0.9823335,0.000086850494,0.0015818052,0.00002997014,0.0018928836,0.00025438014,0.00012978131,0.00008493309,0.013605941],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926025,0.000020482417,0.00022343187,0.0001326469,0.00012449957,0.00023867689],"domain_scores_gemma":[0.99968886,0.00008668869,0.000027078187,0.00012253154,0.000029120301,0.00004568896],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032450975,0.000103636856,0.00014064038,0.00009030696,0.00010816684,0.000038413,0.000040593244,0.000112296904,0.00010495736],"category_scores_gemma":[0.000013637615,0.000093288734,0.00006755828,0.000362301,0.0000033815095,0.00008028885,0.000012462193,0.000053297346,0.0008224081],"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":[0.000051610892,0.000008969507,0.00040174005,0.00012904596,0.000058362886,0.0000036879273,0.00019317654,0.1803919,0.1212139,0.00021411707,0.64822567,0.049107846],"study_design_scores_gemma":[0.0016736797,0.00029226445,0.0036897846,0.00013536896,0.00004609379,0.000008998972,0.00032752592,0.91097593,0.021073375,0.0004200219,0.06083043,0.00052651844],"about_ca_topic_score_codex":0.00008681462,"about_ca_topic_score_gemma":0.000021658565,"teacher_disagreement_score":0.730584,"about_ca_system_score_codex":0.000045195062,"about_ca_system_score_gemma":0.0000062379913,"threshold_uncertainty_score":0.9999556},"labels":[],"label_agreement":null},{"id":"W4391223796","doi":"10.1016/b978-0-323-99134-6.00007-4","title":"Convolutional neural networks: Basic concepts and applications in manufacturing","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence","score_opus":0.013171857949719552,"score_gpt":0.22999381303635222,"score_spread":0.21682195508663266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391223796","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006931242,0.0054532476,0.0001319401,0.0000049904934,0.0011311966,0.0007402211,0.00003455422,0.0002564574,0.99155426],"genre_scores_gemma":[0.26532888,0.00009213831,0.000010553036,0.000025567651,0.0015420621,0.0001531975,0.000034639514,0.00011898078,0.732694],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999002,0.000008944696,0.00036853485,0.00028605587,0.00013986585,0.00019461385],"domain_scores_gemma":[0.9996193,0.00006051864,0.000046202684,0.0001844878,0.000016538688,0.00007292118],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011724436,0.0002864568,0.00032631512,0.00018850552,0.000057123343,0.00006448689,0.000070062124,0.00037966948,0.00005469016],"category_scores_gemma":[0.0000014531673,0.00027919203,0.000097806216,0.000014767839,0.0000518496,0.00003432356,0.000043220814,0.0006483232,0.00005543134],"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":[0.000004693598,0.000001177236,0.000005868247,0.00016870046,0.0000604716,0.000016184531,0.00006108543,0.0038968285,0.00001035201,0.006158252,0.00012376174,0.9894926],"study_design_scores_gemma":[0.00020590042,0.00001680465,0.00003792825,0.000352595,0.000041214906,0.000030567513,0.0000126874365,0.008611283,0.000039182247,0.0028822182,0.98742723,0.00034238366],"about_ca_topic_score_codex":0.0000012081447,"about_ca_topic_score_gemma":0.000022940394,"teacher_disagreement_score":0.9891502,"about_ca_system_score_codex":0.000121990844,"about_ca_system_score_gemma":0.0000125322385,"threshold_uncertainty_score":0.999966},"labels":[],"label_agreement":null},{"id":"W4391713537","doi":"10.21611/qirt.2023.02","title":"Automated Defect Detection for Epoxy-Carbon Prepreg Laminates in Data Fusion Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Epoxy; Materials science; Fusion; Carbon fibers; Sensor fusion; Composite material; Computer science; Artificial intelligence; Composite number","score_opus":0.05560663397446722,"score_gpt":0.2789331319417843,"score_spread":0.2233264979673171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391713537","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":[0.96961385,0.00007487344,0.014956975,0.00000791033,0.0012684272,0.0012302527,0.000028289689,0.006140507,0.006678935],"genre_scores_gemma":[0.9989961,0.000009819883,0.00020476831,0.000002325447,0.00014297426,0.00014148379,0.00013096466,0.000038861286,0.00033272768],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989498,0.000046545516,0.0002933929,0.00031942222,0.00013492587,0.00025586764],"domain_scores_gemma":[0.9993448,0.0001119117,0.00003096989,0.00044973826,0.000026608077,0.000035929468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077953073,0.0001377186,0.00018530263,0.0003179983,0.000055578777,0.000045104473,0.00014832735,0.00021277866,0.0000057003017],"category_scores_gemma":[0.00013075591,0.00012501497,0.000052769898,0.00080826104,0.000007001559,0.00015436996,0.00007711215,0.0001236554,0.000031884618],"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":[0.00029629905,0.00017487151,0.0021272057,0.0014916593,0.0002519249,0.000020926533,0.0010614574,0.2446405,0.49333838,0.00014251305,0.02219656,0.23425771],"study_design_scores_gemma":[0.000565327,0.00004286051,0.0018085182,0.000031481588,0.00001008843,0.0000066199827,0.00014559174,0.9749364,0.02048031,0.000030544605,0.0017873594,0.00015489414],"about_ca_topic_score_codex":0.0001823902,"about_ca_topic_score_gemma":0.00014854266,"teacher_disagreement_score":0.7302959,"about_ca_system_score_codex":0.000068461966,"about_ca_system_score_gemma":0.000008538647,"threshold_uncertainty_score":0.50979626},"labels":[],"label_agreement":null},{"id":"W4391844683","doi":"10.1007/s00170-024-13168-9","title":"Review of current vision-based robotic machine-tending applications","year":2024,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Artificial intelligence; Economic shortage; Process (computing); Robotics; Investment (military); Engineering; Manufacturing; Robot; Computer science; Manufacturing engineering; Risk analysis (engineering); Machine learning; Business; Government (linguistics); Marketing","score_opus":0.013368749243364977,"score_gpt":0.3047140328708129,"score_spread":0.29134528362744794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391844683","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":[0.044263463,0.30230635,0.6339283,0.0051123933,0.012178455,0.0008453628,0.000029381468,0.00072027533,0.0006160089],"genre_scores_gemma":[0.99106055,0.0077889212,0.00081141636,0.000041908304,0.00024548877,0.00001917822,0.000002334404,0.000020173422,0.00001000683],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99901265,0.000015448313,0.0005260385,0.00008623583,0.0002603882,0.00009923095],"domain_scores_gemma":[0.9993848,0.00011510318,0.00017485628,0.00016872257,0.00013394558,0.000022543914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002930815,0.00010479621,0.00020673251,0.00047871357,0.000030361169,0.000023303324,0.00047291798,0.000059373153,0.0000320416],"category_scores_gemma":[0.000054638655,0.000071292525,0.00013313133,0.00018980625,0.00003787061,0.00010877188,0.000042578158,0.0004403934,0.0000114093955],"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":[0.000025992396,0.000029436174,0.000006162606,0.0015684732,0.00018877258,0.0000185692,0.00001778374,0.10670358,0.023022832,0.0019962636,0.0016244387,0.8647977],"study_design_scores_gemma":[0.00050239725,0.00014132124,0.00002861982,0.01860389,0.000092801354,0.00044770708,0.000045790413,0.009955044,0.564603,0.006506352,0.39885992,0.00021319053],"about_ca_topic_score_codex":0.0000010933317,"about_ca_topic_score_gemma":7.3568157e-7,"teacher_disagreement_score":0.94679713,"about_ca_system_score_codex":0.00013406288,"about_ca_system_score_gemma":0.000027371967,"threshold_uncertainty_score":0.2907225},"labels":[],"label_agreement":null},{"id":"W4392002532","doi":"10.3389/fmtec.2024.1277152","title":"Anomaly detection in automated fibre placement: learning with data limitations","year":2024,"lang":"en","type":"article","venue":"Frontiers in Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Anomaly (physics); Computer science; Artificial intelligence; Pattern recognition (psychology); Physics","score_opus":0.019080941560642245,"score_gpt":0.22862610551928683,"score_spread":0.2095451639586446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392002532","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":[0.8883398,0.00070464436,0.10284016,0.00007387415,0.0017526218,0.00035237722,0.0000070589513,0.0052833636,0.0006460649],"genre_scores_gemma":[0.9973473,0.000036479545,0.0023868913,0.0000018155016,0.000046692003,0.000045995144,0.000021101654,0.00003982073,0.00007389688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989852,0.000033703265,0.0002660292,0.0003344472,0.00010322368,0.00027740796],"domain_scores_gemma":[0.9995749,0.000038129594,0.000025271698,0.0003327748,0.000007747533,0.000021203865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021245523,0.00015713548,0.00019925545,0.0015409222,0.00006485428,0.00006303078,0.00020796135,0.00031166172,0.0000051985458],"category_scores_gemma":[0.000036214846,0.00015253124,0.000016559809,0.0007573515,0.000031695985,0.00026347936,0.00006716521,0.000691073,0.000018353207],"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":[0.00010867258,0.00003135757,0.008386934,0.0004612592,0.00018772644,0.00033411407,0.00046256345,0.59012234,0.0031672223,0.000057357072,0.004921215,0.39175925],"study_design_scores_gemma":[0.000630867,0.000157963,0.0022022629,0.00039912533,0.000017359696,0.000062824256,0.0009156187,0.9065251,0.046996832,0.00022079093,0.041551497,0.0003198035],"about_ca_topic_score_codex":0.000047479818,"about_ca_topic_score_gemma":0.00029156305,"teacher_disagreement_score":0.39143944,"about_ca_system_score_codex":0.00023832166,"about_ca_system_score_gemma":0.000011888135,"threshold_uncertainty_score":0.62200433},"labels":[],"label_agreement":null},{"id":"W4392133341","doi":"10.1016/j.dsp.2024.104441","title":"High-quality matched transfer generation adversarial network for synthetic cross-material surface defect images","year":2024,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Adversarial system; Computer science; Quality (philosophy); Transfer (computing); Artificial intelligence; Surface (topology); Generative adversarial network; Image quality; Pattern recognition (psychology); Algorithm; Image (mathematics); Mathematics; Physics; Geometry","score_opus":0.0251444511360259,"score_gpt":0.2707836076186486,"score_spread":0.24563915648262272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392133341","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":[0.58455473,0.00038411593,0.4114152,0.000007873562,0.0021143456,0.00033315708,0.00020719234,0.0005962904,0.00038710108],"genre_scores_gemma":[0.99643004,0.0000018338603,0.00013619981,0.000006371531,0.0031223604,0.000031350886,0.00012102842,0.00006927061,0.00008155464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986581,0.00002368989,0.00045567934,0.00031193212,0.00021750241,0.00033308897],"domain_scores_gemma":[0.999642,0.00010417903,0.0000244399,0.00008869879,0.00007443319,0.00006621221],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00042796324,0.00022654785,0.0002760548,0.000043056094,0.00018966974,0.00201108,0.00007210651,0.00019871276,0.000045857818],"category_scores_gemma":[0.000022596492,0.00020621272,0.00018846437,0.00021002605,0.000033632143,0.0009609258,0.000010579354,0.00013758431,0.000028248522],"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":[0.00037382374,0.000026879457,0.000053295564,0.0012142584,0.00013948455,0.000012030109,0.00024364624,0.47178802,0.29599774,0.0003405514,0.0013219203,0.22848837],"study_design_scores_gemma":[0.0020328304,0.0003518167,0.0001273027,0.0008736675,0.00015862094,0.000046980167,0.00009578013,0.5360045,0.4483519,0.001933095,0.008580815,0.0014427288],"about_ca_topic_score_codex":0.000019739879,"about_ca_topic_score_gemma":0.000001920012,"teacher_disagreement_score":0.41187528,"about_ca_system_score_codex":0.000094807314,"about_ca_system_score_gemma":0.000047143345,"threshold_uncertainty_score":0.9990249},"labels":[],"label_agreement":null},{"id":"W4392158173","doi":"10.1109/globecom54140.2023.10436741","title":"Long-Term Prediction of Remaining Useful Life for Industrial IoT","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Dalhousie University","funders":"","keywords":"Term (time); Internet of Things; Computer science; Computer security","score_opus":0.10249363030594588,"score_gpt":0.26686081680857354,"score_spread":0.16436718650262766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392158173","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":[0.9825103,0.0000140720595,0.011577178,0.000013333563,0.0030672548,0.00043885925,0.000038179234,0.00076870143,0.0015721205],"genre_scores_gemma":[0.9982766,0.0000042250217,0.000038211037,0.0000047626636,0.0011676749,0.000038801376,0.000020253898,0.000026239812,0.00042324362],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992111,0.000017037712,0.00034382677,0.00011428715,0.00014476471,0.00016898141],"domain_scores_gemma":[0.9996115,0.00010536804,0.000044665914,0.00013150352,0.00004678431,0.00006015926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040612425,0.00008863064,0.0001767591,0.00019005974,0.000048862803,0.000019258521,0.000049570954,0.00022516026,0.000050478433],"category_scores_gemma":[0.00017728421,0.000082447645,0.00008204866,0.0003810218,0.000008214581,0.000058065783,0.000013023856,0.00010982002,0.000032914482],"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":[0.0010598731,0.000108112275,0.18602508,0.0012338049,0.0010185731,0.000018699491,0.0020300755,0.14579625,0.13235487,0.0013117286,0.34683403,0.18220888],"study_design_scores_gemma":[0.022361968,0.0027734423,0.23929726,0.001416539,0.00030579552,0.000044241177,0.0018039421,0.41066185,0.25922373,0.00028659072,0.060149662,0.0016749672],"about_ca_topic_score_codex":0.0000123418695,"about_ca_topic_score_gemma":0.000004824037,"teacher_disagreement_score":0.2866844,"about_ca_system_score_codex":0.000032464806,"about_ca_system_score_gemma":0.000021941134,"threshold_uncertainty_score":0.33621174},"labels":[],"label_agreement":null},{"id":"W4392248401","doi":"10.1109/icce59016.2024.10444225","title":"XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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 New Brunswick","funders":"Ministry of Education","keywords":"Computer science; Segmentation; Visual inspection; Quality (philosophy); Artificial intelligence; Image segmentation; Computer vision; Natural language processing","score_opus":0.04250936752773567,"score_gpt":0.3291103359543989,"score_spread":0.28660096842666327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392248401","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":[0.2134127,0.000076088276,0.7786144,0.000011150245,0.0020315577,0.0003374539,0.0000046659,0.0013091173,0.004202883],"genre_scores_gemma":[0.9986391,0.000009714419,0.00033446183,0.000009141303,0.00040378343,0.000086605985,0.000012100732,0.00002440093,0.00048070843],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993403,0.000019770618,0.00025048642,0.00015020136,0.00011827348,0.00012098479],"domain_scores_gemma":[0.99979615,0.000058203143,0.000013622202,0.00006716356,0.00003583262,0.000029054154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026353172,0.00009142946,0.00011164552,0.0001067382,0.0000642951,0.000101937876,0.000021505792,0.00009679663,0.000024494897],"category_scores_gemma":[0.000010115294,0.00008223812,0.00007541125,0.00019187898,0.0000048652673,0.00030250166,0.000004421693,0.00007688454,0.00006004032],"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":[0.00007910712,0.000033298616,0.000008204128,0.0009211721,0.00015579593,0.0000014314758,0.00104379,0.21277462,0.6448137,0.010920119,0.007815298,0.12143343],"study_design_scores_gemma":[0.0002516088,0.000067837325,0.00002744727,0.000042942604,0.000012002525,0.0000021803817,0.00027162165,0.8252769,0.17213863,0.0010805888,0.00070066657,0.00012757893],"about_ca_topic_score_codex":0.00007340734,"about_ca_topic_score_gemma":0.000037959155,"teacher_disagreement_score":0.7852264,"about_ca_system_score_codex":0.00011390694,"about_ca_system_score_gemma":0.000010330287,"threshold_uncertainty_score":0.3353573},"labels":[],"label_agreement":null},{"id":"W4392309880","doi":"10.1007/978-3-031-54288-6_2","title":"Wood Surface Defects Detection Based on AI Algorithm Yolov7","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Surface (topology); Materials science; Computer science; Artificial intelligence; Mathematics; Geometry","score_opus":0.010542781387665774,"score_gpt":0.2087247725814742,"score_spread":0.19818199119380844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392309880","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":[0.0008787446,0.03584115,0.8720999,0.00003284826,0.025495604,0.0022244295,0.00008124202,0.0012079335,0.062138144],"genre_scores_gemma":[0.9926811,0.00023309179,0.000028275266,0.00006137456,0.002821769,0.000034240446,0.000030524105,0.00021514067,0.0038945237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980425,0.00005907185,0.00059810054,0.0005685743,0.00033480301,0.00039694406],"domain_scores_gemma":[0.9989905,0.0003667482,0.000104410385,0.00037256346,0.000054143668,0.00011167281],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00045846388,0.00062728766,0.0007674071,0.00034060236,0.00009630108,0.00027316398,0.00009310019,0.0017847759,0.000019579795],"category_scores_gemma":[0.000019454383,0.00054672407,0.00019406756,0.000181876,0.000028432174,0.00004932173,0.000023721266,0.0017372868,0.000048678703],"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":[0.000023457107,0.0000035537687,0.000004725288,0.00028503666,0.000059711863,0.000054489006,0.000023717052,0.9566631,0.000045036428,0.00017128514,0.00029131098,0.042374607],"study_design_scores_gemma":[0.00037023664,0.00021894112,0.000003187199,0.002132114,0.000059260576,0.0000474499,0.0000025690533,0.9530591,0.00011648958,0.00041223667,0.043026097,0.0005523509],"about_ca_topic_score_codex":0.00010396202,"about_ca_topic_score_gemma":0.00014043847,"teacher_disagreement_score":0.99180233,"about_ca_system_score_codex":0.00027306343,"about_ca_system_score_gemma":0.00002100869,"threshold_uncertainty_score":0.9996984},"labels":[],"label_agreement":null},{"id":"W4392505527","doi":"10.1061/9780784484449.050","title":"Depth Completion from Sparse 3D Maps for Automated Defect Quantification","year":2022,"lang":"en","type":"article","venue":"Lifelines 2022","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Completion (oil and gas wells); Artificial intelligence; Computer vision; Pattern recognition (psychology); Geology","score_opus":0.04713922301587206,"score_gpt":0.26872322858455444,"score_spread":0.22158400556868238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392505527","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":[0.863046,0.0011223322,0.11090029,0.00015514439,0.014921243,0.0020001777,0.0018737396,0.0052652494,0.00071579986],"genre_scores_gemma":[0.9957764,0.000008035013,0.0008098515,0.000045685283,0.0014598914,0.00042723666,0.0012756463,0.00004875522,0.00014849739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988944,0.000100201636,0.00036230654,0.00022580575,0.00023803466,0.00017925182],"domain_scores_gemma":[0.9994493,0.00013010975,0.00007878599,0.00024246484,0.00005503775,0.000044270844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037155504,0.00013427259,0.00019880458,0.00012949402,0.0002933403,0.00004264346,0.000110934496,0.000070715476,0.00013735669],"category_scores_gemma":[0.00006531511,0.00014637486,0.00012513752,0.00032185126,0.000007305528,0.00006868637,0.000036153866,0.00016894849,0.00006824486],"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":[0.00029270162,0.000105130806,0.0017274116,0.00010029385,0.00019634982,0.0000072302837,0.0003532074,0.53244704,0.12268038,0.0002451095,0.31706232,0.024782792],"study_design_scores_gemma":[0.0006913092,0.00008848212,0.0014925301,0.000009298705,0.000033894165,0.000007744639,0.00018100641,0.6887043,0.0016779568,0.00008120107,0.3068243,0.00020798064],"about_ca_topic_score_codex":0.00019569743,"about_ca_topic_score_gemma":0.00007107188,"teacher_disagreement_score":0.15625724,"about_ca_system_score_codex":0.000118512165,"about_ca_system_score_gemma":0.000016561882,"threshold_uncertainty_score":0.5968994},"labels":[],"label_agreement":null},{"id":"W4392639505","doi":"10.21203/rs.3.rs-3913857/v2","title":"WITHDRAWN: Surface multi-spectral reflectivity and texture material recognition in non-constrained environments","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Reflectivity; Texture (cosmology); Computer science; Materials science; Surface (topology); Artificial intelligence; Pattern recognition (psychology); Optics; Mathematics; Image (mathematics); Physics; Geometry","score_opus":0.060368364637912994,"score_gpt":0.3461791058281061,"score_spread":0.2858107411901931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392639505","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":[0.9948444,0.00037216907,0.00029238284,0.000042602063,0.001142739,0.001480989,0.0003590875,0.0001730759,0.0012925747],"genre_scores_gemma":[0.99853,0.00018437226,0.00020740833,0.0000018150275,0.00049995974,0.0001312623,0.00008596551,0.000077174554,0.00028207604],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99763125,0.00032290645,0.00036525523,0.00061050954,0.000533244,0.0005368524],"domain_scores_gemma":[0.99943274,0.00009314283,0.00003553204,0.00027041652,0.000034308163,0.00013387896],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0014274536,0.0003104607,0.00039917836,0.0004384901,0.00010098138,0.0003262285,0.00011718252,0.0008445841,0.000099678735],"category_scores_gemma":[0.000064697946,0.00030337693,0.00008455037,0.00033055045,0.00009009824,0.00008496033,0.0003613568,0.002634979,0.00014428688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013635922,0.0005377835,0.002638214,0.0151773635,0.0006194136,0.0010114734,0.005694939,0.03373666,0.78599256,0.000051801027,0.0046837344,0.14849249],"study_design_scores_gemma":[0.012568975,0.0022399528,0.09833762,0.02852595,0.00019103674,0.00038772903,0.004199833,0.3988745,0.42580405,0.013349692,0.0091382945,0.0063823946],"about_ca_topic_score_codex":0.0005191786,"about_ca_topic_score_gemma":0.00023161685,"teacher_disagreement_score":0.36513782,"about_ca_system_score_codex":0.0005711299,"about_ca_system_score_gemma":0.00006570935,"threshold_uncertainty_score":0.9999418},"labels":[],"label_agreement":null},{"id":"W4392936942","doi":"10.1016/j.jmsy.2024.03.007","title":"Time series prediction for production quality in a machining system using spatial-temporal multi-task graph learning","year":2024,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Machining; Series (stratigraphy); Task (project management); Graph; Computer science; Production (economics); Time series; Quality (philosophy); Artificial intelligence; Machine learning; Engineering; Theoretical computer science; Mechanical engineering; Geology; Systems engineering","score_opus":0.03039115748438443,"score_gpt":0.26765598877720115,"score_spread":0.23726483129281672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392936942","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":[0.8917374,0.00097631913,0.09583556,0.000008760384,0.010472146,0.00057350675,0.0000148271665,0.0003441515,0.00003732407],"genre_scores_gemma":[0.99742013,0.000010428078,0.00030068296,5.077567e-7,0.0020116637,0.000020952573,0.000005205993,0.00006222989,0.00016820461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975411,0.00028462408,0.0013278797,0.00022114231,0.00036208113,0.0002631153],"domain_scores_gemma":[0.9992508,0.00009917493,0.00035205067,0.00012694273,0.00009774336,0.000073287905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003078616,0.00022879001,0.00055481796,0.00068882125,0.00014205305,0.00022705483,0.00008598449,0.00021290519,0.0000020899215],"category_scores_gemma":[0.000091131384,0.0002020948,0.00021372695,0.00018946097,0.000019116338,0.0006402669,0.000013485446,0.000569568,0.0000062044132],"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":[0.00016406695,0.000016367705,0.0010039379,0.0034739259,0.00018737103,0.000042308096,0.0012773363,0.9704922,0.019788561,0.000010175652,0.00012415597,0.0034196253],"study_design_scores_gemma":[0.0019681212,0.00055729394,0.003151876,0.011229469,0.00017385912,0.002676933,0.0068115657,0.9183037,0.04579848,0.000015419439,0.008610604,0.00070266443],"about_ca_topic_score_codex":0.00042250307,"about_ca_topic_score_gemma":0.000025038195,"teacher_disagreement_score":0.105682716,"about_ca_system_score_codex":0.0006434424,"about_ca_system_score_gemma":0.00004628017,"threshold_uncertainty_score":0.8241187},"labels":[],"label_agreement":null},{"id":"W4392974616","doi":"10.1016/j.procs.2024.02.035","title":"Defect detection in additive manufacturing using image processing techniques","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"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 du Québec en Abitibi-Témiscamingue","funders":"Ministère des relations internationales et de la Francophonie","keywords":"Computer science; Image processing; Computer vision; Artificial intelligence; Image (mathematics)","score_opus":0.01436283497835114,"score_gpt":0.2544084405960031,"score_spread":0.24004560561765195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392974616","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":[0.3667307,0.00014855925,0.6303839,0.0000053397853,0.0011540483,0.00023531921,0.0000013591703,0.0009268834,0.00041389366],"genre_scores_gemma":[0.9899508,0.0000066493117,0.009601762,0.000016287939,0.00037533845,0.000025758325,3.233699e-7,0.000020845715,0.000002278032],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988615,0.000013339039,0.00021363104,0.00035494208,0.0002576742,0.00029894704],"domain_scores_gemma":[0.9997402,0.000030662097,0.000022006523,0.00009844622,0.00005293864,0.00005575765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055260153,0.00014033515,0.00012309343,0.00059960515,0.00013572187,0.0004756009,0.00014235171,0.000064457396,0.000003347241],"category_scores_gemma":[0.000017292165,0.00013070951,0.00004546046,0.0010442346,0.00006843649,0.0010983485,0.000054628734,0.00025974758,0.000015041785],"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":[0.0000024070584,0.0000037439966,0.000006419601,0.00011702172,0.0000024456167,0.000016570895,0.00033139705,0.0010873365,0.11152678,0.0000053691556,0.000032913602,0.8868676],"study_design_scores_gemma":[0.000031641306,0.000021092748,0.00009634202,0.00024341045,0.0000021459566,0.0000587983,0.000010096313,0.49295756,0.5057254,0.00005535997,0.0006876215,0.000110579254],"about_ca_topic_score_codex":0.000021870768,"about_ca_topic_score_gemma":0.0000074124655,"teacher_disagreement_score":0.886757,"about_ca_system_score_codex":0.00030070808,"about_ca_system_score_gemma":0.0000799091,"threshold_uncertainty_score":0.53301793},"labels":[],"label_agreement":null},{"id":"W4393099452","doi":"10.1109/icaect60202.2024.10469334","title":"A Quarter Century Journey: Evolution of Object Detection Methods","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Computer science; Object (grammar); Artificial intelligence; History; Archaeology","score_opus":0.016402856319368168,"score_gpt":0.2884135822244957,"score_spread":0.27201072590512754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393099452","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":[0.074688755,0.0018746707,0.90659565,0.0000072952403,0.005334446,0.00015769218,0.0000024723734,0.00071206834,0.01062697],"genre_scores_gemma":[0.9985505,0.000020431378,0.00095535227,0.0000016816255,0.00031518575,0.000009463657,4.8543194e-7,0.000018240598,0.00012867634],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993306,0.000079664285,0.00025314433,0.000107449414,0.00011163327,0.00011751762],"domain_scores_gemma":[0.9997478,0.00006115582,0.000016427626,0.00010873137,0.000032270193,0.000033585657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004928958,0.000087527995,0.00012947699,0.00023955549,0.000028346945,0.000040206178,0.00003106973,0.00013370285,0.000106716514],"category_scores_gemma":[0.000021521415,0.00007190089,0.0001053183,0.00039478377,0.0000071273166,0.00014439144,0.000005793243,0.00016333174,0.00006112986],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001702656,0.000008367409,0.000017557322,0.00020477909,0.000092028415,0.0000027802673,0.00035894357,0.0020475949,0.70089203,0.0008487108,0.0024311915,0.293079],"study_design_scores_gemma":[0.00034083135,0.0002466621,0.0006372775,0.0002143695,0.000058397683,0.00009473046,0.0010576013,0.30230793,0.61560506,0.000581262,0.07855307,0.00030281616],"about_ca_topic_score_codex":0.0000946917,"about_ca_topic_score_gemma":0.000012926933,"teacher_disagreement_score":0.92386174,"about_ca_system_score_codex":0.00015592476,"about_ca_system_score_gemma":0.0000144664,"threshold_uncertainty_score":0.29320332},"labels":[],"label_agreement":null},{"id":"W4393100908","doi":"10.54254/2755-2721/51/20241591","title":"A new frontier in electronics manufacturing: Optimized deep learning techniques for PCB image reconstruction","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Industrial Vision Systems and Defect Detection","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":"McMaster University","funders":"","keywords":"Printed circuit board; Electronics; Computer science; Deep learning; Artificial intelligence; Benchmark (surveying); Autofocus; Computer engineering; Engineering; Electrical engineering; Focus (optics)","score_opus":0.004411998900722455,"score_gpt":0.19560267668279666,"score_spread":0.19119067778207421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393100908","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":[0.023727484,0.00061618676,0.9740057,0.0000098008295,0.0003234864,0.0002672889,0.0000013178068,0.00061969523,0.00042906642],"genre_scores_gemma":[0.8556323,0.000039496597,0.14383893,0.0000036568879,0.00029313672,0.00009025593,0.0000156316,0.00004370853,0.000042878874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994059,0.0000038229578,0.00019698098,0.00015869374,0.00007271013,0.00016186075],"domain_scores_gemma":[0.9998218,0.0000822722,0.000012605654,0.00003341021,0.000010204157,0.000039750477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012233773,0.0001254406,0.00014437227,0.0002072741,0.000035018526,0.00009902608,0.000027669317,0.00009409327,0.00000764629],"category_scores_gemma":[0.000005626119,0.00013538447,0.000038304777,0.000108054875,0.0000040656637,0.00010566375,0.00000814113,0.00022552136,0.0000031045665],"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":[0.000011056817,0.0000011794995,9.096706e-7,0.00007908184,0.000022780107,0.0000010638952,0.00007017502,0.69816923,0.0037071311,0.0015678715,0.000045887627,0.29632363],"study_design_scores_gemma":[0.0003633322,0.00002759892,0.000040314655,0.00006992082,0.000008614989,0.000030181298,0.00003458502,0.9783221,0.010783485,0.0015564193,0.008590453,0.00017298944],"about_ca_topic_score_codex":0.00000506455,"about_ca_topic_score_gemma":7.541507e-7,"teacher_disagreement_score":0.8319048,"about_ca_system_score_codex":0.00008533599,"about_ca_system_score_gemma":0.000015418045,"threshold_uncertainty_score":0.5520819},"labels":[],"label_agreement":null},{"id":"W4393275150","doi":"10.2316/j.2024.206-0935","title":"PATENT SEARCH CLASSIFICATION MODEL FOR SERVICE ROBOTS FIELD USING DEEP LEARNING APPROACH","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Field (mathematics); Computer science; Artificial intelligence; Service (business); Robot; Deep learning; Machine learning; Business; Mathematics","score_opus":0.0992669932511405,"score_gpt":0.30617750257725,"score_spread":0.20691050932610952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393275150","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":[0.077895924,0.0002283226,0.9204196,0.00018267575,0.0010250981,0.00008743669,0.000001839187,0.000051431314,0.0001077029],"genre_scores_gemma":[0.9841283,0.00006090363,0.015356333,0.000017316223,0.00038772082,0.000002426668,0.000007830645,0.000014884964,0.000024316156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928284,0.000018152421,0.00031279275,0.00007210732,0.00023919475,0.000074927535],"domain_scores_gemma":[0.99947226,0.0000659143,0.00006761336,0.000031629992,0.00032898324,0.000033601358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003336224,0.00006977031,0.0000959734,0.00021045582,0.000046883302,0.00019674249,0.000067810615,0.00008329141,0.0000019837444],"category_scores_gemma":[0.000029045714,0.00006238154,0.000055510256,0.00009258274,0.0000038420117,0.00026287397,0.000010951096,0.00017212851,0.0000012603476],"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":[0.000012715383,0.0000068866852,0.000021943126,0.00006674819,0.00005716535,0.0000012315293,0.0003329562,0.95163035,0.007105024,0.0009142188,0.00003419855,0.039816562],"study_design_scores_gemma":[0.00021019354,0.000036310947,0.00011683467,0.0001353619,0.000022072532,0.00007431135,0.00012230303,0.9982923,0.0005478581,0.00027620647,0.00010296586,0.00006326746],"about_ca_topic_score_codex":0.0000057424736,"about_ca_topic_score_gemma":9.693688e-7,"teacher_disagreement_score":0.90623236,"about_ca_system_score_codex":0.00009933836,"about_ca_system_score_gemma":0.000025564992,"threshold_uncertainty_score":0.25438455},"labels":[],"label_agreement":null},{"id":"W4393335160","doi":"10.48550/arxiv.2403.18910","title":"A Geometric Explanation of the Likelihood OOD Detection Paradox","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Industrial Vision Systems and Defect Detection","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":"Canadian Institute for Advanced Research","keywords":"Maximum likelihood; Econometrics; Computer science; Mathematical economics; Statistics; Mathematics","score_opus":0.04486886149411207,"score_gpt":0.16488559129505773,"score_spread":0.12001672980094566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393335160","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":[0.9610259,0.00030552625,0.030476019,0.0000063201214,0.0045658713,0.0004167314,0.00003517946,0.00032342528,0.0028450636],"genre_scores_gemma":[0.9993831,0.000075176744,0.0000052362016,0.0000027648873,0.00020622704,0.0000021677797,0.000004135276,0.000031173335,0.00029002968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905545,0.0000704548,0.0002463633,0.0003419981,0.0001093039,0.00017643528],"domain_scores_gemma":[0.9992241,0.00005219067,0.00012657499,0.00046166888,0.000084176136,0.00005128416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022885289,0.00020708625,0.00024386417,0.00059989595,0.00006828465,0.00003952344,0.00025247695,0.00043952483,0.000021428901],"category_scores_gemma":[0.00003801956,0.00018717154,0.00027144473,0.0015493924,0.00002386954,0.000062447776,0.00026447355,0.00071660546,0.0000683153],"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":[0.00005859813,0.00004497087,0.00040042365,0.00092993875,0.00039799965,0.000035580804,0.00023931735,0.976971,0.0038042117,0.0014387014,0.00090909767,0.014770191],"study_design_scores_gemma":[0.001419334,0.00020015449,0.0040688505,0.0012423916,0.00081104954,0.000030373525,0.000583117,0.88409317,0.07622342,0.026222073,0.003917941,0.0011881089],"about_ca_topic_score_codex":0.00021904045,"about_ca_topic_score_gemma":0.000096039264,"teacher_disagreement_score":0.09287778,"about_ca_system_score_codex":0.00027766658,"about_ca_system_score_gemma":0.000046574754,"threshold_uncertainty_score":0.7632634},"labels":[],"label_agreement":null},{"id":"W4393414289","doi":"10.5281/zenodo.7693278","title":"Iterative Bleaching Extends Multiplexity (IBEX) Knowledge-Base","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Knowledge base; Base (topology); Mathematics; Computer science; Artificial intelligence; Mathematical analysis","score_opus":0.038596629757461934,"score_gpt":0.26708555418757784,"score_spread":0.22848892443011593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393414289","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019245515,0.00035292184,0.0029930938,0.000032110664,0.0015503359,0.00076567277,0.9589604,0.001820474,0.033332527],"genre_scores_gemma":[0.00548717,0.00013446515,0.000049365568,0.00003162975,0.00077721855,2.1569232e-7,0.9914352,0.00087441114,0.0012102741],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99760807,0.00054670376,0.0004894958,0.0005314203,0.0003443475,0.0004799656],"domain_scores_gemma":[0.99836075,0.0000683701,0.00011841388,0.00077829993,0.00047760492,0.00019653824],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009437626,0.00036116838,0.0003835801,0.0006796899,0.0019184223,0.0010918933,0.00089571916,0.0003762628,0.004745428],"category_scores_gemma":[0.0007932034,0.00038365228,0.00013623342,0.0007853044,0.00007764306,0.00021666447,0.0007918214,0.0011577272,0.0035196904],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003149862,0.000061280916,3.939993e-8,0.00032233706,0.00008452418,0.000014565043,0.00021081849,0.00043971327,0.0002622322,0.000050599898,0.96102345,0.03749896],"study_design_scores_gemma":[0.00054299296,0.00012598775,0.0000046220607,0.0002765202,0.000037346024,0.00004244197,0.00009459515,0.0014661299,0.00028236365,0.0000124945145,0.9967652,0.00034928345],"about_ca_topic_score_codex":0.000070036825,"about_ca_topic_score_gemma":0.0000034346049,"teacher_disagreement_score":0.037149675,"about_ca_system_score_codex":0.0004707364,"about_ca_system_score_gemma":0.000009643742,"threshold_uncertainty_score":0.99994504},"labels":[],"label_agreement":null},{"id":"W4393429433","doi":"10.5281/zenodo.2025778","title":"Krill production circuit's - DATASET","year":2018,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Krill; Production (economics); Fishery; Oceanography; Biology; Geology; Economics; Microeconomics","score_opus":0.04941003097231952,"score_gpt":0.2471640279330441,"score_spread":0.19775399696072457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393429433","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009468287,0.0000639268,0.00027334472,0.000027667074,0.002047229,0.0005956032,0.99127483,0.0011435555,0.0044791596],"genre_scores_gemma":[0.0008982198,0.00014263493,0.000009052443,0.000028961387,0.0022034335,9.703855e-8,0.99525714,0.0012977298,0.00016272978],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978929,0.0002575328,0.00040916228,0.00055625825,0.0004807331,0.00040342202],"domain_scores_gemma":[0.99824613,0.000013172781,0.00012833595,0.0010742367,0.00037258895,0.00016553841],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00085758744,0.00028499382,0.00027479578,0.00044434212,0.0014726608,0.0008180132,0.0008721768,0.00033118116,0.010939325],"category_scores_gemma":[0.00054996053,0.0003053961,0.000066768946,0.00059654133,0.00010546963,0.00027184087,0.00050660426,0.0006218102,0.025356393],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017223772,0.000028904773,1.2292978e-8,0.00017404795,0.000056279685,0.000007928483,0.0000365713,0.00006922586,0.00034482466,0.000008145146,0.9883623,0.010894532],"study_design_scores_gemma":[0.00021647445,0.00016670756,0.0000035500964,0.00008564012,0.00003517319,0.00019929663,0.000029417659,0.00005416194,0.0002571204,0.000016121096,0.9986202,0.00031613154],"about_ca_topic_score_codex":0.000046712084,"about_ca_topic_score_gemma":0.0000012428006,"teacher_disagreement_score":0.014417068,"about_ca_system_score_codex":0.00027012895,"about_ca_system_score_gemma":0.0000035946669,"threshold_uncertainty_score":0.9999398},"labels":[],"label_agreement":null},{"id":"W4393460691","doi":"10.5281/zenodo.5907001","title":"ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.036861670955754884,"score_gpt":0.25078934535583436,"score_spread":0.21392767440007948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393460691","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012808926,0.00009531619,0.0006653897,0.00001788887,0.00069618115,0.0012810539,0.99531204,0.0007002155,0.00110383],"genre_scores_gemma":[0.0005841636,0.000090823545,0.000011787432,0.00003808695,0.00046214383,0.00000120471,0.99735236,0.0012967217,0.00016269083],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974975,0.00036909332,0.0005075131,0.00054594537,0.00054075895,0.0005392172],"domain_scores_gemma":[0.9987651,0.00005830685,0.0001285076,0.00075531565,0.00014538073,0.00014738292],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017283229,0.00029485094,0.0003558775,0.00066808815,0.0014088422,0.0005931039,0.0009692339,0.00030063416,0.063766025],"category_scores_gemma":[0.0005257847,0.00034075038,0.0001388129,0.0006994763,0.000032733173,0.00021523115,0.000769875,0.0008200597,0.0038225076],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008131667,0.000080817656,1.2383668e-7,0.00026283963,0.000069370595,0.000012177475,0.00006209527,0.0006115334,0.00015606343,0.000009227567,0.9959483,0.0027061272],"study_design_scores_gemma":[0.0010661025,0.00032340825,0.0000072216485,0.000053780463,0.000056040346,0.00008136252,0.00007713271,0.0012948225,0.000030296505,0.0000072170765,0.996709,0.00029362566],"about_ca_topic_score_codex":0.00004059237,"about_ca_topic_score_gemma":0.000002081933,"teacher_disagreement_score":0.05994352,"about_ca_system_score_codex":0.000521427,"about_ca_system_score_gemma":0.000005642939,"threshold_uncertainty_score":0.99990445},"labels":[],"label_agreement":null},{"id":"W4393475419","doi":"10.5281/zenodo.5508261","title":"TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Materials science; Computer graphics (images); Computer science","score_opus":0.03710859822394473,"score_gpt":0.21639728732178,"score_spread":0.1792886890978353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393475419","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039741118,0.0028394235,0.00028819163,0.000028389177,0.00032065707,0.00082389114,0.9551462,0.0004618993,0.0003502637],"genre_scores_gemma":[0.37855002,0.0021657115,0.000054664368,0.000015779018,0.00037312845,4.0894798e-7,0.617883,0.000929101,0.000028173017],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983167,0.00028684316,0.00047045018,0.0003835369,0.00029804432,0.00024441208],"domain_scores_gemma":[0.9992788,0.000020290276,0.00011382589,0.00026210042,0.00020781583,0.00011718701],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038659247,0.00025312038,0.0004187055,0.00019843968,0.0004707931,0.00050597714,0.00025492988,0.0002367532,0.0009567439],"category_scores_gemma":[0.00019267938,0.00024767002,0.000036575748,0.00018569684,0.00010516084,0.00016703727,0.00020953384,0.0003502984,0.000055947097],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022920237,0.000051298204,7.856019e-7,0.002196883,0.00031540164,0.000040185372,0.0011646529,0.00024318838,0.20278877,0.0000042159277,0.78157073,0.011394709],"study_design_scores_gemma":[0.0006985188,0.00021135948,0.000041316747,0.0008331719,0.00009447102,0.000065421984,0.00047995456,0.00015798071,0.037530545,0.0000026257442,0.9595919,0.00029271838],"about_ca_topic_score_codex":0.0005674414,"about_ca_topic_score_gemma":0.000007897705,"teacher_disagreement_score":0.33880892,"about_ca_system_score_codex":0.00006779639,"about_ca_system_score_gemma":0.0000061353016,"threshold_uncertainty_score":0.99999756},"labels":[],"label_agreement":null},{"id":"W4393495264","doi":"10.5281/zenodo.5907002","title":"ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Data mining","score_opus":0.036861670955754884,"score_gpt":0.25078934535583436,"score_spread":0.21392767440007948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393495264","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012808926,0.00009531619,0.0006653897,0.00001788887,0.00069618115,0.0012810539,0.99531204,0.0007002155,0.00110383],"genre_scores_gemma":[0.0005841636,0.000090823545,0.000011787432,0.00003808695,0.00046214383,0.00000120471,0.99735236,0.0012967217,0.00016269083],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974975,0.00036909332,0.0005075131,0.00054594537,0.00054075895,0.0005392172],"domain_scores_gemma":[0.9987651,0.00005830685,0.0001285076,0.00075531565,0.00014538073,0.00014738292],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017283229,0.00029485094,0.0003558775,0.00066808815,0.0014088422,0.0005931039,0.0009692339,0.00030063416,0.063766025],"category_scores_gemma":[0.0005257847,0.00034075038,0.0001388129,0.0006994763,0.000032733173,0.00021523115,0.000769875,0.0008200597,0.0038225076],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008131667,0.000080817656,1.2383668e-7,0.00026283963,0.000069370595,0.000012177475,0.00006209527,0.0006115334,0.00015606343,0.000009227567,0.9959483,0.0027061272],"study_design_scores_gemma":[0.0010661025,0.00032340825,0.0000072216485,0.000053780463,0.000056040346,0.00008136252,0.00007713271,0.0012948225,0.000030296505,0.0000072170765,0.996709,0.00029362566],"about_ca_topic_score_codex":0.00004059237,"about_ca_topic_score_gemma":0.000002081933,"teacher_disagreement_score":0.05994352,"about_ca_system_score_codex":0.000521427,"about_ca_system_score_gemma":0.000005642939,"threshold_uncertainty_score":0.99990445},"labels":[],"label_agreement":null},{"id":"W4393508681","doi":"10.5281/zenodo.7570822","title":"Defectors: A Large Scale Python Dataset for Defect Prediction","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Python (programming language); Computer science; Scale (ratio); Programming language; Cartography; Geography","score_opus":0.03739181245637486,"score_gpt":0.2548971986705139,"score_spread":0.21750538621413906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393508681","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001745398,0.0000750765,0.0030416618,0.000019229832,0.0015100568,0.0012723529,0.9912428,0.0021369797,0.0005272985],"genre_scores_gemma":[0.00042807916,0.0001435538,0.000031981064,0.000022625134,0.0011902204,0.0000010169243,0.99589586,0.0021472718,0.0001393894],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99752504,0.00027893583,0.0005061812,0.0006084114,0.00047554282,0.0006058743],"domain_scores_gemma":[0.9981493,0.00007579029,0.00013889112,0.0011293441,0.00030216362,0.00020452717],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014098459,0.00034268503,0.00036954484,0.0005980895,0.0017012188,0.0008183089,0.0010081601,0.0004537326,0.001560857],"category_scores_gemma":[0.0009417612,0.0003713684,0.000198277,0.000774207,0.00004611923,0.00023515151,0.00077794725,0.000625322,0.023506178],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052946005,0.000043662938,1.0267002e-7,0.0004309254,0.00010691285,0.000006343989,0.00006816685,0.00032713232,0.00020134727,0.000005079142,0.9957622,0.002995138],"study_design_scores_gemma":[0.00080607756,0.000319678,0.000012465821,0.00014127849,0.000091281814,0.00006094572,0.00008464844,0.001212374,0.0000859865,0.000017506307,0.9968403,0.00032746844],"about_ca_topic_score_codex":0.00005982405,"about_ca_topic_score_gemma":0.000006588478,"teacher_disagreement_score":0.02194532,"about_ca_system_score_codex":0.00031925784,"about_ca_system_score_gemma":0.000005113873,"threshold_uncertainty_score":0.9998738},"labels":[],"label_agreement":null},{"id":"W4393528085","doi":"10.5281/zenodo.7708984","title":"Defectors: A Large Scale Python Dataset for Defect Prediction","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Python (programming language); Computer science; Scale (ratio); Programming language; Cartography; Geography","score_opus":0.03739181245637486,"score_gpt":0.2548971986705139,"score_spread":0.21750538621413906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393528085","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001745398,0.0000750765,0.0030416618,0.000019229832,0.0015100568,0.0012723529,0.9912428,0.0021369797,0.0005272985],"genre_scores_gemma":[0.00042807916,0.0001435538,0.000031981064,0.000022625134,0.0011902204,0.0000010169243,0.99589586,0.0021472718,0.0001393894],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99752504,0.00027893583,0.0005061812,0.0006084114,0.00047554282,0.0006058743],"domain_scores_gemma":[0.9981493,0.00007579029,0.00013889112,0.0011293441,0.00030216362,0.00020452717],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014098459,0.00034268503,0.00036954484,0.0005980895,0.0017012188,0.0008183089,0.0010081601,0.0004537326,0.001560857],"category_scores_gemma":[0.0009417612,0.0003713684,0.000198277,0.000774207,0.00004611923,0.00023515151,0.00077794725,0.000625322,0.023506178],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052946005,0.000043662938,1.0267002e-7,0.0004309254,0.00010691285,0.000006343989,0.00006816685,0.00032713232,0.00020134727,0.000005079142,0.9957622,0.002995138],"study_design_scores_gemma":[0.00080607756,0.000319678,0.000012465821,0.00014127849,0.000091281814,0.00006094572,0.00008464844,0.001212374,0.0000859865,0.000017506307,0.9968403,0.00032746844],"about_ca_topic_score_codex":0.00005982405,"about_ca_topic_score_gemma":0.000006588478,"teacher_disagreement_score":0.02194532,"about_ca_system_score_codex":0.00031925784,"about_ca_system_score_gemma":0.000005113873,"threshold_uncertainty_score":0.9998738},"labels":[],"label_agreement":null},{"id":"W4393551005","doi":"10.5281/zenodo.4500787","title":"U.S. Test System with High Spatial and Temporal Resolution for Renewable Integration Studies","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Renewable energy; Test (biology); Computer science; Environmental science; Engineering; Geology; Electrical engineering; Paleontology","score_opus":0.03678355055796939,"score_gpt":0.236920817410432,"score_spread":0.2001372668524626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393551005","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004826624,0.00075170037,0.02268556,0.000066181805,0.0010439485,0.0015595685,0.97111076,0.001277415,0.0010222319],"genre_scores_gemma":[0.042505223,0.0002874605,0.00018789199,0.0000098996225,0.0008831085,9.432896e-7,0.9549739,0.0009410054,0.00021057295],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998453,0.000173007,0.0003707941,0.00041413982,0.0003146593,0.0002743701],"domain_scores_gemma":[0.99855405,0.00006513397,0.00014568497,0.00039268442,0.0007446332,0.000097838056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006111136,0.00025215774,0.00035937596,0.00027345298,0.0012657276,0.0006614251,0.00024409193,0.00023314227,0.00019823687],"category_scores_gemma":[0.00058484153,0.00022906058,0.000045470046,0.0003629758,0.00007054201,0.0001685073,0.00024071794,0.0003166111,0.00021315906],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006639696,0.000020463574,1.6747009e-7,0.00072389725,0.000088268745,0.000010097292,0.00006778527,0.00051755947,0.00033641452,0.000024540715,0.9922948,0.0058496427],"study_design_scores_gemma":[0.00065295876,0.0005150779,0.0000075886887,0.0005472458,0.00006194882,0.00013580141,0.00065327715,0.00180453,0.0004068779,0.000004844538,0.994956,0.00025382978],"about_ca_topic_score_codex":0.00048952963,"about_ca_topic_score_gemma":0.000046018344,"teacher_disagreement_score":0.04202256,"about_ca_system_score_codex":0.00039163342,"about_ca_system_score_gemma":0.000006899036,"threshold_uncertainty_score":0.9735081},"labels":[],"label_agreement":null},{"id":"W4393631426","doi":"10.1109/tetci.2024.3378651","title":"A Small Object Real-Time Detection Method for Power Line Inspection in Low-Illuminance Environments","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Illuminance; Line (geometry); Computer science; Power (physics); Computer vision; Object (grammar); Artificial intelligence; Optics; Mathematics; Physics","score_opus":0.030497015736016094,"score_gpt":0.2980562263637444,"score_spread":0.2675592106277283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393631426","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":[0.04301987,0.00006104091,0.9534926,0.000041759005,0.0024951904,0.00042037785,0.000016458804,0.00026126078,0.00019148292],"genre_scores_gemma":[0.9888545,0.00008617901,0.010474262,0.000013173393,0.00013940046,0.00015958385,0.0000044304406,0.000037750706,0.00023068555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998664,0.000064271575,0.00051568943,0.00034944934,0.00018623432,0.00022033381],"domain_scores_gemma":[0.9994474,0.00032709027,0.000034207165,0.00012496805,0.000028997549,0.0000373045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042149017,0.00018865037,0.00019306602,0.0005811494,0.00009328192,0.00005110632,0.00008336951,0.00016974249,0.000033063952],"category_scores_gemma":[0.000011392345,0.0002139453,0.000109825785,0.0006458585,0.000021785083,0.00015315005,0.0000012849492,0.00037948968,0.000049735532],"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":[0.000033562297,0.000042640408,0.0000015296462,0.00005515826,0.000017571352,0.000005151777,0.00034897297,0.87506115,0.0027448402,0.00010140419,0.0000068265394,0.12158119],"study_design_scores_gemma":[0.00014766282,0.0001500832,0.0000738205,0.00026500894,0.000008151702,0.000011028397,0.00007254004,0.9497086,0.047456868,0.001302608,0.0006068422,0.00019681004],"about_ca_topic_score_codex":0.00014263928,"about_ca_topic_score_gemma":0.0001907459,"teacher_disagreement_score":0.94583464,"about_ca_system_score_codex":0.00045572963,"about_ca_system_score_gemma":0.00002702794,"threshold_uncertainty_score":0.8724436},"labels":[],"label_agreement":null},{"id":"W4393660359","doi":"10.5281/zenodo.7945452","title":"In Situ Exfoliation Method of Large-Area 2D Materials","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"In situ; Exfoliation joint; Materials science; Geology; Chemistry; Nanotechnology","score_opus":0.04476437954848911,"score_gpt":0.2870348132420401,"score_spread":0.242270433693551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393660359","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00079149933,0.000035410318,0.0027252894,0.000028076054,0.0012358128,0.0006499886,0.99093145,0.0007223822,0.002880079],"genre_scores_gemma":[0.006111439,0.00016911395,0.000059862992,0.000017466753,0.000380271,1.8518028e-7,0.99171156,0.0013054392,0.00024465576],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800247,0.00043987678,0.0005591989,0.000303961,0.00037623575,0.00031825528],"domain_scores_gemma":[0.9990147,0.000044238586,0.0001652115,0.0004645632,0.00023677899,0.00007451356],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0016221146,0.0001992955,0.00036853534,0.00071144395,0.00031508724,0.00026842253,0.00051914284,0.0003153831,0.0038593768],"category_scores_gemma":[0.00052820024,0.00021780291,0.000060076334,0.0008242419,0.000020780592,0.0001685818,0.00041377102,0.00035823887,0.010541932],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026403586,0.000028266095,2.2120506e-8,0.0002901431,0.00003459033,0.000012473073,0.00009827179,0.0003585776,0.012953977,0.000038279086,0.9840704,0.0020885668],"study_design_scores_gemma":[0.00043477616,0.00008634795,0.000023337778,0.00013405064,0.000018089213,0.000027139005,0.00008625862,0.00014464464,0.0056776977,0.000028661956,0.99314874,0.00019023207],"about_ca_topic_score_codex":0.00016017973,"about_ca_topic_score_gemma":0.0000056455933,"teacher_disagreement_score":0.009078335,"about_ca_system_score_codex":0.00019905886,"about_ca_system_score_gemma":0.000003262459,"threshold_uncertainty_score":0.99705124},"labels":[],"label_agreement":null},{"id":"W4393745689","doi":"10.5281/zenodo.5907847","title":"ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","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","funders":"","keywords":"Computer science","score_opus":0.036861670955754884,"score_gpt":0.25078934535583436,"score_spread":0.21392767440007948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393745689","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012808926,0.00009531619,0.0006653897,0.00001788887,0.00069618115,0.0012810539,0.99531204,0.0007002155,0.00110383],"genre_scores_gemma":[0.0005841636,0.000090823545,0.000011787432,0.00003808695,0.00046214383,0.00000120471,0.99735236,0.0012967217,0.00016269083],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974975,0.00036909332,0.0005075131,0.00054594537,0.00054075895,0.0005392172],"domain_scores_gemma":[0.9987651,0.00005830685,0.0001285076,0.00075531565,0.00014538073,0.00014738292],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017283229,0.00029485094,0.0003558775,0.00066808815,0.0014088422,0.0005931039,0.0009692339,0.00030063416,0.063766025],"category_scores_gemma":[0.0005257847,0.00034075038,0.0001388129,0.0006994763,0.000032733173,0.00021523115,0.000769875,0.0008200597,0.0038225076],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008131667,0.000080817656,1.2383668e-7,0.00026283963,0.000069370595,0.000012177475,0.00006209527,0.0006115334,0.00015606343,0.000009227567,0.9959483,0.0027061272],"study_design_scores_gemma":[0.0010661025,0.00032340825,0.0000072216485,0.000053780463,0.000056040346,0.00008136252,0.00007713271,0.0012948225,0.000030296505,0.0000072170765,0.996709,0.00029362566],"about_ca_topic_score_codex":0.00004059237,"about_ca_topic_score_gemma":0.000002081933,"teacher_disagreement_score":0.05994352,"about_ca_system_score_codex":0.000521427,"about_ca_system_score_gemma":0.000005642939,"threshold_uncertainty_score":0.99990445},"labels":[],"label_agreement":null},{"id":"W4393775667","doi":"10.5281/zenodo.7570821","title":"Defectors: A Large Scale Python Dataset for Defect Prediction","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Python (programming language); Computer science; Programming language","score_opus":0.03739181245637486,"score_gpt":0.2548971986705139,"score_spread":0.21750538621413906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393775667","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001745398,0.0000750765,0.0030416618,0.000019229832,0.0015100568,0.0012723529,0.9912428,0.0021369797,0.0005272985],"genre_scores_gemma":[0.00042807916,0.0001435538,0.000031981064,0.000022625134,0.0011902204,0.0000010169243,0.99589586,0.0021472718,0.0001393894],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99752504,0.00027893583,0.0005061812,0.0006084114,0.00047554282,0.0006058743],"domain_scores_gemma":[0.9981493,0.00007579029,0.00013889112,0.0011293441,0.00030216362,0.00020452717],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014098459,0.00034268503,0.00036954484,0.0005980895,0.0017012188,0.0008183089,0.0010081601,0.0004537326,0.001560857],"category_scores_gemma":[0.0009417612,0.0003713684,0.000198277,0.000774207,0.00004611923,0.00023515151,0.00077794725,0.000625322,0.023506178],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052946005,0.000043662938,1.0267002e-7,0.0004309254,0.00010691285,0.000006343989,0.00006816685,0.00032713232,0.00020134727,0.000005079142,0.9957622,0.002995138],"study_design_scores_gemma":[0.00080607756,0.000319678,0.000012465821,0.00014127849,0.000091281814,0.00006094572,0.00008464844,0.001212374,0.0000859865,0.000017506307,0.9968403,0.00032746844],"about_ca_topic_score_codex":0.00005982405,"about_ca_topic_score_gemma":0.000006588478,"teacher_disagreement_score":0.02194532,"about_ca_system_score_codex":0.00031925784,"about_ca_system_score_gemma":0.000005113873,"threshold_uncertainty_score":0.9998738},"labels":[],"label_agreement":null},{"id":"W4393792900","doi":"10.5281/zenodo.7693279","title":"Iterative Bleaching Extends Multiplexity (IBEX) Knowledge-Base","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Base (topology); Knowledge base; Computer science; Psychology; Artificial intelligence; Mathematics","score_opus":0.06097144975372208,"score_gpt":0.27409057242670243,"score_spread":0.21311912267298033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393792900","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035966563,0.0001693667,0.0013405598,0.000029010225,0.0018447348,0.00073232775,0.9820207,0.0034450137,0.01005864],"genre_scores_gemma":[0.003932282,0.00014174564,0.00003007868,0.000018177572,0.001163969,2.4979732e-7,0.99143136,0.0023524405,0.0009297205],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99748534,0.0005184739,0.0004918426,0.0005443064,0.00041696636,0.0005430662],"domain_scores_gemma":[0.99841595,0.00007538488,0.00012740157,0.0007227773,0.00041811776,0.00024039511],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011915273,0.0003618679,0.00037274585,0.0006801615,0.0019393066,0.0011253498,0.0008571976,0.00036853895,0.0029523126],"category_scores_gemma":[0.00084442936,0.00038369387,0.0001368709,0.00091160333,0.00008139579,0.00022767352,0.0007729684,0.001144253,0.065149695],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025812456,0.000046080473,3.4588474e-8,0.00023930789,0.00007950688,0.000027856426,0.0002690497,0.000628557,0.00034304662,0.000024997495,0.9745646,0.023751128],"study_design_scores_gemma":[0.0004960051,0.00015119494,0.000011713135,0.00019690485,0.000029256911,0.000059261736,0.0001371677,0.0019032648,0.00016866952,0.000015463005,0.99644953,0.0003815887],"about_ca_topic_score_codex":0.00012504254,"about_ca_topic_score_gemma":0.000006619455,"teacher_disagreement_score":0.062197383,"about_ca_system_score_codex":0.00043116196,"about_ca_system_score_gemma":0.0000062326776,"threshold_uncertainty_score":0.9999116},"labels":[],"label_agreement":null},{"id":"W4393842139","doi":"10.5281/zenodo.7945453","title":"In Situ Exfoliation Method of Large-Area 2D Materials","year":2023,"lang":"en","type":"dataset","venue":"University of Groningen research database (University of Groningen / Centre for Information Technology)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"In situ; Exfoliation joint; Geology; Materials science; Environmental science; Nanotechnology; Physics; Meteorology","score_opus":0.03269689162141007,"score_gpt":0.2845017269773116,"score_spread":0.2518048353559015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393842139","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013412225,0.00007911882,0.016356483,0.00012372862,0.00046725132,0.0014934784,0.96767384,0.00019671452,0.00019716118],"genre_scores_gemma":[0.0155051835,0.0015247102,0.00757984,0.0000048505563,0.00006879133,0.0000020162734,0.9747979,0.000049071466,0.00046760813],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977225,0.0001744327,0.0005391108,0.00030289424,0.00066610845,0.0005949659],"domain_scores_gemma":[0.9975802,0.00025295644,0.0006244722,0.0007056512,0.00074286485,0.00009388011],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021210562,0.00026833234,0.00076343666,0.00451376,0.00028652677,0.000018614905,0.0009772672,0.00095034303,0.00018856364],"category_scores_gemma":[0.00035014705,0.00037569404,0.00016608316,0.0018704184,0.00023634,0.0013839555,0.0006252378,0.0006684343,0.00014471235],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042156433,0.00007911702,0.00001672046,0.002257471,0.00028871754,0.000023087268,0.0010887934,0.0008297052,0.0040827394,0.00076806056,0.98842806,0.0017159503],"study_design_scores_gemma":[0.0030463347,0.00027772624,0.000076708435,0.00096908543,0.00015621027,0.000004563449,0.021429712,0.0021977832,0.011743164,0.00008370139,0.95958567,0.00042934905],"about_ca_topic_score_codex":0.004109943,"about_ca_topic_score_gemma":0.0031002404,"teacher_disagreement_score":0.02884241,"about_ca_system_score_codex":0.00050049153,"about_ca_system_score_gemma":0.0001773757,"threshold_uncertainty_score":0.9998695},"labels":[],"label_agreement":null},{"id":"W4393950777","doi":"10.3390/electronics13071368","title":"Robustness Assessment of AI-Based 2D Object Detection Systems: A Method and Lessons Learned from Two Industrial Cases","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Industrial Vision Systems and Defect Detection","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":"Thornhill Medical (Canada)","funders":"Agencia Estatal de Investigación; Association Nationale de la Recherche et de la Technologie; European Commission","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Machine learning; Systems engineering; Engineering; Chemistry","score_opus":0.06235466887569336,"score_gpt":0.3441968338894969,"score_spread":0.28184216501380355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393950777","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":[0.32576534,0.007870175,0.6630817,0.0000614721,0.0021435535,0.0004095957,0.000058159847,0.00045493772,0.00015507154],"genre_scores_gemma":[0.99878854,0.00011445835,0.00032948735,0.000004368116,0.00060958555,0.000049144634,0.000015607242,0.000052134892,0.000036703954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848026,0.00024762657,0.0003998383,0.00031028377,0.00026242324,0.00029958028],"domain_scores_gemma":[0.99914056,0.00045836213,0.000060897895,0.00021594296,0.00005888206,0.0000653767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007252609,0.00021204435,0.00037182873,0.00024523184,0.00008636077,0.0001699999,0.00006864221,0.00028529647,0.000016528196],"category_scores_gemma":[0.00006708362,0.0002017607,0.00009979936,0.00048271692,0.000016650749,0.00013483434,0.000016244312,0.0007464387,0.0000017444154],"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":[0.000058958536,0.000014732577,0.00004904726,0.00015072859,0.00020894401,0.000023117413,0.00005117945,0.7551438,0.0491911,0.00048156577,0.00016665347,0.19446015],"study_design_scores_gemma":[0.00090121676,0.0002841913,0.000018422901,0.00022839523,0.00011991266,0.00004140584,0.000075507356,0.92525095,0.060759153,0.00006492282,0.012030863,0.00022505748],"about_ca_topic_score_codex":0.00054201134,"about_ca_topic_score_gemma":0.0002634568,"teacher_disagreement_score":0.67302316,"about_ca_system_score_codex":0.0003741875,"about_ca_system_score_gemma":0.00027764775,"threshold_uncertainty_score":0.82275623},"labels":[],"label_agreement":null},{"id":"W4394045213","doi":"10.5281/zenodo.5508260","title":"TransProteus, Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers from images","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer graphics (images); Computer science; Materials science","score_opus":0.03710859822394473,"score_gpt":0.21639728732178,"score_spread":0.1792886890978353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394045213","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039741118,0.0028394235,0.00028819163,0.000028389177,0.00032065707,0.00082389114,0.9551462,0.0004618993,0.0003502637],"genre_scores_gemma":[0.37855002,0.0021657115,0.000054664368,0.000015779018,0.00037312845,4.0894798e-7,0.617883,0.000929101,0.000028173017],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983167,0.00028684316,0.00047045018,0.0003835369,0.00029804432,0.00024441208],"domain_scores_gemma":[0.9992788,0.000020290276,0.00011382589,0.00026210042,0.00020781583,0.00011718701],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038659247,0.00025312038,0.0004187055,0.00019843968,0.0004707931,0.00050597714,0.00025492988,0.0002367532,0.0009567439],"category_scores_gemma":[0.00019267938,0.00024767002,0.000036575748,0.00018569684,0.00010516084,0.00016703727,0.00020953384,0.0003502984,0.000055947097],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022920237,0.000051298204,7.856019e-7,0.002196883,0.00031540164,0.000040185372,0.0011646529,0.00024318838,0.20278877,0.0000042159277,0.78157073,0.011394709],"study_design_scores_gemma":[0.0006985188,0.00021135948,0.000041316747,0.0008331719,0.00009447102,0.000065421984,0.00047995456,0.00015798071,0.037530545,0.0000026257442,0.9595919,0.00029271838],"about_ca_topic_score_codex":0.0005674414,"about_ca_topic_score_gemma":0.000007897705,"teacher_disagreement_score":0.33880892,"about_ca_system_score_codex":0.00006779639,"about_ca_system_score_gemma":0.0000061353016,"threshold_uncertainty_score":0.99999756},"labels":[],"label_agreement":null},{"id":"W4394626911","doi":"10.23977/acss.2024.080214","title":"A study of ancient glass subclassification based on K-means algorithm","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Algorithm; Computer science","score_opus":0.025020101175142608,"score_gpt":0.26819413431726835,"score_spread":0.24317403314212574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394626911","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":[0.28105658,0.0066847196,0.70628315,0.0000050021376,0.0047391555,0.00071952515,0.000012269494,0.00017758048,0.00032203514],"genre_scores_gemma":[0.9994,0.00003761342,0.00022573852,0.000005867856,0.00025496952,0.000052362277,0.0000017010368,0.000014072124,0.000007644087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989847,0.00008766864,0.00038007443,0.0002159929,0.00021165205,0.00011989635],"domain_scores_gemma":[0.9995982,0.00015363659,0.00003706754,0.00014657606,0.000031299216,0.00003321168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041692602,0.00011757528,0.00023219336,0.00021874801,0.000026971224,0.0000778486,0.000055606874,0.000058366837,0.0000014286139],"category_scores_gemma":[0.0000029204346,0.000096540185,0.000030169884,0.00031752617,0.000009584217,0.00013630275,0.000008271373,0.000106091946,0.000004094832],"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":[0.000008610294,0.00007512186,0.00033103678,0.0002588086,0.00001570171,0.000020333237,0.00051125285,0.8264143,0.00044797192,0.00008173213,0.00014408497,0.17169106],"study_design_scores_gemma":[0.0003612744,0.00047885385,0.00037536034,0.00044814768,0.0000046830733,0.000005245435,0.00023687817,0.99179393,0.00014914919,0.0000069276284,0.0060381824,0.0001013454],"about_ca_topic_score_codex":0.00003149571,"about_ca_topic_score_gemma":0.000008810591,"teacher_disagreement_score":0.71834344,"about_ca_system_score_codex":0.000046196717,"about_ca_system_score_gemma":0.000008862975,"threshold_uncertainty_score":0.39367947},"labels":[],"label_agreement":null},{"id":"W4394966962","doi":"10.1109/icjece.2024.3368454","title":"Visual Geometry Group Network for Flexible Printed Circuit Board Surface Defect Classification","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Overfitting; Convolutional neural network; Artificial intelligence; Weighting; Pattern recognition (psychology); Sample (material); Computer science; Regularization (linguistics); Surface (topology); Artificial neural network; Mathematics; Geometry; Chemistry; Physics","score_opus":0.016678156952562306,"score_gpt":0.21366196754272462,"score_spread":0.19698381059016232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394966962","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":[0.21517168,0.0040552383,0.77837294,0.000015810068,0.002102543,0.00012515023,0.0000027860915,0.00011550386,0.000038374248],"genre_scores_gemma":[0.99743086,0.000025746154,0.000959055,0.000012767045,0.001510895,0.0000033595436,0.0000023123155,0.000032477117,0.000022534701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912405,0.000013251531,0.000317744,0.000121945886,0.00009170776,0.00033127965],"domain_scores_gemma":[0.99936765,0.00017455395,0.000028181983,0.000054894204,0.000058005604,0.0003166812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028455543,0.00013607487,0.00021858548,0.00031273934,0.00006245958,0.00019132691,0.00007361991,0.00012799856,0.000005373301],"category_scores_gemma":[0.000025671186,0.00012832109,0.00013668777,0.00056790595,0.0000070418205,0.00012263282,0.000004119099,0.00032815346,0.000002796969],"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":[0.000026772423,0.000010842211,0.0009713975,0.0003937954,0.00048840704,0.00009905329,0.0001260691,0.75595945,0.006152727,0.008547928,0.013494698,0.21372883],"study_design_scores_gemma":[0.00020967754,0.00027788314,0.0020862892,0.0001744583,0.000031336294,0.00016915014,0.000002979725,0.9451201,0.00028750137,0.00008525866,0.051379915,0.0001754479],"about_ca_topic_score_codex":0.00007242969,"about_ca_topic_score_gemma":0.000038068993,"teacher_disagreement_score":0.78225917,"about_ca_system_score_codex":0.00018289176,"about_ca_system_score_gemma":0.00008252312,"threshold_uncertainty_score":0.52327824},"labels":[],"label_agreement":null},{"id":"W4395081343","doi":"10.18280/ria.380232","title":"Classification of Surface Defects in Steel Sheets Using Developed NasNet-Mobile CNN and Few Samples","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Surface (topology); Artificial intelligence; Pattern recognition (psychology); Computer science; Materials science; Mathematics; Geometry","score_opus":0.08954837074349042,"score_gpt":0.29919124868394037,"score_spread":0.20964287794044995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395081343","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":[0.96633387,0.0024975832,0.029159723,0.000008402225,0.00063323736,0.00032805192,0.0000063733396,0.00011887217,0.00091387023],"genre_scores_gemma":[0.99911034,0.00016383894,0.0005138854,0.0000025208285,0.00005719058,0.0000126938085,0.0000034793657,0.00002571955,0.00011032173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989403,0.000046504545,0.00047674516,0.00024188333,0.00010609564,0.00018849166],"domain_scores_gemma":[0.9995253,0.00016605349,0.000039738374,0.00017970511,0.000045002827,0.00004417869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043664762,0.00013167158,0.0002120386,0.00017164544,0.00004573686,0.0000637657,0.00007161585,0.00013357501,0.000057904876],"category_scores_gemma":[0.00004771573,0.00013254384,0.000046314202,0.0006434478,0.00004155973,0.00014288582,0.000022392533,0.00016733748,0.000050877356],"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":[0.000014441644,0.000024286193,0.00080963597,0.0006160006,0.00002080969,0.000009660898,0.0018576708,0.4782741,0.46393716,0.0010611074,0.00011642177,0.05325871],"study_design_scores_gemma":[0.000031819363,0.000045134766,0.0005634296,0.00053149933,0.000009833667,0.000017886947,0.0011271749,0.7951274,0.19811301,0.00012266378,0.00413954,0.00017061268],"about_ca_topic_score_codex":0.00008988314,"about_ca_topic_score_gemma":0.000046244,"teacher_disagreement_score":0.31685328,"about_ca_system_score_codex":0.000089992376,"about_ca_system_score_gemma":0.000033969005,"threshold_uncertainty_score":0.5404981},"labels":[],"label_agreement":null},{"id":"W4395685145","doi":"10.18280/ijsse.140230","title":"Enhancing Remaining Useful Life Predictions in Predictive Maintenance of MOSFETs: The Efficacy of Integrated Particle Filter-Gaussian Process Regression Models","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Tenaga Nasional; Tenaga Nasional Berhad","keywords":"Particle filter; Kriging; Gaussian process; Process (computing); Regression; Regression analysis; Computer science; Reliability engineering; Gaussian; Statistics; Engineering; Artificial intelligence; Machine learning; Mathematics; Kalman filter; Physics","score_opus":0.011475484661220189,"score_gpt":0.24233337713379194,"score_spread":0.23085789247257174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395685145","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":[0.85663,0.0012531676,0.1403915,0.000115923955,0.001291815,0.00012305437,0.0000424557,0.00004701379,0.00010501375],"genre_scores_gemma":[0.99936205,0.00032921584,0.000085303975,0.0000035515998,0.00019570139,0.0000031977174,0.0000021990306,0.000015088118,0.000003689691],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869126,0.000030846473,0.0007559171,0.00009404978,0.0003083759,0.00011955966],"domain_scores_gemma":[0.99930596,0.0002383219,0.00012952165,0.00007057197,0.00019934853,0.00005625155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062661833,0.00011486254,0.0002372043,0.00022337149,0.000023793498,0.00003232817,0.00014411629,0.000086643995,0.000008375655],"category_scores_gemma":[0.00027575373,0.00008086021,0.00008303697,0.00029206942,0.000027583164,0.00040061178,0.0000236987,0.00042832506,2.5243045e-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":[0.00029937905,0.000028184135,0.00019613751,0.00016322982,0.00019901933,0.000021740836,0.006847605,0.9836999,0.006148193,0.0007609238,0.000025260664,0.0016103834],"study_design_scores_gemma":[0.0006542557,0.000093201576,0.0016165919,0.0040086294,0.000023357612,0.00009374745,0.0013059409,0.97764575,0.013977013,0.00023315487,0.00026220584,0.00008614186],"about_ca_topic_score_codex":0.000021449932,"about_ca_topic_score_gemma":0.000008101396,"teacher_disagreement_score":0.142732,"about_ca_system_score_codex":0.0001020349,"about_ca_system_score_gemma":0.000059038557,"threshold_uncertainty_score":0.32973838},"labels":[],"label_agreement":null},{"id":"W4395959946","doi":"10.1088/978-0-7503-6244-3ch12","title":"Applications of NeRF and 3D Gaussian splatting for synthesis of 3D scenes","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Computer graphics (images); Gaussian; Computer vision; Artificial intelligence; Physics","score_opus":0.017423434863581496,"score_gpt":0.2320902226160331,"score_spread":0.2146667877524516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395959946","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013515675,0.0014899767,0.030602438,0.00001220981,0.00046029533,0.0009903619,0.00020100432,0.00020057453,0.965908],"genre_scores_gemma":[0.60128707,0.00039057902,0.0075023724,0.000007546958,0.0016830007,0.00042923054,0.000027787877,0.00033066643,0.38834172],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992987,0.0000021286276,0.00038044446,0.00014823928,0.00009096975,0.000079519144],"domain_scores_gemma":[0.99951905,0.00015906131,0.000084615924,0.0001612705,0.00004742108,0.000028593347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013592493,0.00014858988,0.0003356748,0.00019670342,0.000026449678,0.000014628395,0.000042837477,0.000288064,0.00006697951],"category_scores_gemma":[0.0000109568655,0.0001297056,0.000090346635,0.000033710956,0.00002139839,0.000024083158,0.000017059325,0.00010188831,0.000010906538],"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":[0.000030467223,0.000009796162,0.00000942204,0.0076055573,0.00061384775,0.0000014235959,0.00012465107,0.0007155641,0.0047354302,0.19355077,0.0019313624,0.7906717],"study_design_scores_gemma":[0.0003685486,0.0001288837,0.000009234904,0.0035181365,0.0006723101,0.000027906255,0.00013151266,0.016659198,0.03525748,0.0055554225,0.936893,0.000778359],"about_ca_topic_score_codex":0.000028081386,"about_ca_topic_score_gemma":0.000009739507,"teacher_disagreement_score":0.9349616,"about_ca_system_score_codex":0.000018865576,"about_ca_system_score_gemma":0.000010490242,"threshold_uncertainty_score":0.5289241},"labels":[],"label_agreement":null},{"id":"W4396213255","doi":"10.61091/jcmcc119-27","title":"The-Application-of-Servo-Control-Technology-in-Robot-Positioning-and-Tracking-System-via-Heuristic-Algorithm","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Servo control; Servomechanism; Servo; Heuristic; Kalman filter; Robot; Tracking (education); Stability (learning theory); Eye tracking; Control engineering; Engineering; Machine learning","score_opus":0.006506582128901672,"score_gpt":0.23191260612484382,"score_spread":0.22540602399594215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396213255","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":[0.49366355,0.007412726,0.4551672,0.00016684235,0.041915216,0.00082572567,0.000006840179,0.00034386135,0.0004980405],"genre_scores_gemma":[0.9975975,0.00005682455,0.0011575593,0.0000013666419,0.0011412926,0.000005906448,4.140595e-7,0.000037649752,0.0000014960291],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783105,0.00006825829,0.0012973438,0.0001614798,0.0003899997,0.00025187232],"domain_scores_gemma":[0.998139,0.00087827543,0.00040153347,0.00018529194,0.00031061846,0.00008528347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001799043,0.00022563724,0.00062237977,0.00035512037,0.00023309219,0.00027545908,0.00021494417,0.0002567358,5.808765e-7],"category_scores_gemma":[0.00015325697,0.00017929277,0.00012533611,0.0005600912,0.00007671783,0.00014685669,0.0000579507,0.00061216514,0.0000019795834],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006350291,0.00012318302,0.00017498611,0.0011035975,0.00031713807,0.00005673528,0.00063376647,0.0016576893,0.0039447835,0.9069189,0.000106125044,0.08489963],"study_design_scores_gemma":[0.003672328,0.00060201547,0.00012555189,0.0019625174,0.0001792217,0.00057089573,0.0005937143,0.67747706,0.0025293848,0.31055734,0.001345394,0.0003845657],"about_ca_topic_score_codex":0.0000135579885,"about_ca_topic_score_gemma":5.0999097e-7,"teacher_disagreement_score":0.6758194,"about_ca_system_score_codex":0.00012252294,"about_ca_system_score_gemma":0.00005138711,"threshold_uncertainty_score":0.73113465},"labels":[],"label_agreement":null},{"id":"W4396713560","doi":"10.1007/978-3-031-59057-3_2","title":"Robot Vision and Deep Learning for Automated Planogram Compliance in Retail","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Image stitching; Computer science; Artificial intelligence; Computer vision; Identification (biology); Robot; Concatenation (mathematics); Deep learning; Mathematics","score_opus":0.05634033942380258,"score_gpt":0.3080339893937218,"score_spread":0.2516936499699192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396713560","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035594762,0.014136851,0.42877078,0.0005947942,0.0044553047,0.0052100066,0.000078356046,0.003664103,0.53953034],"genre_scores_gemma":[0.9700832,0.006292997,0.021288631,0.00010632691,0.000104144085,0.00012597334,0.00016655041,0.000045150147,0.0017870262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990429,0.000011482647,0.00050961797,0.00014120119,0.00015159424,0.0001431739],"domain_scores_gemma":[0.9992424,0.00014711978,0.00008513713,0.00038016032,0.00010067737,0.000044493558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007016952,0.00015006293,0.0002069037,0.0007584749,0.00020565579,0.0003661609,0.00031517417,0.00016048283,0.0000025318218],"category_scores_gemma":[0.000022473798,0.00014693518,0.000025199324,0.00026946113,0.00018479498,0.0013874728,0.00025183128,0.00042932466,0.000017945607],"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":[0.000009185226,0.000005693807,0.000046477922,0.00035188746,0.000009340792,4.512033e-7,0.0017074748,0.04923834,0.000033058554,0.028775755,0.00018861031,0.91963375],"study_design_scores_gemma":[0.00019428108,0.00004296656,0.00043999,0.00057274656,0.0000029353048,0.000010818465,0.000024564035,0.8617983,0.0000040558493,0.00040114103,0.13636951,0.00013872044],"about_ca_topic_score_codex":0.000008468646,"about_ca_topic_score_gemma":0.000021604392,"teacher_disagreement_score":0.9665237,"about_ca_system_score_codex":0.00011100133,"about_ca_system_score_gemma":0.000024840296,"threshold_uncertainty_score":0.5991843},"labels":[],"label_agreement":null},{"id":"W4396713670","doi":"10.1007/978-3-031-59057-3","title":"Robotics, Computer Vision and Intelligent Systems","year":2024,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Samsung; Akademia Górniczo-Hutnicza im. Stanislawa Staszica; Université Claude Bernard Lyon 1; Universidad Carlos III de Madrid; Universität Bremen; Universidade de Lisboa; Turun Yliopisto; Egypt-Japan University of Science and Technology; Universiti Malaysia Perlis; Slovenská technická univerzita v Bratislave; Università degli Studi della Campania Luigi Vanvitelli; Kungliga Tekniska Högskolan; Università degli Studi di Trento; RMIT University; International Science and Technology Center; Universidade de Coimbra; University of Glasgow; Imperial College London; Kyungpook National University; Universidad de Alcalá; Universidad de Guanajuato; Sveučilište u Zagrebu; Harvard University; Trent University; Nottingham Trent University; Politechnika Warszawska; Staffordshire University; Instituto Politécnico Nacional; York University; Chuo University; Universidad Autónoma de Yucatán; Université du Québec à Chicoutimi; Gebze Teknik Üniversitesi; Universidad de Alicante; Southeast University; Friedrich-Alexander-Universität Erlangen-Nürnberg","keywords":"Robotics; Artificial intelligence; Computer science; Computer vision; Robot","score_opus":0.031435570261879736,"score_gpt":0.27991560724093517,"score_spread":0.24848003697905544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396713670","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":[0.0004240857,0.0099685425,0.7783292,0.00027858556,0.010639144,0.0017375165,0.000056410052,0.0007609391,0.19780558],"genre_scores_gemma":[0.8470046,0.04841168,0.078777894,0.0009743141,0.0032606232,0.00031378164,0.0006771022,0.00024123711,0.020338776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874735,0.000029076302,0.0006319025,0.00016253968,0.00026986323,0.00015924772],"domain_scores_gemma":[0.99883723,0.00012279509,0.00008606683,0.0007211143,0.00015157866,0.00008123322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008398212,0.0001899061,0.00024721684,0.0008961451,0.00019979036,0.0009370642,0.0004885971,0.0001851547,0.0000016082406],"category_scores_gemma":[0.000008992639,0.0001710703,0.000029613493,0.00039935447,0.00029886703,0.0017816548,0.00063777337,0.00048297952,0.000090965055],"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":[0.0000049681025,0.000015524489,0.000015071483,0.0009326138,0.000033957724,0.0000016380096,0.00303281,0.15064022,0.000008635785,0.080760896,0.035093036,0.72946066],"study_design_scores_gemma":[0.0000709881,0.000041046096,0.00004399911,0.00055182196,0.000005285567,0.00003236422,0.000020288016,0.7383814,0.0000021669944,0.00020104938,0.26050165,0.00014796204],"about_ca_topic_score_codex":0.000012609571,"about_ca_topic_score_gemma":0.0000034816355,"teacher_disagreement_score":0.8465805,"about_ca_system_score_codex":0.0002433413,"about_ca_system_score_gemma":0.00009918494,"threshold_uncertainty_score":0.90361315},"labels":[],"label_agreement":null},{"id":"W4396837492","doi":"10.2139/ssrn.4819854","title":"Image-Based Finite Element Model Stiffness and Vbmd by Single and Dual Energy Ct Reconstruction Kernel","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Kernel (algebra); Stiffness; Finite element method; Dual energy; Dual (grammatical number); Element (criminal law); Mathematics; Structural engineering; Computer science; Pure mathematics; Medicine; Engineering; Internal medicine; Art; Political science; Bone mineral","score_opus":0.010335974811456644,"score_gpt":0.21204866322339017,"score_spread":0.20171268841193352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396837492","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":[0.60557926,0.019208599,0.37056565,0.00015553077,0.0031347538,0.00032181118,0.000108990265,0.00034298748,0.0005824249],"genre_scores_gemma":[0.99621665,0.002492196,0.0001815567,0.000012207496,0.0005875624,0.00002468101,0.000022384675,0.00008374029,0.00037903056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976994,0.00008979846,0.0005237906,0.00038018986,0.00026508493,0.0010417441],"domain_scores_gemma":[0.9994371,0.000056423156,0.00015210341,0.0001698865,0.00006529978,0.00011921143],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009531998,0.00036747238,0.00037624297,0.00028000772,0.00014868117,0.00037749734,0.00008114412,0.00026184996,0.000009711093],"category_scores_gemma":[0.00002820565,0.0003535766,0.00012018089,0.00010977197,0.00004143959,0.00010237959,0.00010589283,0.0029200604,0.0000034524153],"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":[0.00017003149,0.00007475487,0.000024037443,0.0005471297,0.0009924313,0.00003588057,0.00016046259,0.2474781,0.03263543,0.0026036666,0.0016706655,0.71360743],"study_design_scores_gemma":[0.0011646011,0.00019308423,0.0000038765334,0.00043215582,0.00017411278,0.0010340788,0.00029345823,0.9322245,0.002957567,0.05972919,0.0011946538,0.00059868797],"about_ca_topic_score_codex":0.00014776495,"about_ca_topic_score_gemma":0.00015472191,"teacher_disagreement_score":0.7130087,"about_ca_system_score_codex":0.0010816096,"about_ca_system_score_gemma":0.00064671773,"threshold_uncertainty_score":0.99989164},"labels":[],"label_agreement":null},{"id":"W4398294068","doi":"10.7910/dvn/br37r3","title":"Brooks 2017/2018 controlled release pass detection data","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Industrial Vision Systems and Defect Detection","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 Calgary","funders":"","keywords":"Computer science","score_opus":0.04278312129089893,"score_gpt":0.2521994853285982,"score_spread":0.20941636403769928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398294068","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000538799,0.000010390797,0.0009943119,8.3517983e-7,0.010991971,0.0012717781,0.9857938,0.0003181984,0.0005647797],"genre_scores_gemma":[0.0010022082,0.00030729058,0.000023830868,0.00005129332,0.0018534723,0.000066582434,0.99604774,0.00008190118,0.0005656728],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99706143,0.00018691446,0.00083437894,0.00082296686,0.0005882886,0.00050600583],"domain_scores_gemma":[0.9945133,0.00018011355,0.00030636517,0.004710733,0.00008404977,0.00020544861],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00079888315,0.0006029885,0.0010506413,0.0003978063,0.00016070058,0.0002644991,0.0011621434,0.0010430563,0.0040493812],"category_scores_gemma":[0.0004391964,0.0005693685,0.0002095738,0.00022867302,0.000039870512,0.00063261116,0.00048661896,0.0010375145,0.21436764],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030247576,0.000022926162,2.6734307e-7,0.00020008926,0.00025108593,0.00005193935,0.0000042633546,0.0004183295,0.00028904324,4.995299e-7,0.9969983,0.0014607805],"study_design_scores_gemma":[0.0041727773,0.00008284161,0.0000052247046,0.00017109152,0.00034762765,0.000055928183,0.000025924717,0.007113631,0.00012092862,0.0000018072406,0.9872956,0.0006065723],"about_ca_topic_score_codex":0.00064746843,"about_ca_topic_score_gemma":0.00040158912,"teacher_disagreement_score":0.21031825,"about_ca_system_score_codex":0.00023767799,"about_ca_system_score_gemma":0.00009251193,"threshold_uncertainty_score":0.99967575},"labels":[],"label_agreement":null},{"id":"W4398787632","doi":"10.23977/jaip.2024.070210","title":"Research on periodic intelligent inspection and maintenance of offshore platform equipment","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Submarine pipeline; Marine engineering; Engineering; Construction engineering; Computer science; Forensic engineering; Geotechnical engineering","score_opus":0.1385219177261825,"score_gpt":0.3941637408425306,"score_spread":0.2556418231163481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398787632","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":[0.7875246,0.005737136,0.17900996,0.0013549066,0.011493165,0.000698866,0.000010618122,0.00021138866,0.0139593715],"genre_scores_gemma":[0.9977069,0.0011175367,0.00034385064,0.000014802235,0.0007455464,0.000003991585,2.7680875e-7,0.000021575508,0.000045541456],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979342,0.00011772494,0.00089501514,0.00015313976,0.0006705238,0.00022939613],"domain_scores_gemma":[0.99819034,0.000865502,0.0001693054,0.00013839509,0.00053515285,0.000101326674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035988016,0.00013019524,0.00025300906,0.00064755767,0.0001129442,0.0002023191,0.000121342644,0.00013569508,0.000051607145],"category_scores_gemma":[0.0007536244,0.00010438476,0.00009838455,0.00068198185,0.00010336576,0.0006355585,0.000029745705,0.001014517,0.000071191396],"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":[0.0014052273,0.0002202593,0.000012066001,0.00036295815,0.00027924436,0.00025173416,0.0065612677,0.031066263,0.025978481,0.03846862,0.0045218137,0.89087206],"study_design_scores_gemma":[0.00013426802,0.005509035,0.00006862644,0.0031984472,0.00014711406,0.0015838152,0.064659536,0.14263794,0.6152478,0.00997922,0.15632123,0.0005129893],"about_ca_topic_score_codex":0.00004608699,"about_ca_topic_score_gemma":0.000012688996,"teacher_disagreement_score":0.89035904,"about_ca_system_score_codex":0.00027642166,"about_ca_system_score_gemma":0.000088579494,"threshold_uncertainty_score":0.44076282},"labels":[],"label_agreement":null},{"id":"W4399398407","doi":"10.46254/an14.20240529","title":"Predictive Modeling of Opportunistic Maintenance Strategy in PVC Manufacturing: A Machine Learning and Simulation Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Western University","funders":"","keywords":"Computer science; Reliability engineering; Manufacturing engineering; Artificial intelligence; Engineering","score_opus":0.034520027901930816,"score_gpt":0.2546260644377476,"score_spread":0.22010603653581678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399398407","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":[0.30064324,0.00039448863,0.68251735,0.000002372668,0.00013964306,0.00022175329,0.0000074720097,0.00025864094,0.015815042],"genre_scores_gemma":[0.9995355,0.000031923897,0.00010284235,7.314217e-7,0.00004131614,0.0000072643,0.0000070021283,0.00001690939,0.00025649232],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994095,0.000022433016,0.00023528567,0.00013621342,0.000088837616,0.0001077611],"domain_scores_gemma":[0.9998479,0.000047786714,0.000014625955,0.000045886594,0.000013777865,0.000030009172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021792502,0.000088954475,0.00013555418,0.00015238806,0.000019321222,0.000032694956,0.00001996631,0.000082556064,0.00001333848],"category_scores_gemma":[0.000016773269,0.00007568607,0.000023220367,0.000098827055,0.000007533276,0.000106397165,0.000010627098,0.00021683042,0.0000013437872],"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":[0.00001309923,0.0000034376556,0.000040361614,0.0001434987,0.000011576193,0.0000037344098,0.0001105848,0.9924375,0.00027929913,0.00015530284,0.0000034175903,0.0067982096],"study_design_scores_gemma":[0.00015897152,0.00004679047,0.000040388695,0.000102502134,0.000006338429,0.000006513501,0.0002546609,0.99877894,0.00026117373,0.00011447066,0.0001551043,0.000074167634],"about_ca_topic_score_codex":0.00011310287,"about_ca_topic_score_gemma":0.000006236323,"teacher_disagreement_score":0.6988923,"about_ca_system_score_codex":0.000040981602,"about_ca_system_score_gemma":0.00000963266,"threshold_uncertainty_score":0.30863884},"labels":[],"label_agreement":null},{"id":"W4399424010","doi":"10.1117/12.3013954","title":"Surface moisture detection using thermal imaging and computer vision","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"McMaster University","funders":"","keywords":"Computer science; Computer vision; Surface (topology); Moisture; Thermal; Artificial intelligence; Remote sensing; Materials science; Geology; Physics; Mathematics; Meteorology","score_opus":0.01132029288982304,"score_gpt":0.2375426231661168,"score_spread":0.22622233027629376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399424010","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":[0.83945763,0.00054091477,0.15628563,0.000007691722,0.001892212,0.00008260782,8.485008e-7,0.00055689417,0.0011755816],"genre_scores_gemma":[0.99904215,0.000005396451,0.00041275183,0.0000095065925,0.00044045175,6.422544e-7,4.739993e-7,0.000022736438,0.00006585777],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954677,0.00001784422,0.00011605215,0.00013088205,0.00008099343,0.000107434105],"domain_scores_gemma":[0.9998587,0.000023485833,0.000006328473,0.00006574917,0.000013774597,0.000031963442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013063014,0.00009230253,0.00008023704,0.00006114767,0.000063843116,0.0001872006,0.000018451336,0.00006771383,0.00002090614],"category_scores_gemma":[0.0000013194173,0.000074892945,0.000031349737,0.00013455178,0.000007905348,0.00019846865,0.00001547258,0.00013887441,0.000020446605],"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":[0.000012079226,0.000004082535,0.00026554402,0.00013561765,0.00003708415,0.000031054857,0.00023699402,0.10312086,0.5950057,0.000080197206,0.0006148127,0.300456],"study_design_scores_gemma":[0.00008661929,0.000019072031,0.00060001074,0.000066178625,0.000007642908,0.000072375784,0.000031816424,0.97757316,0.0165217,0.000008931073,0.0049097687,0.00010271258],"about_ca_topic_score_codex":0.00010649179,"about_ca_topic_score_gemma":0.000010357354,"teacher_disagreement_score":0.8744523,"about_ca_system_score_codex":0.000045074357,"about_ca_system_score_gemma":0.000004084901,"threshold_uncertainty_score":0.30540454},"labels":[],"label_agreement":null},{"id":"W4399425748","doi":"10.2139/ssrn.4856345","title":"Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis","year":2024,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Quality (philosophy); Computer science; Business; Artificial intelligence; Engineering; Process management; Risk analysis (engineering)","score_opus":0.023857547019172178,"score_gpt":0.3073749668265575,"score_spread":0.2835174198073853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399425748","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":[0.5839003,0.011453687,0.40274125,0.000058142672,0.0015181128,0.00018668787,0.0000010788341,0.00010187951,0.00003889798],"genre_scores_gemma":[0.99203056,0.006867048,0.0002514681,0.0000024800177,0.0007338551,0.000043471373,6.4487455e-7,0.00003832738,0.000032171483],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99830216,0.00004846362,0.0003778818,0.00018345518,0.00016339714,0.00092464715],"domain_scores_gemma":[0.99953324,0.00025276784,0.000032891727,0.00009624548,0.000018468752,0.00006636567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001536531,0.00015164215,0.00019372978,0.00020911233,0.00009652321,0.00018145874,0.00007482981,0.00018763683,0.000005197365],"category_scores_gemma":[0.0000623693,0.0001360692,0.00009959032,0.00011682812,0.000008461747,0.00018673875,0.000016740098,0.0015441463,0.000006527116],"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":[0.000084917854,0.000029011399,0.0026669083,0.00020722076,0.00018300569,0.000008203598,0.0009325289,0.014477235,0.0007032327,0.017980214,0.00002850842,0.962699],"study_design_scores_gemma":[0.0009268883,0.0007217018,0.002918595,0.001470688,0.0000666341,0.00030360933,0.0029014442,0.061507132,0.03845059,0.88271207,0.007227173,0.0007934466],"about_ca_topic_score_codex":0.00004854422,"about_ca_topic_score_gemma":0.000104042345,"teacher_disagreement_score":0.96190554,"about_ca_system_score_codex":0.000854626,"about_ca_system_score_gemma":0.000083324405,"threshold_uncertainty_score":0.67086333},"labels":[],"label_agreement":null},{"id":"W4399513380","doi":"10.1038/s41467-024-48768-2","title":"Using scalable computer vision to automate high-throughput semiconductor characterization","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":16,"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":"Division of Materials Research; Office of Energy Efficiency; Materials Research Science and Engineering Center, Harvard University; Finnish Center for Artificial Intelligence; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Simons Foundation; University of Toronto; Office of Energy Efficiency and Renewable Energy; U.S. Department of Energy; National Science Foundation","keywords":"Throughput; Computer science; Scalability; Characterization (materials science); Computer architecture; Nanotechnology; Materials science; Database; Operating system","score_opus":0.031511524377865,"score_gpt":0.31400930471582356,"score_spread":0.2824977803379586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399513380","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":[0.8470657,0.0010722218,0.13733214,0.0016123932,0.008446635,0.00098905,0.00014891825,0.0025385418,0.00079443713],"genre_scores_gemma":[0.9834249,0.00003152648,0.015572749,0.00022334428,0.0004600137,0.00002901512,0.000104719715,0.000048250433,0.000105465064],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99916273,0.00006312293,0.00026003763,0.0001936318,0.00014828524,0.00017219201],"domain_scores_gemma":[0.99886775,0.00007169633,0.00001945169,0.00085033773,0.00009381004,0.000096973046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020280997,0.00013972443,0.00015465659,0.00028486768,0.00019055033,0.00024300122,0.00033002623,0.0002993896,0.000026750007],"category_scores_gemma":[0.000021400456,0.00013597822,0.000049084756,0.0009242241,0.000011466731,0.0002769113,0.00017049111,0.00042614897,0.0003460363],"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":[0.000007898782,0.000027774902,0.000028006629,0.000070213006,0.00006144773,0.0000027949254,0.0005575517,0.0148625765,0.92539084,0.003148033,0.021473983,0.034368888],"study_design_scores_gemma":[0.00010673393,0.00005578248,0.001012139,0.0003903686,0.000022119442,0.000022441936,0.000014411229,0.44887343,0.011514438,0.00002489677,0.53769296,0.00027026187],"about_ca_topic_score_codex":0.000030542866,"about_ca_topic_score_gemma":0.000020094742,"teacher_disagreement_score":0.9138764,"about_ca_system_score_codex":0.00019377956,"about_ca_system_score_gemma":0.000024766694,"threshold_uncertainty_score":0.5545031},"labels":[],"label_agreement":null},{"id":"W4399598501","doi":"10.1016/j.aei.2024.102637","title":"DiffDD: A surface defect detection framework with diffusion probabilistic model","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Probabilistic logic; Diffusion; Surface (topology); Computer science; Artificial intelligence; Mathematics; Physics; Geometry; Thermodynamics","score_opus":0.005806832307022,"score_gpt":0.19766245934299104,"score_spread":0.19185562703596903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399598501","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":[0.34676233,0.00017888682,0.6502437,0.0000017510947,0.000717382,0.00024872783,0.000006034144,0.0014902196,0.00035094307],"genre_scores_gemma":[0.9741674,0.000040253748,0.025518343,0.0000060990537,0.000094641626,0.0000441835,0.0000054241355,0.00007786792,0.000045757897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998887,0.00000546095,0.00041260672,0.000120115794,0.00025325146,0.00032157532],"domain_scores_gemma":[0.99945605,0.00011702941,0.000030757405,0.00026060076,0.00004054319,0.00009501479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013588331,0.000275418,0.00022755757,0.00015454268,0.00006812078,0.00013603858,0.000085339154,0.00019478358,0.0000048061447],"category_scores_gemma":[0.00004831406,0.00022948676,0.00008923966,0.0005332332,0.000012019931,0.0005265654,0.000020455585,0.0005006956,0.000051923736],"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":[0.00001106089,0.0000037822833,0.0000025041406,0.0006177985,0.00003416983,0.00000273045,0.0005623534,0.98210925,0.0036238404,0.0004379714,0.000015916716,0.012578644],"study_design_scores_gemma":[0.00020809857,0.00008874997,0.000013781019,0.0006175407,0.000028692197,0.000039621184,0.00006210119,0.99195945,0.0032790825,0.00013599549,0.003254958,0.00031190057],"about_ca_topic_score_codex":0.0000023291689,"about_ca_topic_score_gemma":0.0000023450878,"teacher_disagreement_score":0.6274051,"about_ca_system_score_codex":0.00020771075,"about_ca_system_score_gemma":0.000018258223,"threshold_uncertainty_score":0.93581986},"labels":[],"label_agreement":null},{"id":"W4399639892","doi":"10.1061/jcemd4.coeng-15115","title":"Automated Detection and Segmentation of Mechanical, Electrical, and Plumbing Components in Indoor Environments by Using the YOLACT++ Architecture","year":2024,"lang":"en","type":"article","venue":"Journal of Construction Engineering and Management","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Concordia University","funders":"","keywords":"Architecture; Computer science; Segmentation; Artificial intelligence; Geography","score_opus":0.008282077559180461,"score_gpt":0.20361752804022065,"score_spread":0.1953354504810402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399639892","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":[0.88394743,0.00089237205,0.11457352,0.000009787756,0.00040110154,0.00013025597,0.0000011771423,0.000038989347,0.000005386399],"genre_scores_gemma":[0.99853706,0.00051011326,0.0009031171,0.0000019563568,0.000032614993,0.0000025991758,3.491432e-7,0.0000103017655,0.0000019103893],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940974,0.00002310771,0.0002925957,0.00007322635,0.00012229486,0.00007906767],"domain_scores_gemma":[0.999839,0.00003112904,0.000059205115,0.000034386943,0.0000063268158,0.00002994415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022655772,0.00008371629,0.00013066115,0.00025487543,0.000032340737,0.00005155374,0.000020287938,0.000052191073,8.688494e-7],"category_scores_gemma":[0.000005574229,0.00006818991,0.000020575157,0.00015866512,0.000016289814,0.0001071318,0.000013287552,0.0001732315,6.61918e-8],"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":[0.000045198205,0.000011824653,0.00012373811,0.00040577273,0.00023831255,0.000011897558,0.00022984292,0.083757,0.762425,0.00008256241,0.000029541849,0.15263933],"study_design_scores_gemma":[0.0008090782,0.00012904775,0.0012630887,0.000397426,0.00009322401,0.00053985097,0.00025868247,0.9739537,0.020723285,0.000039027167,0.0016653838,0.00012819853],"about_ca_topic_score_codex":0.000009636964,"about_ca_topic_score_gemma":6.801715e-7,"teacher_disagreement_score":0.89019674,"about_ca_system_score_codex":0.000060367413,"about_ca_system_score_gemma":0.0000021099684,"threshold_uncertainty_score":0.2780704},"labels":[],"label_agreement":null},{"id":"W4399653828","doi":"10.23977/jeeem.2024.070201","title":"Optimization of agricultural broken film cleaning device based on electrostatic adsorption principle","year":2024,"lang":"en","type":"article","venue":"Journal of Electrotechnology Electrical Engineering and Management","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adsorption; Materials science; Agriculture; Nanotechnology; Chemistry; Biology; Ecology; Physical chemistry","score_opus":0.005027384223506192,"score_gpt":0.20377073994654518,"score_spread":0.198743355723039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399653828","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":[0.2289512,0.0020852997,0.76700073,0.000162806,0.00050128356,0.0004098586,0.0000017412688,0.0005100962,0.00037699306],"genre_scores_gemma":[0.99653846,0.00041971504,0.0028864765,0.000011556937,0.000077596356,0.000012699919,0.0000020527218,0.000026268113,0.000025193063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893665,0.000020555815,0.00044135508,0.00013295791,0.00021073647,0.00025774233],"domain_scores_gemma":[0.9996496,0.00007376348,0.00008067533,0.00009169006,0.000057285637,0.00004698748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028367256,0.0001634697,0.00026376266,0.0007753522,0.0000275812,0.000037134327,0.0000923164,0.00015157234,0.0000050021986],"category_scores_gemma":[0.000052030722,0.00013475542,0.00008605128,0.00084704545,0.000009756192,0.00009805746,0.000011488539,0.0005268113,0.0000016408945],"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":[0.000030271804,0.000018539367,0.000002579573,0.00021920842,0.00012115602,0.000014799111,0.000009680587,0.95636624,0.032511897,0.0026367186,0.00026183057,0.0078071062],"study_design_scores_gemma":[0.00039650494,0.0010287401,0.00015003467,0.00028396683,0.000078304096,0.00010703283,0.000009746922,0.9625298,0.03294303,0.000035134348,0.0022890964,0.00014861501],"about_ca_topic_score_codex":0.0000012597965,"about_ca_topic_score_gemma":2.198907e-7,"teacher_disagreement_score":0.76758724,"about_ca_system_score_codex":0.00017623749,"about_ca_system_score_gemma":0.000014206003,"threshold_uncertainty_score":0.5495167},"labels":[],"label_agreement":null},{"id":"W4399798506","doi":"10.1049/icp.2024.0440","title":"Evaluating OLTC condition based on feature extraction from vibro-acoustic signals","year":2023,"lang":"en","type":"article","venue":"IET conference proceedings.","topic":"Industrial Vision Systems and Defect Detection","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":"Hydro-Québec; Université du Québec à Chicoutimi","funders":"","keywords":"Feature extraction; Computer science; Extraction (chemistry); Speech recognition; Feature (linguistics); Pattern recognition (psychology); Artificial intelligence","score_opus":0.06882331137763358,"score_gpt":0.32943964434842193,"score_spread":0.26061633297078834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399798506","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":[0.9762451,0.000020802618,0.010812673,0.00011334165,0.0018145401,0.00055320025,0.000058401554,0.0018508981,0.008531067],"genre_scores_gemma":[0.99867666,0.000007834913,0.00024011386,0.000045808858,0.0005745241,0.000109848166,0.00007651347,0.00004219114,0.00022652996],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986198,0.000019248415,0.00026794896,0.0003252201,0.00047381368,0.00029395687],"domain_scores_gemma":[0.99933636,0.00011755005,0.000103238686,0.00010615455,0.0002454724,0.0000911988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042456875,0.00022753428,0.00023677779,0.00023481656,0.00016011286,0.00024371997,0.0000850032,0.0003074186,0.0003362582],"category_scores_gemma":[0.00021535181,0.00022154668,0.00007485128,0.000537089,0.000017478507,0.0002874902,0.00001233888,0.00041983117,0.00047945397],"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":[0.000085260115,0.00001888997,0.00014041013,0.00011205199,0.00003140337,0.0000128872125,0.00039385783,0.035527073,0.9384183,0.00008415727,0.013409388,0.011766272],"study_design_scores_gemma":[0.00060857553,0.00024193502,0.0017221814,0.00039638285,0.00003361232,0.000006435282,0.00054129685,0.90820336,0.086171344,0.00033828753,0.0014388957,0.0002976757],"about_ca_topic_score_codex":0.000023522594,"about_ca_topic_score_gemma":0.0000028511192,"teacher_disagreement_score":0.8726763,"about_ca_system_score_codex":0.000112711554,"about_ca_system_score_gemma":0.000041058192,"threshold_uncertainty_score":0.90344113},"labels":[],"label_agreement":null},{"id":"W4399870559","doi":"10.23977/acss.2024.080114","title":"Deep Data Augmentation for Defect Detection Enhancement: A Diffusion Model Based Approach","year":2024,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Diffusion; Computer science; Artificial intelligence; Physics","score_opus":0.038125498073012014,"score_gpt":0.2838703420864776,"score_spread":0.24574484401346558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399870559","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":[0.01063613,0.012212032,0.97392845,0.0000022282559,0.0021340086,0.00077691086,0.000019421883,0.0001654164,0.00012538506],"genre_scores_gemma":[0.99483067,0.00020222174,0.0042212494,0.000008709974,0.00042444016,0.00021342885,0.000058843383,0.000026421658,0.000014001735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988636,0.000055082608,0.00036873497,0.00037823262,0.00015589801,0.00017848656],"domain_scores_gemma":[0.999526,0.00014873754,0.000038523365,0.00022158462,0.000025585465,0.000039539267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053614355,0.00015864638,0.00022205876,0.00017759304,0.00007489417,0.00019945094,0.000102644364,0.00009858984,0.0000012198936],"category_scores_gemma":[0.000005534959,0.00013926305,0.00004801659,0.00019821498,0.000009589041,0.00053601677,0.000030423409,0.000101133985,0.0000023248372],"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":[0.000019954547,0.000012097826,0.000008072856,0.0007354126,0.000015564681,9.219227e-7,0.00010166506,0.8100224,0.0031575568,0.000056523724,0.00005363444,0.18581618],"study_design_scores_gemma":[0.00048384027,0.000101755264,0.0000028790564,0.00030361794,0.000014839279,0.0000069001367,0.000034317476,0.99492383,0.0008574933,0.00008959253,0.0030186384,0.00016231743],"about_ca_topic_score_codex":0.000017408276,"about_ca_topic_score_gemma":0.000015453572,"teacher_disagreement_score":0.9841946,"about_ca_system_score_codex":0.00006706043,"about_ca_system_score_gemma":0.000009899107,"threshold_uncertainty_score":0.5678983},"labels":[],"label_agreement":null},{"id":"W4400015332","doi":"10.1016/j.compag.2024.109141","title":"Improving 3D reconstruction for accurate measurement of appearance characteristics in shiny fruits using post-harvest particle film: A case study on tomatoes","year":2024,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Ferdowsi University of Mashhad","keywords":"Particle (ecology); Horticulture; Computer vision; Materials science; Artificial intelligence; Environmental science; Computer science; Biology; Ecology","score_opus":0.02470376821928793,"score_gpt":0.24321935307247916,"score_spread":0.21851558485319122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400015332","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":[0.9932799,0.0010095114,0.0044283867,0.000008424438,0.00066041015,0.00054532755,0.000008404351,0.000055117358,0.000004562243],"genre_scores_gemma":[0.9997271,0.000023714554,0.00009694764,0.000004302626,0.00010944948,0.000023948814,0.0000015257295,0.000010990226,0.0000020533057],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991657,0.00003550482,0.00030988897,0.00018762007,0.000103631624,0.00019763295],"domain_scores_gemma":[0.99976784,0.00003064342,0.000045496792,0.00006999999,0.000058119433,0.00002788344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000336515,0.00012735026,0.00020372243,0.000078419536,0.000046194895,0.000093186085,0.00003568277,0.00008875292,3.3890913e-7],"category_scores_gemma":[0.000019169413,0.00009841178,0.000032651536,0.00024719152,0.000006386994,0.00012653074,0.000011954975,0.00021875331,2.889073e-7],"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":[0.0002961202,0.00044629443,0.006338442,0.0021837892,0.00030017,0.0006303111,0.008997898,0.0855071,0.4340976,0.00042579274,0.00026424968,0.46051225],"study_design_scores_gemma":[0.0012697087,0.0008816703,0.0047383923,0.001242933,0.000039085753,0.00084837875,0.0021991741,0.9790563,0.009164776,0.00002030648,0.00018322218,0.00035604532],"about_ca_topic_score_codex":0.00011831876,"about_ca_topic_score_gemma":0.00039692828,"teacher_disagreement_score":0.8935492,"about_ca_system_score_codex":0.00020512716,"about_ca_system_score_gemma":0.000027126458,"threshold_uncertainty_score":0.4013116},"labels":[],"label_agreement":null},{"id":"W4400038831","doi":"10.3390/app14135503","title":"A Defect Detection Method Based on YOLOv7 for Automated Remanufacturing","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":13,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remanufacturing; Computer science; Artificial intelligence; Object detection; Segmentation; Inference; Process (computing); Machine learning; Transfer of learning; Field (mathematics); Object (grammar); Computer vision; Data mining; Pattern recognition (psychology); Engineering; Mathematics","score_opus":0.026502238561161573,"score_gpt":0.2988873500524747,"score_spread":0.2723851114913131,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400038831","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":[0.24426186,0.00010554202,0.7197095,0.00007635468,0.0041722236,0.0015682317,0.00000829281,0.0063724825,0.0237255],"genre_scores_gemma":[0.99663293,6.827019e-7,0.0029018044,0.000058776823,0.00015355542,0.00020999687,0.0000010243047,0.000018213896,0.000023010562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990936,0.000021315174,0.00016917047,0.00028804058,0.00021375081,0.0002141374],"domain_scores_gemma":[0.99949676,0.00032687484,0.000017880056,0.0001097269,0.000010415475,0.00003833955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010247597,0.00012126819,0.00012267288,0.00026381583,0.00021754463,0.00019273483,0.000087040185,0.00009094461,0.00001222573],"category_scores_gemma":[0.000025094449,0.00009564223,0.00009351987,0.00049185305,0.000024032337,0.00007479835,0.000006202537,0.000116718584,0.00005542566],"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":[0.0004643837,0.000015685719,0.0000010798599,0.00017889717,0.000036772006,0.0000028711381,0.00015039073,0.72271985,0.11212234,0.001030362,0.0032963804,0.15998101],"study_design_scores_gemma":[0.0007650632,0.00020062305,0.000008676983,0.000037641017,0.00000893989,0.0000024674662,0.00003666279,0.8216472,0.16616759,0.00016567686,0.010842883,0.00011652318],"about_ca_topic_score_codex":0.0000105083645,"about_ca_topic_score_gemma":0.00000650281,"teacher_disagreement_score":0.7523711,"about_ca_system_score_codex":0.000067094195,"about_ca_system_score_gemma":0.00002276078,"threshold_uncertainty_score":0.3900177},"labels":[],"label_agreement":null},{"id":"W4400056842","doi":"10.1142/s0218488524400075","title":"A Computer Vision Engineering Management System for Automated Defect Detection in Electronic Components Manufacturing","year":2024,"lang":"en","type":"article","venue":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Manufacturing engineering; Systems engineering; Engineering; Engineering drawing; Artificial intelligence; Software engineering","score_opus":0.009491870133713701,"score_gpt":0.2484039897911871,"score_spread":0.23891211965747342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400056842","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":[0.65544146,0.0031508969,0.32207265,0.000020442669,0.017840048,0.0007351923,0.000022000775,0.0005896166,0.00012768818],"genre_scores_gemma":[0.9987883,0.000033484175,0.000091599395,0.000002033038,0.00096730434,0.00005400838,0.0000096549875,0.000041112322,0.000012528699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842244,0.00006726376,0.00075570657,0.00019897093,0.00029848173,0.00025711936],"domain_scores_gemma":[0.99931186,0.00019805317,0.000116026145,0.00008673006,0.00021385193,0.00007349706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008897381,0.00022452936,0.00035669823,0.0009894397,0.000045913857,0.00030527674,0.00016432065,0.00013274736,0.0000012005911],"category_scores_gemma":[0.000008949358,0.0001924092,0.0001996645,0.00020488135,0.000009864508,0.00020117988,0.000022446915,0.0002513182,0.000005541163],"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":[0.00029450015,0.000041919586,0.000023127095,0.0023106337,0.0004983667,0.00012367239,0.00010899937,0.94971836,0.006873048,0.00041038104,0.00018015105,0.039416857],"study_design_scores_gemma":[0.0016206548,0.0001873992,0.00024332084,0.0035761253,0.000043644202,0.00025287198,0.00008595471,0.9805832,0.002208348,0.000006108339,0.011003194,0.00018921058],"about_ca_topic_score_codex":0.00004913765,"about_ca_topic_score_gemma":0.000022532156,"teacher_disagreement_score":0.3433468,"about_ca_system_score_codex":0.0009581506,"about_ca_system_score_gemma":0.000040638675,"threshold_uncertainty_score":0.78462195},"labels":[],"label_agreement":null},{"id":"W4400133724","doi":"10.1101/2024.06.23.599080","title":"ColocZStats: A Z-Stack Signal Colocalization Extension Tool for 3D Slicer","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Colocalization; Stack (abstract data type); Extension (predicate logic); SIGNAL (programming language); Computer science; Physics; Programming language; Neuroscience; Biology","score_opus":0.017283899190980473,"score_gpt":0.2265531946106313,"score_spread":0.20926929541965084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400133724","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":[0.7913315,0.0031909696,0.18063664,0.00007623491,0.014368535,0.0049952143,0.0011660268,0.004181097,0.000053738902],"genre_scores_gemma":[0.99359316,0.00011181031,0.0032772562,0.000078085766,0.0016804009,0.00091391103,0.0000017872732,0.00032013166,0.000023451617],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971655,0.000092965,0.000838855,0.00090131833,0.00042696006,0.00057440123],"domain_scores_gemma":[0.9981149,0.00012171584,0.00019791836,0.0007774785,0.0006063932,0.00018162806],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008144471,0.00063736906,0.00068084407,0.0004295535,0.00017559114,0.00044620037,0.00024403546,0.0011895357,0.000070781905],"category_scores_gemma":[0.00015141624,0.00067349,0.00026586797,0.00061579864,0.000043844593,0.00012826645,0.00023908149,0.0008810871,0.00019987518],"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":[0.00022055345,0.00010658685,0.00009858543,0.004388713,0.0005853255,0.000078835765,0.00004074849,0.035080064,0.9220785,0.00091249513,0.036313184,0.00009643551],"study_design_scores_gemma":[0.0017433323,0.00034418367,0.0011229013,0.0026244782,0.00056634605,1.2445929e-7,0.000010747911,0.4391916,0.33176085,0.000025023348,0.22015503,0.0024553845],"about_ca_topic_score_codex":0.000029179155,"about_ca_topic_score_gemma":0.0000025406323,"teacher_disagreement_score":0.5903176,"about_ca_system_score_codex":0.00063686655,"about_ca_system_score_gemma":0.000317606,"threshold_uncertainty_score":0.9995716},"labels":[],"label_agreement":null},{"id":"W4400275268","doi":"10.1109/lgrs.2024.3422842","title":"Efficient Roof Vertex Clustering for Wireframe Simplification Based on the Extended Multiclass Twin Support Vector Machine","year":2024,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"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":"National Key Research and Development Program of China; University of Calgary; National Science Foundation","keywords":"Cluster analysis; Vertex (graph theory); Computer science; Support vector machine; Artificial intelligence; Algorithm; Pattern recognition (psychology); Theoretical computer science; Graph","score_opus":0.017979539787144883,"score_gpt":0.24103786569765057,"score_spread":0.22305832591050567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400275268","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":[0.41827545,0.000023143155,0.5765057,0.001519331,0.0030190207,0.00036854835,0.00000949533,0.00021830466,0.000060967093],"genre_scores_gemma":[0.99767375,0.0000018325385,0.0012616499,0.0007817757,0.00020340965,8.844889e-7,0.0000025896497,0.000025469632,0.000048646903],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893725,0.00003807157,0.00021055552,0.0003046082,0.00022742609,0.00028206385],"domain_scores_gemma":[0.9994169,0.00026548398,0.000028049362,0.00020709995,0.000023724344,0.00005875039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057123124,0.00015597511,0.00013041537,0.00014053298,0.00025122066,0.0002086855,0.00007178312,0.00007641901,0.0000022462405],"category_scores_gemma":[0.000056746852,0.00010744778,0.00007164345,0.00027771402,0.00006911085,0.00004226634,0.000008660854,0.00018798838,0.000013811032],"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":[0.000033979875,0.000006378001,7.838211e-7,0.00010861801,0.0000127727835,0.000016279335,0.00039170973,0.16409107,0.5864859,0.0000129691325,0.0012789723,0.24756053],"study_design_scores_gemma":[0.00018582665,0.000057862126,0.00010824109,0.00016722157,0.000012145059,0.00002183561,0.00003587366,0.98600894,0.010330315,0.000006484881,0.0029215566,0.00014371792],"about_ca_topic_score_codex":0.00009009841,"about_ca_topic_score_gemma":0.0000110630035,"teacher_disagreement_score":0.82191783,"about_ca_system_score_codex":0.00009270037,"about_ca_system_score_gemma":0.00001901904,"threshold_uncertainty_score":0.43815932},"labels":[],"label_agreement":null},{"id":"W4400287666","doi":"10.1121/10.0026720","title":"Novel auditory alerts that foster efficient detection and discrimination in complex auditory environments: Dual-task conditions","year":2024,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Task (project management); Computer science; Dual (grammatical number); Speech recognition; Human–computer interaction; Engineering","score_opus":0.019957841791084667,"score_gpt":0.23996557708787877,"score_spread":0.2200077352967941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400287666","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":[0.21500273,0.00042915222,0.78001773,0.0007441181,0.0033507985,0.00027814542,0.000030667383,0.000032543445,0.00011409693],"genre_scores_gemma":[0.9989222,0.00013279791,0.0003324847,0.00007309567,0.00049517665,0.0000026864145,6.0826744e-7,0.000014546637,0.000026404326],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990848,0.00007825468,0.00031594856,0.00007065952,0.00032437945,0.0001259929],"domain_scores_gemma":[0.99939823,0.00030930317,0.00011903265,0.0001134104,0.00002039131,0.000039633924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041605876,0.000102880986,0.00017526597,0.000042416705,0.00011003149,0.000024120378,0.00008721625,0.000081515274,0.000020989397],"category_scores_gemma":[0.000037230337,0.000060601094,0.00015753687,0.00015541211,0.00020766965,0.00008466267,0.00004565341,0.0003749795,0.0000035185267],"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":[0.000034502016,0.000067793124,0.000009479971,0.00011008692,0.000133853,0.000001278657,0.0017203437,0.18262514,0.7766166,0.0000015539335,0.019611642,0.019067733],"study_design_scores_gemma":[0.0005192779,0.00017373155,0.012046032,0.00031418283,0.00021129237,0.00014851462,0.003430941,0.967649,0.0056032646,0.00007685179,0.009680516,0.00014639748],"about_ca_topic_score_codex":0.00001895232,"about_ca_topic_score_gemma":0.0000013626986,"teacher_disagreement_score":0.78502387,"about_ca_system_score_codex":0.00017043849,"about_ca_system_score_gemma":0.000013959629,"threshold_uncertainty_score":0.24712409},"labels":[],"label_agreement":null},{"id":"W4400335907","doi":"10.1145/3649405.3659507","title":"A Framework for Evaluating the Impact of Production Quality on Coding Demonstration Videos","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Computer science; Production (economics); Coding (social sciences); Quality (philosophy); Mathematics; Statistics","score_opus":0.16031946335886366,"score_gpt":0.4434181242685702,"score_spread":0.28309866090970653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400335907","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":[0.6930007,0.000057865283,0.30374193,0.00005277139,0.0012945024,0.0005614268,0.000005170756,0.00022150727,0.0010641626],"genre_scores_gemma":[0.9986835,0.0000024247613,0.0007169196,0.0000024004692,0.00046234974,0.000050631374,0.0000017280271,0.0000129841865,0.00006707083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993451,0.000056295445,0.00025959272,0.00011095245,0.00013858726,0.00008944577],"domain_scores_gemma":[0.9993554,0.0004171808,0.00003220077,0.00012893582,0.00005196586,0.00001435345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012777168,0.00007380908,0.00009924736,0.00005861899,0.00007019224,0.00005627042,0.000029658015,0.00008737622,0.000023127513],"category_scores_gemma":[0.0005317232,0.000043637458,0.00012243725,0.00021158729,0.00000788112,0.00008653758,0.0000029979828,0.00014025447,0.000007707452],"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":[0.00021566615,0.000027127864,0.00019733884,0.00047588118,0.00025291022,2.5622288e-7,0.0013105937,0.35820886,0.32206824,0.05448406,0.0028938146,0.25986525],"study_design_scores_gemma":[0.0002020513,0.00095204683,0.0018196914,0.0006710234,0.000038295493,0.000008255799,0.00047421848,0.8416728,0.14575641,0.00803134,0.00017181884,0.00020207951],"about_ca_topic_score_codex":0.000068476314,"about_ca_topic_score_gemma":0.0000034955756,"teacher_disagreement_score":0.4834639,"about_ca_system_score_codex":0.000090308204,"about_ca_system_score_gemma":0.000025080422,"threshold_uncertainty_score":0.17794839},"labels":[],"label_agreement":null},{"id":"W4400680796","doi":"10.1109/cefc61729.2024.10586063","title":"Mesh Error Estimation Using Graph Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial neural network; Estimation; Graph; Theoretical computer science; Artificial intelligence; Algorithm; Engineering","score_opus":0.03162838175913973,"score_gpt":0.26896070127846117,"score_spread":0.23733231951932143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400680796","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":[0.20025,0.00041129428,0.7910111,0.000009925038,0.004262254,0.0001238691,0.000001009762,0.0011087226,0.0028218024],"genre_scores_gemma":[0.99906987,0.000002475659,0.00041430615,0.0000089434925,0.0003472505,0.0000041076646,0.0000023907419,0.00002051413,0.00013016694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995498,0.0000126061095,0.00014659701,0.00009110028,0.00007985998,0.00012001677],"domain_scores_gemma":[0.9998515,0.00002277559,0.000006493105,0.000076219665,0.0000110858045,0.000031911597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010871038,0.00007889394,0.00007817774,0.00010557889,0.000042381635,0.000109818655,0.000025537067,0.00009201173,0.0000806189],"category_scores_gemma":[0.0000051416864,0.00006661587,0.000055663524,0.0002960983,0.00000497255,0.00016317841,0.000006086261,0.00012786443,0.000028819351],"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":[0.0000015071081,8.83237e-7,0.000007006692,0.000020033709,0.000012566887,0.0000045640763,0.00002518334,0.9642783,0.0006141358,0.0002218731,0.0017388592,0.03307511],"study_design_scores_gemma":[0.00005046179,0.000011334161,0.000021503094,0.000032518237,0.000009722407,0.000024004945,0.000017701901,0.9981954,0.0006089045,0.000059826543,0.00088671426,0.000081913095],"about_ca_topic_score_codex":0.00004668182,"about_ca_topic_score_gemma":0.000004656484,"teacher_disagreement_score":0.79881984,"about_ca_system_score_codex":0.000035834317,"about_ca_system_score_gemma":0.0000037065943,"threshold_uncertainty_score":0.27165163},"labels":[],"label_agreement":null},{"id":"W4400878462","doi":"10.1109/qomex61742.2024.10598253","title":"Perceptual Crack Detection for Rendered 3D Textured Meshes","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Polygon mesh; Computer science; Perception; Artificial intelligence; Computer vision; Computer graphics (images); Psychology","score_opus":0.01911427564154966,"score_gpt":0.2420274374705555,"score_spread":0.22291316182900583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400878462","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":[0.114400774,0.0010148776,0.83001417,0.00007119429,0.011741605,0.0010180508,0.000033578395,0.004621277,0.037084445],"genre_scores_gemma":[0.99558884,0.0000078746425,0.00037544267,0.000011061611,0.00083103223,0.000061387895,0.000004899746,0.00003654533,0.0030829096],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938464,0.000013138214,0.00018358216,0.00016037046,0.000101125566,0.00015713622],"domain_scores_gemma":[0.99975055,0.000066758286,0.0000075210082,0.000106962834,0.000027805232,0.000040373852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015445684,0.00011183611,0.00011596817,0.00011083425,0.00006708035,0.00011815015,0.000035859382,0.00017114232,0.00023069691],"category_scores_gemma":[0.000027945996,0.00009237674,0.000099317294,0.00017400061,0.0000073286133,0.00013397385,0.0000059137606,0.0001226633,0.00017050192],"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":[0.00005956837,0.000010819802,0.00000411898,0.00030754943,0.00015059946,0.0000053448593,0.00082960207,0.0043299804,0.23222534,0.00052886264,0.0642165,0.6973317],"study_design_scores_gemma":[0.000492256,0.0002046549,0.000105641935,0.000074508585,0.000042325773,0.00004478144,0.0006403118,0.3917117,0.08600912,0.00026710943,0.52005273,0.00035482322],"about_ca_topic_score_codex":0.000028276389,"about_ca_topic_score_gemma":0.000051510237,"teacher_disagreement_score":0.8811881,"about_ca_system_score_codex":0.00007515163,"about_ca_system_score_gemma":0.000009234151,"threshold_uncertainty_score":0.3767014},"labels":[],"label_agreement":null},{"id":"W4400937202","doi":"10.1149/1945-7111/ad6713","title":"Corrosion of Lithium-ion Battery Cylindrical Cell Hardware: Understanding the Mechanisms and Exploring Effective Solutions","year":2024,"lang":"en","type":"article","venue":"Journal of The Electrochemical Society","topic":"Industrial Vision Systems and Defect Detection","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":"E-One Moli Energy (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Corrosion; Lithium (medication); Battery (electricity); Ion; Electrochemistry; Lithium-ion battery; Materials science; Computer science; Chemical engineering; Process engineering; Metallurgy; Chemistry; Engineering; Physics; Electrode; Power (physics); Thermodynamics; Organic chemistry; Physical chemistry; Psychology","score_opus":0.031299791521403594,"score_gpt":0.2219990286709247,"score_spread":0.19069923714952108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400937202","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":[0.14187545,0.0022796108,0.85293454,0.00042238177,0.0020773818,0.00021836317,0.000002354597,0.000053467706,0.00013642757],"genre_scores_gemma":[0.99913335,0.00016893528,0.000244648,0.000018223767,0.00038907453,0.0000063731654,2.3158056e-7,0.000017226186,0.00002196137],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908704,0.00006446442,0.00030215518,0.00008788435,0.0002733418,0.0001851437],"domain_scores_gemma":[0.9994006,0.00033033104,0.00008571241,0.00009164687,0.000044607885,0.000047086032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063098484,0.00010881696,0.00018334533,0.00002858565,0.00016788956,0.00005421613,0.00011539245,0.00013494027,0.000004297058],"category_scores_gemma":[0.00004467006,0.00006059295,0.00033648204,0.00028535892,0.00004073691,0.0001297376,0.000048658756,0.00077214534,9.603534e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015938085,0.000008763281,0.0000055401842,0.00007083976,0.000044898345,6.214564e-7,0.000620215,0.00018619599,0.9970724,0.0003668316,0.0013270047,0.00028077484],"study_design_scores_gemma":[0.00029509555,0.00015009212,0.00004187236,0.00034570615,0.0001056808,0.00011544723,0.0007734809,0.015431022,0.97877544,0.0030634154,0.00079936604,0.000103354076],"about_ca_topic_score_codex":0.0000016675718,"about_ca_topic_score_gemma":2.0009e-7,"teacher_disagreement_score":0.8572579,"about_ca_system_score_codex":0.0003736463,"about_ca_system_score_gemma":0.000024492985,"threshold_uncertainty_score":0.33546302},"labels":[],"label_agreement":null},{"id":"W4400956229","doi":"10.1016/j.aei.2024.102717","title":"Defect detection on multi-type rail surfaces via IoU decoupling and multi-information alignment","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Science Basic Research Program of Shaanxi Province; Natural Science Foundation of Hunan Province; China Scholarship Council; University of Waterloo; National Natural Science Foundation of China","keywords":"Decoupling (probability); Computer science; Artificial intelligence; Engineering; Materials science; Control engineering","score_opus":0.010984771624871291,"score_gpt":0.2271057419252353,"score_spread":0.216120970300364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400956229","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":[0.22520335,0.00037399208,0.77007765,0.0000035487067,0.0027374956,0.00032703436,0.000007378549,0.0011509138,0.00011865417],"genre_scores_gemma":[0.9864205,0.00016299725,0.013231956,0.00002674962,0.000054619508,0.00003151235,0.000014415456,0.00004118221,0.0000160754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988641,0.0000058450737,0.000552887,0.00009361749,0.00020603521,0.00027750226],"domain_scores_gemma":[0.99955475,0.00008759148,0.000048826827,0.00016886955,0.00004737062,0.00009259158],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023636721,0.00026212333,0.00020256425,0.00030425665,0.00007747005,0.00018077342,0.00005999327,0.00015033537,0.000004288248],"category_scores_gemma":[0.000053812437,0.00025278362,0.00007015294,0.00034685244,0.000008946195,0.0011574491,0.000021009351,0.00029684626,0.00013304297],"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":[0.00000842017,0.000004596691,0.0000044442654,0.0003304359,0.00004376071,0.0000010205539,0.00055291934,0.8714962,0.0098229125,0.000026754722,0.000030467436,0.117678076],"study_design_scores_gemma":[0.00045852474,0.000120946184,0.0000847645,0.00022960025,0.000016128235,0.000024220988,0.00013021208,0.93110025,0.0317083,0.0000019841884,0.03584966,0.00027539342],"about_ca_topic_score_codex":0.0000060917923,"about_ca_topic_score_gemma":0.000004122663,"teacher_disagreement_score":0.7612171,"about_ca_system_score_codex":0.00021537692,"about_ca_system_score_gemma":0.0000074520035,"threshold_uncertainty_score":0.99999243},"labels":[],"label_agreement":null},{"id":"W4400976566","doi":"10.1109/icca62789.2024.10591948","title":"An Insulator Missing Defect Detection Method Based on Unmanned Aerial Vehicles","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Concordia University","funders":"Fundamental Research Funds for the Key Research Program of Chongqing Science and Technology Commission; National Natural Science Foundation of China; China Association for Science and Technology","keywords":"Computer science; Aeronautics; Real-time computing; Artificial intelligence; Remote sensing; Computer vision; Engineering; Geography","score_opus":0.01711927792261114,"score_gpt":0.2797260088490328,"score_spread":0.26260673092642167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400976566","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":[0.48348206,0.00008889221,0.50497985,0.000022403452,0.0046337633,0.00026515214,0.0000061814258,0.002445849,0.0040758313],"genre_scores_gemma":[0.99741846,8.699937e-7,0.00146825,0.000033564746,0.00095873413,0.000017036049,0.0000036424724,0.000054137552,0.000045302095],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900585,0.00012966647,0.00023212783,0.00024975865,0.00018868371,0.00019392621],"domain_scores_gemma":[0.9995174,0.00013755604,0.000014222049,0.00021306565,0.000023129496,0.00009462378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051851355,0.00016782248,0.00016244009,0.00027534002,0.00010706543,0.0002499045,0.000054502565,0.00021317397,0.00009161825],"category_scores_gemma":[0.000035927784,0.00014365937,0.00013417087,0.00037407383,0.0000066029047,0.00018577187,0.0000044642993,0.00021987759,0.00011532582],"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":[0.000106155676,0.000019520303,0.0000096905615,0.00009142846,0.000040852316,0.000013831026,0.00007938096,0.13093363,0.4717952,0.00014594995,0.00033420365,0.39643016],"study_design_scores_gemma":[0.00025791503,0.0002075087,0.00008922238,0.00006586986,0.000016448183,0.000007414561,0.00002225443,0.73613775,0.25549835,0.000063687075,0.00747934,0.00015423432],"about_ca_topic_score_codex":0.0000812906,"about_ca_topic_score_gemma":0.000014787145,"teacher_disagreement_score":0.6052041,"about_ca_system_score_codex":0.00012627324,"about_ca_system_score_gemma":0.000022414417,"threshold_uncertainty_score":0.5858259},"labels":[],"label_agreement":null},{"id":"W4401111058","doi":"10.1109/intcec61833.2024.10603365","title":"Real-Time Waste Detection Based on YOLOv8","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Computer science","score_opus":0.010153191000734349,"score_gpt":0.21580019109699564,"score_spread":0.2056470000962613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401111058","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":[0.48628852,0.00005834813,0.05906237,0.000058575148,0.0072074947,0.00044545697,0.000009585295,0.0060686525,0.440801],"genre_scores_gemma":[0.99685013,0.0000037281045,0.00004913008,0.000010425792,0.00047314828,0.00001424203,0.0000019070867,0.000030129488,0.002567158],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945015,0.000020642887,0.00014075455,0.00013351065,0.00012924352,0.00012568184],"domain_scores_gemma":[0.99974644,0.000059749676,0.0000059467297,0.00013468556,0.000012476543,0.00004071809],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000164423,0.000097624885,0.000090681955,0.00017439015,0.000037268816,0.000076195494,0.000029059496,0.00011937643,0.00038126882],"category_scores_gemma":[0.000009963306,0.000079995676,0.00006714996,0.00025728883,0.000004181448,0.00006676372,0.0000035208268,0.00012985639,0.0017570781],"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":[0.000051191513,0.000013747683,0.0000038762923,0.00013825131,0.000044209177,0.00003094332,0.000063400825,0.14118019,0.67867535,0.00022051539,0.019774335,0.15980397],"study_design_scores_gemma":[0.000118048556,0.000107605345,0.000013055457,0.000060938684,0.0000061608603,0.000004562088,0.000015979169,0.8123251,0.16940525,0.000016075892,0.017820869,0.00010632311],"about_ca_topic_score_codex":0.00005269899,"about_ca_topic_score_gemma":0.0000049869072,"teacher_disagreement_score":0.67114496,"about_ca_system_score_codex":0.0000867294,"about_ca_system_score_gemma":0.00000840941,"threshold_uncertainty_score":0.99902016},"labels":[],"label_agreement":null},{"id":"W4401247751","doi":"10.1109/iceib61477.2024.10602607","title":"Improved Fault Diagnosis Method for PLC-Based Manufacturing Processes with Validation through a Cyber-Physical System","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National Science and Technology Council","keywords":"Cyber-physical system; Computer science; Fault (geology); Reliability engineering; Embedded system; Systems engineering; Engineering; Operating system","score_opus":0.021862614113756555,"score_gpt":0.28068919432609607,"score_spread":0.2588265802123395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401247751","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":[0.09490314,0.000080041,0.90065694,0.000033958575,0.00059782394,0.0009155823,0.000026820951,0.0015599423,0.0012257525],"genre_scores_gemma":[0.98819786,0.0000023927587,0.010031168,0.000010109384,0.00053149974,0.0010315797,0.000014884375,0.00006381153,0.00011667183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909174,0.000028788285,0.00022267974,0.00027837337,0.00016471159,0.00021370094],"domain_scores_gemma":[0.9991801,0.00052913796,0.000028883487,0.00014821431,0.00007075765,0.000042955766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018153888,0.0001991737,0.00024262654,0.00008725585,0.00008496606,0.00021629763,0.00006037023,0.00011616535,0.00000936022],"category_scores_gemma":[0.000033718723,0.00013960076,0.00009011479,0.0002497686,0.000006592293,0.00027421187,0.0000069692296,0.00012342601,0.000016699987],"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":[0.00066855265,0.00024222516,0.00007299027,0.05234889,0.0014186266,0.000057276007,0.003981819,0.7053964,0.050140638,0.004014169,0.008749187,0.17290923],"study_design_scores_gemma":[0.0003835651,0.00015583339,0.0000028775487,0.000432841,0.00006332193,0.000007972752,0.00023378192,0.29193747,0.6995004,0.000026761178,0.0070663528,0.00018885816],"about_ca_topic_score_codex":0.00015066747,"about_ca_topic_score_gemma":0.00002889652,"teacher_disagreement_score":0.89329475,"about_ca_system_score_codex":0.00014046162,"about_ca_system_score_gemma":0.000054098102,"threshold_uncertainty_score":0.5692754},"labels":[],"label_agreement":null},{"id":"W4401304378","doi":"10.2316/j.2024.201-0430","title":"VISUAL RECOGNITION SYSTEM OF WELDING ROBOT BASED ON MACHINE LEARNING","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robot welding; Artificial intelligence; Welding; Computer science; Robot; Computer vision; Human–computer interaction; Engineering; Mechanical engineering","score_opus":0.009744247765139933,"score_gpt":0.20695090996305637,"score_spread":0.19720666219791644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401304378","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":[0.3654024,0.01360133,0.60975754,0.00003203167,0.005416278,0.0010319911,0.000035584166,0.001508917,0.0032139067],"genre_scores_gemma":[0.99956733,0.000009283285,0.000009869402,0.000003215695,0.00029562012,0.000046453384,0.0000029328585,0.00002792229,0.000037360867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990334,0.00010946104,0.0003304305,0.00016867598,0.00017431495,0.00018369834],"domain_scores_gemma":[0.9996725,0.00011975326,0.00004250614,0.000083029474,0.000029882625,0.000052342093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066019496,0.00013421466,0.00026463973,0.00019825864,0.00007325032,0.000094639596,0.000031176474,0.000107709275,0.000011571567],"category_scores_gemma":[0.000016231614,0.0001139555,0.0000774838,0.00016364879,0.0000067977,0.000083609586,0.000004003235,0.00018172592,0.000023012435],"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":[0.0003291032,0.00004082882,0.0005772694,0.0041803406,0.00041400496,0.00005435522,0.00026089652,0.71956354,0.07001747,0.0077860546,0.00006828276,0.19670787],"study_design_scores_gemma":[0.0009543322,0.00027912928,0.000026371668,0.000864311,0.00003827837,0.000029359315,0.00030565285,0.99337715,0.0028912146,0.0000055600767,0.0011058217,0.00012280325],"about_ca_topic_score_codex":0.00014798819,"about_ca_topic_score_gemma":0.0000026896457,"teacher_disagreement_score":0.6341649,"about_ca_system_score_codex":0.000103103586,"about_ca_system_score_gemma":0.000015170423,"threshold_uncertainty_score":0.46469706},"labels":[],"label_agreement":null},{"id":"W4401360951","doi":"10.1109/iccsc62074.2024.10617179","title":"Improved Real Time Printed Circuit Board Fault Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Printed circuit board; Computer science; Fault detection and isolation; Fault (geology); Artificial intelligence; Geology; Operating system","score_opus":0.012815593482775481,"score_gpt":0.22170078289700876,"score_spread":0.20888518941423329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401360951","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":[0.65989137,0.00014066715,0.13461089,0.000023423123,0.007307532,0.00068485993,0.000009687868,0.008829569,0.188502],"genre_scores_gemma":[0.9934349,0.000009931856,0.00003041543,0.0000045693955,0.00049131707,0.00002304624,0.0000023643613,0.000036952606,0.005966491],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993182,0.000017529755,0.00021243203,0.00017215412,0.000109445806,0.00017026234],"domain_scores_gemma":[0.99973124,0.000031462434,0.0000098283745,0.00014330562,0.0000285685,0.00005561179],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015926622,0.00011862313,0.00012261201,0.00013780808,0.0000419694,0.00012091272,0.00004390664,0.00017469515,0.00031252814],"category_scores_gemma":[0.000014558743,0.00010220724,0.00008455434,0.00027734137,0.0000064838714,0.00012935836,0.000010700991,0.00019152249,0.0010667833],"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":[0.000007416564,0.0000031731192,0.0000019302615,0.000047957667,0.00004483852,0.0000058326877,0.00006893825,0.00039170106,0.86290705,0.00009331836,0.001533688,0.13489416],"study_design_scores_gemma":[0.00027459287,0.000121295976,0.00011893393,0.00006488933,0.000022056003,0.000037109192,0.000051241368,0.6280787,0.30475965,0.0000819463,0.06610387,0.0002857176],"about_ca_topic_score_codex":0.00023668956,"about_ca_topic_score_gemma":0.000029199442,"teacher_disagreement_score":0.627687,"about_ca_system_score_codex":0.00012643608,"about_ca_system_score_gemma":0.000011185907,"threshold_uncertainty_score":0.999711},"labels":[],"label_agreement":null},{"id":"W4401361321","doi":"10.1109/iccsc62074.2024.10617066","title":"Metal Structural Defect Detection Based-On Deep Learning and Grad-Cam","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Artificial intelligence; Deep learning; Computer science; Pattern recognition (psychology)","score_opus":0.007879298604716976,"score_gpt":0.2251227208735634,"score_spread":0.2172434222688464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401361321","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":[0.93499815,0.0008210217,0.050771754,0.000011056307,0.0026935637,0.00019594694,0.0000014785447,0.0015965598,0.008910464],"genre_scores_gemma":[0.9993896,0.0000068314134,0.000062555024,0.0000067246224,0.00027776626,0.0000103892635,0.000001850446,0.000029510004,0.0002147787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993624,0.000041943367,0.00013988889,0.00017419027,0.00013337073,0.00014820596],"domain_scores_gemma":[0.9997744,0.00008786697,0.000009183831,0.000065236614,0.000012304314,0.00005101945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017333492,0.00013191128,0.00011845001,0.00018513092,0.000100839694,0.00013636748,0.000020976542,0.00011293689,0.000095067044],"category_scores_gemma":[0.000027837432,0.00010499803,0.00009260564,0.00022212883,0.000009470273,0.00011445888,0.0000072893554,0.00029841383,0.00006787075],"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":[0.00004546485,0.000003624787,0.00043480966,0.00022514194,0.00012975208,0.0000211277,0.00015338893,0.09351517,0.042860832,0.00041374005,0.00013821314,0.86205876],"study_design_scores_gemma":[0.00020189378,0.00019742564,0.0004376678,0.00003930404,0.000029143519,0.00004680532,0.00006433507,0.9628436,0.023005268,0.00006011013,0.0128937615,0.00018066325],"about_ca_topic_score_codex":0.000034204928,"about_ca_topic_score_gemma":0.00002697749,"teacher_disagreement_score":0.86932844,"about_ca_system_score_codex":0.000054115168,"about_ca_system_score_gemma":0.0000039062515,"threshold_uncertainty_score":0.42816955},"labels":[],"label_agreement":null},{"id":"W4401453425","doi":"10.11591/ijece.v14i5.pp5185-5199","title":"Phase structures-based hybrid approaches for defect detection in vials","year":2024,"lang":"en","type":"article","venue":"International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Artificial intelligence; Blob detection; Computer science; Thresholding; Robustness (evolution); Pattern recognition (psychology); Quality assurance; Segmentation; Computer vision; Image processing; Edge detection; Image (mathematics); Engineering","score_opus":0.01152147113764312,"score_gpt":0.2465392237231744,"score_spread":0.2350177525855313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401453425","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":[0.32144636,0.008693037,0.663737,0.00007439406,0.0058503104,0.0001463325,0.000012938241,0.000026333135,0.0000132947125],"genre_scores_gemma":[0.9971636,0.00029063088,0.0006629072,0.00001666807,0.0018221384,0.000005414611,0.0000030426797,0.000031635314,0.0000039550423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980307,0.000046047102,0.0009774971,0.00015911953,0.00055241195,0.0002341992],"domain_scores_gemma":[0.998801,0.00029390515,0.00025565692,0.000050662646,0.0004819106,0.00011689703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007219618,0.0002200877,0.00040876653,0.0010968237,0.000025260475,0.00037361184,0.00025948227,0.00011019044,0.0000027716967],"category_scores_gemma":[0.00006523543,0.00018507062,0.00027026326,0.0001667172,0.00001407558,0.0003295311,0.000022041913,0.000522494,3.6908557e-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":[0.001172838,0.00026273553,0.000114412054,0.00020546712,0.0030404546,0.0007066595,0.00031945857,0.66994655,0.025470097,0.009834505,0.00063886354,0.28828794],"study_design_scores_gemma":[0.003143289,0.0014381879,0.00011779854,0.0004003598,0.000055987788,0.0031542266,0.000014496982,0.97320366,0.005903945,0.0004440036,0.0119001735,0.00022384354],"about_ca_topic_score_codex":0.0000047747476,"about_ca_topic_score_gemma":0.0000017030701,"teacher_disagreement_score":0.67571723,"about_ca_system_score_codex":0.00038594977,"about_ca_system_score_gemma":0.000104543295,"threshold_uncertainty_score":0.7546961},"labels":[],"label_agreement":null},{"id":"W4401536395","doi":"10.1007/s10044-024-01318-4","title":"Multilinear principal component analysis-based tensor decomposition for fabric weave pattern recognition from high-dimensional streaming data","year":2024,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Principal component analysis; Pattern recognition (psychology); Dimensionality reduction; Local binary patterns; Tensor (intrinsic definition); Multilinear map; Image (mathematics); Histogram; Mathematics","score_opus":0.03777358483708904,"score_gpt":0.29570965215031475,"score_spread":0.2579360673132257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401536395","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":[0.30518854,0.00013568654,0.69107676,0.000045263514,0.00007386715,0.00035092884,0.0029653667,0.00015735692,0.0000062374456],"genre_scores_gemma":[0.9830095,0.000014652158,0.0011927417,0.00003482695,0.00038872406,0.00031028545,0.015012791,0.000026491187,0.000009950545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985926,0.000043284577,0.00042242795,0.000566102,0.00020047998,0.00017511524],"domain_scores_gemma":[0.9990132,0.000246604,0.0000743535,0.00049058715,0.00008167016,0.00009358258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000225317,0.0001950736,0.00036839006,0.00059238897,0.00017387386,0.00019219505,0.0001253243,0.00011356208,0.00010017056],"category_scores_gemma":[0.000005673987,0.00018239628,0.00023949149,0.0010571152,0.000017467852,0.00011503928,0.000040036895,0.00013577787,0.00003757865],"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":[0.000014922711,0.0001199732,0.0077351695,0.00009310477,0.005226951,0.000003150875,0.000049010432,0.06984391,0.0052226204,0.0000036958652,0.00016668407,0.9115208],"study_design_scores_gemma":[0.00029235758,0.000015302941,0.020761248,0.000033747274,0.004391389,7.1194637e-7,0.000026899079,0.9709424,0.0022327437,0.000016487269,0.0010743305,0.00021241356],"about_ca_topic_score_codex":0.0014100439,"about_ca_topic_score_gemma":0.00061879837,"teacher_disagreement_score":0.9113084,"about_ca_system_score_codex":0.000059424085,"about_ca_system_score_gemma":0.000010900209,"threshold_uncertainty_score":0.74379045},"labels":[],"label_agreement":null},{"id":"W4401559026","doi":"10.1016/j.measurement.2024.115488","title":"Rapid detection technology of sewing thread based on DeblurGAN-BSV3 defuzzification algorithm and ST-FPN detection algorithm","year":2024,"lang":"en","type":"article","venue":"Measurement","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Support Program for Longyuan Youth and Fundamental Research Funds for the Universities of Gansu Province; Shanghai International Science and Technology; Ministry of Education of the People's Republic of China","keywords":"Algorithm; Thread (computing); Computer science","score_opus":0.028468826098467064,"score_gpt":0.21994943314776097,"score_spread":0.1914806070492939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401559026","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":[0.016671985,0.0018116316,0.9770873,0.00003380617,0.0025619378,0.000559626,0.000014249605,0.0008638736,0.0003955856],"genre_scores_gemma":[0.99775636,0.00004996986,0.0017663831,0.000007139238,0.00026098834,0.00010030526,0.000003909406,0.000046164405,0.000008787649],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984365,0.00006879908,0.00041105694,0.00032785122,0.00053540285,0.00022038586],"domain_scores_gemma":[0.9994098,0.00004025234,0.0000604971,0.00025744538,0.00017592173,0.0000560669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000987407,0.00021428546,0.00023000478,0.00069028797,0.00012277512,0.00006381554,0.000061571125,0.00028542004,0.000014874292],"category_scores_gemma":[0.000060684597,0.00020501806,0.00007946316,0.00072295463,0.00002800129,0.00011669613,0.000011035323,0.00030366186,0.000021122869],"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":[0.0000112924345,0.000017495817,0.0000065305176,0.000087066524,0.00004229019,0.0000022086747,0.00006125446,0.001759457,0.10532297,0.0000054904726,0.000027728614,0.8926562],"study_design_scores_gemma":[0.00039167225,0.0003256532,0.00017395115,0.0002576923,0.00004547514,0.00001847948,0.00014689068,0.3650072,0.6270565,0.00011735591,0.0062683886,0.00019075563],"about_ca_topic_score_codex":0.000041305244,"about_ca_topic_score_gemma":0.000014713108,"teacher_disagreement_score":0.98108435,"about_ca_system_score_codex":0.00042069945,"about_ca_system_score_gemma":0.000030250798,"threshold_uncertainty_score":0.8360394},"labels":[],"label_agreement":null},{"id":"W4401567455","doi":"10.1109/access.2024.3443343","title":"Yield Maximization of Flip-Flop Circuits Based on Deep Neural Network and Polyhedral Estimation of Nonlinear Constraints","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Industrial Vision Systems and Defect Detection","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Yield (engineering); Nonlinear system; Electronic circuit; Artificial neural network; Flip-flop; Maximization; Flip; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Engineering; Materials science; Electrical engineering","score_opus":0.029912349242162316,"score_gpt":0.2704851450613114,"score_spread":0.24057279581914906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401567455","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":[0.7989082,0.00019468582,0.19682752,0.000016983919,0.0025839573,0.00026907268,0.000022832523,0.0001640863,0.0010126632],"genre_scores_gemma":[0.9995242,0.000004951836,0.00016224086,0.000014128673,0.0002573363,0.00000562077,0.000008101836,0.000017966386,0.000005484981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993233,0.000022684011,0.000280859,0.00011766657,0.00014588117,0.00010962377],"domain_scores_gemma":[0.99964416,0.00013163056,0.000047809648,0.00010570275,0.000038374263,0.000032349246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014873272,0.000096200696,0.00016202568,0.00013149834,0.00002645576,0.000060150265,0.00006195812,0.00011261416,0.000035006535],"category_scores_gemma":[0.00004042652,0.000091074005,0.00003940967,0.0003193929,0.000029659432,0.00019102285,0.000006988606,0.00010985108,0.0000027472122],"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":[0.000012294736,0.000006174484,0.00024469593,0.00015041699,0.000013553767,0.0000026244595,0.00003987236,0.88228154,0.0018033421,0.000030506237,0.00011150721,0.115303464],"study_design_scores_gemma":[0.00018870582,0.00006281409,0.0009317378,0.00024535472,0.000016108637,0.0000046757154,0.0000065584086,0.98155016,0.016852569,0.00003311096,0.000027402364,0.00008077962],"about_ca_topic_score_codex":0.00003229107,"about_ca_topic_score_gemma":0.000006196259,"teacher_disagreement_score":0.20061597,"about_ca_system_score_codex":0.00001861748,"about_ca_system_score_gemma":0.000014754879,"threshold_uncertainty_score":0.371389},"labels":[],"label_agreement":null},{"id":"W4401747364","doi":"10.21203/rs.3.rs-4823989/v1","title":"AUTOMATING QUALITY CONTROL: REAL-TIME DEFECT DETECTION AND AUTOMATED DECISION-MAKING WITH AI AND DOOSAN ROBOTICS","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Robotics; Artificial intelligence; Control (management); Quality (philosophy); Computer science; Machine learning; Real-time computing; Computer vision; Robot","score_opus":0.029847705037688885,"score_gpt":0.3741793195677305,"score_spread":0.3443316145300416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401747364","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":[0.97637546,0.0014062594,0.014489978,0.000045978457,0.00071496156,0.0018654105,0.0000933343,0.003811311,0.0011972778],"genre_scores_gemma":[0.9983958,0.00011633659,0.0008341647,0.000003842787,0.00035725726,0.00012791694,0.00000938418,0.00012820236,0.000027087652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962286,0.0007130923,0.00066996104,0.0007343943,0.001042221,0.0006117793],"domain_scores_gemma":[0.99701273,0.001751668,0.00010560178,0.00052885566,0.00039292287,0.00020821618],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0038140477,0.00041698795,0.0006906387,0.0007946071,0.00038380528,0.00093580724,0.00013665795,0.00077523035,0.000017650316],"category_scores_gemma":[0.0007830832,0.00034953404,0.00013327617,0.00065690273,0.000103676146,0.00011987541,0.00044976332,0.0022901234,0.00004690965],"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":[0.0012102742,0.000079402416,0.002693645,0.017284509,0.0014820233,0.0004097882,0.0020149758,0.4228889,0.034408003,0.0004008559,0.0016088105,0.5155188],"study_design_scores_gemma":[0.0007960086,0.0003369088,0.008569035,0.0071245204,0.00007705979,0.00009367075,0.00018841024,0.9805395,0.00039212318,0.0012984915,0.00006801466,0.0005162771],"about_ca_topic_score_codex":0.00048182986,"about_ca_topic_score_gemma":0.00019516071,"teacher_disagreement_score":0.55765057,"about_ca_system_score_codex":0.00040211112,"about_ca_system_score_gemma":0.00013645088,"threshold_uncertainty_score":0.9998957},"labels":[],"label_agreement":null},{"id":"W4401928963","doi":"10.18280/jesa.570412","title":"A Novel Method for Knitted Fabric Defect Classification Using Image Processing and Weighted Voting Classifiers","year":2024,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Voting; Artificial intelligence; Pattern recognition (psychology); Computer science; Image processing; Image (mathematics); Computer vision","score_opus":0.047376849377211525,"score_gpt":0.3041465544647863,"score_spread":0.25676970508757474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401928963","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":[0.08694235,0.0023591158,0.907998,0.000029658815,0.0010182029,0.0004416474,0.000015978878,0.00078619574,0.00040884977],"genre_scores_gemma":[0.89339507,0.000029395755,0.105530865,0.0000108783,0.00074329594,0.000026988348,0.0000040843784,0.0001371145,0.00012232807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980819,0.00016629015,0.0007558689,0.0003028243,0.00029008952,0.00040297888],"domain_scores_gemma":[0.99898475,0.00027760045,0.00020115482,0.000117402946,0.00025987896,0.00015923436],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012912629,0.00029394336,0.00038629308,0.0005554166,0.000525026,0.0012570707,0.00010730833,0.00019772167,0.000012886926],"category_scores_gemma":[0.00022480324,0.00025354774,0.00021040381,0.00074709416,0.000044893903,0.00073889975,0.000024132407,0.00046667087,0.000010773286],"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":[0.000016972615,0.000011239734,0.000032948312,0.0012460131,0.00015550166,0.000021940805,0.00042509296,0.001983118,0.29711202,0.0002215765,0.00045359085,0.69832],"study_design_scores_gemma":[0.0004914768,0.000073485004,0.0028575908,0.0012781932,0.00016567117,0.003007087,0.0002786038,0.98800606,0.0015011022,0.00030739492,0.0017387654,0.0002945756],"about_ca_topic_score_codex":0.000019084338,"about_ca_topic_score_gemma":0.0000029528599,"teacher_disagreement_score":0.98602295,"about_ca_system_score_codex":0.0005129081,"about_ca_system_score_gemma":0.00012677256,"threshold_uncertainty_score":0.99999166},"labels":[],"label_agreement":null},{"id":"W4402135212","doi":"10.1007/978-3-031-61539-9_10","title":"Defect Quantification Using Novel Civil RGB-D Dataset","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"RGB color model; Artificial intelligence; Computer science; Natural language processing; Cartography; Geography","score_opus":0.03550762803157004,"score_gpt":0.2458455380414127,"score_spread":0.21033791000984267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402135212","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":[0.0003691685,0.009623891,0.9498208,0.0000434512,0.010185625,0.0010367476,0.0019205495,0.0016847184,0.025315026],"genre_scores_gemma":[0.9915384,0.00024597152,0.0016590249,0.0000740486,0.0024558026,0.000057103494,0.0016733975,0.000881674,0.0014145633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978211,0.0000075375297,0.00071040547,0.0006238295,0.00037978985,0.00045737287],"domain_scores_gemma":[0.9988616,0.00024808184,0.000089564535,0.00066036754,0.000041308267,0.00009905087],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003667887,0.00071349787,0.00065151707,0.00088905124,0.000053463864,0.0001634821,0.00022356007,0.00096988917,0.00019959525],"category_scores_gemma":[0.00011630337,0.00075558084,0.00026538703,0.00027626354,0.00002005471,0.00013961508,0.00007228838,0.0015779792,0.0001575544],"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":[0.000007126127,0.000005502209,0.0000017897906,0.0005842947,0.00015132395,0.000031708096,0.0000633703,0.973398,0.021569964,0.0015122208,0.00084760704,0.0018271484],"study_design_scores_gemma":[0.00034783178,0.000037430433,0.0000041311264,0.0023973987,0.00015787549,0.00013465647,0.0000015522306,0.5435571,0.0025307601,0.0009028956,0.44873372,0.0011946553],"about_ca_topic_score_codex":0.00006701148,"about_ca_topic_score_gemma":0.0006372634,"teacher_disagreement_score":0.9911693,"about_ca_system_score_codex":0.0005256681,"about_ca_system_score_gemma":0.000039385923,"threshold_uncertainty_score":0.99948955},"labels":[],"label_agreement":null},{"id":"W4402259599","doi":"10.1109/jphotov.2024.3450009","title":"Using SegFormer for Effective Semantic Cell Segmentation for Fault Detection in Photovoltaic Arrays","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Photovoltaics","topic":"Industrial Vision Systems and Defect Detection","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 Calgary","funders":"","keywords":"Photovoltaic system; Computer science; Segmentation; Fault detection and isolation; Artificial intelligence; Optoelectronics; Materials science; Electrical engineering","score_opus":0.026971205775092415,"score_gpt":0.2888259171006518,"score_spread":0.2618547113255594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402259599","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":[0.5470754,0.0002715922,0.44754103,0.0000012333387,0.004085267,0.0009363473,0.000016776601,0.000043640193,0.000028729153],"genre_scores_gemma":[0.996583,0.000035466892,0.0021170003,0.000009738151,0.0010548747,0.00009636632,0.0000023015148,0.00006754178,0.00003372762],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99862397,0.000040929728,0.000682831,0.00015736849,0.00022371416,0.00027121586],"domain_scores_gemma":[0.99915636,0.00031349208,0.00016594706,0.000084526175,0.00021010623,0.000069553826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008571124,0.00020419057,0.00034381385,0.0005795784,0.00008253695,0.00011103413,0.000070585746,0.0002159491,0.000004666697],"category_scores_gemma":[0.00005536895,0.00018738706,0.0002861478,0.00041064515,0.000012535543,0.00041421,0.000003937664,0.0003320042,0.0000037228338],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014708182,0.000016156828,0.000019133819,0.0006014269,0.000056855424,0.0000074419413,0.00038596423,0.084137894,0.9072045,9.523213e-7,0.0002972831,0.0071253004],"study_design_scores_gemma":[0.0009676859,0.00030488073,0.0000091078255,0.00025120162,0.00006746292,0.00006547804,0.00022292003,0.37110677,0.62495357,0.000147884,0.0017740297,0.0001290244],"about_ca_topic_score_codex":0.000059548292,"about_ca_topic_score_gemma":0.000060918894,"teacher_disagreement_score":0.4495076,"about_ca_system_score_codex":0.00054879783,"about_ca_system_score_gemma":0.000060982467,"threshold_uncertainty_score":0.7641423},"labels":[],"label_agreement":null},{"id":"W4402307091","doi":"10.18280/ts.410417","title":"Optimizing and Assessing the Quality of E-Commerce Product Images Using Deep Learning Techniques","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Product (mathematics); Computer science; Artificial intelligence; Deep learning; Machine learning; Data science; Mathematics","score_opus":0.046755354118479656,"score_gpt":0.3242456481312153,"score_spread":0.27749029401273567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402307091","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":[0.8033101,0.0020822263,0.19265456,0.000040530897,0.00026186512,0.000293121,0.0000017640294,0.00041163422,0.00094417157],"genre_scores_gemma":[0.9972662,0.000024624558,0.002378855,0.000005685815,0.00027389708,0.000010572738,0.0000013052834,0.000022755406,0.00001612518],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990503,0.00013326343,0.00034608302,0.00014935761,0.00018234781,0.00013864679],"domain_scores_gemma":[0.99967265,0.00013557973,0.000049445553,0.00008415333,0.000034659904,0.000023483819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012533768,0.00011701023,0.00016831879,0.00009198487,0.0001346049,0.00019215004,0.000053273434,0.00004771837,0.000027482763],"category_scores_gemma":[0.000025511632,0.0000859408,0.000055227643,0.00018124592,0.00003515192,0.00023784622,0.000022985285,0.00024526875,9.4745815e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075045896,0.000010097815,0.00040900797,0.0003870166,0.000071789604,0.000003026982,0.001200771,0.014710965,0.8237726,0.00013618146,0.00006250785,0.1592285],"study_design_scores_gemma":[0.0003788829,0.00014138965,0.004784928,0.0010417468,0.00011450884,0.000058389996,0.0022443137,0.30096275,0.6827868,0.000100135076,0.0068607735,0.0005253927],"about_ca_topic_score_codex":0.00007356482,"about_ca_topic_score_gemma":0.000001512503,"teacher_disagreement_score":0.28625178,"about_ca_system_score_codex":0.00004560524,"about_ca_system_score_gemma":0.000010561686,"threshold_uncertainty_score":0.35045642},"labels":[],"label_agreement":null},{"id":"W4402329819","doi":"10.1016/j.compeleceng.2024.109615","title":"Enhanced defect detection on wind turbine blades using binary segmentation masks and YOLO","year":2024,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Binary number; Turbine; Segmentation; Turbine blade; Computer science; Artificial intelligence; Marine engineering; Computer vision; Aerospace engineering; Environmental science; Engineering; Mathematics","score_opus":0.011853212295887552,"score_gpt":0.2234253770144981,"score_spread":0.21157216471861057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402329819","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":[0.5700052,0.0008352149,0.42688364,0.0000042163283,0.0013768601,0.00016824924,0.0000010462119,0.00068394304,0.00004167052],"genre_scores_gemma":[0.9986252,0.00003389859,0.0007269751,0.000011053494,0.0005217299,0.000009644257,0.0000034164168,0.00005750785,0.000010541131],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896216,0.000024859299,0.00025278772,0.00028974778,0.00017038285,0.0003000812],"domain_scores_gemma":[0.999607,0.00015537425,0.000018010354,0.000108644614,0.000017693954,0.00009326314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013630165,0.00023733788,0.00022039325,0.0004411174,0.000074491145,0.00015277922,0.000052808406,0.00016966257,0.000004591884],"category_scores_gemma":[0.000021031821,0.00023672197,0.00009591139,0.0006793923,0.000008567566,0.00017545688,0.000018197477,0.00039476444,0.000015073784],"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":[0.000013782747,0.0000053854665,0.0000015332556,0.00008320816,0.00006427153,0.000014475444,0.00007833496,0.4538671,0.4680668,0.0000345758,0.000038831306,0.07773168],"study_design_scores_gemma":[0.00021764266,0.00022184152,0.00011834425,0.00016132287,0.00003057867,0.000052370622,0.0000048663032,0.8294366,0.1691269,0.000008405504,0.00040287606,0.00021827396],"about_ca_topic_score_codex":0.0000101160695,"about_ca_topic_score_gemma":4.179647e-7,"teacher_disagreement_score":0.42862007,"about_ca_system_score_codex":0.0002817301,"about_ca_system_score_gemma":0.000010393762,"threshold_uncertainty_score":0.96532416},"labels":[],"label_agreement":null},{"id":"W4402351233","doi":"10.1109/access.2024.3456688","title":"WrapperRL: Reinforcement Learning Agent for Feature Selection in High-Dimensional Industrial Data","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Industrial Vision Systems and Defect Detection","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":"Western University","funders":"","keywords":"Reinforcement learning; Computer science; Feature selection; Selection (genetic algorithm); Artificial intelligence; Feature (linguistics); Machine learning","score_opus":0.09123863707268354,"score_gpt":0.32066307934443805,"score_spread":0.22942444227175451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402351233","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":[0.90920264,0.0006487954,0.06496573,0.0001987802,0.021150928,0.0018287638,0.00008423705,0.0012446479,0.0006754996],"genre_scores_gemma":[0.9973974,0.000011468048,0.00004518353,0.00002016333,0.001818241,0.00006378448,0.00014456025,0.000036851663,0.00046230247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998948,0.000034412587,0.0002755315,0.00029380727,0.00021407414,0.00023422627],"domain_scores_gemma":[0.9996628,0.00008120413,0.000029461196,0.00015671979,0.000029903988,0.00003989168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004699023,0.00014729978,0.00017735852,0.0002512927,0.00008492195,0.00024982146,0.00020986196,0.00026124174,0.000041468717],"category_scores_gemma":[0.0000480601,0.00013613392,0.000044700188,0.00048245353,0.000006387057,0.0005610308,0.000053767795,0.00050718716,0.000024221354],"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":[0.00008220441,0.000009103587,0.00023219257,0.00009995617,0.00007121656,0.000010028761,0.00005695513,0.83548117,0.006112218,0.000054643806,0.119884,0.037906308],"study_design_scores_gemma":[0.0012084171,0.00012781161,0.00028634813,0.00030450083,0.00003217422,0.000014314876,0.000021389593,0.82566667,0.013868041,0.00003821772,0.15813068,0.0003014539],"about_ca_topic_score_codex":0.00028428715,"about_ca_topic_score_gemma":0.00007168816,"teacher_disagreement_score":0.088194825,"about_ca_system_score_codex":0.00017384916,"about_ca_system_score_gemma":0.00004951941,"threshold_uncertainty_score":0.555138},"labels":[],"label_agreement":null},{"id":"W4402438896","doi":"10.11159/htff24.198","title":"An Efficient Platform to Capture Flow Features In Industrial Applications","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Flow (mathematics); Mechanics","score_opus":0.008679791804270065,"score_gpt":0.2161888303001475,"score_spread":0.20750903849587743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402438896","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":[0.9957617,0.000026135214,0.000026682272,0.00007042193,0.0028083234,0.0006511947,0.000040895877,0.0003484301,0.0002662522],"genre_scores_gemma":[0.9988036,0.000005868302,0.0003002265,0.00001472793,0.0006158571,0.00015663732,0.000002815683,0.00004388127,0.00005636893],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896425,0.0000025279812,0.00032208822,0.00027412525,0.00019645372,0.00024055115],"domain_scores_gemma":[0.9996571,0.000039187427,0.000028615645,0.00012063534,0.000035457368,0.00011902532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002059914,0.00021260264,0.00026459893,0.00023628496,0.000038821712,0.00019689366,0.00022121936,0.00019685845,0.000012765477],"category_scores_gemma":[0.00003733686,0.00016446576,0.000058656413,0.00052007893,0.000015200267,0.000092473434,0.00006420824,0.00036695827,0.000002849403],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006171938,0.000018749342,9.872933e-7,0.00021686546,0.000018249162,6.890686e-7,0.00002380158,0.015743377,0.97715324,0.0036922053,0.0010961692,0.001973918],"study_design_scores_gemma":[0.00026027762,0.000034847093,0.0000030543324,0.0005773634,0.000016539212,0.000007968851,0.00004382486,0.043916762,0.94632244,0.00005634011,0.008557563,0.00020299964],"about_ca_topic_score_codex":0.000012477982,"about_ca_topic_score_gemma":0.000002989008,"teacher_disagreement_score":0.030830812,"about_ca_system_score_codex":0.00008342036,"about_ca_system_score_gemma":0.000009402617,"threshold_uncertainty_score":0.6706719},"labels":[],"label_agreement":null},{"id":"W4402439054","doi":"10.11159/icmie24.115","title":"A Novel Approach Integrating Deep Learning and Machine Vision for Tool Wear Detection","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science and Technology Council","keywords":"Computer science; Artificial intelligence; Machine vision; Deep learning; Machine learning; Computer vision","score_opus":0.007798180845319869,"score_gpt":0.21108963923032467,"score_spread":0.20329145838500481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402439054","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":[0.9928908,0.0000979491,0.0029594386,0.000028383633,0.002582737,0.00062391005,0.000017009512,0.0005750687,0.0002247468],"genre_scores_gemma":[0.99711317,0.000032236258,0.0022686115,0.000004312252,0.0003510015,0.0000863974,0.0000021637572,0.000059269056,0.000082846214],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990317,0.000003224586,0.0003306599,0.0002786425,0.00014549356,0.00021022816],"domain_scores_gemma":[0.99969643,0.000089176014,0.00005363186,0.00005606584,0.000047736874,0.000056938596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031465298,0.00022498566,0.00027952055,0.00014510776,0.00009171556,0.00026200232,0.00009618057,0.00014440819,0.0000036434906],"category_scores_gemma":[0.0001386241,0.00017015357,0.00008067578,0.00022444928,0.000022095594,0.00015409492,0.00007753522,0.00032614288,3.989695e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062958046,0.000008494609,5.5835795e-7,0.00085597904,0.00003270341,9.562802e-8,0.0000143028765,0.0003043358,0.9894193,0.001811481,0.000026734526,0.0074630873],"study_design_scores_gemma":[0.00024663997,0.000057671055,8.0372666e-7,0.00043378738,0.000026772777,0.00001593663,0.00003790954,0.32976413,0.66672206,0.000042983487,0.0025128,0.00013849682],"about_ca_topic_score_codex":0.0000054976817,"about_ca_topic_score_gemma":8.259204e-7,"teacher_disagreement_score":0.3294598,"about_ca_system_score_codex":0.00004628733,"about_ca_system_score_gemma":0.0000031463408,"threshold_uncertainty_score":0.6938661},"labels":[],"label_agreement":null},{"id":"W4402464070","doi":"10.11159/mvml24.110","title":"Pedestrian Equipment Anomaly Detection with Computer Vision in Warehouses","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"İzmir Yüksek Teknoloji Enstitüsü","keywords":"Pedestrian; Anomaly detection; Computer science; Pedestrian detection; Computer vision; Artificial intelligence; Computer graphics (images); Engineering; Transport engineering","score_opus":0.007039719484417521,"score_gpt":0.2033959830926665,"score_spread":0.19635626360824895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402464070","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":[0.99204814,0.00063033315,0.0042539914,0.000025252946,0.0024078425,0.00029310217,7.698064e-7,0.0002117577,0.00012882723],"genre_scores_gemma":[0.99957085,0.000017592281,0.000098700504,0.000004771695,0.00022321567,0.000016004593,3.9933575e-8,0.000014699359,0.00005415905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894863,0.000004864943,0.00023864632,0.0002826111,0.00028673402,0.00023853018],"domain_scores_gemma":[0.9997132,0.00006091409,0.000032908105,0.000065377375,0.000053770746,0.00007385511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036605442,0.00015623623,0.00020798978,0.00048246852,0.00008931992,0.0003858735,0.00013823363,0.000047063626,2.0829793e-7],"category_scores_gemma":[0.0000067006845,0.00010024453,0.000026267177,0.0012576078,0.000055917848,0.00022306712,0.000049040496,0.0002348489,4.7358557e-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":[0.00027102773,0.00013701251,0.0056775743,0.0040449337,0.0001850413,0.000042276326,0.00089993706,0.4409906,0.10733231,0.021153308,0.0010754038,0.41819057],"study_design_scores_gemma":[0.0001982891,0.00034850702,0.0027456046,0.0009961432,0.0000061150104,0.00007518434,0.0000057448674,0.9893718,0.004544638,0.000003829296,0.0015603965,0.0001437745],"about_ca_topic_score_codex":0.000033641816,"about_ca_topic_score_gemma":0.000005980989,"teacher_disagreement_score":0.54838115,"about_ca_system_score_codex":0.00008336404,"about_ca_system_score_gemma":0.000015564545,"threshold_uncertainty_score":0.40878534},"labels":[],"label_agreement":null},{"id":"W4402464218","doi":"10.11159/icbes24.155","title":"Using Fixed Probe Geometries with OBI Systems During Measurements from Subjects with Different Forehead Angulations","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Forehead; Computer science; Geology; Medicine; Anatomy","score_opus":0.020390411558785803,"score_gpt":0.2118125950740595,"score_spread":0.1914221835152737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402464218","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":[0.992076,0.0012075464,0.0046479455,0.000009288181,0.0013856194,0.00042535368,0.0000037215823,0.00018786395,0.00005666673],"genre_scores_gemma":[0.99952954,0.0000058250807,0.00013665236,0.0000016296011,0.00021071915,0.000028429877,1.8772695e-7,0.000022332799,0.000064698244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864876,0.0000050923777,0.0002497168,0.00031812023,0.000494322,0.00028396232],"domain_scores_gemma":[0.99955606,0.000061750645,0.00005955313,0.000093347866,0.00012965738,0.00009961285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002149266,0.00021259615,0.00029160222,0.00045775305,0.00020763036,0.000607841,0.00015348698,0.00004716436,2.0669036e-7],"category_scores_gemma":[0.000012213579,0.00012382826,0.000027734748,0.0014192249,0.00007065154,0.000248722,0.00004209164,0.00020708427,1.3766557e-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":[0.00025180847,0.00008800652,0.03661521,0.004926829,0.0006811977,0.000014767681,0.0008561555,0.6813535,0.25968033,0.01114912,0.0002686152,0.0041144458],"study_design_scores_gemma":[0.0003209492,0.00015187384,0.0060793185,0.0026867404,0.000034074936,0.00006396012,0.000020351727,0.9748479,0.015416192,0.0000034596528,0.00015257012,0.00022262555],"about_ca_topic_score_codex":0.000064570835,"about_ca_topic_score_gemma":0.0000034687557,"teacher_disagreement_score":0.29349437,"about_ca_system_score_codex":0.00010940895,"about_ca_system_score_gemma":0.000021878715,"threshold_uncertainty_score":0.5861425},"labels":[],"label_agreement":null},{"id":"W4402473501","doi":"10.1109/access.2024.3459629","title":"Enhancing Object Detection in Dense Images: Adjustable Non-Maximum Suppression for Single-Class Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Industrial Vision Systems and Defect Detection","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":"Simon Fraser University","funders":"Seoul National University of Science and Technology","keywords":"Object detection; Computer science; Class (philosophy); Artificial intelligence; Computer vision; Object-class detection; Pattern recognition (psychology); Face detection","score_opus":0.023794452963942135,"score_gpt":0.2747826563352934,"score_spread":0.2509882033713513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402473501","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":[0.61548316,0.00031652956,0.37162846,0.0000064447736,0.010220322,0.00076662336,0.000013502063,0.0006568397,0.0009081186],"genre_scores_gemma":[0.99841017,0.000020280435,0.000054949694,0.000009441792,0.0010090804,0.00026983707,0.000004430674,0.000088591085,0.00013323534],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984216,0.000052619707,0.0004895283,0.00039717904,0.00021525953,0.0004238139],"domain_scores_gemma":[0.99933046,0.00023265586,0.00005151713,0.00023209353,0.00008033668,0.000072956325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004957917,0.00025112735,0.00027989905,0.00056199793,0.00014224535,0.00048349687,0.00014726672,0.00033092865,0.000022016478],"category_scores_gemma":[0.00009489384,0.00024520705,0.00013550716,0.0008063668,0.000013032676,0.0010815567,0.000026265794,0.00037169905,0.000046389945],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007301944,0.000014247014,0.000010259827,0.00039706196,0.000019388903,0.000015335683,0.00015077187,0.010654144,0.9081863,4.1066707e-7,0.00038381093,0.08009526],"study_design_scores_gemma":[0.00048539284,0.00012815719,0.000069792724,0.00045063952,0.000025104931,0.00003578302,0.000110209214,0.07858271,0.91630965,0.000115494506,0.0034197413,0.00026734013],"about_ca_topic_score_codex":0.0003493356,"about_ca_topic_score_gemma":0.00085990847,"teacher_disagreement_score":0.382927,"about_ca_system_score_codex":0.00045368896,"about_ca_system_score_gemma":0.000030602634,"threshold_uncertainty_score":0.9999253},"labels":[],"label_agreement":null},{"id":"W4402477158","doi":"10.11159/icceia24.142","title":"Plasma Treatment for Mould Inactivation on Building Materials: Surface Coverage Evaluation with Automated Image Analysis","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Grantová Agentura České Republiky","keywords":"Plasma; Image (mathematics); Computer science; Materials science; Computer vision; Physics","score_opus":0.025034674676954846,"score_gpt":0.2878476797334924,"score_spread":0.26281300505653754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402477158","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":[0.9939458,0.000056498677,0.00018318635,0.00036351566,0.00067356316,0.0010496997,0.000028660015,0.0030441193,0.0006549637],"genre_scores_gemma":[0.99876916,0.000017145505,0.0005394029,0.000002583491,0.00004110511,0.00014176096,0.000003917377,0.00003212351,0.0004527829],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989816,0.000006931742,0.00027195312,0.00026645968,0.00029930266,0.00017376129],"domain_scores_gemma":[0.99945104,0.00012511387,0.0001358603,0.00015251471,0.000120247576,0.0000152323155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028308202,0.00021805418,0.00030897665,0.00057711545,0.00009382483,0.00021736948,0.00017419302,0.0001440129,0.0000067710976],"category_scores_gemma":[0.00012626668,0.0001355716,0.000102493046,0.0014178742,0.00003477295,0.00021868754,0.000024960764,0.00012685754,0.0000027131196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004813568,0.000046410405,0.00020875213,0.00025485342,0.0013723612,8.8277744e-7,0.000108319284,0.059001297,0.8716821,0.0044479063,0.006162605,0.05623316],"study_design_scores_gemma":[0.0005396407,0.00024433288,0.00009353074,0.00041480744,0.00030653604,0.0000013479669,0.0001370879,0.11759285,0.8786297,0.00033804117,0.0015643683,0.00013778877],"about_ca_topic_score_codex":0.00003418617,"about_ca_topic_score_gemma":0.000015151717,"teacher_disagreement_score":0.058591556,"about_ca_system_score_codex":0.0004240133,"about_ca_system_score_gemma":0.000024742858,"threshold_uncertainty_score":0.55284494},"labels":[],"label_agreement":null},{"id":"W4402501855","doi":"10.11159/icceia24.139","title":"Crack Density and Length Detection using Machine Learning","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Technology Karnataka, Surathkal","keywords":"Computer science; Artificial intelligence","score_opus":0.017870640965423314,"score_gpt":0.23306317456331532,"score_spread":0.215192533597892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402501855","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":[0.99436456,0.000999426,0.000068172725,0.0001740788,0.0012913545,0.00019490461,0.0000010024439,0.0021143376,0.00079213944],"genre_scores_gemma":[0.99875045,0.0000774614,0.00013209728,0.0000030568744,0.00006666989,0.0000052675637,7.5445904e-8,0.000024677396,0.00094022776],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993184,0.0000037168804,0.00018945071,0.0001863286,0.00015039393,0.00015166891],"domain_scores_gemma":[0.9997533,0.000049750273,0.000060384496,0.000081250255,0.00003696956,0.000018340725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016441873,0.00014931438,0.00017739327,0.00033746177,0.000129404,0.00012608911,0.00014474434,0.00013891379,0.0000022846182],"category_scores_gemma":[0.00015802629,0.00010707807,0.000059211317,0.0006160516,0.000065678134,0.00016849501,0.000103148814,0.0005369611,0.0000030143049],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007820885,0.000011552767,0.005456021,0.00053041003,0.00016241954,0.0000032607043,0.00014355815,0.0013628706,0.6446803,0.0030177175,0.0012195617,0.34333408],"study_design_scores_gemma":[0.00014289873,0.000057592493,0.00014603723,0.00052057934,0.000030398345,0.00003011974,0.00021141737,0.059855774,0.9250697,0.00094767974,0.012841042,0.00014674613],"about_ca_topic_score_codex":0.0000485471,"about_ca_topic_score_gemma":0.000036861322,"teacher_disagreement_score":0.34318733,"about_ca_system_score_codex":0.00006876167,"about_ca_system_score_gemma":0.0000069147713,"threshold_uncertainty_score":0.4366517},"labels":[],"label_agreement":null},{"id":"W4402915489","doi":"10.1109/icip51287.2024.10647386","title":"Semi-Supervised 3D Object Detection With Channel Augmentation Using Transformation Equivariance","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Transformation (genetics); Computer science; Channel (broadcasting); Object (grammar); Computer vision; Object detection; Artificial intelligence; Pattern recognition (psychology); Telecommunications","score_opus":0.026956262695923678,"score_gpt":0.2452905986870429,"score_spread":0.2183343359911192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402915489","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":[0.26459634,0.00010742077,0.7280274,0.0000071332524,0.0012093293,0.00031007,0.000004028277,0.0008351546,0.004903125],"genre_scores_gemma":[0.99925894,0.0000113641645,0.00025981024,0.0000099082545,0.0002961188,0.000029313478,0.000007741864,0.00003463334,0.000092184375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992434,0.000025172465,0.00022810533,0.00014888121,0.00018757574,0.00016689523],"domain_scores_gemma":[0.99980116,0.000021076641,0.0000147070205,0.00008983054,0.000033657896,0.00003957927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018732174,0.00013839071,0.00011445792,0.00018855443,0.000095596566,0.00015342452,0.000030468082,0.0001168183,0.00006355389],"category_scores_gemma":[0.0000030438362,0.000113744856,0.000043853724,0.00048074487,0.0000071609875,0.00065401767,0.0000029029031,0.0001481417,0.00005239764],"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":[0.00008020539,0.000010099931,0.0000040961017,0.0004535128,0.00011306061,0.000009461985,0.0023878564,0.47268897,0.4170815,0.00011993458,0.00008714442,0.106964156],"study_design_scores_gemma":[0.00033829125,0.000085074666,0.000020674544,0.00014419811,0.000026121395,0.000043231692,0.0003251835,0.8760709,0.121991344,0.000021465861,0.0007785633,0.0001549355],"about_ca_topic_score_codex":0.00012443088,"about_ca_topic_score_gemma":0.000048362912,"teacher_disagreement_score":0.7346626,"about_ca_system_score_codex":0.00018700871,"about_ca_system_score_gemma":0.000018883942,"threshold_uncertainty_score":0.46383804},"labels":[],"label_agreement":null},{"id":"W4403061186","doi":"10.1109/iotaai62601.2024.10692829","title":"Inkjet printer ink drop feature detection based on machine vision","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Inkwell; Computer science; Drop (telecommunication); Computer vision; Artificial intelligence; Machine vision; Feature (linguistics); Drop out; Computer graphics (images); Speech recognition","score_opus":0.007294618276654879,"score_gpt":0.23038494562807557,"score_spread":0.2230903273514207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403061186","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":[0.20976278,0.0006383579,0.6137871,0.00070262095,0.019494252,0.0012367377,0.000034139033,0.00811105,0.14623292],"genre_scores_gemma":[0.9975184,0.0000036508425,0.000085583706,0.00006752249,0.00041135532,0.000016253742,0.000006549325,0.000041635692,0.001849027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992637,0.000032252752,0.00015864449,0.00020331793,0.00019197559,0.00015007865],"domain_scores_gemma":[0.99967885,0.000049600596,0.000010108872,0.00018774993,0.000019219977,0.00005448446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020226896,0.00015528475,0.00012275385,0.00026555863,0.00005301866,0.00014583023,0.000049525817,0.00022347618,0.00026763824],"category_scores_gemma":[0.00001515613,0.00011615511,0.00009563223,0.0003015238,0.000006168152,0.00011513211,0.000011437084,0.00041011182,0.00046429664],"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":[0.00019944957,0.00005117662,0.000068400914,0.00040515387,0.00008816593,0.00006955055,0.00014853153,0.041894004,0.19039145,0.00032649352,0.045099683,0.7212579],"study_design_scores_gemma":[0.00024501322,0.00021333592,0.00019801168,0.0001641904,0.000007793724,0.0000124323915,0.0000094883335,0.76360935,0.068657674,0.00001876323,0.16670829,0.00015564222],"about_ca_topic_score_codex":0.00002913024,"about_ca_topic_score_gemma":0.000034897806,"teacher_disagreement_score":0.7877556,"about_ca_system_score_codex":0.000121877616,"about_ca_system_score_gemma":0.000007899163,"threshold_uncertainty_score":0.5967749},"labels":[],"label_agreement":null},{"id":"W4403061359","doi":"10.1109/iotaai62601.2024.10692413","title":"GSBF-YOLO: A steel strip surface defect detection technique based on improved YOLOv8","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"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":"Chinese Academy of Sciences; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Materials science; Surface (topology); Computer science; Geometry; Mathematics","score_opus":0.011122923566913683,"score_gpt":0.22354426415235149,"score_spread":0.2124213405854378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403061359","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":[0.1911639,0.00030595154,0.7595428,0.000045306562,0.005205137,0.0018960767,0.000032472206,0.0069652195,0.03484308],"genre_scores_gemma":[0.99836487,0.0000048662628,0.00038132336,0.000027100032,0.00025698697,0.000101863465,0.0000043895643,0.00006908426,0.00078954594],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987928,0.0000737594,0.00031082035,0.00032784758,0.0002046635,0.0002900969],"domain_scores_gemma":[0.9993881,0.00016072002,0.000022617258,0.00030495733,0.000040151634,0.0000834335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053332915,0.0002486695,0.00021740797,0.00025377717,0.00008399714,0.00015859386,0.000082656275,0.00036799206,0.00014940175],"category_scores_gemma":[0.000044884884,0.00021033874,0.00021560783,0.0006094495,0.000012606073,0.00013357658,0.0000127574895,0.00046750178,0.0002474147],"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":[0.00008254521,0.000026928885,0.000015364865,0.00021850703,0.00006824332,0.000019756262,0.000032183652,0.04513993,0.9085694,0.00008648389,0.0026791336,0.043061543],"study_design_scores_gemma":[0.00029700954,0.00037129308,0.000044805478,0.00011927693,0.000020266429,0.000016652632,0.000029763232,0.5452447,0.43228215,0.000029290744,0.021284115,0.00026066933],"about_ca_topic_score_codex":0.00014873964,"about_ca_topic_score_gemma":0.000046053414,"teacher_disagreement_score":0.8072009,"about_ca_system_score_codex":0.00024747427,"about_ca_system_score_gemma":0.00003583567,"threshold_uncertainty_score":0.85773647},"labels":[],"label_agreement":null},{"id":"W4403211608","doi":"10.1109/icecet61485.2024.10698277","title":"Wind Turbine surface defects detection Based-on NasNetMobile architecture","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Turbine; Architecture; Wind power; Marine engineering; Computer science; Environmental science; Aerospace engineering; Engineering; Electrical engineering; Geography","score_opus":0.008701378462610215,"score_gpt":0.21286771248172498,"score_spread":0.20416633401911477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403211608","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":[0.8921814,0.0004986183,0.066783816,0.000038539547,0.005226808,0.00049311674,0.000012794724,0.0029166853,0.031848215],"genre_scores_gemma":[0.99849945,0.000003473383,0.00007701058,0.000037843896,0.00052281277,0.0000055448486,0.000004450055,0.00005514958,0.00079429016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907094,0.000040192564,0.00019145751,0.00024726472,0.00021964381,0.00023048345],"domain_scores_gemma":[0.99954486,0.00012994956,0.000010733299,0.00021553003,0.000020610369,0.00007833363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021211355,0.0001962223,0.00015364954,0.00017135759,0.00006886852,0.00012971484,0.000057129797,0.00020339614,0.00024586325],"category_scores_gemma":[0.000020349553,0.00015662736,0.00012030009,0.0004778175,0.000010545998,0.00007613733,0.000008408079,0.0003880505,0.000478912],"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":[0.000023215112,0.0000083075165,0.000013480544,0.00011128953,0.000020369154,0.000017239445,0.000044668628,0.9045816,0.058869123,0.000035531102,0.0025482674,0.03372689],"study_design_scores_gemma":[0.0003573617,0.00034610427,0.00011531423,0.00018851364,0.000019596413,0.000036069105,0.000028753611,0.6340329,0.23640664,0.00007802539,0.12804566,0.00034504742],"about_ca_topic_score_codex":0.000037443235,"about_ca_topic_score_gemma":0.000025070445,"teacher_disagreement_score":0.2705487,"about_ca_system_score_codex":0.000108324595,"about_ca_system_score_gemma":0.000016238822,"threshold_uncertainty_score":0.6387078},"labels":[],"label_agreement":null},{"id":"W4403249565","doi":"10.1016/j.procir.2024.08.372","title":"Structured-light 3D Scanning Performance in Offline and In-process Measurement of 3D Printed Parts","year":2024,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"3d printed; 3d scanning; Structured light; Process (computing); 3D printing; Engineering drawing; Materials science; Computer science; Engineering; Manufacturing engineering; Mechanical engineering; Artificial intelligence","score_opus":0.018850634467483758,"score_gpt":0.23720026490591084,"score_spread":0.2183496304384271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403249565","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":[0.9959789,0.0016208544,0.00021926033,0.000012540573,0.0005819766,0.00023840152,0.0000018781082,0.00010258887,0.0012436215],"genre_scores_gemma":[0.9997204,0.000038553306,0.000053667416,0.000002702678,0.00012108012,0.000032830594,9.735121e-7,0.000018879995,0.0000109280145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991189,0.000009973115,0.00031623786,0.00016600873,0.00022540257,0.00016345007],"domain_scores_gemma":[0.9997911,0.0000117836125,0.000026528644,0.00007673314,0.000060303486,0.000033560078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036753487,0.00011451696,0.0001836977,0.00023863591,0.000016319607,0.000026795187,0.000045750825,0.00009413138,0.000010120129],"category_scores_gemma":[0.00005497253,0.00010271706,0.000015158803,0.00039438833,0.000009594996,0.00013967343,0.000012890643,0.00021210869,0.0000031421205],"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":[0.00041641935,0.000105467756,0.17612094,0.022174703,0.00021301469,0.00008474939,0.016929273,0.17377467,0.15544415,0.00014842473,0.000519642,0.45406854],"study_design_scores_gemma":[0.0023541541,0.0002469664,0.10270416,0.0067913104,0.000045652596,0.0000837217,0.0005323152,0.5441665,0.33134723,0.00015117187,0.010687989,0.0008888334],"about_ca_topic_score_codex":0.000011590139,"about_ca_topic_score_gemma":0.00005078245,"teacher_disagreement_score":0.45317972,"about_ca_system_score_codex":0.00008297763,"about_ca_system_score_gemma":0.000040179733,"threshold_uncertainty_score":0.418868},"labels":[],"label_agreement":null},{"id":"W4403291952","doi":"10.1007/s10462-024-10978-x","title":"A systematic review of computer vision-based personal protective equipment compliance in industry practice: advancements, challenges and future directions","year":2024,"lang":"en","type":"review","venue":"Artificial Intelligence Review","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Science Fund of the Republic of Serbia","keywords":"Compliance (psychology); Personal protective equipment; Computer science; Risk analysis (engineering); Medicine; Psychology; Coronavirus disease 2019 (COVID-19); Pathology","score_opus":0.11885685547743512,"score_gpt":0.3976492832622018,"score_spread":0.27879242778476665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403291952","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":[5.599748e-8,0.99039215,0.00083754625,0.0001985538,0.0011533576,0.007079384,0.000036018533,0.000103513106,0.00019940274],"genre_scores_gemma":[0.0000042203023,0.9974352,0.00016335968,0.00010324609,0.00040562786,0.0017996641,0.000014961318,0.00006502875,0.00000867712],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9954323,0.0007373243,0.0024835602,0.00057793304,0.0004651716,0.000303701],"domain_scores_gemma":[0.99814326,0.0004327648,0.0006713729,0.00043563222,0.00021300415,0.00010397841],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020906148,0.00059964834,0.0034264724,0.00030159924,0.000059137856,0.000048312213,0.00021148963,0.0004952593,0.000059619317],"category_scores_gemma":[0.00029844113,0.0004472989,0.0004977088,0.0010682575,0.00005607005,0.00018200996,0.00006535112,0.0013806246,0.0001332931],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012013543,0.000025871666,1.274139e-9,0.5136818,0.000088274996,0.0000071850263,0.000033090022,0.0000017644434,5.2012307e-8,0.00008723008,0.000052038446,0.4860215],"study_design_scores_gemma":[0.000009447118,0.000090030095,1.618994e-8,0.65351075,0.0011310539,0.000058203335,0.000064380845,0.0005504602,0.0000010876971,0.000018653169,0.3443178,0.0002481129],"about_ca_topic_score_codex":0.000014095037,"about_ca_topic_score_gemma":0.000010527168,"teacher_disagreement_score":0.48577335,"about_ca_system_score_codex":0.00035379294,"about_ca_system_score_gemma":0.0001521416,"threshold_uncertainty_score":0.9997979},"labels":[],"label_agreement":null},{"id":"W4403677342","doi":"10.1109/case59546.2024.10711635","title":"Visual Deformation Detection Using Soft Material Simulation for Pre-training of Condition Assessment Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Training (meteorology); Deformation (meteorology); Training set; Artificial intelligence; Materials science; Composite material","score_opus":0.05294791845711259,"score_gpt":0.3545796985898713,"score_spread":0.3016317801327587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403677342","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":[0.45154604,0.0000059332065,0.54654187,5.9751017e-7,0.001196453,0.00030739623,0.000013969095,0.0002216906,0.00016603479],"genre_scores_gemma":[0.9984556,8.2823846e-7,0.0010352071,0.0000019034293,0.0003828458,0.00004135098,0.000043924974,0.000026049229,0.000012294393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992056,0.000022520002,0.0003910694,0.000109091045,0.00014979567,0.0001219097],"domain_scores_gemma":[0.9997296,0.00007314698,0.00004943861,0.000054430515,0.000070139045,0.00002329974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032567154,0.00010141751,0.0001368244,0.00019158995,0.00006844936,0.00009380816,0.000019952673,0.0001426442,0.000027814398],"category_scores_gemma":[0.0000102335825,0.00009579805,0.00007322675,0.0001447147,0.0000063429575,0.0006886751,0.000005645015,0.00006414276,0.0000018026012],"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":[0.000032092346,0.0000033769704,9.11145e-7,0.00018454729,0.000021928658,9.9675894e-8,0.00020268455,0.83379066,0.14085534,0.00020739029,0.0000048864244,0.024696061],"study_design_scores_gemma":[0.00030486242,0.00009880121,0.00002720574,0.000091707545,0.00002669369,0.0000044487037,0.00015230748,0.9241891,0.07430802,0.0005942151,0.00010603404,0.00009661159],"about_ca_topic_score_codex":0.000033310786,"about_ca_topic_score_gemma":0.0000049106256,"teacher_disagreement_score":0.5469096,"about_ca_system_score_codex":0.00017078238,"about_ca_system_score_gemma":0.000025025756,"threshold_uncertainty_score":0.39065313},"labels":[],"label_agreement":null},{"id":"W4403852319","doi":"10.1007/978-3-031-73404-5","title":"Computer Vision – ECCV 2024","year":2024,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of California, Los Angeles; University at Buffalo; Wuhan University; Tsinghua University; DeepMind; Harbin Institute of Technology; Universiteit van Amsterdam; Sun Yat-sen University; University of Science and Technology of China; National University of Singapore; University of Bristol; Northwestern University; York University; Korea Advanced Institute of Science and Technology; Shanghai Jiao Tong University; Zhejiang University; Hong Kong Baptist University; Microsoft Research; Nanjing University; Peking University; Baidu; California Institute of Technology; University of Illinois at Urbana-Champaign; National Taiwan University; University of Minnesota; University of Oxford","keywords":"Computer science; Computer graphics (images); Computer vision; Artificial intelligence","score_opus":0.011947100520395092,"score_gpt":0.24193132055468078,"score_spread":0.22998422003428567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403852319","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":[0.0002362967,0.00092829147,0.96340144,0.00004798095,0.026431292,0.00035678994,0.000010475272,0.0004860488,0.00810136],"genre_scores_gemma":[0.76951754,0.00028070086,0.12602083,0.0019544219,0.061659303,0.00011461067,0.000099316465,0.00093527074,0.039417997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975563,0.0000277345,0.00046618402,0.00080617995,0.0006638959,0.000479663],"domain_scores_gemma":[0.99899065,0.0001999561,0.000055861867,0.00055405276,0.000081721744,0.0001177594],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062921876,0.00043792426,0.00046318924,0.00090749364,0.00010886292,0.0005702485,0.00056869833,0.00056937936,0.00005927877],"category_scores_gemma":[0.000016265003,0.00037252912,0.00015147189,0.00096913363,0.00015916984,0.00020261406,0.00029504503,0.0012295585,0.00053630077],"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":[0.000004019292,0.0000065005443,0.0000020070988,0.00019775357,0.000015128633,0.00010794849,0.00019802689,0.32860103,0.00020718508,0.000056307912,0.018111523,0.6524926],"study_design_scores_gemma":[0.00015638418,0.00020997558,0.000013184575,0.0013067276,0.00001271793,0.000106948006,1.0073026e-7,0.9075815,0.00076243695,0.0058025126,0.08350753,0.00053997955],"about_ca_topic_score_codex":0.000011851997,"about_ca_topic_score_gemma":0.000025567802,"teacher_disagreement_score":0.83738065,"about_ca_system_score_codex":0.00061653566,"about_ca_system_score_gemma":0.00023970417,"threshold_uncertainty_score":0.9998727},"labels":[],"label_agreement":null},{"id":"W4403863359","doi":"10.1109/tii.2024.3463705","title":"Multi-Source Domain Generalization for Machine Remaining Useful Life Prediction via Risk Minimization-Based Test-Time Adaptation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Generalization; Minification; Adaptation (eye); Domain adaptation; Machine learning; Artificial intelligence; Mathematics","score_opus":0.03353389467160457,"score_gpt":0.23176515290889138,"score_spread":0.19823125823728682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403863359","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":[0.017240271,0.000024635725,0.9752463,0.000034971137,0.0039218455,0.001249056,0.00091287633,0.001302337,0.00006768561],"genre_scores_gemma":[0.98293805,0.000026830705,0.014997628,0.00008577355,0.000929367,0.00031134792,0.00027296023,0.00013012635,0.0003079344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977862,0.000095508796,0.0012084949,0.0002021801,0.0003923138,0.00031525563],"domain_scores_gemma":[0.99854225,0.0006839713,0.00019459343,0.00026315072,0.0001486741,0.0001673587],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070888316,0.00033882717,0.0003189872,0.0006605632,0.00042800448,0.00028743077,0.00010798478,0.0006255427,0.00007842621],"category_scores_gemma":[0.00014808375,0.00034151896,0.00021377103,0.00083686755,0.000030102492,0.0006324898,0.0000010384125,0.00060428306,0.00009839914],"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":[0.000106682615,0.000041298375,0.000011158127,0.000103333754,0.000081449216,3.7715182e-7,0.0010607522,0.96711725,0.0008594263,0.000002762191,0.0014153982,0.029200101],"study_design_scores_gemma":[0.0023272915,0.00032613828,0.000006112692,0.00020780148,0.00015443866,0.000006240544,0.00028539408,0.9781754,0.009046584,0.000007995973,0.009145113,0.00031151428],"about_ca_topic_score_codex":0.000057093737,"about_ca_topic_score_gemma":0.000015752208,"teacher_disagreement_score":0.96569777,"about_ca_system_score_codex":0.00031635,"about_ca_system_score_gemma":0.00014988928,"threshold_uncertainty_score":0.9999037},"labels":[],"label_agreement":null},{"id":"W4403869526","doi":"10.3390/automation5040031","title":"Capacity Constraint Analysis Using Object Detection for Smart Manufacturing","year":2024,"lang":"en","type":"article","venue":"Automation","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Ontario Centre of Innovation; University of Windsor","keywords":"Constraint (computer-aided design); Computer science; Object (grammar); Artificial intelligence; Manufacturing engineering; Computer vision; Engineering; Mechanical engineering","score_opus":0.031650019673483465,"score_gpt":0.25599751483479594,"score_spread":0.22434749516131247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403869526","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":[0.5665519,0.000028466417,0.43141055,0.0000026687446,0.0009608107,0.0001746166,0.000009857022,0.00064761005,0.00021349259],"genre_scores_gemma":[0.9991554,0.000001429991,0.00054390106,0.0000028589484,0.0002204261,0.000029097757,0.000010873916,0.000018250512,0.000017777598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936014,0.000023457225,0.00023166255,0.00014404072,0.00010817254,0.00013250133],"domain_scores_gemma":[0.9997575,0.00006496487,0.000025875994,0.00009730901,0.00002573103,0.000028608829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029841924,0.000100694764,0.00014156383,0.00038697835,0.000097321536,0.0001339755,0.00002490447,0.00012158257,0.000024204523],"category_scores_gemma":[0.000018978813,0.00009953476,0.00015871684,0.00037458388,0.000008525422,0.0002111902,0.0000038625553,0.000092396156,0.000017945104],"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":[0.000014353985,0.000009394302,0.000085064545,0.0005568262,0.0012149675,0.0000038434455,0.00079017796,0.4007953,0.24024493,0.00024614,0.00014716067,0.35589182],"study_design_scores_gemma":[0.000081479164,0.000015856223,0.00072160974,0.00003165522,0.00017767808,0.000010267911,0.000034163848,0.803164,0.19461323,0.000106029845,0.0009461595,0.00009786846],"about_ca_topic_score_codex":0.00010537837,"about_ca_topic_score_gemma":0.000057172907,"teacher_disagreement_score":0.43260345,"about_ca_system_score_codex":0.00021057593,"about_ca_system_score_gemma":0.000009988922,"threshold_uncertainty_score":0.40589097},"labels":[],"label_agreement":null},{"id":"W4403889654","doi":"10.1007/978-3-031-72754-2_7","title":"Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Segmentation; Image segmentation; Computer graphics (images); Pattern recognition (psychology)","score_opus":0.016770745282669752,"score_gpt":0.2439766510671669,"score_spread":0.22720590578449715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403889654","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":[0.0053458675,0.00022962624,0.97695655,0.000037451267,0.0044745645,0.00049480284,0.000113869646,0.00019214158,0.0121551445],"genre_scores_gemma":[0.97857314,0.000040379593,0.018847248,0.000063443855,0.0013925249,0.000017953762,0.000027105501,0.00007762915,0.0009605569],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986152,0.0000088796205,0.0003335438,0.00043902773,0.00039961087,0.00020375274],"domain_scores_gemma":[0.9993302,0.0001556847,0.00007435786,0.000290214,0.0000839774,0.00006553143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020540951,0.0002388996,0.00033361203,0.0004260741,0.000044422508,0.00010057397,0.00022309861,0.00022306704,0.000041676183],"category_scores_gemma":[0.000017235638,0.00021811051,0.00006717499,0.00027976927,0.00008013169,0.00014098483,0.00010601028,0.00036016246,0.000053294334],"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":[0.00002486236,0.0000064671176,0.000008991425,0.00007392421,0.00003682318,0.000026155312,0.0009309798,0.4428866,0.025034841,0.00024274692,0.00020256337,0.530525],"study_design_scores_gemma":[0.00057356077,0.0004053419,0.00017623573,0.002154959,0.000058532914,0.000017566685,0.000003895147,0.42560175,0.52894336,0.03805614,0.0030153755,0.0009933054],"about_ca_topic_score_codex":0.00024383863,"about_ca_topic_score_gemma":0.000103510225,"teacher_disagreement_score":0.97322726,"about_ca_system_score_codex":0.00022458062,"about_ca_system_score_gemma":0.00005593327,"threshold_uncertainty_score":0.88942885},"labels":[],"label_agreement":null},{"id":"W4403901637","doi":"10.1016/j.mlwa.2024.100600","title":"Review of machine learning applications for defect detection in composite materials","year":2024,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Composite number; Computer science; Materials science; Artificial intelligence; Machine learning; Composite material","score_opus":0.010480366394013512,"score_gpt":0.2567022874481734,"score_spread":0.2462219210541599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403901637","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":[0.010332864,0.042236455,0.9400228,0.00009944763,0.00018008467,0.004547497,0.00012051851,0.0012465002,0.0012137953],"genre_scores_gemma":[0.99175304,0.0018866372,0.001068149,0.000022295495,0.00018339281,0.004581998,0.00025562654,0.00008350468,0.00016537779],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987751,0.00009278506,0.00050864974,0.00028813593,0.00013385309,0.00020146657],"domain_scores_gemma":[0.999336,0.00021944336,0.00010375749,0.0002137382,0.00007690194,0.000050168084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006192785,0.00019598684,0.00034601733,0.0002815751,0.00016350548,0.000057903344,0.00010405021,0.00010371463,0.000041475574],"category_scores_gemma":[0.00003574506,0.00017370228,0.00010347794,0.000900942,0.000023248494,0.00008926062,0.000018129642,0.0004310954,0.000043384738],"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":[0.00016899346,0.00014520167,0.0022227846,0.043460883,0.00040716474,0.0000032150945,0.00035995283,0.12905683,0.36040777,0.0027115764,0.0003269751,0.46072865],"study_design_scores_gemma":[0.00079170667,0.00030497342,0.0003124363,0.004653523,0.00020549959,0.000069743604,0.000032764114,0.110511534,0.03936238,0.00014883405,0.84308004,0.0005265536],"about_ca_topic_score_codex":0.0001673032,"about_ca_topic_score_gemma":0.00006503373,"teacher_disagreement_score":0.98142016,"about_ca_system_score_codex":0.000090416346,"about_ca_system_score_gemma":0.000020075142,"threshold_uncertainty_score":0.70833737},"labels":[],"label_agreement":null},{"id":"W4403917867","doi":"10.1109/tci.2024.3488563","title":"OccCasNet: Occlusion-Aware Cascade Cost Volume for Light Field Depth Estimation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Imaging","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Cascade; Volume (thermodynamics); Computer science; Computer vision; Field (mathematics); Occlusion; Estimation; Artificial intelligence; Mathematics; Medicine; Engineering; Physics","score_opus":0.014686970408777276,"score_gpt":0.2719464141417567,"score_spread":0.25725944373297943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403917867","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":[0.002239247,0.00012087509,0.9914654,0.00046130834,0.003916022,0.0005000616,0.00007494928,0.00076488726,0.0004572717],"genre_scores_gemma":[0.99685013,0.000004433372,0.002299445,0.00012232234,0.00026325864,0.00010666146,0.000026505199,0.00005161873,0.0002756511],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998968,0.000023124028,0.00031764558,0.00025101437,0.00023246056,0.00020775362],"domain_scores_gemma":[0.99934375,0.00036384823,0.000024047857,0.00010577937,0.00009001562,0.000072546856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001514089,0.00018198128,0.0001500988,0.00032333663,0.00027805415,0.00020718189,0.00006768608,0.00009389839,0.000082190985],"category_scores_gemma":[0.0000072235753,0.00018893107,0.00015267388,0.00031339013,0.0000112380885,0.00033007513,8.966672e-7,0.0002632687,0.00016072183],"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":[0.000015737181,0.000012838188,0.0000029768826,0.00007329098,0.000035962992,0.000009702308,0.00013361107,0.8336931,0.00035990909,0.00005097947,0.0075244913,0.15808743],"study_design_scores_gemma":[0.00029393545,0.00004293138,0.000026998485,0.00017098476,0.000033025408,0.00009494788,0.000045820434,0.9797138,0.008569786,0.0003109432,0.010493905,0.00020291997],"about_ca_topic_score_codex":0.000026137091,"about_ca_topic_score_gemma":0.000011771659,"teacher_disagreement_score":0.99461085,"about_ca_system_score_codex":0.00017361183,"about_ca_system_score_gemma":0.00004662751,"threshold_uncertainty_score":0.77043855},"labels":[],"label_agreement":null},{"id":"W4404315085","doi":"10.1016/j.aei.2024.102933","title":"A Unet-inspired spatial-attention transformer model for segmenting gear tooth surface defects","year":2024,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus","funders":"","keywords":"Transformer; Tooth surface; Computer science; Artificial intelligence; Engineering; Electrical engineering; Mechanical engineering","score_opus":0.011205981938493658,"score_gpt":0.2203848774359911,"score_spread":0.20917889549749746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404315085","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":[0.1935796,0.0002495154,0.8027343,0.0000039922375,0.0013210681,0.0005019236,0.000031662505,0.001184965,0.00039292593],"genre_scores_gemma":[0.97025937,0.00004263429,0.029199926,0.000010085644,0.00012865651,0.00007364686,0.00003725751,0.00008593475,0.00016250409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870855,0.000003622087,0.00058105827,0.00011294795,0.00019918603,0.00039466476],"domain_scores_gemma":[0.9996018,0.00006806897,0.0000362776,0.00016721488,0.000045976605,0.00008070101],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023666867,0.0002541818,0.00024160428,0.00015947728,0.00007677817,0.00013509818,0.00008083885,0.00016030388,0.000004022357],"category_scores_gemma":[0.000022759328,0.00025852388,0.00015552269,0.00027787976,0.0000065412014,0.0007168795,0.000007670368,0.00022447862,0.000032017706],"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":[0.0000063292873,0.0000029368869,0.0000031733039,0.0010271826,0.000043912827,7.140048e-7,0.0006214183,0.9571022,0.019671947,0.00022540009,0.00012929393,0.021165524],"study_design_scores_gemma":[0.0004663309,0.0000449089,0.000009815926,0.00033447682,0.00003320495,0.000007006637,0.00007713871,0.97571737,0.009517666,0.00002542485,0.01346639,0.00030025002],"about_ca_topic_score_codex":0.0000043977866,"about_ca_topic_score_gemma":0.0000036419344,"teacher_disagreement_score":0.77667975,"about_ca_system_score_codex":0.00015162062,"about_ca_system_score_gemma":0.000022231388,"threshold_uncertainty_score":0.9999867},"labels":[],"label_agreement":null},{"id":"W4404420827","doi":"10.1007/978-981-97-9003-6_3","title":"DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learning","year":2024,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Generative grammar; Computer science; Nanotechnology; Artificial intelligence; Materials science","score_opus":0.03255737050998604,"score_gpt":0.24917878970862428,"score_spread":0.21662141919863825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404420827","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":[0.16957891,0.0071702506,0.68224764,0.00023323542,0.0031471772,0.003681114,0.00007230591,0.00091274927,0.13295661],"genre_scores_gemma":[0.9955026,0.0016178626,0.002263437,0.000030043275,0.0001608066,0.00002166226,0.00002738462,0.000019042642,0.00035719326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899566,0.000032903383,0.0004090051,0.00020114964,0.00020946741,0.00015182355],"domain_scores_gemma":[0.9992323,0.00013572737,0.000121791985,0.00027022464,0.00016552894,0.00007442551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073752826,0.00020821669,0.00022621811,0.00046747134,0.00044364927,0.0005253579,0.00013567296,0.00019562281,0.0000022758275],"category_scores_gemma":[0.000030476478,0.00018964424,0.000021058277,0.00024757587,0.00029575927,0.0020624143,0.00019435905,0.00068487047,0.000006355277],"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":[0.00004584464,0.000008050475,0.0001758941,0.00016977376,0.000102094236,0.0000020931234,0.007131507,0.12459064,0.0025829955,0.025962781,0.00016514478,0.83906317],"study_design_scores_gemma":[0.0005254108,0.00019445935,0.00009901547,0.00035207983,0.00002695864,0.00006856162,0.00014525476,0.9689508,0.0004927949,0.00020607155,0.02862154,0.00031701938],"about_ca_topic_score_codex":0.000021991149,"about_ca_topic_score_gemma":0.00006445355,"teacher_disagreement_score":0.8443602,"about_ca_system_score_codex":0.00015803907,"about_ca_system_score_gemma":0.00006411083,"threshold_uncertainty_score":0.7733468},"labels":[],"label_agreement":null},{"id":"W4404427741","doi":"10.18280/ts.410544","title":"An End-to-End Object Detection System in Indoor Environments Using Lightweight Neural Network","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Northern Border University","keywords":"End-to-end principle; Artificial neural network; Computer science; Real-time computing; Artificial intelligence","score_opus":0.016951953278375943,"score_gpt":0.22754507717888414,"score_spread":0.2105931239005082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404427741","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":[0.9352231,0.00025236333,0.06070199,0.000003886202,0.0025442997,0.0005994864,0.000012723116,0.00035588403,0.00030626424],"genre_scores_gemma":[0.99810565,0.0000026856096,0.00011797226,0.00001462136,0.0016100988,0.00006438081,0.000008099233,0.00005909807,0.000017427188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982713,0.00012463931,0.00050271343,0.00034430152,0.00034856691,0.00040848283],"domain_scores_gemma":[0.9996183,0.000041360807,0.000033369855,0.00016332448,0.00000768808,0.00013598989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005254317,0.0002481665,0.0002507842,0.00031606434,0.00011219722,0.0001747097,0.00010163187,0.00016373291,0.00014093792],"category_scores_gemma":[0.0000022921727,0.00023734967,0.000088627356,0.0005092872,0.000010453076,0.00031012396,0.00001825975,0.00027794414,0.000085753905],"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":[0.000093255985,0.00003066675,0.00060712406,0.00014632885,0.0000752462,0.00010023859,0.0005123828,0.6902847,0.27034196,0.000043311054,0.00008575664,0.037679005],"study_design_scores_gemma":[0.0006267653,0.00042975493,0.0029762683,0.00035867782,0.000042328502,0.0000717342,0.00021583436,0.9430209,0.043388512,0.000007695644,0.00844441,0.00041715044],"about_ca_topic_score_codex":0.00009515442,"about_ca_topic_score_gemma":0.000049339662,"teacher_disagreement_score":0.25273615,"about_ca_system_score_codex":0.0005660723,"about_ca_system_score_gemma":0.000014748454,"threshold_uncertainty_score":0.9678839},"labels":[],"label_agreement":null},{"id":"W4404626171","doi":"10.1016/j.measurement.2024.116255","title":"HDFA-Net: A high-dimensional decoupled frequency attention network for steel surface defect detection","year":2024,"lang":"en","type":"article","venue":"Measurement","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Hebei Agricultural University; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Net (polyhedron); Surface (topology); Tunable metamaterials; Acoustics; Materials science; Structural engineering; Engineering; Computer science; Physics; Optoelectronics; Mathematics; Geometry","score_opus":0.03027119543456828,"score_gpt":0.2271543156478063,"score_spread":0.19688312021323803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404626171","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":[0.7376918,0.005070216,0.24052422,0.000046948106,0.013506791,0.0015132389,0.000026159261,0.001223263,0.00039732296],"genre_scores_gemma":[0.9978375,0.000010782804,0.00071278244,0.00001321888,0.0011000463,0.00014307693,0.000014619232,0.00006369116,0.000104324696],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982842,0.00006563614,0.00040155632,0.00031938613,0.000568526,0.0003606545],"domain_scores_gemma":[0.9994443,0.000058998907,0.000038851627,0.00019561696,0.00018207126,0.00008012663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015015965,0.00022413395,0.00022661262,0.000094938485,0.00018014487,0.00012279692,0.00006158858,0.00018448212,0.00005453869],"category_scores_gemma":[0.00004415081,0.00020886202,0.00024323532,0.00033240402,0.000008868891,0.00013904045,0.000010788221,0.0001958195,0.00013145363],"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":[0.000108981025,0.00003913747,0.00020892554,0.0004994788,0.00052559987,0.000009824237,0.00006426331,0.31217253,0.6301195,0.00033190683,0.014104114,0.041815706],"study_design_scores_gemma":[0.0045490745,0.0014567162,0.010181152,0.0023132998,0.0006944763,0.00009030683,0.0000760159,0.7880281,0.10882871,0.0029845124,0.07883291,0.0019647279],"about_ca_topic_score_codex":0.0001485093,"about_ca_topic_score_gemma":0.0003614836,"teacher_disagreement_score":0.52129084,"about_ca_system_score_codex":0.0005792762,"about_ca_system_score_gemma":0.00004284585,"threshold_uncertainty_score":0.8517146},"labels":[],"label_agreement":null},{"id":"W4404783123","doi":"10.18653/v1/2024.emnlp-main.530","title":"The effects of distance on NPI illusive effects in BERT","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","score_opus":0.003329978628301555,"score_gpt":0.20631871900125903,"score_spread":0.20298874037295747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404783123","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":[0.9499589,0.003265813,0.008451021,0.000046145447,0.008387005,0.0008707249,0.0000021643675,0.0003967385,0.028621491],"genre_scores_gemma":[0.9987881,0.000026084666,0.0000058038795,0.000008138754,0.00010377987,0.00003332932,2.983012e-7,0.000014583916,0.0010198872],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995043,0.0000334274,0.00014100164,0.0000908254,0.0001132858,0.000117130316],"domain_scores_gemma":[0.99892527,0.0009130062,0.0000089145915,0.00012424517,0.000009237906,0.000019298439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014096664,0.000080332946,0.000111596084,0.00006333374,0.000027206008,0.00002881892,0.000047449816,0.00006745779,0.0000038329285],"category_scores_gemma":[0.00010021079,0.000048379898,0.00004781368,0.00024594052,0.000011632807,0.000037119033,0.000008088889,0.00014150141,0.000049996663],"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":[0.0001792943,0.000063551815,0.00041288073,0.005590107,0.00025536606,0.00026746775,0.0018339959,0.008070511,0.12061877,0.07896898,0.030903751,0.75283533],"study_design_scores_gemma":[0.0019542498,0.001224597,0.006862375,0.005534164,0.000040857278,0.00001529544,0.00021216038,0.08029936,0.6384002,0.0017098628,0.2630722,0.00067467755],"about_ca_topic_score_codex":0.000041823987,"about_ca_topic_score_gemma":0.000047796253,"teacher_disagreement_score":0.75216067,"about_ca_system_score_codex":0.000057049187,"about_ca_system_score_gemma":0.0000058002342,"threshold_uncertainty_score":0.1972875},"labels":[],"label_agreement":null},{"id":"W4404797509","doi":"10.2139/ssrn.5037945","title":"Fabrication of High Aspect Ratio Afm Tips by Surfactant Added Tmah Etching","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fabrication; Pulmonary surfactant; Etching (microfabrication); Atomic force microscopy; Nanotechnology; Materials science; Aspect ratio (aeronautics); Chemistry; Optoelectronics; Layer (electronics)","score_opus":0.009404678986356918,"score_gpt":0.22498647246588332,"score_spread":0.2155817934795264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404797509","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":[0.8516848,0.023583667,0.11331291,0.00017335112,0.008165219,0.0006944703,0.00009776896,0.0004894899,0.0017983005],"genre_scores_gemma":[0.99486405,0.0030531206,0.000042338717,0.000004405327,0.0011940469,0.000016447026,0.000041000516,0.000096234,0.0006883554],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99701846,0.00014490775,0.00086888776,0.00032025177,0.00048027956,0.0011672435],"domain_scores_gemma":[0.9990209,0.00006445789,0.00034035876,0.00032999946,0.00016249508,0.000081809616],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0019030125,0.0003595368,0.0005489074,0.00032563647,0.00010722006,0.00017718888,0.0002765726,0.00051158736,0.000048888756],"category_scores_gemma":[0.00007955142,0.0003322165,0.00029386106,0.0002748087,0.000016772754,0.000100280966,0.00011125891,0.005906579,0.000059879436],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050115073,0.00027242556,0.00025439842,0.0019854684,0.008169176,0.000045557947,0.0027993768,0.1953312,0.28140038,0.08331518,0.0496727,0.37625298],"study_design_scores_gemma":[0.0038322967,0.0016593876,0.00025375566,0.0033223045,0.0013464264,0.002092957,0.0030746837,0.07878019,0.13455237,0.74483407,0.022547577,0.0037039965],"about_ca_topic_score_codex":0.0005773765,"about_ca_topic_score_gemma":0.00022687997,"teacher_disagreement_score":0.6615189,"about_ca_system_score_codex":0.0019683978,"about_ca_system_score_gemma":0.001154861,"threshold_uncertainty_score":0.999913},"labels":[],"label_agreement":null},{"id":"W4404812842","doi":"10.2139/ssrn.5037705","title":"Automated Ppe Compliance Monitoring in Industrial Environments Using Deep Learning-Based Detection and Pose Estimation","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Artificial intelligence; Compliance (psychology); Pose; Deep learning; Computer science; Psychology","score_opus":0.03131314794030583,"score_gpt":0.2720723080235508,"score_spread":0.240759160083245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404812842","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":[0.9203338,0.0034560757,0.07131511,0.000009843439,0.004086383,0.0003899728,0.0000024824028,0.00038062135,0.000025720201],"genre_scores_gemma":[0.99836195,0.000435967,0.00008339351,0.0000010761037,0.0009832388,0.000018114539,0.000005578918,0.0000840337,0.000026638842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742293,0.00018395449,0.00061511993,0.00033441585,0.00032677458,0.0011168282],"domain_scores_gemma":[0.99949986,0.000043291595,0.00021213484,0.00014178417,0.000021959188,0.00008098908],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0013693081,0.00035066187,0.00037811883,0.00054295006,0.00017749726,0.0002422623,0.00011281924,0.0007053609,0.0000036017323],"category_scores_gemma":[0.00006810495,0.00038024344,0.00010522847,0.00026859157,0.000021311993,0.00012155841,0.00008978688,0.0077100494,0.000015372942],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050717088,0.000011018771,0.000470173,0.000074238786,0.00009170554,0.000009113841,0.000068764246,0.9388634,0.006121895,0.000006456704,0.0000011177535,0.054231416],"study_design_scores_gemma":[0.00090315804,0.00014035315,0.00035912864,0.00089607766,0.00006895212,0.00017883239,0.00019551654,0.9913408,0.003382047,0.002141994,0.00005803272,0.00033507354],"about_ca_topic_score_codex":0.00019038453,"about_ca_topic_score_gemma":0.000098491895,"teacher_disagreement_score":0.07802817,"about_ca_system_score_codex":0.003878503,"about_ca_system_score_gemma":0.00036014692,"threshold_uncertainty_score":0.9999454},"labels":[],"label_agreement":null},{"id":"W4404824419","doi":"10.1007/978-3-031-77918-3_10","title":"Digit Detection: Localizing and Convoluting","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Computer science; Numerical digit; Arithmetic; Mathematics","score_opus":0.015343448701535874,"score_gpt":0.21817310466759737,"score_spread":0.2028296559660615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404824419","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":[0.0011134684,0.0016149083,0.9810034,0.000020941632,0.005057716,0.00020634764,0.0000030788774,0.00037762357,0.010602533],"genre_scores_gemma":[0.99730134,0.00003660582,0.0009940142,0.00003789849,0.0011976879,0.000003664573,7.495132e-7,0.000048364494,0.0003796747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867207,0.0000048904035,0.00029040623,0.00047533662,0.00029224466,0.0002650735],"domain_scores_gemma":[0.9994737,0.00012943198,0.000040825227,0.00022838086,0.00005499523,0.00007269714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028535174,0.00025893757,0.00025888567,0.00045497023,0.0001189569,0.00034998727,0.00015981718,0.00030552747,0.000009395782],"category_scores_gemma":[0.000025255913,0.0002391108,0.000047967158,0.00027835666,0.00015776276,0.00013999853,0.00013509324,0.000654748,0.000042420743],"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":[0.000001990806,9.0179975e-7,0.000010730677,0.00012599945,0.00001219981,0.000036920846,0.00016176682,0.044748783,0.0008023758,0.0004017743,0.000009052371,0.9536875],"study_design_scores_gemma":[0.00017863065,0.000090303074,0.000012709292,0.0012913644,0.000017075878,0.00024060854,5.798357e-7,0.94851,0.011993098,0.020505933,0.016534448,0.00062524545],"about_ca_topic_score_codex":0.000024754974,"about_ca_topic_score_gemma":0.00006216874,"teacher_disagreement_score":0.99618787,"about_ca_system_score_codex":0.00020085037,"about_ca_system_score_gemma":0.00004005478,"threshold_uncertainty_score":0.9750655},"labels":[],"label_agreement":null},{"id":"W4404896194","doi":"10.1016/j.jnlssr.2024.09.002","title":"SH17: A dataset for human safety and personal protective equipment detection in manufacturing industry","year":2024,"lang":"en","type":"article","venue":"Journal of Safety Science and Resilience","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":21,"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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Ontario Centre of Innovation; University of Windsor; Ontario Centres of Excellence","keywords":"Personal protective equipment; Manufacturing; Business; Computer science; Medicine; Marketing; Coronavirus disease 2019 (COVID-19)","score_opus":0.016609710940033415,"score_gpt":0.2802954736490305,"score_spread":0.2636857627089971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404896194","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":[0.98772097,0.0004856345,0.010510978,0.00010547372,0.0006541079,0.00035379897,0.000052390562,0.000018654082,0.00009801451],"genre_scores_gemma":[0.9996791,0.00006914266,0.000059728205,0.000009594402,0.0001588746,0.000004943672,7.363426e-7,0.000005742187,0.00001214551],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890333,0.000019452547,0.00035166866,0.00018319444,0.00034352366,0.00019883084],"domain_scores_gemma":[0.9996633,0.000064130196,0.000060450053,0.000057847566,0.00005891867,0.00009535946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019588564,0.00009558879,0.00015144146,0.00036997144,0.00028683033,0.00013543552,0.000094796815,0.00011416668,0.000004097598],"category_scores_gemma":[0.00008181938,0.00007263199,0.00002541792,0.00034504136,0.0001648481,0.00069876004,0.00003444934,0.0004878365,5.990012e-7],"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":[0.0006030416,0.0000338063,0.00030971662,0.0005684551,0.00004337215,0.00008473664,0.0033921818,0.009268758,0.40426326,0.00020210285,0.00021501935,0.5810156],"study_design_scores_gemma":[0.005443852,0.003937438,0.117294125,0.005501546,0.00010774619,0.0038923305,0.012846173,0.20184273,0.5741302,0.0025092505,0.07096273,0.0015319106],"about_ca_topic_score_codex":0.000031447118,"about_ca_topic_score_gemma":0.000025936095,"teacher_disagreement_score":0.5794836,"about_ca_system_score_codex":0.00024143977,"about_ca_system_score_gemma":0.00008780279,"threshold_uncertainty_score":0.29618466},"labels":[],"label_agreement":null},{"id":"W4404986388","doi":"10.48550/arxiv.2411.15391","title":"Disk and Partial Disk Inspection: Worst- to Average-Case and Pareto Upper Bounds","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Industrial Vision Systems and Defect Detection","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Business","score_opus":0.045589624203499324,"score_gpt":0.18076161595716786,"score_spread":0.13517199175366854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404986388","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":[0.98421526,0.00027701404,0.010135218,0.000028529177,0.0024747169,0.0003858215,0.000062962106,0.0005780663,0.0018424272],"genre_scores_gemma":[0.9983051,0.0001580856,0.000016369713,0.000013835703,0.00053700287,0.0000040374293,0.0000062606778,0.000045235967,0.0009140739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876547,0.00005439628,0.00020630186,0.0006579951,0.000065078544,0.0002507462],"domain_scores_gemma":[0.99927217,0.00004528432,0.00004053065,0.00035133166,0.000041318403,0.00024937082],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016443939,0.0003098406,0.00031736816,0.00029947414,0.00019723957,0.00024293334,0.00008883156,0.0003872265,0.000038575246],"category_scores_gemma":[0.00002067795,0.00033553026,0.000108291184,0.00036738545,0.00005212409,0.00011153986,0.00046956347,0.0006677258,0.00008471831],"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":[0.0007654406,0.00013538798,0.015760878,0.0024875973,0.0016265834,0.017367072,0.005792525,0.89334595,0.00056770357,0.030243015,0.013008648,0.018899204],"study_design_scores_gemma":[0.002552162,0.0005058942,0.0032806497,0.0013829166,0.0009479676,0.0015042516,0.002309049,0.9074895,0.0010021642,0.010584629,0.065293305,0.0031475462],"about_ca_topic_score_codex":0.00035842846,"about_ca_topic_score_gemma":0.00017754437,"teacher_disagreement_score":0.05228466,"about_ca_system_score_codex":0.00019587204,"about_ca_system_score_gemma":0.000027558573,"threshold_uncertainty_score":0.9999097},"labels":[],"label_agreement":null},{"id":"W4405002180","doi":"10.3390/s24237664","title":"Multimodal Material Classification Using Visual Attention","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"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 Moncton; Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.030582760571114788,"score_gpt":0.28287387229844135,"score_spread":0.25229111172732654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405002180","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":[0.9911929,0.00002826117,0.0022228095,0.0000068628183,0.0051154075,0.00011486276,0.000008781954,0.00058604917,0.00072410423],"genre_scores_gemma":[0.99881476,0.0000030232652,0.000086382315,0.0000015961892,0.0009309119,0.0000036142624,0.00001121058,0.000028358252,0.00012015731],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994521,0.000026883707,0.00016890636,0.00012391231,0.00010891637,0.00011926056],"domain_scores_gemma":[0.999859,0.000012869143,0.000011663726,0.000069539165,0.000017620589,0.000029352328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011223429,0.00008401166,0.00007981829,0.000115445175,0.000047023026,0.000112240705,0.000020651178,0.00011728377,0.00005995976],"category_scores_gemma":[0.000007010207,0.00007996195,0.00005206111,0.00015621893,0.000008377876,0.00009085246,0.000005016206,0.00009038696,0.00016769183],"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":[0.000018251925,0.0000071261866,0.00008755009,0.00009764616,0.000035870522,0.000013327477,0.00012321849,0.019904101,0.95559615,0.00015958883,0.00036223032,0.023594934],"study_design_scores_gemma":[0.0000995864,0.000018527022,0.0013522136,0.00006165904,0.000013645485,0.000023769575,0.000111438385,0.97350526,0.020863986,0.000012276442,0.0038218936,0.0001157417],"about_ca_topic_score_codex":0.00005921153,"about_ca_topic_score_gemma":0.0000018010014,"teacher_disagreement_score":0.9536012,"about_ca_system_score_codex":0.00009133592,"about_ca_system_score_gemma":0.0000071845407,"threshold_uncertainty_score":0.32607538},"labels":[],"label_agreement":null},{"id":"W4405024469","doi":"10.3390/ndt2040032","title":"Advanced Defect Detection on Curved Aeronautical Surfaces Through Infrared Imaging and Deep Learning","year":2024,"lang":"en","type":"article","venue":"NDT","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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 du Québec à Rimouski; Université Laval","funders":"Agency for Science, Technology and Research","keywords":"Aerospace; Robustness (evolution); Deep learning; Computer science; Artificial intelligence; Segmentation; Computer vision; Machine learning; Reliability engineering; Engineering; Aerospace engineering","score_opus":0.011116248139633708,"score_gpt":0.24097362125415403,"score_spread":0.2298573731145203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405024469","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":[0.9572924,0.003524227,0.027761541,0.00001844163,0.0021666507,0.00021025301,0.0000017696855,0.0012454531,0.0077793063],"genre_scores_gemma":[0.99939144,0.0000856417,0.00012183268,0.000010137915,0.00019223221,0.000015666468,0.0000019259085,0.000036187754,0.00014494127],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923515,0.000056746834,0.00017078697,0.00021028514,0.0001373365,0.00018968854],"domain_scores_gemma":[0.99968445,0.00015221731,0.000013897957,0.00009022728,0.000017499864,0.00004172378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001903241,0.00013830034,0.00014092647,0.00007998205,0.00011202222,0.00013634026,0.000029135972,0.00009787448,0.00003101543],"category_scores_gemma":[0.00007837639,0.00012713505,0.00006366865,0.00020855069,0.000016649461,0.00023968692,0.000013959398,0.00037639198,0.000087357155],"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":[0.00008252255,0.000010525501,0.000775319,0.00022536359,0.00008521282,0.000038159746,0.0010037097,0.062059987,0.054289907,0.00032652702,0.00022031402,0.88088244],"study_design_scores_gemma":[0.0011944644,0.0004433022,0.0058381194,0.0005720218,0.00006956787,0.00011273023,0.0007666654,0.74322,0.07762259,0.0009444056,0.1684705,0.00074566243],"about_ca_topic_score_codex":0.000021984592,"about_ca_topic_score_gemma":0.000012921274,"teacher_disagreement_score":0.8801368,"about_ca_system_score_codex":0.00008802444,"about_ca_system_score_gemma":0.0000052295095,"threshold_uncertainty_score":0.5184417},"labels":[],"label_agreement":null},{"id":"W4405429414","doi":"10.1109/lra.2024.3518102","title":"Visual-Tactile Inference of 2.5D Object Shape From Marker Texture","year":2024,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Industrial Vision Systems and Defect Detection","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":"York University; McGill University","funders":"","keywords":"Artificial intelligence; Texture (cosmology); Object (grammar); Inference; Computer vision; Computer science; Pattern recognition (psychology); Image (mathematics)","score_opus":0.013930661616736752,"score_gpt":0.24429209424746293,"score_spread":0.23036143263072617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405429414","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":[0.87031114,0.00026107676,0.12607905,0.00019740452,0.0022915744,0.0001438534,0.000022756874,0.000402257,0.00029086482],"genre_scores_gemma":[0.9992855,0.000022466143,0.00032049388,0.000065475695,0.0002502239,0.0000049039554,0.000009338042,0.0000193375,0.000022237668],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993713,0.000022912625,0.00023398163,0.000129032,0.0001393757,0.000103411956],"domain_scores_gemma":[0.99971324,0.00011913005,0.00003262165,0.00008160673,0.00001929121,0.000034081088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000906754,0.00011105792,0.0001482001,0.000117487594,0.00003430572,0.00010564473,0.000035162313,0.000105112355,0.000062170206],"category_scores_gemma":[0.000013561377,0.00010007735,0.0000473047,0.0001708249,0.000014946164,0.00016038124,0.0000072567336,0.00013898555,0.000029862342],"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":[0.000013816521,0.00001999989,0.0005633852,0.00046232957,0.00021878682,0.000028813609,0.00085281045,0.3430682,0.53534144,0.00032383154,0.016342819,0.10276375],"study_design_scores_gemma":[0.00013517877,0.00002125235,0.0038282922,0.00022521675,0.000023919392,0.000005005283,0.000027711152,0.98617494,0.007722918,0.00003509219,0.0016606393,0.00013984935],"about_ca_topic_score_codex":0.00004163618,"about_ca_topic_score_gemma":0.0000028041975,"teacher_disagreement_score":0.6431067,"about_ca_system_score_codex":0.0000281245,"about_ca_system_score_gemma":0.000009710048,"threshold_uncertainty_score":0.4081036},"labels":[],"label_agreement":null},{"id":"W4405518936","doi":"10.2316/j.2025.201-0430","title":"VISUAL RECOGNITION SYSTEM OF WELDING ROBOT BASED ON MACHINE LEARNING, 9-16. SI","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robot welding; Welding; Robot; Computer science; Artificial intelligence; Computer vision; Human–computer interaction; Engineering; Mechanical engineering","score_opus":0.00970199198883991,"score_gpt":0.20928316615515605,"score_spread":0.19958117416631613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405518936","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":[0.3715593,0.015603835,0.59819144,0.000039881994,0.0063334987,0.001315851,0.000050944695,0.00170588,0.005199378],"genre_scores_gemma":[0.99945784,0.000011430617,0.000006986972,0.000004677481,0.00035040072,0.00005852006,0.0000046035216,0.00003343394,0.000072084316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887526,0.00013098182,0.00037890594,0.00019856078,0.00020419456,0.00021211755],"domain_scores_gemma":[0.99961656,0.00012603645,0.000054282005,0.00010260132,0.00003694791,0.00006357668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070975017,0.00015998489,0.0003095238,0.00021625345,0.00008013041,0.0001068072,0.000038538616,0.0001330722,0.000020061172],"category_scores_gemma":[0.000020130963,0.00013568377,0.00009538245,0.00017796879,0.000008559309,0.000089890666,0.0000049457144,0.0002076589,0.00003086521],"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":[0.0006181691,0.0000897908,0.0010283189,0.006967575,0.0007757357,0.000078830795,0.00037282985,0.61632913,0.0790443,0.013625365,0.00026407806,0.2808059],"study_design_scores_gemma":[0.0012196929,0.00038548405,0.000024830542,0.00091495254,0.000050625687,0.000034451023,0.0003255253,0.99092025,0.0032690945,0.000008266714,0.0026915665,0.00015524442],"about_ca_topic_score_codex":0.0002565258,"about_ca_topic_score_gemma":0.000006067249,"teacher_disagreement_score":0.6278986,"about_ca_system_score_codex":0.00016101652,"about_ca_system_score_gemma":0.000018961135,"threshold_uncertainty_score":0.5533024},"labels":[],"label_agreement":null},{"id":"W4405519039","doi":"10.2316/j.2025.201-0490","title":"RECOGNITION METHOD OF SURFACE DEFECTS OF MECHANICAL PARTS BASED ON MACHINE VISION, 182-191.","year":2024,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Machine vision; Artificial intelligence; Computer vision; Surface (topology); Computer science; Pattern recognition (psychology); Mathematics; Geometry","score_opus":0.013013226055918658,"score_gpt":0.24584784105720128,"score_spread":0.23283461500128264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405519039","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":[0.08808436,0.007879021,0.8979011,0.000038411163,0.0034210864,0.0012213128,0.00021004751,0.00036854378,0.000876105],"genre_scores_gemma":[0.99950975,0.000030973955,0.00017795757,0.0000096034055,0.00015188608,0.00003692347,0.000008668774,0.000038263803,0.000035975638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838483,0.00025849658,0.0005821937,0.0002608245,0.00029717767,0.00021645208],"domain_scores_gemma":[0.99908006,0.00043143932,0.00009524891,0.00022826614,0.00008800665,0.00007697514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013153016,0.00020070636,0.0005450092,0.00016057168,0.000039627674,0.0000472567,0.00006072828,0.00022637488,0.000031457905],"category_scores_gemma":[0.00005165682,0.00016288414,0.00014447155,0.00024169181,0.000011762122,0.00007852197,0.000008870037,0.00020308164,0.000013579539],"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":[0.0010221589,0.00020645968,0.0001092309,0.006203972,0.0009286617,0.000048903275,0.00025815086,0.41347298,0.37561116,0.014217332,0.0018878593,0.18603314],"study_design_scores_gemma":[0.0016387064,0.0006580538,0.000017830202,0.0009765539,0.00011174592,0.000022872175,0.00006437553,0.9701322,0.023089038,0.00018764776,0.0029139936,0.00018703108],"about_ca_topic_score_codex":0.0002250818,"about_ca_topic_score_gemma":0.000012646939,"teacher_disagreement_score":0.9114254,"about_ca_system_score_codex":0.00006295269,"about_ca_system_score_gemma":0.000039672595,"threshold_uncertainty_score":0.66422224},"labels":[],"label_agreement":null},{"id":"W4405552769","doi":"10.1007/978-3-031-73632-2_1","title":"A Dynamic Look-Ahead Approach to Sorting in a Big Data Age","year":2024,"lang":"en","type":"book-chapter","venue":"Studies in big data","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Sorting; Computer science; Big data; Data science; Data mining; Algorithm","score_opus":0.32265779706480247,"score_gpt":0.34932323035095303,"score_spread":0.026665433286150564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405552769","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00092555815,0.051948935,0.0038675542,0.00014128676,0.036755458,0.0037281523,0.009853253,0.0011152196,0.89166456],"genre_scores_gemma":[0.6569263,0.014628388,0.00346471,0.0003247815,0.01760595,0.00060481066,0.018497443,0.0017942236,0.2861534],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971077,0.00002373207,0.0009250644,0.0011614185,0.00036293938,0.000419135],"domain_scores_gemma":[0.99679416,0.000098700046,0.00009414089,0.002920159,0.000029668112,0.00006314213],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011537228,0.000495787,0.0008915089,0.0007444484,0.000063270454,0.00008940419,0.0012243419,0.00041201015,0.000003729904],"category_scores_gemma":[0.00023228645,0.00046634488,0.000053906504,0.00029802322,0.00005768186,0.00013729023,0.0037593942,0.0010388504,0.00020114648],"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":[0.00014751831,0.00010629816,0.0000399668,0.008002286,0.0025948065,0.0019791138,0.006671943,0.004444402,0.00019223278,0.0043412987,0.28562772,0.6858524],"study_design_scores_gemma":[0.0008131865,0.000077061326,0.000019314959,0.005493435,0.00017492125,0.00005452402,0.0025356004,0.021517565,0.000004414667,0.002240396,0.9656762,0.0013934171],"about_ca_topic_score_codex":0.00025610314,"about_ca_topic_score_gemma":0.0041370494,"teacher_disagreement_score":0.68445903,"about_ca_system_score_codex":0.00035890282,"about_ca_system_score_gemma":0.000039370887,"threshold_uncertainty_score":0.9997788},"labels":[],"label_agreement":null},{"id":"W4405603465","doi":"10.2316/j.2025.206-0935","title":"PATENT SEARCH CLASSIFICATION MODEL FOR SERVICE ROBOTS FIELD USING DEEP LEARNING APPROACH, 15-22. SI","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Field (mathematics); Artificial intelligence; Robot; Deep learning; Service (business); Computer science; Machine learning; Engineering; Business; Mathematics","score_opus":0.08773949370482367,"score_gpt":0.30116899895661925,"score_spread":0.21342950525179558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405603465","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":[0.08998782,0.00025403392,0.9081484,0.000189623,0.001120659,0.00010246914,0.0000024343249,0.000059018967,0.0001355371],"genre_scores_gemma":[0.9855993,0.00006607864,0.013797735,0.000023420804,0.0004494423,0.0000029321868,0.0000101167525,0.000017915128,0.000033076347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916404,0.000022419174,0.0003616736,0.000085522275,0.00027782013,0.00008850159],"domain_scores_gemma":[0.99938476,0.00007959558,0.000085144464,0.000038023347,0.0003713666,0.000041110845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035774586,0.00008326187,0.000112482376,0.00023634576,0.000055634675,0.00022867131,0.00007886881,0.00009793902,0.000003540776],"category_scores_gemma":[0.000037948896,0.0000750225,0.000066182656,0.00010534352,0.000004713351,0.00030762237,0.0000136532735,0.00020173972,0.0000017911751],"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":[0.000015716361,0.000008769,0.000026687067,0.000076143486,0.00006692349,0.0000017122966,0.00033941353,0.9522486,0.008200195,0.00089890993,0.00005183905,0.03806514],"study_design_scores_gemma":[0.00024859677,0.000043696426,0.00012769521,0.00016813693,0.00002660626,0.000080603575,0.00013740148,0.9980419,0.0005995846,0.00030865186,0.0001410881,0.00007602699],"about_ca_topic_score_codex":0.000007121677,"about_ca_topic_score_gemma":0.000001305941,"teacher_disagreement_score":0.89561146,"about_ca_system_score_codex":0.00011419176,"about_ca_system_score_gemma":0.000030364126,"threshold_uncertainty_score":0.30593288},"labels":[],"label_agreement":null},{"id":"W4405804303","doi":"10.1109/ehb64556.2024.10805659","title":"A Vision-Based Approach for the Automated Evaluation of the MoCA Clock-Drawing Test Using a YOLO Neural Network","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Computer science; Test (biology); Artificial intelligence; Computer vision; Geology","score_opus":0.05483285133485303,"score_gpt":0.3060574463805807,"score_spread":0.25122459504572764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405804303","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":[0.4693078,0.0023184852,0.5119847,0.00016921261,0.00672703,0.0044658193,0.000026039224,0.0024013289,0.0025995926],"genre_scores_gemma":[0.9987942,5.023562e-7,0.00064150465,0.000020742695,0.0004060096,0.00007759289,0.0000029657265,0.000029360523,0.00002714388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896735,0.000091315684,0.00028764398,0.00013989401,0.0003415171,0.00017225463],"domain_scores_gemma":[0.99905974,0.00054386334,0.000037954916,0.00022606555,0.00011333144,0.000019056028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014610908,0.00011544412,0.00012916114,0.00005310512,0.00019507617,0.00010468818,0.000103149556,0.00010905625,0.000016890763],"category_scores_gemma":[0.0001485582,0.000060904767,0.00014458447,0.0006102186,0.000018657538,0.00007061963,0.000017137078,0.00013742685,0.0000016625864],"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":[0.0000063778366,0.0000058633286,0.000040248568,0.00005370076,0.000025897472,7.309373e-8,0.00004781713,0.9876557,0.003475956,0.000033433957,0.0018713397,0.0067835897],"study_design_scores_gemma":[0.00028760574,0.000036822585,0.00022169121,0.00009257409,0.00011264123,0.000004745128,0.0000725994,0.9966708,0.0018373309,0.000020078078,0.0005650421,0.000078111894],"about_ca_topic_score_codex":0.000065224594,"about_ca_topic_score_gemma":0.000005927638,"teacher_disagreement_score":0.52948636,"about_ca_system_score_codex":0.000098872406,"about_ca_system_score_gemma":0.00007027801,"threshold_uncertainty_score":0.24836244},"labels":[],"label_agreement":null},{"id":"W4405948632","doi":"10.3390/machines13010011","title":"Advancements in Smart Nondestructive Evaluation of Industrial Machines: A Comprehensive Review of Computer Vision and AI Techniques for Infrastructure Maintenance","year":2024,"lang":"en","type":"review","venue":"Machines","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":14,"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 du Québec à Trois-Rivières; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Trois-Rivières","keywords":"Computer science; Engineering; Construction engineering; Systems engineering; Engineering management","score_opus":0.04490477775198462,"score_gpt":0.36855710059731694,"score_spread":0.3236523228453323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405948632","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":[0.00016076834,0.9927445,0.0013943963,0.0000064240326,0.0013472504,0.003902929,0.00030384795,0.00006303098,0.000076875636],"genre_scores_gemma":[0.0003497154,0.9978461,0.0008842063,0.000016631777,0.0003475348,0.00035367327,0.00013216703,0.00006320847,0.0000067413284],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977098,0.00023433375,0.0011950919,0.00033860918,0.00035946595,0.00016270224],"domain_scores_gemma":[0.99885947,0.00013890931,0.0003906751,0.0002411296,0.00033623172,0.000033561206],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007878703,0.00043754166,0.0018533022,0.00045373046,0.000022854812,0.000020865788,0.00012656834,0.00040793457,0.000013425551],"category_scores_gemma":[0.00011336721,0.0003074313,0.00027849682,0.0005373907,0.000041828243,0.00011484224,0.0000758558,0.00048913946,9.865745e-7],"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":[0.00001603921,0.0000068523314,0.000007809999,0.08792577,0.000089474175,7.646116e-7,0.000020256142,0.000067650195,0.0000066226844,0.000017987819,0.002257928,0.90958285],"study_design_scores_gemma":[0.0009103663,0.00032987786,0.000018073226,0.32950372,0.0011707471,0.00005271953,0.0000069781113,0.017810466,0.000016165053,0.0005479367,0.6492111,0.00042183208],"about_ca_topic_score_codex":0.000042795033,"about_ca_topic_score_gemma":0.000007793381,"teacher_disagreement_score":0.90916103,"about_ca_system_score_codex":0.00017266555,"about_ca_system_score_gemma":0.000095049014,"threshold_uncertainty_score":0.9999378},"labels":[],"label_agreement":null},{"id":"W4406203377","doi":"10.61091/jcmcc123-17","title":"Practical Research on Machine Vision Technology in Automated Inspection of Precision Components in Intelligent Manufacturing","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hough transform; Artificial intelligence; Computer vision; Machine vision; Computer science; Calibration; Edge detection; Enhanced Data Rates for GSM Evolution; Object detection; Pattern recognition (psychology); Image processing; Image (mathematics); Mathematics","score_opus":0.05230977664981139,"score_gpt":0.3565162120400167,"score_spread":0.3042064353902053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406203377","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":[0.9851897,0.00032284204,0.001316495,0.00007062268,0.012266806,0.00033368723,0.0000010240644,0.00021630342,0.00028255687],"genre_scores_gemma":[0.99900943,0.000073126605,0.00050694606,7.758795e-7,0.00037180015,0.0000023489392,6.688952e-7,0.00003407752,8.0471614e-7],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972001,0.00019713493,0.0013936865,0.00019251856,0.00073545176,0.00028116142],"domain_scores_gemma":[0.9982546,0.0010838617,0.00023442648,0.00016202734,0.00019045055,0.0000746185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003713548,0.00019977227,0.00061278546,0.002013207,0.00007966984,0.00012756005,0.00016013932,0.00033597264,0.0000023622479],"category_scores_gemma":[0.00038954226,0.00017354685,0.00008595978,0.0010144081,0.0000544927,0.00019002124,0.0001102229,0.0014169426,0.000004070171],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021151511,0.00449597,0.0008694081,0.0045120097,0.00043327242,0.0010996107,0.0057148845,0.034749813,0.06257881,0.8313391,0.0023228426,0.049769085],"study_design_scores_gemma":[0.004137143,0.0026793343,0.00061688636,0.0075929235,0.000026946029,0.0002582996,0.00076364825,0.78175724,0.050921034,0.15023074,0.00066845486,0.00034733576],"about_ca_topic_score_codex":0.000027081363,"about_ca_topic_score_gemma":0.0000018348062,"teacher_disagreement_score":0.7470074,"about_ca_system_score_codex":0.0003638182,"about_ca_system_score_gemma":0.000055949145,"threshold_uncertainty_score":0.70770353},"labels":[],"label_agreement":null},{"id":"W4406267132","doi":"10.1109/vtc2024-fall63153.2024.10757938","title":"Integrating Visual Geometry and Mask Region CNN for Enhanced UAV Detection and Identification","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Identification (biology); Computer vision; Computer science; Artificial intelligence; Computer graphics (images); Computational geometry; Geometry; Mathematics","score_opus":0.014445711546838817,"score_gpt":0.25941419112286807,"score_spread":0.24496847957602924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406267132","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":[0.49259144,0.00026360742,0.5054875,0.000008599548,0.00079165626,0.00019383122,8.813064e-7,0.00028313752,0.00037938246],"genre_scores_gemma":[0.9990016,0.0000381895,0.00014045576,0.0000039841893,0.00027189832,0.000050245188,0.0000025482177,0.000019489458,0.0004715853],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946237,0.000012377438,0.00019586539,0.00017212926,0.000061618055,0.0000956383],"domain_scores_gemma":[0.99979186,0.000079443074,0.000017718934,0.000054778804,0.0000250665,0.00003115679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022051463,0.000086715634,0.00009114574,0.00017990888,0.00007838297,0.00019254973,0.000014353178,0.00011514787,0.0000038980097],"category_scores_gemma":[0.00004961634,0.00007576764,0.000027834798,0.00019998584,0.000009339086,0.00020338532,0.0000073497836,0.000095935866,0.000006766842],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013110065,0.000002071311,0.0000089812265,0.00018893382,0.000018909412,5.341926e-7,0.0002346426,0.00006200049,0.5029641,0.0003095332,0.00013138478,0.49606583],"study_design_scores_gemma":[0.00030963478,0.00015834213,0.00031913878,0.00015638868,0.00002839782,0.000040785802,0.0010222747,0.3316502,0.6613966,0.00039694557,0.0043073185,0.00021395028],"about_ca_topic_score_codex":0.000025089834,"about_ca_topic_score_gemma":0.000021311,"teacher_disagreement_score":0.5064102,"about_ca_system_score_codex":0.000041189036,"about_ca_system_score_gemma":0.0000036922584,"threshold_uncertainty_score":0.30897146},"labels":[],"label_agreement":null},{"id":"W4406330302","doi":"10.3390/machines13010049","title":"Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models","year":2025,"lang":"en","type":"article","venue":"Machines","topic":"Industrial Vision Systems and Defect Detection","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":"Université du Québec à Trois-Rivières; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada; Université du Québec à Trois-Rivières","keywords":"Transformer; Computer science; Hyperparameter; Artificial intelligence; Transfer of learning; Machine learning; Deep learning; Engineering","score_opus":0.025626864789561513,"score_gpt":0.27864102862624357,"score_spread":0.25301416383668207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406330302","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":[0.73152506,0.00032007875,0.26589724,0.000011046688,0.00022908297,0.0003140975,0.0000059482613,0.00018383987,0.0015135845],"genre_scores_gemma":[0.999558,0.000018763873,0.00021779293,0.0000072373177,0.000050158993,0.000040887808,0.000011519343,0.00002827916,0.00006739505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895424,0.0000887952,0.0002876766,0.00028990433,0.00016549169,0.00021389955],"domain_scores_gemma":[0.99963546,0.00008552002,0.000013232498,0.00019773439,0.000034380788,0.00003369883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069730915,0.00019091189,0.00023768043,0.0002716216,0.00012508813,0.0000775549,0.00008263008,0.00010577202,0.000008814794],"category_scores_gemma":[0.000036481466,0.00015405935,0.000040489653,0.00045172803,0.000018050832,0.00022732285,0.000012152266,0.00035255958,0.000003187492],"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":[0.0001147894,0.00001684873,0.0007298939,0.00020186408,0.00005110606,0.0000032151263,0.00055550615,0.8342263,0.04408178,0.000030582734,0.000009712943,0.119978376],"study_design_scores_gemma":[0.0010613729,0.0000969113,0.0010467744,0.00015613809,0.000044829347,0.000012653944,0.0001490853,0.9927667,0.0041604126,0.000030205048,0.00029493455,0.00018000134],"about_ca_topic_score_codex":0.00038936283,"about_ca_topic_score_gemma":0.00052981975,"teacher_disagreement_score":0.26803288,"about_ca_system_score_codex":0.000048852347,"about_ca_system_score_gemma":0.00001440575,"threshold_uncertainty_score":0.6282358},"labels":[],"label_agreement":null},{"id":"W4406485747","doi":"10.1016/0967-0653(96)81265-8","title":"10.1016/0967-0653(96)81265-8","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"LOFAR; Computer science; Telecommunications","score_opus":0.007626537179248296,"score_gpt":0.16499417508615397,"score_spread":0.15736763790690567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406485747","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001609479,0.000048740618,0.000012815642,0.000015358073,0.000014971235,0.0001802178,0.000009151837,0.0005136266,0.99759567],"genre_scores_gemma":[0.0033798658,1.4491428e-7,0.000022134129,0.000006501641,0.00045205632,0.000019118643,0.000005588368,0.000035174748,0.99607944],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927497,0.000023036788,0.00019568643,0.00013945992,0.00014649199,0.00022033008],"domain_scores_gemma":[0.99961334,0.00002408457,0.000011626881,0.00021618688,0.000022721166,0.00011204582],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00013660667,0.00012923442,0.00015916696,0.00008322665,0.000058106274,0.00004810315,0.00009127908,0.00012153887,0.9925123],"category_scores_gemma":[0.000014832812,0.00012638085,0.000062075975,0.00023698264,0.0000066367334,0.000083536055,0.00000993325,0.00013159197,0.9967928],"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":[0.000022012775,0.0000059905424,2.05035e-8,0.000005712855,0.00001250403,0.0000027841895,0.00001220123,0.0037066163,0.00015297341,2.6723825e-7,0.26628512,0.7297938],"study_design_scores_gemma":[0.00020148033,0.00007766396,0.000004335157,0.000020606629,0.0000070123547,0.000010939169,0.0000010541714,0.001981776,0.0008680287,0.0000016338341,0.9966629,0.00016253567],"about_ca_topic_score_codex":0.000027011703,"about_ca_topic_score_gemma":1.7070813e-7,"teacher_disagreement_score":0.7303778,"about_ca_system_score_codex":0.000051355157,"about_ca_system_score_gemma":0.000006860959,"threshold_uncertainty_score":0.5153662},"labels":[],"label_agreement":null},{"id":"W4406505770","doi":"10.1016/s0967-0653(98)81391-4","title":"10.1016/s0967-0653(98)81391-4","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Code (set theory); Reading (process); Programming language","score_opus":0.007905887626211729,"score_gpt":0.1690128251597129,"score_spread":0.16110693753350117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406505770","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007934586,0.00004554962,0.000010490374,0.00001397594,0.000013510859,0.00017526155,0.000007750996,0.00048756052,0.9984524],"genre_scores_gemma":[0.0015942848,1.6252724e-7,0.000022636323,0.0000058755295,0.0004561549,0.000018627336,0.000004405951,0.00003451035,0.99786335],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992894,0.00002248601,0.00019153763,0.00013661377,0.00014444457,0.00021550283],"domain_scores_gemma":[0.9996221,0.000023289682,0.000011474766,0.00021112117,0.0000224747,0.00010955359],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00013374706,0.00012735646,0.00016261663,0.00008193171,0.000055409077,0.000047070283,0.000090059206,0.00012004648,0.9969996],"category_scores_gemma":[0.000014364911,0.00012427042,0.00006125052,0.0002305037,0.0000065502445,0.000081300386,0.000009940355,0.0001302462,0.9991171],"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":[0.000021524802,0.0000053002746,4.7565583e-9,0.0000052585306,0.000011633975,0.000002638528,0.0000107693995,0.0030984688,0.00007913064,2.1663386e-7,0.39071974,0.6060453],"study_design_scores_gemma":[0.00020862934,0.00007529493,0.000002432346,0.000020890382,0.000007082312,0.0000099289,0.000001047067,0.001864366,0.0007087812,0.0000016126395,0.99694026,0.00015969995],"about_ca_topic_score_codex":0.00003072063,"about_ca_topic_score_gemma":2.0049396e-7,"teacher_disagreement_score":0.6062205,"about_ca_system_score_codex":0.000050625553,"about_ca_system_score_gemma":0.0000068551326,"threshold_uncertainty_score":0.50676006},"labels":[],"label_agreement":null},{"id":"W4406575354","doi":"10.1007/978-981-97-7426-5_3","title":"Enhancing Textile Quality Assurance: An Advanced Deep Learning Approach for Efficient Fabric Defect Detection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Textile; Quality assurance; Computer science; Materials science; Artificial intelligence; Engineering; Operations management; Composite material","score_opus":0.013806359941448387,"score_gpt":0.24445736353102585,"score_spread":0.23065100358957746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406575354","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":[0.0017374171,0.0128292795,0.97288257,9.59948e-7,0.0028830974,0.0017967967,0.000011256279,0.00032348273,0.0075351703],"genre_scores_gemma":[0.99651164,0.00016214882,0.00022130189,0.0000073815318,0.0012988477,0.00025597896,0.00007020697,0.00010615547,0.0013663206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975763,0.00016840544,0.0008940302,0.0006595383,0.0002524393,0.00044929032],"domain_scores_gemma":[0.99840295,0.0007647543,0.00026919198,0.0003296687,0.00013984277,0.00009361257],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012008831,0.00054212223,0.0009854644,0.00032050034,0.00026047652,0.00017569307,0.000110859386,0.0012904408,0.0000035076164],"category_scores_gemma":[0.00019841385,0.0005110977,0.00024736347,0.0001841103,0.000024605562,0.00006790785,0.000026961172,0.001127568,9.811096e-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":[0.000059951344,0.0000055200726,0.000012084088,0.00083021284,0.000057343103,0.0000011434262,0.00009654975,0.85726196,0.0002938151,0.00012111375,0.000005036543,0.14125527],"study_design_scores_gemma":[0.00066185195,0.00017072835,0.000019928146,0.00087666756,0.000059068163,0.000016933773,0.00003925642,0.9915571,0.00016581286,0.00008262056,0.0057731364,0.0005768979],"about_ca_topic_score_codex":0.00009416867,"about_ca_topic_score_gemma":0.00034604935,"teacher_disagreement_score":0.9947742,"about_ca_system_score_codex":0.0003371842,"about_ca_system_score_gemma":0.000027472273,"threshold_uncertainty_score":0.99973404},"labels":[],"label_agreement":null},{"id":"W4406576237","doi":"10.1016/s1553-3212(10)70017-0","title":"10.1016/s1553-3212(10)70017-0","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Neuroscience; Psychology","score_opus":0.007153557487722314,"score_gpt":0.16210242170981876,"score_spread":0.15494886422209644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406576237","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001960438,0.000046620324,0.000010464005,0.000015014899,0.000013370355,0.0001848348,0.000009791259,0.0005149547,0.99724454],"genre_scores_gemma":[0.002504996,1.595684e-7,0.000026204658,0.000005662659,0.00047438545,0.000021235895,0.0000056783133,0.000038183116,0.9969235],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992223,0.000024515275,0.00020896432,0.00015104562,0.00015692378,0.00023627396],"domain_scores_gemma":[0.9995743,0.000026729678,0.000013735796,0.00023865979,0.000027826725,0.000118760974],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00014640208,0.00014031814,0.00017372522,0.000092238115,0.00006457946,0.000052640935,0.00009917402,0.00013187081,0.99517256],"category_scores_gemma":[0.000018638864,0.00013787177,0.000067134555,0.00023984283,0.0000072858793,0.00008661712,0.000011550254,0.00012920504,0.9966642],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003258145,0.0000069696034,1.29277735e-8,0.0000063547322,0.000013952671,0.0000032900994,0.000010990452,0.0029831403,0.00022641766,2.2762491e-7,0.50024307,0.49647295],"study_design_scores_gemma":[0.00023361763,0.00009467142,0.000005965025,0.000022081433,0.000007700453,0.0000108345475,0.0000010086463,0.0023893276,0.0007517122,0.0000016258724,0.9963067,0.00017473058],"about_ca_topic_score_codex":0.00002593064,"about_ca_topic_score_gemma":1.7860674e-7,"teacher_disagreement_score":0.49629822,"about_ca_system_score_codex":0.000053796593,"about_ca_system_score_gemma":0.000008293277,"threshold_uncertainty_score":0.56222475},"labels":[],"label_agreement":null},{"id":"W4406577979","doi":"10.1016/s1544-8800(10)70039-7","title":"10.1016/s1544-8800(10)70039-7","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Medicine; Computer science","score_opus":0.006655601504985975,"score_gpt":0.160210780900165,"score_spread":0.15355517939517901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406577979","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018361987,0.000047380527,0.000012957741,0.000014650254,0.000013554153,0.00018727306,0.000009682542,0.0005109039,0.9973674],"genre_scores_gemma":[0.002939894,1.3893751e-7,0.000025745812,0.000005178367,0.00045466668,0.00002047151,0.0000052866912,0.00003840821,0.9965102],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992217,0.000026439728,0.00021046169,0.00015056788,0.00015685271,0.00023399112],"domain_scores_gemma":[0.99957836,0.000028187133,0.000013313755,0.00023386153,0.000028683835,0.000117573836],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00015009279,0.00014021907,0.00017305254,0.00009148777,0.00006216373,0.000050871615,0.000096139825,0.00013354857,0.99480426],"category_scores_gemma":[0.000019415224,0.00013773647,0.000066570334,0.0002552484,0.0000071914633,0.00008356848,0.000011212609,0.0001291746,0.99624455],"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":[0.00003192413,0.000006491762,1.19183365e-8,0.0000060643238,0.000013015741,0.0000028017034,0.0000100567395,0.0037554177,0.00024909005,1.9346894e-7,0.4730965,0.52282846],"study_design_scores_gemma":[0.00022957071,0.000096010255,0.000007939257,0.000021950025,0.000007761801,0.0000106126545,9.651557e-7,0.0023192766,0.00078407285,0.0000013922578,0.99634594,0.00017449663],"about_ca_topic_score_codex":0.000030644405,"about_ca_topic_score_gemma":2.0205255e-7,"teacher_disagreement_score":0.52324945,"about_ca_system_score_codex":0.00005659072,"about_ca_system_score_gemma":0.000008479268,"threshold_uncertainty_score":0.56167305},"labels":[],"label_agreement":null},{"id":"W4406707857","doi":"10.1007/978-3-031-75653-5_9","title":"Enhanced 3D X-Ray Tomography: Deep Learning–Based Advanced Algorithms for Fiber Instance Segmentation","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Segmentation; Tomography; Artificial intelligence; Computer science; Algorithm; Deep learning; Fiber; X-ray; Computer vision; Materials science; Optics; Physics","score_opus":0.014558232648159895,"score_gpt":0.2410604029821021,"score_spread":0.2265021703339422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406707857","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016120767,0.0016709124,0.46134204,0.000009603008,0.003890332,0.0018427427,0.00007523914,0.0017012222,0.5293067],"genre_scores_gemma":[0.022817673,0.00013230304,0.016794983,0.000046779507,0.001156826,0.00051454356,0.00031691752,0.00040141388,0.95781857],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984546,0.000009463561,0.0005133183,0.00045272376,0.00029318914,0.00027671168],"domain_scores_gemma":[0.99933666,0.000107176216,0.0001140471,0.00022955939,0.00013180371,0.00008076356],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001612254,0.00045077014,0.000444271,0.0003725678,0.000102215956,0.000093017305,0.00008876689,0.0005599934,0.0006363783],"category_scores_gemma":[0.0000119671095,0.0004344831,0.00033280047,0.00010879059,0.000020975383,0.0001254299,0.0000130334765,0.00052318943,0.00036143788],"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":[0.00015921691,0.000011791878,4.0713928e-7,0.0010046413,0.00043309876,0.0000115528655,0.00017129008,0.298105,0.0052795145,0.002778328,0.0019168985,0.69012827],"study_design_scores_gemma":[0.0015290051,0.0004426367,0.0000018945269,0.00082542055,0.00016075264,0.000002639519,0.00006399255,0.10503195,0.013315449,0.0007125429,0.8768401,0.0010735856],"about_ca_topic_score_codex":0.0000051845504,"about_ca_topic_score_gemma":0.000015750238,"teacher_disagreement_score":0.8749232,"about_ca_system_score_codex":0.00018773753,"about_ca_system_score_gemma":0.000022134833,"threshold_uncertainty_score":0.9998107},"labels":[],"label_agreement":null},{"id":"W4406803664","doi":"10.3390/jimaging11020033","title":"Investigating the Potential of Latent Space for the Classification of Paint Defects","year":2025,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"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":"HORIZON EUROPE Marie Sklodowska-Curie Actions; European Commission; Canadian Institute of Steel Construction","keywords":"Interpretability; Computer science; Workload; Space (punctuation); Dual (grammatical number); Machine learning; Artificial intelligence; Mechanism (biology); Data science; Risk analysis (engineering); Human–computer interaction","score_opus":0.01893554143466337,"score_gpt":0.2533821251765314,"score_spread":0.23444658374186805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406803664","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":[0.6937939,0.0011290556,0.30201176,0.0009515216,0.0015806705,0.00025056442,0.000001878856,0.000013120065,0.0002675687],"genre_scores_gemma":[0.9994919,0.00001504012,0.00028811482,0.000018078941,0.0001630844,0.0000025946074,1.2421737e-7,0.0000062162508,0.000014904798],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993075,0.000045833192,0.00040496732,0.000034407156,0.00013474654,0.00007251301],"domain_scores_gemma":[0.99914694,0.00025733977,0.00029230982,0.00009609762,0.00019304105,0.000014297139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010543095,0.000052152234,0.00012904941,0.00008597385,0.00006701422,0.000022561768,0.00009661623,0.000025839656,0.0000014374893],"category_scores_gemma":[0.00026178214,0.00002907658,0.00012624018,0.00016109087,0.000030059538,0.00006488258,0.000011201126,0.00015139788,1.7819134e-7],"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":[0.000032729276,0.000013786645,0.0036230648,0.00020618776,0.00016154414,8.477836e-7,0.0005067644,0.08703954,0.84861827,0.0016164212,0.0037092,0.05447167],"study_design_scores_gemma":[0.0013938881,0.00009427353,0.052517682,0.0010842448,0.00027077954,0.00005274054,0.0031924923,0.7199924,0.21572623,0.0015325216,0.0040147044,0.0001280165],"about_ca_topic_score_codex":0.000015483458,"about_ca_topic_score_gemma":0.0000013678764,"teacher_disagreement_score":0.63295287,"about_ca_system_score_codex":0.00003632652,"about_ca_system_score_gemma":0.000037214006,"threshold_uncertainty_score":0.11857085},"labels":[],"label_agreement":null},{"id":"W4406857259","doi":"10.1109/access.2025.3534444","title":"Hierarchical Feature Attention Learning Network for Detecting Object and Discriminative Parts in Fine-Grained Visual Classification","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Industrial Vision Systems and Defect Detection","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 British Columbia","funders":"Korea Institute of Marine Science and Technology promotion; Hankuk University of Foreign Studies","keywords":"Discriminative model; Computer science; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Object (grammar); Object detection; Feature learning; Feature extraction; Visual attention; Contextual image classification; Machine learning; Computer vision; Image (mathematics)","score_opus":0.03249424113555014,"score_gpt":0.32411103432275407,"score_spread":0.2916167931872039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406857259","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":[0.9071269,0.00013299854,0.09034583,0.00009352403,0.0011720948,0.00051992794,0.0000019808958,0.00015456446,0.00045217297],"genre_scores_gemma":[0.99917877,0.000008442469,0.00009455662,0.0000114603845,0.00036228646,0.0001383542,0.000009903111,0.000016438167,0.00017976423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991811,0.00008813295,0.00022244854,0.00021107032,0.00008400517,0.00021324928],"domain_scores_gemma":[0.9995683,0.00023401502,0.000052540054,0.000071138944,0.00004478895,0.000029219203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038364285,0.00012288436,0.00018119471,0.00017439833,0.00017258154,0.00014166726,0.0000655332,0.00017543991,0.0000010975239],"category_scores_gemma":[0.00017565567,0.00011771307,0.000047910682,0.00043497139,0.00001599951,0.00021537057,0.0000197368,0.00034502652,8.037055e-7],"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":[0.00063185306,0.00007542732,0.07624971,0.0008230794,0.00017336207,0.000009554364,0.0011186509,0.13461779,0.048545077,0.0010894691,0.005243728,0.7314223],"study_design_scores_gemma":[0.0025401036,0.00023151637,0.22117594,0.00087718497,0.00006367644,0.000006759059,0.000605469,0.7579311,0.010510046,0.0016181074,0.003941656,0.000498402],"about_ca_topic_score_codex":0.000034206976,"about_ca_topic_score_gemma":0.00021559568,"teacher_disagreement_score":0.7309239,"about_ca_system_score_codex":0.00007147591,"about_ca_system_score_gemma":0.000013196401,"threshold_uncertainty_score":0.48001996},"labels":[],"label_agreement":null},{"id":"W4406894939","doi":"10.1109/mepcon63025.2024.10850157","title":"Classification of Defects in Outdoor Ceramic Insulators using Machine Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ceramic; Computer science; Materials science; Environmental science; Artificial intelligence; Composite material","score_opus":0.0328520535303999,"score_gpt":0.2618725876540826,"score_spread":0.22902053412368273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406894939","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":[0.9907728,0.00043738823,0.0056709168,0.000002587937,0.00065646484,0.00010418332,0.0000013245233,0.00025435933,0.002100018],"genre_scores_gemma":[0.99973893,0.000009340349,0.00006937278,0.0000011898808,0.000063456726,0.0000028417642,0.0000022693957,0.00002102211,0.00009155062],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994444,0.000030534393,0.00023864738,0.00009975347,0.00008985347,0.000096782496],"domain_scores_gemma":[0.9998429,0.00003817705,0.000016923703,0.000069114285,0.000013289832,0.000019636667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020519542,0.000074111485,0.00011721847,0.0002709242,0.000017469523,0.000027171169,0.000026910522,0.0000987373,0.000032737207],"category_scores_gemma":[0.000025105122,0.000066807654,0.000042001513,0.00042671038,0.0000058290825,0.00009482089,0.0000071551704,0.00019614735,0.00002172829],"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":[0.000014878561,0.000016221711,0.030958598,0.00040007257,0.000039774197,0.000009205939,0.00056099513,0.2010031,0.7466506,0.0023015554,0.000040599636,0.018004436],"study_design_scores_gemma":[0.00013867297,0.000022655628,0.0058652605,0.00012051243,0.0000060143966,0.000005702158,0.000082196646,0.97639114,0.016029472,0.000040767005,0.0012116735,0.00008593425],"about_ca_topic_score_codex":0.00028934077,"about_ca_topic_score_gemma":0.000047223766,"teacher_disagreement_score":0.77538806,"about_ca_system_score_codex":0.00009924328,"about_ca_system_score_gemma":0.000012490188,"threshold_uncertainty_score":0.2724337},"labels":[],"label_agreement":null},{"id":"W4407009884","doi":"10.2139/ssrn.5119212","title":"Smart Casting Process Defects Detection System Framework","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Process (computing); Casting; Computer science; Process engineering; Manufacturing engineering; Engineering; Materials science; Metallurgy; Operating system","score_opus":0.010523546697217794,"score_gpt":0.24452490157057863,"score_spread":0.23400135487336082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407009884","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":[0.3545092,0.005678094,0.61778814,0.000014104098,0.014652572,0.0008122856,0.000014917406,0.0012661504,0.0052645435],"genre_scores_gemma":[0.9966685,0.0004198499,0.00007051855,0.000006727279,0.002361659,0.00007430843,0.0000043851296,0.000076640514,0.00031741874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99595445,0.00017887075,0.00079779915,0.00041642465,0.00046817318,0.002184286],"domain_scores_gemma":[0.9988123,0.00011841347,0.00033392294,0.00037321632,0.00024158359,0.000120549266],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00217091,0.0004988579,0.0006148354,0.0005294779,0.00040954706,0.00027381963,0.00035538527,0.0011869641,0.000005769781],"category_scores_gemma":[0.00020300511,0.00049621833,0.00036136492,0.00048168135,0.000015442649,0.00012988759,0.00009934396,0.011066953,0.000035691945],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004246834,0.00010312668,0.0012199198,0.009104691,0.0037723556,0.000102893515,0.0017530943,0.64287996,0.002732769,0.017828044,0.00027091906,0.31980756],"study_design_scores_gemma":[0.0075365417,0.0027713338,0.0005408662,0.053902015,0.0034832144,0.019255042,0.039928287,0.40057653,0.06308841,0.3867518,0.0113307405,0.010835208],"about_ca_topic_score_codex":0.00011800555,"about_ca_topic_score_gemma":0.00034750302,"teacher_disagreement_score":0.6421593,"about_ca_system_score_codex":0.0044491426,"about_ca_system_score_gemma":0.0016514623,"threshold_uncertainty_score":0.99974895},"labels":[],"label_agreement":null},{"id":"W4407055839","doi":"10.1007/s41315-024-00417-z","title":"Automating quality control: real-time defect detection and automated decision-making with ai and doosan robotics","year":2025,"lang":"en","type":"article","venue":"International Journal of Intelligent Robotics and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Robotics; Artificial intelligence; Control (management); Quality (philosophy); Computer science; Machine learning; Engineering; Robot","score_opus":0.00890470699113239,"score_gpt":0.2991091784074154,"score_spread":0.290204471416283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407055839","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":[0.10108648,0.00031981224,0.8974219,0.00013675036,0.00030751078,0.00024443681,0.000008210578,0.00012954352,0.00034534073],"genre_scores_gemma":[0.9934162,0.00029817893,0.006055625,0.000042954467,0.00014723367,0.000010936375,0.00000145145,0.000014397842,0.00001302573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988331,0.000041164563,0.000631618,0.00014160332,0.00024067883,0.00011179723],"domain_scores_gemma":[0.99872273,0.00047355847,0.0002269798,0.000097704535,0.00040914162,0.00006985892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043782813,0.0001433327,0.00025112735,0.0002914973,0.0001301748,0.0002091357,0.00010000646,0.000102032864,0.0000039330644],"category_scores_gemma":[0.000099569035,0.000119232536,0.000057909197,0.00018439116,0.000048376027,0.00013867138,0.00003244768,0.00021248464,0.0000018009879],"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":[0.00020519004,0.000069780595,0.0034733606,0.00008387646,0.0007455432,0.000009605151,0.00014596249,0.5750891,0.01241178,0.009276146,0.00013879454,0.3983509],"study_design_scores_gemma":[0.0013728891,0.0002034392,0.013748699,0.0010512046,0.00021366218,0.00042915836,0.00021225975,0.975955,0.0020783127,0.0033194993,0.0010759145,0.0003399515],"about_ca_topic_score_codex":0.000021756385,"about_ca_topic_score_gemma":0.000019056437,"teacher_disagreement_score":0.8923297,"about_ca_system_score_codex":0.00009058697,"about_ca_system_score_gemma":0.000030781972,"threshold_uncertainty_score":0.4862162},"labels":[],"label_agreement":null},{"id":"W4407079460","doi":"10.1101/2025.01.29.635361","title":"Dude, Everyone Wants Pattern Analysis Tools (DEWPAT): Tools for measuring visual pattern diversity from digital images","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Industrial Vision Systems and Defect Detection","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; University of Toronto","funders":"","keywords":"Diversity (politics); Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology); Computer graphics (images); Sociology; Anthropology","score_opus":0.02949334568094565,"score_gpt":0.22618618639006274,"score_spread":0.19669284070911708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407079460","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":[0.7390913,0.00067817926,0.24753857,0.000011342259,0.0028701148,0.0010790383,0.007744435,0.0009692179,0.000017818496],"genre_scores_gemma":[0.99835265,0.00006501418,0.00017390332,0.000041305375,0.0010113824,0.00021110989,0.000012595136,0.00011400872,0.000018012113],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99636996,0.00010466571,0.0009405188,0.001193253,0.0006722657,0.0007193607],"domain_scores_gemma":[0.9973084,0.0003924157,0.00036046308,0.0011336143,0.00053218723,0.00027292524],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00053685886,0.0008436045,0.0013145081,0.00075018476,0.00039345102,0.0015602773,0.00064701005,0.0009428334,0.00005029718],"category_scores_gemma":[0.00037546485,0.0009531264,0.00078067096,0.0008821322,0.00004175975,0.0008665846,0.0010749553,0.0008472227,0.000060784037],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000665932,0.0011214925,0.47985652,0.0063794535,0.039051805,0.00055381923,0.0003165859,0.054511327,0.39312178,0.000011847526,0.010364183,0.014045245],"study_design_scores_gemma":[0.003525688,0.00016721652,0.52174556,0.0021779463,0.004124911,3.5280102e-8,0.000046022993,0.029207239,0.42857194,0.0000027004887,0.0061623827,0.004268367],"about_ca_topic_score_codex":0.0008806102,"about_ca_topic_score_gemma":0.000033323755,"teacher_disagreement_score":0.25926137,"about_ca_system_score_codex":0.00063742045,"about_ca_system_score_gemma":0.00015605098,"threshold_uncertainty_score":0.9994762},"labels":[],"label_agreement":null},{"id":"W4407129098","doi":"10.1109/tce.2025.3536438","title":"PDFormer: Efficient Vision Transformer for Photovoltaic Defect Detection","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Industrial Vision Systems and Defect Detection","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Photovoltaic system; Transformer; Electrical engineering; Computer science; Electronic engineering; Voltage; Computer vision; Engineering","score_opus":0.0076881483776566235,"score_gpt":0.24327481002629708,"score_spread":0.23558666164864045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407129098","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":[0.101825155,0.00079761294,0.89163285,0.000017513907,0.0027865476,0.001192603,0.00003579482,0.00055778783,0.001154153],"genre_scores_gemma":[0.9986419,0.00021669848,0.00009888909,0.000049442864,0.000043888544,0.0004425277,0.0000038320445,0.000054847435,0.0004479597],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983765,0.00004398861,0.00045878417,0.00034868956,0.00021152613,0.0005605351],"domain_scores_gemma":[0.9992753,0.000202557,0.00004058746,0.00029170452,0.00010985314,0.00007998608],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031459916,0.00031190278,0.00031633367,0.00050275266,0.0004309378,0.00007055797,0.00010705852,0.0003319704,0.0000525721],"category_scores_gemma":[0.0000068801896,0.00031037317,0.00044216675,0.00070629944,0.000035611014,0.00010575367,3.2905703e-7,0.0005295095,0.000065741704],"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":[0.00054486736,0.00016685108,0.00000215365,0.00019190321,0.00043149572,8.0211913e-7,0.000086650776,0.17911784,0.33260006,0.000086656226,0.00065311143,0.4861176],"study_design_scores_gemma":[0.0016870277,0.00043042182,0.000007701997,0.00007965454,0.00019522494,0.000011084388,0.000033197975,0.14238036,0.7915941,0.00008136448,0.06318307,0.00031678553],"about_ca_topic_score_codex":0.00003228079,"about_ca_topic_score_gemma":0.0002918706,"teacher_disagreement_score":0.89681673,"about_ca_system_score_codex":0.0004875041,"about_ca_system_score_gemma":0.00010737189,"threshold_uncertainty_score":0.99993485},"labels":[],"label_agreement":null},{"id":"W4407133399","doi":"10.1038/s41598-025-87046-z","title":"Learning under label noise through few-shot human-in-the-loop refinement","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Industrial Vision Systems and Defect Detection","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":"Queen's University","funders":"","keywords":"Computer science; Shot (pellet); Noise (video); Loop (graph theory); Human-in-the-loop; Shot noise; Artificial intelligence; Mathematics; Telecommunications; Chemistry; Image (mathematics); Detector","score_opus":0.044278161496202485,"score_gpt":0.30073549876268074,"score_spread":0.25645733726647824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407133399","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":[0.89467084,0.00024961773,0.0012667679,0.00011277647,0.013556756,0.00041763045,4.3150675e-7,0.00024338542,0.089481786],"genre_scores_gemma":[0.9848707,0.0000024782419,0.00003621781,0.00003596393,0.000088846704,0.000036981608,0.000010810458,0.000011986273,0.014906022],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983951,0.00006713193,0.00052522065,0.00037244472,0.00037744656,0.00026268317],"domain_scores_gemma":[0.99926203,0.000035092944,0.000084738116,0.0005313614,0.00006219395,0.000024592659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001810879,0.00012925973,0.00016567066,0.000186394,0.00041949583,0.00031201247,0.0001054873,0.00010102073,0.000078795405],"category_scores_gemma":[0.000066236295,0.00010226543,0.000063967214,0.00092524197,0.000047044326,0.0001246682,0.000033127548,0.00029049438,0.000036430276],"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":[0.000029706316,0.00033160966,0.0055025727,0.00039995715,0.00016674382,0.000980179,0.0049154386,0.15771437,0.45047125,0.0069417553,0.352597,0.019949416],"study_design_scores_gemma":[0.0010872814,0.000099678036,0.0023804002,0.00044692727,0.000052114065,0.00016085689,0.002617812,0.0031392353,0.048684612,0.00998028,0.93077105,0.0005797707],"about_ca_topic_score_codex":0.000113102535,"about_ca_topic_score_gemma":0.00007993429,"teacher_disagreement_score":0.57817405,"about_ca_system_score_codex":0.00010876953,"about_ca_system_score_gemma":0.000037562997,"threshold_uncertainty_score":0.41702634},"labels":[],"label_agreement":null},{"id":"W4407135179","doi":"10.1080/10589759.2025.2461231","title":"Multi-scale contrast-to-noise ratio (MS-CNR): a novel metric for quantitative defect characterisation without manual region specification","year":2025,"lang":"en","type":"article","venue":"Nondestructive Testing And Evaluation","topic":"Industrial Vision Systems and Defect Detection","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":"Université Laval","funders":"Spanish National Plan for Scientific and Technical Research and Innovation","keywords":"Metric (unit); Contrast (vision); Scale (ratio); Noise (video); Contrast-to-noise ratio; Computer science; Engineering; Artificial intelligence; Cartography; Geography; Operations management","score_opus":0.11580245071107821,"score_gpt":0.3529758245786024,"score_spread":0.23717337386752418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407135179","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":[0.558028,0.0000514515,0.43943655,0.000022058166,0.00051565527,0.0014429424,0.000014643567,0.00015016126,0.00033854233],"genre_scores_gemma":[0.96126837,0.0000030014187,0.037942577,0.000019479598,0.00018490391,0.0004618416,0.00003770965,0.000026930526,0.00005517672],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857694,0.00012876076,0.0004404619,0.0003851178,0.00026062335,0.00020808961],"domain_scores_gemma":[0.9982737,0.0005806961,0.0001649588,0.00016695586,0.0007501044,0.000063619125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012744819,0.00021027177,0.00027147637,0.00051850395,0.0002670668,0.00011601895,0.000056306813,0.00015972699,0.000003408286],"category_scores_gemma":[0.001970599,0.00021564338,0.0000627952,0.0008641525,0.000029336716,0.00029979524,0.000012963905,0.00014768375,0.000007719725],"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":[0.00049772405,0.00009623798,0.019806996,0.00023415878,0.00017205795,3.793533e-7,0.0022784881,0.033045545,0.7643146,0.00068558595,0.00023472372,0.17863353],"study_design_scores_gemma":[0.0022532789,0.00024046941,0.289962,0.00018478307,0.0001558049,0.000010924932,0.0005381587,0.6961893,0.00976399,0.00037922635,0.00007707124,0.00024504616],"about_ca_topic_score_codex":0.000086568296,"about_ca_topic_score_gemma":0.000037311704,"teacher_disagreement_score":0.7545506,"about_ca_system_score_codex":0.00032073577,"about_ca_system_score_gemma":0.000068691,"threshold_uncertainty_score":0.8793682},"labels":[],"label_agreement":null},{"id":"W4407285227","doi":"10.1093/jcag/gwae059.009","title":"A9 IMPLEMENTATION STRATEGIES TO OPTIMIZE DIAGNOSTIC ACCURACY OF COMPUTER-ASSISTED OPTICAL POLYP DIAGNOSIS","year":2025,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Industrial Vision Systems and Defect Detection","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":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Medical physics; Medicine","score_opus":0.010425243076299455,"score_gpt":0.25645258278281613,"score_spread":0.24602733970651666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407285227","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":[0.98982453,0.000014775576,0.005043287,0.002511019,0.0021779402,0.00022998372,0.000043146745,0.000007641518,0.00014766329],"genre_scores_gemma":[0.9990374,0.0000107283195,0.0005988117,0.00020000536,0.00012390869,0.0000073346205,0.0000017642582,0.000007430001,0.000012618378],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99861455,0.00017960892,0.00072163023,0.00006430201,0.00020385746,0.00021602845],"domain_scores_gemma":[0.9984724,0.00047457818,0.00052230764,0.00010956427,0.00031333684,0.00010779753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005958725,0.00009273367,0.00033460138,0.00038264133,0.000059380756,0.000035173063,0.00018961835,0.00011903829,0.000028425633],"category_scores_gemma":[0.00053781184,0.000079472564,0.00015668268,0.00024023336,0.000016731678,0.00010807323,0.000021427,0.00019626437,0.0000012409257],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003877442,0.00010569734,0.70326763,0.00012968142,0.0013999991,0.000011941571,0.0009479805,0.20188792,0.004399885,0.0006017413,0.07277634,0.014083402],"study_design_scores_gemma":[0.00291718,0.0013954984,0.98678815,0.00028664686,0.00022194284,0.000025310432,0.00079697603,0.0012706647,0.0034384057,0.000110826644,0.0026409335,0.00010744835],"about_ca_topic_score_codex":0.0035600774,"about_ca_topic_score_gemma":0.099158846,"teacher_disagreement_score":0.28352052,"about_ca_system_score_codex":0.0008338753,"about_ca_system_score_gemma":0.00023412908,"threshold_uncertainty_score":0.9172792},"labels":[],"label_agreement":null},{"id":"W4407485539","doi":"10.1016/j.ress.2025.110890","title":"Reconstruction of 3-D pipeline defect profile based on MFL signals and hybrid neural networks","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Industrial Vision Systems and Defect Detection","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":"University of Alberta","funders":"China Scholarship Council","keywords":"Pipeline (software); Artificial neural network; Computer science; Pattern recognition (psychology); Engineering; Data mining; Artificial intelligence; Mechanical engineering","score_opus":0.004702136041949613,"score_gpt":0.19076433267526952,"score_spread":0.1860621966333199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407485539","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":[0.36251852,0.00046685504,0.62801063,0.00002670257,0.004191369,0.0012991318,0.00006320538,0.0011471086,0.0022764625],"genre_scores_gemma":[0.9993716,0.00000770615,0.00032505047,0.0000055121113,0.0001696983,0.00004861994,0.000009908299,0.00003335215,0.0000285648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982946,0.00010687444,0.0008064464,0.00033085558,0.00019006104,0.00027116758],"domain_scores_gemma":[0.99890304,0.00038128791,0.000088114015,0.00043751643,0.00010928452,0.0000807605],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010251703,0.0002725493,0.0005335325,0.00024404847,0.00007306862,0.000029832923,0.00008845392,0.00020118435,0.000011147852],"category_scores_gemma":[0.00018014543,0.0002595029,0.00018566386,0.00042654545,0.000025654246,0.00008747534,0.000016942517,0.0003270988,0.0000031765735],"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":[0.00008129122,0.000010857491,0.00050345267,0.0010918931,0.000025606038,0.000001463381,0.0000073864157,0.98383373,0.0012976632,0.000077049466,0.00012173205,0.01294787],"study_design_scores_gemma":[0.00056802324,0.00007811396,0.0011839782,0.000850071,0.000035924833,0.000018866755,0.000023412576,0.9909754,0.005622566,0.0000026398645,0.00044598972,0.00019505493],"about_ca_topic_score_codex":0.00004361524,"about_ca_topic_score_gemma":0.0000011941503,"teacher_disagreement_score":0.63685304,"about_ca_system_score_codex":0.00028577837,"about_ca_system_score_gemma":0.000024468518,"threshold_uncertainty_score":0.9999857},"labels":[],"label_agreement":null},{"id":"W4407574641","doi":"10.1109/tase.2025.3542076","title":"Automatic Point Cloud Clustering for Surface Defect Diagnosis","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; Science and Technology Program of Zhejiang Province; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Cluster analysis; Cloud computing; Point cloud; Computer science; Point (geometry); Artificial intelligence; Data mining; Mathematics; Geometry; Operating system","score_opus":0.014630938530411555,"score_gpt":0.2473186272512891,"score_spread":0.23268768872087756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407574641","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":[0.3157144,0.000039725885,0.68083525,0.000040750743,0.0021870842,0.00033313135,0.0000064152146,0.00068954873,0.00015369635],"genre_scores_gemma":[0.9976134,0.00002418163,0.0020811793,0.000029621962,0.00004078692,0.00015349036,4.2192403e-7,0.0000182452,0.00003870345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989679,0.000009827418,0.00029357077,0.00022783701,0.0002313001,0.00026960653],"domain_scores_gemma":[0.9994326,0.00019416014,0.000025245055,0.00017807701,0.00009302914,0.00007686246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006659808,0.00016194547,0.00017963367,0.00045264114,0.000314053,0.0001763454,0.00010513026,0.000091684306,0.000012932252],"category_scores_gemma":[0.00004020828,0.00016459131,0.00007951921,0.0009631202,0.000036371544,0.00041841276,0.0000017184697,0.00013494311,0.000010003026],"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":[0.000004107845,0.000011165603,0.0000048495226,0.0001855918,0.000024660354,3.14382e-7,0.0001682577,0.92105347,0.017465416,0.000070795424,0.00021040575,0.060800992],"study_design_scores_gemma":[0.00031997173,0.000041176834,0.00016918745,0.00017851527,0.000021483644,0.0000050898975,0.000067559675,0.9331979,0.06491196,0.000014509008,0.00091755926,0.00015506842],"about_ca_topic_score_codex":0.000011817093,"about_ca_topic_score_gemma":0.000005484295,"teacher_disagreement_score":0.68189895,"about_ca_system_score_codex":0.00022044958,"about_ca_system_score_gemma":0.000041948606,"threshold_uncertainty_score":0.6711839},"labels":[],"label_agreement":null},{"id":"W4407693069","doi":"10.1109/tim.2025.3542885","title":"Dual-Contrastive Multiview Graph Attention Network for Industrial Fault Diagnosis Under Domain and Label Shift","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Western University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Dual (grammatical number); Graph; Graph theory; Theoretical computer science; Artificial intelligence; Mathematics","score_opus":0.05278858484618111,"score_gpt":0.26761186121341707,"score_spread":0.21482327636723597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407693069","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":[0.36009896,0.00019692209,0.63524264,0.00019599558,0.002679592,0.0012935792,0.00004967935,0.00012227641,0.00012039042],"genre_scores_gemma":[0.9984918,0.00020984355,0.0003611743,0.00009734556,0.00010199353,0.000687497,0.0000059220342,0.00001608149,0.000028348368],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989162,0.000075208394,0.000345105,0.00023538635,0.00023183833,0.00019627],"domain_scores_gemma":[0.999639,0.000077778386,0.00005259026,0.00008512847,0.00007491676,0.00007059728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046809067,0.00018345543,0.00020562088,0.00018131267,0.00032101694,0.00008211992,0.000023037097,0.00016337357,0.0000122602805],"category_scores_gemma":[0.000005987742,0.0001818685,0.00006874186,0.000251849,0.0000313011,0.0001460298,7.4011047e-7,0.00017080654,0.0000021900944],"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":[0.0007390261,0.00033768092,0.0008294768,0.00031525444,0.0010935471,0.0000012259071,0.0007515204,0.04402268,0.011819472,0.001308157,0.0024995639,0.9362824],"study_design_scores_gemma":[0.22818433,0.008511256,0.07472134,0.014466389,0.0060016797,0.000077093406,0.023254186,0.07215256,0.46109545,0.0217344,0.08164831,0.008153013],"about_ca_topic_score_codex":0.000058867256,"about_ca_topic_score_gemma":0.00021219702,"teacher_disagreement_score":0.9281294,"about_ca_system_score_codex":0.00017472317,"about_ca_system_score_gemma":0.000025401418,"threshold_uncertainty_score":0.7416382},"labels":[],"label_agreement":null},{"id":"W4407809852","doi":"10.1177/14759217251316532","title":"Learning monocular depth estimation for defect measurement from civil RGB-D dataset","year":2025,"lang":"en","type":"article","venue":"Structural Health Monitoring","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monocular; Artificial intelligence; RGB color model; Computer science; Computer vision; Geology; Remote sensing","score_opus":0.044100749615316195,"score_gpt":0.3271636585766125,"score_spread":0.2830629089612963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407809852","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":[0.86160827,0.0040812492,0.11710819,0.000092560294,0.014065026,0.0016674207,0.00030842377,0.00083497236,0.00023387348],"genre_scores_gemma":[0.99651724,0.000022626527,0.0024620732,0.000011940383,0.0006616811,0.00007298167,0.00021511594,0.000024932764,0.000011396304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986589,0.00007454318,0.00041641327,0.00024461243,0.00026526916,0.0003402837],"domain_scores_gemma":[0.9994658,0.00008431786,0.00008339104,0.00020561766,0.000070901595,0.00008994975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048676366,0.00018409893,0.00026736554,0.00012738394,0.0003935303,0.00008061457,0.0000934174,0.00011571229,0.00000490243],"category_scores_gemma":[0.0001241243,0.00018006505,0.000084861815,0.00019645318,0.000007328166,0.00016027456,0.000022716315,0.00027589095,0.000006172641],"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":[0.000088062494,0.00000409548,0.005982884,0.0006843853,0.00018456067,0.0000014325129,0.00040241622,0.18742594,0.0045049996,0.000045564,0.0032113714,0.7974643],"study_design_scores_gemma":[0.005004895,0.0006386872,0.07584185,0.0026713721,0.00017994399,0.000009037761,0.0012088994,0.7699404,0.068046674,0.0022499093,0.07288484,0.001323496],"about_ca_topic_score_codex":0.00091234694,"about_ca_topic_score_gemma":0.00008867043,"teacher_disagreement_score":0.7961408,"about_ca_system_score_codex":0.0005973842,"about_ca_system_score_gemma":0.00006387968,"threshold_uncertainty_score":0.7342839},"labels":[],"label_agreement":null},{"id":"W4407919697","doi":"10.1016/j.procs.2025.01.184","title":"Real time contaminants detection in wood panel manufacturing process using YOLO algorithms","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Université du Québec en Abitibi-Témiscamingue","funders":"Mitacs","keywords":"Computer science; Process (computing); Algorithm; Real-time computing","score_opus":0.01993010311556979,"score_gpt":0.2562083574813149,"score_spread":0.2362782543657451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407919697","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":[0.90195,0.000014125642,0.095439404,0.0000035470478,0.0016170398,0.0002674699,0.0000010817153,0.00024182284,0.00046552232],"genre_scores_gemma":[0.99830306,0.000003948039,0.0014471182,0.000009396069,0.00018965962,0.000017435303,3.2205793e-7,0.0000101998285,0.000018891938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875784,0.000015767493,0.0002796154,0.00034271443,0.00025508943,0.00034896756],"domain_scores_gemma":[0.9996417,0.000031146672,0.00004382608,0.00014790804,0.00007685466,0.000058564252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048647527,0.00014563858,0.00018530869,0.00048420439,0.00016663424,0.00014206297,0.00023638023,0.000099842786,0.0000023938119],"category_scores_gemma":[0.000020781541,0.0001416102,0.000027138827,0.0010087041,0.000054680917,0.00046235987,0.00006399177,0.00018555053,0.000034585933],"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":[0.000036707544,0.00005632357,0.0014504461,0.0003649337,0.00001946747,0.000025201174,0.001604389,0.0943852,0.18287094,0.000012722444,0.00003306971,0.7191406],"study_design_scores_gemma":[0.0002804233,0.000029631643,0.004844394,0.00013240619,0.00000342759,0.000019139106,0.000027894606,0.722812,0.27162755,0.00005236808,0.000035667475,0.00013509385],"about_ca_topic_score_codex":0.00006165294,"about_ca_topic_score_gemma":0.00001221307,"teacher_disagreement_score":0.7190055,"about_ca_system_score_codex":0.00023789197,"about_ca_system_score_gemma":0.00010085174,"threshold_uncertainty_score":0.57746965},"labels":[],"label_agreement":null},{"id":"W4407920613","doi":"10.18280/jesa.580103","title":"A Data-Driven Predictive Maintenance Approach for Industry 4.0 Using LSTM with Cross-Validation and the IDAIC Framework","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Predictive maintenance; Computer science; Cross-validation; Artificial intelligence; Machine learning; Data mining; Reliability engineering; Engineering","score_opus":0.0342240491220125,"score_gpt":0.2936154988614713,"score_spread":0.2593914497394588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407920613","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":[0.2728692,0.0005096065,0.72416955,0.000028232482,0.0005628395,0.0007040479,0.00006939234,0.00018166973,0.0009054726],"genre_scores_gemma":[0.9809869,0.000038744653,0.018204825,0.00003214917,0.00044336964,0.000040840485,0.000009464679,0.000045572506,0.0001981118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845,0.00020790685,0.0005329734,0.00025326415,0.0002616726,0.00029421705],"domain_scores_gemma":[0.99878806,0.00029215758,0.00023990835,0.0003933584,0.00021000054,0.000076544835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001118357,0.00022074257,0.0003712842,0.00016336788,0.0005104448,0.0006513895,0.00030740933,0.00029866214,0.0000056864865],"category_scores_gemma":[0.0004109658,0.0001374433,0.000065315166,0.0004422125,0.00019332432,0.0005268479,0.0000888329,0.00084159855,0.0000013227823],"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":[0.0014226591,0.00007796131,0.007513825,0.0014733134,0.0016766619,0.000029304028,0.0020318327,0.87738615,0.0008587814,0.006099949,0.005418802,0.096010774],"study_design_scores_gemma":[0.002105164,0.000092368304,0.016589142,0.0010730056,0.00015613725,0.0005727554,0.00044318492,0.9764277,0.00015128529,0.0015825636,0.0006172128,0.00018950022],"about_ca_topic_score_codex":0.000019232806,"about_ca_topic_score_gemma":0.0000017553149,"teacher_disagreement_score":0.7081177,"about_ca_system_score_codex":0.00018590805,"about_ca_system_score_gemma":0.00009147919,"threshold_uncertainty_score":0.62813634},"labels":[],"label_agreement":null},{"id":"W4407959434","doi":"10.1016/j.inffus.2025.103039","title":"IFIFusion: A independent feature information fusion model for surface defect detection","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Information fusion; Computer science; Feature (linguistics); Surface (topology); Fusion; Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.009003721558036406,"score_gpt":0.21982935195863812,"score_spread":0.2108256304006017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407959434","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":[0.28285843,0.00006516714,0.7036187,0.000100646845,0.0028814583,0.0015637986,0.00006123822,0.00078121386,0.008069375],"genre_scores_gemma":[0.9981128,0.000052275376,0.0008474523,0.00021374357,0.000097932076,0.000116131894,0.0002198064,0.000014924881,0.0003249483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839383,0.000034012326,0.0007270482,0.00013053836,0.0004135217,0.00030105803],"domain_scores_gemma":[0.9989786,0.00006972712,0.00019374506,0.0002991819,0.00037775282,0.0000810012],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061091554,0.00027179075,0.00025309154,0.00054742687,0.00044798802,0.00026641536,0.00014225382,0.00056088134,0.000025233996],"category_scores_gemma":[0.00011582545,0.00025612448,0.00021104896,0.0006536593,0.000012911156,0.0028291775,0.000067856774,0.0003417535,0.00015293916],"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":[0.00038389018,0.000018320012,0.00004785194,0.0004939863,0.000047134945,2.1885705e-7,0.0016933687,0.6426286,0.015347247,0.0004668343,0.019183885,0.31968862],"study_design_scores_gemma":[0.001490543,0.000073479205,0.00026424808,0.00013735818,0.000030533127,0.0000074810355,0.00031268026,0.88235307,0.020531347,0.00022817218,0.0943063,0.00026478662],"about_ca_topic_score_codex":0.00004877735,"about_ca_topic_score_gemma":0.00003289073,"teacher_disagreement_score":0.71525437,"about_ca_system_score_codex":0.00034115103,"about_ca_system_score_gemma":0.00006845399,"threshold_uncertainty_score":0.9999891},"labels":[],"label_agreement":null},{"id":"W4408062863","doi":"10.1007/978-981-96-2260-3_13","title":"Small Target Detection Algorithm Based on Lightweighted YOLOv5","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.008053102259215596,"score_gpt":0.18419240077260496,"score_spread":0.17613929851338936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408062863","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":[0.000039446208,0.0010940445,0.9686856,0.000023379567,0.002286683,0.00062650716,0.000023374341,0.0011105124,0.026110398],"genre_scores_gemma":[0.936364,0.00038765767,0.035344046,0.0006579264,0.008012234,0.00050292583,0.00036233335,0.0015832218,0.016785676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977815,0.0000246964,0.00065655966,0.0005715033,0.0003632114,0.000602486],"domain_scores_gemma":[0.9987023,0.0005989284,0.00007864893,0.00042536485,0.00006878402,0.00012601298],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00020966232,0.00076339743,0.00079037633,0.0015345307,0.00006470549,0.00006636345,0.00020681335,0.0017320251,0.00007394276],"category_scores_gemma":[0.00026023402,0.0007665901,0.0002959449,0.000578096,0.000011640453,0.000041513496,0.000021946893,0.002455204,0.000046419183],"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":[0.000035379755,0.000013679519,0.0000010086204,0.0001190177,0.000068880414,0.000051645206,0.000010037881,0.7295861,0.0013806954,0.00024588124,0.000097719334,0.26838997],"study_design_scores_gemma":[0.00053073553,0.00022253134,0.00000587554,0.00048054394,0.000038806604,0.000010467977,8.1590805e-8,0.9166473,0.026575001,0.0006071148,0.054171886,0.0007096436],"about_ca_topic_score_codex":0.00001897663,"about_ca_topic_score_gemma":0.000014300083,"teacher_disagreement_score":0.93632454,"about_ca_system_score_codex":0.0008786337,"about_ca_system_score_gemma":0.000061104896,"threshold_uncertainty_score":0.99984616},"labels":[],"label_agreement":null},{"id":"W4408135633","doi":"10.18280/mmep.120212","title":"An Adjoint Sensitivity Analysis of PV Solar Cell with Respect to Key Cell Manufacturing Parameters","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sensitivity (control systems); Key (lock); Solar cell; Environmental science; Computer science; Engineering; Electronic engineering; Electrical engineering","score_opus":0.015976984666209507,"score_gpt":0.20553475972795057,"score_spread":0.18955777506174107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408135633","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":[0.4569201,0.000027270782,0.54243994,0.0000036455976,0.00003912492,0.00014112928,0.000003436756,0.00013284737,0.000292531],"genre_scores_gemma":[0.9672301,0.000008001724,0.03265598,0.000004671037,0.000014127518,0.00001944379,0.0000023924351,0.000029504,0.000035823465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890983,0.000025479118,0.0003776881,0.000255467,0.00016879183,0.00026272316],"domain_scores_gemma":[0.99936545,0.00014441708,0.000034769768,0.0002954115,0.00003322451,0.00012672925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004749928,0.00021832755,0.00049582415,0.00049597427,0.000048353966,0.00006872911,0.000059584436,0.0001222112,0.000003468854],"category_scores_gemma":[0.000009456067,0.00018639507,0.00008848926,0.00048643767,0.000011962793,0.00007851302,0.000019505494,0.00021164052,0.000003243061],"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":[0.000012806943,0.000027987868,0.000010177078,0.00065628806,0.00013759134,0.000002588134,0.0004913393,0.9883325,0.009955321,0.0000867444,0.0000044557696,0.00028217767],"study_design_scores_gemma":[0.00016618145,0.00008063288,0.000021089854,0.00024082988,0.00022234012,0.0000019695997,0.00005418135,0.91223395,0.08662497,0.000119231416,0.000043396994,0.00019124415],"about_ca_topic_score_codex":0.00004931922,"about_ca_topic_score_gemma":0.000004071299,"teacher_disagreement_score":0.51031,"about_ca_system_score_codex":0.000042481566,"about_ca_system_score_gemma":0.0000069998155,"threshold_uncertainty_score":0.7600971},"labels":[],"label_agreement":null},{"id":"W4408267267","doi":"10.18280/rcma.350119","title":"Innovations in Defect Detection: Integrating AI and NDT Techniques in Composite Materials","year":2025,"lang":"fr","type":"article","venue":"Revue des composites et des matériaux avancés","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Nondestructive testing; Composite number; Materials science; Engineering; Computer science; Composite material; Physics","score_opus":0.03467112998595337,"score_gpt":0.2977430488596525,"score_spread":0.2630719188736992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408267267","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":[0.7231265,0.006026602,0.2618588,0.0004906403,0.002302432,0.0013274167,0.00006037849,0.00040749958,0.0043997723],"genre_scores_gemma":[0.9704936,0.00075060665,0.027473517,0.0001133694,0.00026907047,0.00018929566,0.000026953088,0.00007170596,0.0006118816],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99698555,0.0005343743,0.0012804942,0.0005134826,0.00013638995,0.0005497199],"domain_scores_gemma":[0.9987424,0.00044366252,0.00017517572,0.00034685686,0.00021419808,0.00007770905],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012820818,0.0004789094,0.00083197176,0.001351277,0.00024560295,0.00047773763,0.0001699632,0.00053683773,0.000044265344],"category_scores_gemma":[0.00024874863,0.0005082302,0.00011633071,0.0019796174,0.00018767486,0.00062802876,0.00014369393,0.00083945604,0.000028674722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002121264,0.0002029726,0.017293794,0.0048095896,0.00016712419,0.00012745176,0.002061382,0.014839593,0.69873273,0.015675053,0.00017955722,0.24569862],"study_design_scores_gemma":[0.003072802,0.00064522313,0.038482852,0.030713733,0.00019879619,0.0009301105,0.0006246875,0.07837028,0.8111937,0.01532576,0.018651955,0.0017901157],"about_ca_topic_score_codex":0.0025110645,"about_ca_topic_score_gemma":0.0030583537,"teacher_disagreement_score":0.24736711,"about_ca_system_score_codex":0.0008127856,"about_ca_system_score_gemma":0.000062256004,"threshold_uncertainty_score":0.9997369},"labels":[],"label_agreement":null},{"id":"W4408323896","doi":"10.1109/mce.2025.3549585","title":"Empirical Studies of Large-Scale Environment Scanning by Consumer Electronics","year":2025,"lang":"en","type":"article","venue":"IEEE Consumer Electronics Magazine","topic":"Industrial Vision Systems and Defect Detection","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":"Huawei Technologies (Canada); University of Ottawa","funders":"","keywords":"Scale (ratio); Electronics; Computer science; Telecommunications; Electrical engineering; Engineering; Cartography; Geography","score_opus":0.018693228089458384,"score_gpt":0.2884893554542798,"score_spread":0.2697961273648214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408323896","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":[0.69794405,0.27582356,0.019578643,0.00027293712,0.0018995419,0.0009128637,0.00009310262,0.00050088955,0.0029744108],"genre_scores_gemma":[0.98496366,0.012062929,0.00016688101,0.00011225965,0.00007957633,0.00008938108,0.000019395631,0.00006944591,0.002436504],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975705,0.00010695296,0.0007501937,0.0004043477,0.00032737674,0.0008405927],"domain_scores_gemma":[0.999016,0.00020154855,0.00012763584,0.0004527216,0.00011138795,0.000090724294],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005568936,0.00037253957,0.00068328937,0.00023960466,0.00015056596,0.000030637744,0.00019495234,0.00027914112,0.00006067506],"category_scores_gemma":[0.00004881682,0.00036994438,0.00016955646,0.00048378317,0.00011524778,0.000103613085,0.000050438244,0.0006415086,0.0001243512],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023856589,0.00032863373,0.005571452,0.000596411,0.0023530822,0.000010347369,0.0005595271,0.0059445105,0.4043557,0.00037838172,0.555366,0.02429743],"study_design_scores_gemma":[0.0017260802,0.00020904628,0.00012368831,0.00013612215,0.00020777147,0.000012402762,0.00010750924,0.0053429203,0.14019403,0.00018122913,0.8513183,0.0004408947],"about_ca_topic_score_codex":0.0000052529817,"about_ca_topic_score_gemma":0.000055942197,"teacher_disagreement_score":0.29595235,"about_ca_system_score_codex":0.0005259014,"about_ca_system_score_gemma":0.00014978882,"threshold_uncertainty_score":0.99987525},"labels":[],"label_agreement":null},{"id":"W4408338546","doi":"10.1016/j.procs.2025.02.094","title":"Artificial intelligence applied in adaptive manufacturing process monitoring: a state-of-the-art in the era of automation.","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada; Polytechnique Montréal","funders":"Mitacs","keywords":"Computer science; Automation; Process (computing); State (computer science); Artificial intelligence; Manufacturing engineering; Data science; Industrial engineering; Algorithm; Operating system; Mechanical engineering","score_opus":0.02072597934605534,"score_gpt":0.25827091849520106,"score_spread":0.23754493914914573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408338546","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":[0.84426713,0.000017813201,0.15412994,0.000020043453,0.00076591654,0.00040023704,6.388185e-7,0.000032243355,0.0003660709],"genre_scores_gemma":[0.9990486,0.0000011543707,0.00085994584,0.000005774419,0.00004189426,0.000037760325,5.8962534e-8,0.0000029372165,0.0000018806561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990459,0.000016743907,0.0003465056,0.00015557928,0.0002838386,0.00015140494],"domain_scores_gemma":[0.9996487,0.000075222466,0.000059769704,0.0001468451,0.00005649948,0.000012963666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063600356,0.00007711185,0.00012000514,0.00019226511,0.00005187741,0.00003937464,0.00040426684,0.00003062593,4.7110777e-7],"category_scores_gemma":[0.000027299744,0.00005337976,0.000020365082,0.00138798,0.000081643564,0.0001475602,0.00005320086,0.00020721478,0.0000017034863],"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":[0.000025242098,0.000049426828,0.0017269154,0.00021824872,0.00000497317,0.000001057176,0.008305454,0.5308803,0.0035295212,0.0009087163,0.000012709481,0.45433745],"study_design_scores_gemma":[0.000058897782,0.000025706931,0.019399721,0.00029650886,0.0000017485665,0.0000017036914,0.00025604054,0.34746638,0.62940186,0.002996965,0.000012808992,0.000081640035],"about_ca_topic_score_codex":0.000014100454,"about_ca_topic_score_gemma":0.00001181716,"teacher_disagreement_score":0.6258724,"about_ca_system_score_codex":0.000058748345,"about_ca_system_score_gemma":0.00010187546,"threshold_uncertainty_score":0.21767636},"labels":[],"label_agreement":null},{"id":"W4408461231","doi":"10.3390/engproc2024076105","title":"Harnessing Artificial Intelligence for Optimum Performance in Industrial Automation","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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 Regina","funders":"","keywords":"Automation; Computer science; Artificial intelligence; Manufacturing engineering; Engineering; Mechanical engineering","score_opus":0.05510318710442167,"score_gpt":0.28157016526599765,"score_spread":0.226466978161576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408461231","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":[0.5280864,0.000023511955,0.459592,0.000050431136,0.0030341814,0.00053005497,0.0000017469297,0.0003285137,0.0083531095],"genre_scores_gemma":[0.9989704,0.0000024161718,0.00054154434,0.000009365904,0.0002143622,0.000056180794,0.0000029131693,0.000007770242,0.0001950355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933267,0.000013627101,0.00033722818,0.00010601984,0.0000651128,0.00014534469],"domain_scores_gemma":[0.99978876,0.0000620847,0.000020630354,0.00008405954,0.000028486129,0.00001599316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033392038,0.000079959515,0.00012088276,0.0002336612,0.000060632305,0.00006479177,0.000052152373,0.00016555557,0.000021419339],"category_scores_gemma":[0.00006058929,0.000077450684,0.000032169246,0.0004054349,0.000007699233,0.00016465539,0.000008726034,0.00012868484,0.000014598232],"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":[0.000060510614,0.000014561383,0.0003564883,0.00006885108,0.000009986369,3.381664e-7,0.0000943613,0.16301368,0.0024861793,0.0036023464,0.0006867571,0.82960594],"study_design_scores_gemma":[0.00019809703,0.00003406766,0.00031645616,0.0001374001,0.0000043392356,6.8098035e-7,0.00014868185,0.931243,0.063517,0.00046772166,0.0038205523,0.00011198519],"about_ca_topic_score_codex":0.000017635448,"about_ca_topic_score_gemma":0.00001580479,"teacher_disagreement_score":0.82949394,"about_ca_system_score_codex":0.00009843004,"about_ca_system_score_gemma":0.000029222518,"threshold_uncertainty_score":0.31583473},"labels":[],"label_agreement":null},{"id":"W4408565313","doi":"10.1109/tim.2025.3551981","title":"PV-PASBLS: A Multimodal Point-View Fusion Model Based on Parameter Adaptive Stacked Broad Learning System for 3-D Shape Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Science and Technology Department of Zhejiang Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Fusion; Computer science; Point (geometry); Sensor fusion; Computer vision; Pattern recognition (psychology); Mathematics; Geometry","score_opus":0.05555673282643434,"score_gpt":0.258635297729881,"score_spread":0.20307856490344664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408565313","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":[0.08614087,0.000027175138,0.91024905,0.00004705685,0.0012581726,0.0013800763,0.000051965788,0.00029794103,0.00054768834],"genre_scores_gemma":[0.9972413,0.00004493365,0.001838503,0.00009996645,0.000032695745,0.00064320903,0.000014700237,0.000028844863,0.000055827382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855876,0.000109957815,0.00043051702,0.00030665557,0.00038722786,0.00020688846],"domain_scores_gemma":[0.99945015,0.000085981235,0.000075798656,0.00011997103,0.00019081695,0.00007727762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004985815,0.00023647926,0.00023964152,0.00035889455,0.0003497255,0.0000793639,0.000040700543,0.00015841603,0.000023043947],"category_scores_gemma":[0.000010996179,0.00023357454,0.00013302354,0.00021387145,0.000016539825,0.00015861234,5.1610755e-7,0.0002516014,0.000014278397],"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":[0.0006959411,0.000108815,0.0000021932296,0.00026384243,0.000087708104,3.427122e-7,0.00021160423,0.4677088,0.012192246,0.000017983342,0.00008086943,0.51862967],"study_design_scores_gemma":[0.00322987,0.00053593866,0.00001618023,0.00082229736,0.00009829746,0.0000011084447,0.00074362947,0.90614337,0.08782131,0.000023711125,0.00035776995,0.00020650923],"about_ca_topic_score_codex":0.00003170194,"about_ca_topic_score_gemma":0.000026059908,"teacher_disagreement_score":0.91110045,"about_ca_system_score_codex":0.00058469013,"about_ca_system_score_gemma":0.00004932224,"threshold_uncertainty_score":0.9524893},"labels":[],"label_agreement":null},{"id":"W4408611506","doi":"10.2139/ssrn.5185506","title":"A Novel Spectral Index for Detecting Large Plastic Accumulations Using Sentinel‐2 Imagery","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Index (typography); Spectral analysis; Spectral index; Remote sensing; Geology; Computer science; Artificial intelligence; Physics; World Wide Web; Spectral line; Spectroscopy; Astronomy","score_opus":0.0315932356123442,"score_gpt":0.29323645265175147,"score_spread":0.2616432170394073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408611506","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":[0.20877494,0.00062237174,0.7857194,0.00001297558,0.0039097895,0.00046632413,0.00006315109,0.00021507942,0.00021595332],"genre_scores_gemma":[0.99607456,0.00010368542,0.0005921953,0.000008858558,0.0027778114,0.0000353488,0.000016518974,0.00008041237,0.00031061843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960908,0.00005508889,0.0008051727,0.0003602232,0.0002900212,0.0023986413],"domain_scores_gemma":[0.9988525,0.00027517928,0.00031291696,0.00027149188,0.0001953186,0.000092610695],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0017538939,0.00042573904,0.00052958005,0.0006630663,0.00048714908,0.00027970845,0.00025596484,0.0005817317,0.000011667846],"category_scores_gemma":[0.0003505928,0.0004511371,0.00049865,0.00033452196,0.000012685945,0.00016737614,0.00013213958,0.0056048576,0.000004045277],"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":[0.00017804724,0.00009160554,0.00091967446,0.00047054654,0.001456407,0.0000053813314,0.00020919298,0.9721658,0.015055122,0.0032937096,0.00015628645,0.0059981784],"study_design_scores_gemma":[0.0024554878,0.00007242813,0.00028946195,0.00066948356,0.0003215418,0.00053697,0.00061354256,0.9777369,0.0009082656,0.014792357,0.00084000936,0.0007635671],"about_ca_topic_score_codex":0.00008962965,"about_ca_topic_score_gemma":0.00032506467,"teacher_disagreement_score":0.78729963,"about_ca_system_score_codex":0.0023358562,"about_ca_system_score_gemma":0.0021609478,"threshold_uncertainty_score":0.99979407},"labels":[],"label_agreement":null},{"id":"W4408828762","doi":"10.3390/app15073584","title":"Advanced Diagnostics of Aircraft Structures Using Automated Non-Invasive Imaging Techniques: A Comprehensive Review","year":2025,"lang":"en","type":"review","venue":"Applied Sciences","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Systems engineering; Engineering","score_opus":0.03687860959829866,"score_gpt":0.3427188567204088,"score_spread":0.30584024712211016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408828762","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":[0.000013958702,0.9941302,0.0009756736,0.0000014433973,0.00073170284,0.0018334566,0.00004327635,0.0005952459,0.0016750275],"genre_scores_gemma":[0.00030288883,0.9965475,0.0028286467,0.000029363337,0.00009580661,0.00014931691,0.000013051129,0.000029896246,0.0000035207906],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978903,0.00006567148,0.0009421868,0.0004242496,0.00036731185,0.0003102663],"domain_scores_gemma":[0.99815637,0.0009137123,0.00042235953,0.00033107013,0.00012151857,0.00005494202],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033202977,0.00044025035,0.0018230692,0.0004575346,0.00017211855,0.0000520256,0.0004241324,0.00022226862,0.000009626601],"category_scores_gemma":[0.0001916178,0.0003473575,0.00026846942,0.0017374427,0.00018279899,0.00008873117,0.000097721,0.00031799288,0.000005749301],"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":[8.171304e-7,0.0000049484065,3.296715e-7,0.0937663,0.000049778544,0.000005060306,0.000024687339,0.0007325701,0.00024504523,0.000083899475,0.001956023,0.90313053],"study_design_scores_gemma":[0.00019245879,0.00004638596,0.000001041138,0.30062777,0.0008818623,0.00006295276,0.00018570802,0.0016642829,0.0023590878,0.0001223885,0.6928442,0.0010118482],"about_ca_topic_score_codex":0.000012731971,"about_ca_topic_score_gemma":0.0000015702776,"teacher_disagreement_score":0.9021187,"about_ca_system_score_codex":0.00011925288,"about_ca_system_score_gemma":0.00037826842,"threshold_uncertainty_score":0.99989784},"labels":[],"label_agreement":null},{"id":"W4408840834","doi":"10.1109/tuffc.2025.3553784","title":"Extended Field of View Imaging Through Correlation With an Experimental Database","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Industrial Vision Systems and Defect Detection","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":"Sunnybrook Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Fonds de recherche du Québec – Nature et technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Réseau en Bio-Imagerie du Quebec","keywords":"Computer science; Field (mathematics); Database; Computer graphics (images); Physics; Mathematics","score_opus":0.00930023560076149,"score_gpt":0.2503255232550762,"score_spread":0.2410252876543147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408840834","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":[0.04989372,0.001695339,0.94591874,0.000029214876,0.0005786764,0.00040222277,0.000038726354,0.0001328009,0.0013105712],"genre_scores_gemma":[0.99903274,0.00020815489,0.00053442636,0.00009606558,0.000023715678,0.000053409287,0.0000049523846,0.00002080644,0.000025760619],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999026,0.000053574255,0.00032281288,0.00022814925,0.00016328527,0.00020616491],"domain_scores_gemma":[0.99939585,0.00018291928,0.00005513125,0.00023619896,0.00007713942,0.000052767507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015046429,0.00018811254,0.00026120307,0.0001878003,0.00015388356,0.000042203083,0.00007449821,0.00011118032,0.00002834672],"category_scores_gemma":[0.00000916119,0.00017042567,0.000059151407,0.00046392457,0.000026964712,0.00032106708,2.8826855e-7,0.00032445622,0.000001373549],"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":[0.0015845593,0.0010035009,0.0004234657,0.0003539878,0.0006447968,0.000031232707,0.0010908287,0.0677652,0.558768,0.009001113,0.0002994254,0.35903394],"study_design_scores_gemma":[0.0059759356,0.0019619053,0.00012670513,0.00039005774,0.00029179544,0.000067666915,0.0003705442,0.54326576,0.44587657,0.0005969619,0.0004453692,0.0006307246],"about_ca_topic_score_codex":0.000169807,"about_ca_topic_score_gemma":0.00004106444,"teacher_disagreement_score":0.949139,"about_ca_system_score_codex":0.00007835611,"about_ca_system_score_gemma":0.000051555868,"threshold_uncertainty_score":0.69497573},"labels":[],"label_agreement":null},{"id":"W4408859064","doi":"10.1109/access.2025.3554509","title":"Innovative Approaches for PCB Image Reconstruction: Tailored Datasets, Metrics, and Models","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Image (mathematics); Iterative reconstruction; Data modeling; Data mining; Artificial intelligence; Software engineering","score_opus":0.09787197530495219,"score_gpt":0.3024936786035483,"score_spread":0.20462170329859614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408859064","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":[0.11518772,0.00036103575,0.87382823,0.000050754355,0.0032779782,0.0008391433,0.0003778104,0.00025589677,0.005821413],"genre_scores_gemma":[0.9981238,0.000022980668,0.001382367,0.000022678436,0.00020373397,0.00010535925,0.000052445455,0.000014915376,0.00007168226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993444,0.00001759686,0.00024174852,0.00018963833,0.00007081185,0.00013576985],"domain_scores_gemma":[0.99958766,0.00008142985,0.000042613905,0.000170434,0.00009119597,0.000026646425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000226927,0.000113393944,0.00018206767,0.0003219681,0.00009625993,0.00018907624,0.0001191223,0.00011709441,0.0000046805035],"category_scores_gemma":[0.000053251573,0.00010740105,0.0000254344,0.00097140303,0.00002983631,0.00072175945,0.00003055621,0.0001176938,0.0000017040501],"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":[0.00021657406,0.000045734665,0.00061315356,0.0008498365,0.0004989227,0.0000040168675,0.00024004375,0.026872693,0.005328506,0.007816593,0.13738237,0.82013154],"study_design_scores_gemma":[0.0031195327,0.000090897076,0.00042964154,0.00020516911,0.00009069466,0.00003173519,0.0006207239,0.8129747,0.1420362,0.014840025,0.024838282,0.00072240666],"about_ca_topic_score_codex":0.000040990228,"about_ca_topic_score_gemma":0.000006858832,"teacher_disagreement_score":0.8829361,"about_ca_system_score_codex":0.00004432635,"about_ca_system_score_gemma":0.000018648152,"threshold_uncertainty_score":0.43796876},"labels":[],"label_agreement":null},{"id":"W4408862930","doi":"10.1109/icce63647.2025.10929775","title":"Object Detection Using Efficient Partitioning and Frame Reduction","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Carleton University","funders":"","keywords":"Computer science; Reduction (mathematics); Frame (networking); Object (grammar); Object detection; Computer vision; Artificial intelligence; Pattern recognition (psychology); Mathematics; Geometry; Computer network","score_opus":0.01255307682758148,"score_gpt":0.23707689566805612,"score_spread":0.22452381884047465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408862930","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":[0.8375359,0.00009748345,0.15648097,0.000004626574,0.0014790033,0.00011593125,3.7340993e-7,0.00027406332,0.0040116026],"genre_scores_gemma":[0.99953705,0.000004550607,0.00019171696,0.000005295906,0.00012386891,0.000007768902,4.0968206e-7,0.000007513402,0.00012184244],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995518,0.000019920748,0.00015100985,0.00010965921,0.00006347563,0.00010416653],"domain_scores_gemma":[0.9998465,0.00001443688,0.00001486249,0.00007530452,0.000026226462,0.000022642638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001438512,0.00007085658,0.00008407721,0.00015275169,0.0001382301,0.00005741663,0.000013727172,0.000098801946,0.000015510499],"category_scores_gemma":[0.000018248353,0.00006909463,0.00002534481,0.0002759222,0.000010971891,0.000057879373,0.000008593292,0.00010503115,0.0000066748557],"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":[0.00002648205,0.00001400386,0.0002665213,0.00008739078,0.000052170253,0.0000010317798,0.00019459352,0.43875593,0.5030877,0.0005278627,0.00018662517,0.0567997],"study_design_scores_gemma":[0.00034582362,0.00003120747,0.0012300906,0.000103964165,0.00002448075,0.00003654216,0.00039604932,0.7073865,0.28843266,0.0001528287,0.0017082271,0.00015163406],"about_ca_topic_score_codex":0.00013141052,"about_ca_topic_score_gemma":0.000009039337,"teacher_disagreement_score":0.26863053,"about_ca_system_score_codex":0.00008630339,"about_ca_system_score_gemma":0.0000071508903,"threshold_uncertainty_score":0.2817597},"labels":[],"label_agreement":null},{"id":"W4408863065","doi":"10.1109/cictn64563.2025.10932445","title":"Analyzing Impact of Data Augmentation Techniques in Computer Vision","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images)","score_opus":0.028432617290345158,"score_gpt":0.3470657825304892,"score_spread":0.31863316524014407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408863065","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":[0.44455573,0.00003995033,0.55193645,0.0000054876305,0.00018710364,0.0001686273,0.0000061646124,0.00015507884,0.0029454334],"genre_scores_gemma":[0.9974755,0.000007661467,0.0024357752,0.0000024969263,0.000033451142,0.000001906745,0.000014288752,0.0000039451716,0.000024940551],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958175,0.000018244258,0.00021003836,0.000085547035,0.00004552203,0.000058883445],"domain_scores_gemma":[0.9997309,0.000026065773,0.00001818911,0.0001995689,0.000016166246,0.00000912494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023002404,0.000050493723,0.00010653606,0.00025048954,0.000009953495,0.000016065804,0.000075392076,0.00005673904,0.000022923188],"category_scores_gemma":[0.000004916604,0.00004113984,0.000026634949,0.00033173512,0.0000037505226,0.00020148938,0.000036972986,0.000056599896,0.0000021524038],"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":[0.000035666828,0.000027228036,0.0076013375,0.00007128776,0.00005757547,0.00000159316,0.00004711059,0.019470122,0.060213733,0.00007861595,0.009672649,0.9027231],"study_design_scores_gemma":[0.00067493715,0.00020661141,0.030347053,0.00040570987,0.000013836361,0.0000021765702,0.00003421399,0.8888042,0.07836024,0.00011515333,0.0008683693,0.00016751331],"about_ca_topic_score_codex":0.00046401832,"about_ca_topic_score_gemma":0.00003766224,"teacher_disagreement_score":0.9025556,"about_ca_system_score_codex":0.00006825331,"about_ca_system_score_gemma":0.000009831022,"threshold_uncertainty_score":0.1677634},"labels":[],"label_agreement":null},{"id":"W4409129151","doi":"10.1109/jiot.2025.3557451","title":"A Surrogate Metric-Based Framework for Placing Infrastructure Sensing Units to Enhance Cooperative Vehicle-Infrastructure Perception","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Anhui Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Metric (unit); Perception; Critical infrastructure; Computer network; Computer security; Distributed computing; Telecommunications; Engineering; Operations management","score_opus":0.011170779698732907,"score_gpt":0.28122094489250316,"score_spread":0.27005016519377023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409129151","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":[0.47159857,0.000034378612,0.5253462,0.00004515857,0.0026154036,0.00019256826,0.000006241176,0.000056718178,0.00010475286],"genre_scores_gemma":[0.98208326,0.0000042940583,0.017117694,0.00023302676,0.00041128043,0.0000041735216,0.000002188362,0.000032229982,0.00011186838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985708,0.00008331802,0.000600078,0.00020134886,0.00026145665,0.00028302282],"domain_scores_gemma":[0.9986183,0.00030175346,0.00019697117,0.00015046596,0.00062400143,0.00010851763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005176048,0.00024012347,0.0003949342,0.00060853455,0.00013715954,0.0001978684,0.00018894949,0.00033235995,0.00003836921],"category_scores_gemma":[0.0007036172,0.00021589703,0.00013301699,0.0008919769,0.000022732078,0.000258957,0.000021450633,0.00091843435,0.0000043826753],"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":[0.00065906794,0.000012060608,0.00022005498,0.00027983863,0.00030872936,0.000012914347,0.006230405,0.5731831,0.2794093,0.00014986403,0.016177846,0.12335682],"study_design_scores_gemma":[0.0011718252,0.0004987394,0.0006071441,0.0037516612,0.00009674618,0.00009889053,0.0014049805,0.33480576,0.65022725,0.0014232437,0.0054334034,0.00048034487],"about_ca_topic_score_codex":0.000037022626,"about_ca_topic_score_gemma":0.0000047766675,"teacher_disagreement_score":0.5104847,"about_ca_system_score_codex":0.00035779004,"about_ca_system_score_gemma":0.00009480113,"threshold_uncertainty_score":0.88040257},"labels":[],"label_agreement":null},{"id":"W4409254137","doi":"10.1007/978-3-031-87364-5_22","title":"Near Real-Time Steel Rust Recognition Using Transformer-Based Convolutional Neural Networks","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Transformer; Computer science; Rust (programming language); Speech recognition; Artificial intelligence; Pattern recognition (psychology); Engineering; Electrical engineering; Voltage; Programming language","score_opus":0.01698303630328868,"score_gpt":0.20938298356743207,"score_spread":0.1923999472641434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409254137","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":[0.0025921112,0.0016185838,0.95731664,0.000024348936,0.0046040607,0.0011977938,0.00021177073,0.0013543869,0.031080311],"genre_scores_gemma":[0.9900163,0.00017227126,0.004481241,0.0000757131,0.0027325451,0.000076353324,0.0006263633,0.00051496,0.0013042984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980632,0.000021075686,0.00068766624,0.00042009755,0.0003090487,0.000498903],"domain_scores_gemma":[0.99908173,0.00037268788,0.00008765209,0.00027960827,0.0000813219,0.0000969934],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023523702,0.00064806896,0.00068867824,0.0005399522,0.00010095963,0.000097997916,0.00013846628,0.0012911912,0.00032162043],"category_scores_gemma":[0.00006294128,0.00073502416,0.0002825209,0.00024143649,0.000031434713,0.000114972274,0.000014249584,0.0013524786,0.000018909168],"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":[0.000029963778,0.000004579139,0.000003638808,0.00024185212,0.0000669642,0.000019184272,0.000020926853,0.9819308,0.0012961354,0.000032933895,0.00006930085,0.016283747],"study_design_scores_gemma":[0.0006192094,0.000046993136,0.000011463316,0.0013620805,0.00008077123,0.000020642936,5.1606247e-7,0.99173856,0.00043300327,0.0000850422,0.004923554,0.0006781483],"about_ca_topic_score_codex":0.00008096062,"about_ca_topic_score_gemma":0.00013551756,"teacher_disagreement_score":0.98742414,"about_ca_system_score_codex":0.0005944855,"about_ca_system_score_gemma":0.000087534085,"threshold_uncertainty_score":0.9995101},"labels":[],"label_agreement":null},{"id":"W4409499241","doi":"10.5006/s2009-00024","title":"Detection of Surface Contamination During a Scheduled Inspection and Repair Investigation","year":2009,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Department of National Defence","funders":"","keywords":"Contamination; Computer science; Environmental science","score_opus":0.009426241050677327,"score_gpt":0.1994920299010319,"score_spread":0.19006578885035458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409499241","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":[0.99212664,0.00006411779,0.005780134,0.000009458528,0.0002379717,0.00017982746,4.7077987e-7,0.00075987674,0.0008415161],"genre_scores_gemma":[0.9996392,0.0000147934625,0.00020914787,0.0000040565424,0.00006649889,0.0000023522377,0.000001185891,0.000007634042,0.000055100994],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994422,0.000028443768,0.00023605587,0.00010722136,0.00010697028,0.00007909657],"domain_scores_gemma":[0.99975854,0.000014685354,0.000048707305,0.00008246344,0.00006101263,0.000034604887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018318417,0.00007969665,0.00012135802,0.00011413569,0.000067067835,0.000015452748,0.000014339879,0.000112720074,0.000003925331],"category_scores_gemma":[0.00003456142,0.00008002739,0.000033065528,0.00023705178,0.000012708904,0.00023021235,0.0000032754472,0.00008632235,0.000003203879],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020383088,0.00000501565,0.0007746726,0.00003958256,0.000009413536,3.439095e-7,0.00012807071,0.0025148834,0.9926826,0.00015981281,0.000010983653,0.0036542388],"study_design_scores_gemma":[0.00054114783,0.00013968698,0.13577926,0.000038742266,0.000009549475,0.000014705203,0.00014602339,0.06058817,0.8025075,0.00009792056,0.000037076436,0.00010019052],"about_ca_topic_score_codex":0.000052072028,"about_ca_topic_score_gemma":0.000026364878,"teacher_disagreement_score":0.19017507,"about_ca_system_score_codex":0.00006957393,"about_ca_system_score_gemma":0.000004351435,"threshold_uncertainty_score":0.32634225},"labels":[],"label_agreement":null},{"id":"W4409501000","doi":"10.5006/c2023-19167","title":"Corrosion Scale and Moisture Assessments – an Improvement to On-Stream Inspections for CUI Management","year":2023,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Dalhousie University; Emissions Reduction Alberta","funders":"","keywords":"Corrosion; Moisture; Scale (ratio); Environmental science; Process engineering; Computer science; Reliability engineering; Materials science; Engineering; Metallurgy; Composite material","score_opus":0.02296212535809403,"score_gpt":0.2952306441681912,"score_spread":0.27226851881009717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409501000","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":[0.9211023,0.000004629551,0.05768043,0.000085855114,0.0023303267,0.0017584106,0.000045771434,0.0011556229,0.015836671],"genre_scores_gemma":[0.99562323,0.000010302634,0.0005410988,0.00006078597,0.00015667813,0.00027084793,0.000020638134,0.000024055735,0.003292348],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99937564,0.0000065278905,0.00014353232,0.00018862802,0.00012541213,0.00016026071],"domain_scores_gemma":[0.9997029,0.0000145039685,0.000012235237,0.00015815951,0.000022334023,0.000089885274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000130454,0.000102227765,0.00009746437,0.00017476862,0.0001309662,0.00006497029,0.000039747567,0.00006360845,0.000011878096],"category_scores_gemma":[0.000002577081,0.00008922696,0.00002914529,0.0002593822,0.0000034158347,0.0000700806,0.000025308911,0.000057419347,0.000053237844],"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":[0.00022571076,0.00028481073,0.0017096674,0.00040691113,0.00014183317,0.000010936483,0.00062294886,0.0499074,0.12648194,0.0044226144,0.1710661,0.6447191],"study_design_scores_gemma":[0.008265117,0.01020009,0.05704519,0.0004806931,0.00017282841,0.000013404512,0.009640519,0.2434879,0.15237083,0.0014621735,0.5148063,0.0020550103],"about_ca_topic_score_codex":0.000049724247,"about_ca_topic_score_gemma":0.00010152158,"teacher_disagreement_score":0.64266413,"about_ca_system_score_codex":0.00007617569,"about_ca_system_score_gemma":0.0000023385471,"threshold_uncertainty_score":0.363857},"labels":[],"label_agreement":null},{"id":"W4409524228","doi":"10.5592/co/3crocee.2025.12","title":"RAPID VISUAL SCREENING METHOD FOR RC BUILDING STRUCTURES","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science","score_opus":0.0237570616931714,"score_gpt":0.3290442493669996,"score_spread":0.30528718767382823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409524228","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":[0.010421342,0.00015755612,0.98079205,0.000020249567,0.0012211271,0.00025279197,0.0000026723228,0.0003684586,0.0067637525],"genre_scores_gemma":[0.8613348,0.0000032431933,0.13740805,0.00007817925,0.00039377876,0.00003665015,0.0000024539645,0.000022097729,0.0007207652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994239,0.000023976096,0.00019302055,0.00012627036,0.0000720129,0.00016081531],"domain_scores_gemma":[0.9996743,0.0001595408,0.000016153072,0.00008760626,0.000034673212,0.000027720587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031094826,0.00009897853,0.0001545976,0.00016071102,0.00009782683,0.00005705783,0.000052604548,0.00012206723,0.000058601963],"category_scores_gemma":[0.00006060634,0.000086001295,0.00008318579,0.00020256359,0.0000036782885,0.000063310195,0.000014843922,0.000097317636,0.0000019768834],"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":[0.000040715375,0.0000032227572,0.000026964868,0.00008863788,0.00011083862,5.4222284e-7,0.000037379494,0.021412598,0.06757631,0.012074552,0.013157532,0.8854707],"study_design_scores_gemma":[0.0009917954,0.0000713284,0.00022881106,0.00006548257,0.000032185697,0.000004611753,0.00021642436,0.44689813,0.33724,0.0017103453,0.2123032,0.00023766364],"about_ca_topic_score_codex":0.000046230118,"about_ca_topic_score_gemma":0.0000038894286,"teacher_disagreement_score":0.88523304,"about_ca_system_score_codex":0.000027584909,"about_ca_system_score_gemma":0.000008136168,"threshold_uncertainty_score":0.35070312},"labels":[],"label_agreement":null},{"id":"W4409955003","doi":"10.1016/j.compag.2025.110384","title":"Cascade vision-based weight estimation algorithm for monitoring fattening pig weight","year":2025,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"Science and Technology Program of Gansu Province","keywords":"Cascade; Weight estimation; Artificial intelligence; Algorithm; Estimation; Computer science; Computer vision; Pattern recognition (psychology); Engineering; Mathematics; Statistics","score_opus":0.005040318662290137,"score_gpt":0.2287253161868194,"score_spread":0.22368499752452928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409955003","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":[0.039132725,0.003768237,0.95358586,0.00017736002,0.0023314075,0.00049938867,0.000005535621,0.00022507657,0.00027440922],"genre_scores_gemma":[0.97572744,0.00022809619,0.022846235,0.000070739436,0.00071838475,0.000114108225,0.00004062136,0.000027703973,0.00022666498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913645,0.000021553738,0.00024111429,0.0002162333,0.00009775336,0.00028688472],"domain_scores_gemma":[0.99967927,0.00010677915,0.000035028082,0.00009175196,0.000046479843,0.00004066459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015288478,0.00017329984,0.00020953127,0.00014681923,0.00014088888,0.00009606435,0.00008000077,0.00022123271,7.572804e-7],"category_scores_gemma":[0.000008109813,0.00013566906,0.00006220454,0.00036744628,0.000008356249,0.00009869083,0.000016120099,0.0002966016,9.470292e-7],"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":[0.000022302389,0.000037760223,0.00031591518,0.00017827682,0.00007838118,0.0000052276014,0.0001562911,0.15345387,0.007727968,0.0007670674,0.020451045,0.8168059],"study_design_scores_gemma":[0.0009994891,0.000087620116,0.0006477865,0.00027524584,0.000018605908,0.0000059342865,0.00002468301,0.95098126,0.015980067,0.00034401682,0.03041889,0.0002164229],"about_ca_topic_score_codex":0.000012432916,"about_ca_topic_score_gemma":0.000006829874,"teacher_disagreement_score":0.9365947,"about_ca_system_score_codex":0.00020739905,"about_ca_system_score_gemma":0.000022691667,"threshold_uncertainty_score":0.5532424},"labels":[],"label_agreement":null},{"id":"W4410027753","doi":"10.1109/tfr.2025.3566694","title":"Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark, and Lessons Learned","year":2025,"lang":"en","type":"article","venue":"IEEE transactions on field robotics.","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Field (mathematics); Computer science; Artificial intelligence; Environmental science; Cartography; Geography; Mathematics","score_opus":0.07975084157213388,"score_gpt":0.38232498648923846,"score_spread":0.30257414491710455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410027753","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":[0.02806291,0.00026783525,0.9613192,0.0021743947,0.0018798483,0.0004908915,0.0000032439739,0.00006290852,0.0057387347],"genre_scores_gemma":[0.9978793,0.000076155455,0.0016740741,0.00013075433,0.000031297575,0.000038390306,6.277869e-7,0.000006902535,0.00016245632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990469,0.00017070559,0.00029713346,0.00018410714,0.00018293946,0.000118194526],"domain_scores_gemma":[0.9988661,0.00068069797,0.00003335766,0.0003407405,0.00006210181,0.00001701553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015222377,0.00010179708,0.00017373437,0.00021631939,0.00008851414,0.000026388212,0.00008147903,0.00020395564,0.000056275752],"category_scores_gemma":[0.000096707445,0.00008249423,0.000062831015,0.0003957451,0.000012105107,0.0000762454,0.0000010262078,0.00041183745,0.0000016173743],"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":[0.000032476437,0.000039597995,0.000024237725,0.00005360313,0.000046313606,5.788585e-7,0.00052429107,0.58546025,0.0029960675,0.00022229478,0.00080815743,0.40979216],"study_design_scores_gemma":[0.0030828328,0.00088143646,0.0016477384,0.00052074896,0.0004840846,0.000016536746,0.002271456,0.5422887,0.44129106,0.0028324167,0.0041550878,0.00052787375],"about_ca_topic_score_codex":0.00019302586,"about_ca_topic_score_gemma":0.00015575811,"teacher_disagreement_score":0.96981645,"about_ca_system_score_codex":0.000034688732,"about_ca_system_score_gemma":0.00003553071,"threshold_uncertainty_score":0.33640173},"labels":[],"label_agreement":null},{"id":"W4410137935","doi":"10.1016/j.jafr.2025.102002","title":"Cross temporal scale pig face recognition based on deep learning","year":2025,"lang":"en","type":"article","venue":"Journal of Agriculture and Food Research","topic":"Industrial Vision Systems and Defect Detection","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":"Ministry of Agriculture","funders":"Wuhan Science and Technology Project","keywords":"Artificial intelligence; Scale (ratio); Deep learning; Facial recognition system; Pattern recognition (psychology); Face (sociological concept); Computer science; Speech recognition; Geography; Cartography; Sociology","score_opus":0.037433625599924414,"score_gpt":0.31199858404775627,"score_spread":0.27456495844783185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410137935","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":[0.98509175,0.001037083,0.0031222233,0.00019348731,0.00060263555,0.00019488289,0.0000036006597,0.000039812698,0.009714539],"genre_scores_gemma":[0.9989941,0.00004978733,0.000060287664,0.00001101035,0.0003606976,0.0000035069208,0.0000025645088,0.00000606861,0.0005119442],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989467,0.00015182002,0.00025859935,0.0000879822,0.00037714242,0.00017773543],"domain_scores_gemma":[0.99931335,0.00013542979,0.00005048653,0.00005144066,0.00037393713,0.000075348755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001090162,0.000082394174,0.00015868353,0.000273026,0.0002096221,0.00015736435,0.00006734872,0.0001800867,0.000015901058],"category_scores_gemma":[0.000114738956,0.000051233572,0.000074310825,0.00049378915,0.000023033346,0.00011439238,0.000013350255,0.0010084241,0.00000872738],"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":[0.0017172488,0.00050147256,0.02295286,0.0011475447,0.0006245627,0.00011794556,0.0022864472,0.16574475,0.12878895,0.00008369333,0.0617014,0.61433315],"study_design_scores_gemma":[0.015182049,0.02072249,0.15678427,0.0058712177,0.00016506601,0.00048238854,0.01348104,0.049183484,0.31107122,0.0012760961,0.42422038,0.0015603019],"about_ca_topic_score_codex":0.000008060797,"about_ca_topic_score_gemma":0.000012909921,"teacher_disagreement_score":0.6127728,"about_ca_system_score_codex":0.00006573789,"about_ca_system_score_gemma":0.000021165457,"threshold_uncertainty_score":0.43811572},"labels":[],"label_agreement":null},{"id":"W4410153260","doi":"10.1109/lcomm.2025.3567850","title":"Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR Inference","year":2025,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"National Science Foundation","keywords":"Precoding; Computer science; Inference; Channel (broadcasting); Zero-forcing precoding; Computer network; Algorithm; Telecommunications; Artificial intelligence; MIMO","score_opus":0.032020616096664976,"score_gpt":0.25771112972228377,"score_spread":0.2256905136256188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410153260","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":[0.8486719,0.0011392796,0.07038015,0.0071536433,0.0039558485,0.0012148248,0.00005594413,0.0011077045,0.0663207],"genre_scores_gemma":[0.9986971,0.0000902457,0.00004629889,0.00047964527,0.00011544816,0.00007794802,0.000012919201,0.000016054511,0.00046433017],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992624,0.000067966,0.0002680625,0.00012182128,0.00010523305,0.00017450411],"domain_scores_gemma":[0.9982653,0.00033149516,0.000043086216,0.0012940884,0.000037378653,0.000028635608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018024266,0.00012842129,0.00013237189,0.00014309195,0.00028932645,0.00017014352,0.0005872541,0.00007841948,0.000014700154],"category_scores_gemma":[0.000034652167,0.00010759614,0.000079945414,0.0004414968,0.00008612517,0.00018221696,0.00009827029,0.00035540233,0.00012319257],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052681753,0.00017780319,0.0032352337,0.00013954067,0.00044424192,0.0000045324728,0.003835636,0.10450354,0.181203,0.0046107676,0.6341965,0.0675965],"study_design_scores_gemma":[0.0007391734,0.000019885882,0.006323138,0.00023197722,0.000027270751,0.0000055725905,0.0002364684,0.02765427,0.00573253,0.00016713228,0.95845395,0.0004086396],"about_ca_topic_score_codex":0.000028916711,"about_ca_topic_score_gemma":0.000017613942,"teacher_disagreement_score":0.3242574,"about_ca_system_score_codex":0.00010618772,"about_ca_system_score_gemma":0.000013022076,"threshold_uncertainty_score":0.43876433},"labels":[],"label_agreement":null},{"id":"W4410292100","doi":"10.1016/j.ces.2025.121832","title":"Machine learning for rapid quantitative stucco phase analysis in plasterboard","year":2025,"lang":"en","type":"article","venue":"Chemical Engineering Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"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 Alberta","funders":"Mitacs","keywords":"Phase (matter); Materials science; Composite material; Computer science; Chemistry","score_opus":0.016989548521724768,"score_gpt":0.28337005914253455,"score_spread":0.2663805106208098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410292100","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":[0.6926421,0.00023129422,0.30577797,0.000011599006,0.0004765399,0.00018882198,0.0000052342175,0.00022755787,0.00043890072],"genre_scores_gemma":[0.997779,0.0000038473054,0.002098045,0.0000048019333,0.000026848214,0.000037065427,0.0000040275017,0.000009347754,0.00003700781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990899,0.0000057751517,0.00024883728,0.00023394143,0.00014813791,0.0002734162],"domain_scores_gemma":[0.9995789,0.00017906234,0.000019677746,0.00011774126,0.0000465006,0.000058150228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045068876,0.00012086606,0.00023968099,0.0007038389,0.000050117087,0.000055368524,0.00015998396,0.000070000286,0.0000067163114],"category_scores_gemma":[0.00048486667,0.00011986697,0.00008371034,0.002860809,0.000040279192,0.0001323935,0.000026422476,0.0002163881,0.0000028135898],"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":[0.000022524398,0.000015537331,0.00015478504,0.000050757993,0.00004658671,9.592536e-7,0.0001054972,0.5809586,0.41256043,0.0004039966,0.00001840537,0.005661931],"study_design_scores_gemma":[0.0006067699,0.00003198551,0.00009439855,0.000039604372,0.000024332,4.2052554e-7,0.000022561646,0.8560865,0.14192662,0.000008546957,0.0010479615,0.00011033377],"about_ca_topic_score_codex":0.000008810487,"about_ca_topic_score_gemma":0.0000010020209,"teacher_disagreement_score":0.30513692,"about_ca_system_score_codex":0.0001475237,"about_ca_system_score_gemma":0.000023207249,"threshold_uncertainty_score":0.4888033},"labels":[],"label_agreement":null},{"id":"W4410560163","doi":"10.18280/isi.300420","title":"Impact of Combining RGB and Grayscale Images on Hotspot Detection in Solar Panels Using Inception Resnet V2 Architecture","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Grayscale; Artificial intelligence; Architecture; Residual neural network; Computer science; Hotspot (geology); RGB color model; Computer vision; Pattern recognition (psychology); Remote sensing; Geology; Geography; Deep learning; Image (mathematics); Seismology","score_opus":0.014035341627320602,"score_gpt":0.25173169403336704,"score_spread":0.23769635240604645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410560163","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":[0.9437578,0.000050415074,0.054133832,0.000001995848,0.00035742673,0.00034036738,0.000018154095,0.0001226553,0.0012173973],"genre_scores_gemma":[0.9997347,0.000022228272,0.00014850828,0.0000073941674,0.00003769804,0.000017578328,0.000014186872,0.000011604322,0.00000609536],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99886864,0.00007878126,0.0005926351,0.000102250415,0.000154235,0.0002034276],"domain_scores_gemma":[0.9994883,0.0000847406,0.00015636261,0.00014876216,0.00008530382,0.0000365462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038034405,0.0001792822,0.0002696655,0.0008356406,0.00012094101,0.00010224415,0.00005695253,0.00021374048,0.0000056448694],"category_scores_gemma":[0.00014424478,0.00016835364,0.00007455134,0.0006237108,0.000052866188,0.00084054266,0.000022740971,0.00025752993,0.0000035390271],"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":[0.00042603575,0.00003300299,0.010142869,0.0010575185,0.00010748121,0.0000020702141,0.00636705,0.32919645,0.10441101,0.00008561685,0.000076583885,0.54809433],"study_design_scores_gemma":[0.0041852393,0.0012290352,0.49266493,0.0040350882,0.000075497555,0.00010288705,0.0029469398,0.28943262,0.20125858,0.0026800472,0.00046103538,0.00092809624],"about_ca_topic_score_codex":0.00054309465,"about_ca_topic_score_gemma":0.00003006841,"teacher_disagreement_score":0.5471662,"about_ca_system_score_codex":0.0005183449,"about_ca_system_score_gemma":0.000043424316,"threshold_uncertainty_score":0.6865262},"labels":[],"label_agreement":null},{"id":"W4410799678","doi":"10.1016/j.matchar.2025.115224","title":"A facile methodology to identify microstructural grains on etched surfaces using panoptic segmentation","year":2025,"lang":"en","type":"article","venue":"Materials Characterization","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council; University of Nottingham","keywords":"Materials science; Panopticon; Segmentation; Nanotechnology; Composite material; Metallurgy; Artificial intelligence; Computer science","score_opus":0.06145937781654779,"score_gpt":0.33702490872649216,"score_spread":0.27556553090994435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410799678","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":[0.94954515,0.0000036116128,0.044728108,0.000030319467,0.004819807,0.00050821644,0.00012337265,0.00021110661,0.000030286574],"genre_scores_gemma":[0.9983048,0.0000035135513,0.0009879605,0.0000999848,0.00017200796,0.000033970828,0.0002683173,0.000023812858,0.0001056193],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99886876,0.00021418264,0.0004091469,0.00020935973,0.00010760862,0.00019091007],"domain_scores_gemma":[0.9996372,0.00004329089,0.00008002035,0.0001493065,0.00005452791,0.000035669393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033580285,0.00017028864,0.00027204232,0.00026498726,0.00010542684,0.00018912116,0.000068858324,0.00016480949,0.00011645179],"category_scores_gemma":[0.000064885106,0.00016809416,0.000032911896,0.00030970087,0.000009515768,0.00017465088,0.000023573204,0.000058501446,0.000060382976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059732218,0.000004268833,0.000077333076,0.00006132728,0.000021980513,8.065239e-7,0.00030214174,0.0022233294,0.9948939,0.000034381577,0.00002320176,0.0022975965],"study_design_scores_gemma":[0.00028915345,0.00003751784,0.016742568,0.00007859996,0.00001839459,0.0000035234305,0.00007979522,0.00096716546,0.98116493,0.000028599296,0.00043914438,0.00015062],"about_ca_topic_score_codex":0.000048517308,"about_ca_topic_score_gemma":0.000002593278,"teacher_disagreement_score":0.048759643,"about_ca_system_score_codex":0.00013332684,"about_ca_system_score_gemma":0.000013331496,"threshold_uncertainty_score":0.68546814},"labels":[],"label_agreement":null},{"id":"W4410827183","doi":"10.1007/s10845-025-02623-3","title":"Focus to generalize (F2G): physics-guided attention for sample efficient and generalizable deep learning defect detection","year":2025,"lang":"en","type":"article","venue":"Journal of Intelligent Manufacturing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Toronto","funders":"","keywords":"Focus (optics); Sample (material); Artificial intelligence; Deep learning; Computer science; Cognitive science; Machine learning; Engineering; Psychology; Physics; Optics","score_opus":0.018111740954098485,"score_gpt":0.25848163670915036,"score_spread":0.24036989575505188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410827183","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":[0.45847368,0.0002706908,0.53987086,0.000012072948,0.0010456966,0.00020976034,0.0000011049948,0.000039556267,0.00007658382],"genre_scores_gemma":[0.9968944,0.000071377835,0.0022908452,0.000021255082,0.0005334786,0.000018749151,0.0000013332644,0.000029044033,0.00013949057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99872243,0.00005590246,0.00061152386,0.00016456193,0.0001896888,0.00025589523],"domain_scores_gemma":[0.999343,0.0001492731,0.0001652812,0.00010688741,0.00013755371,0.00009802477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005673099,0.00018764145,0.00031065516,0.00035685988,0.00019566086,0.00012707381,0.000084993946,0.00011239284,0.000009779421],"category_scores_gemma":[0.00013478631,0.00017059578,0.0002559232,0.00016806873,0.000008304428,0.00010040222,0.00003149498,0.00025145593,0.0000047479825],"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":[0.00006402294,0.000012382165,0.00008109031,0.00008780569,0.0001146037,0.0000015144202,0.00012308531,0.7769904,0.03863199,0.000039885654,0.00012667672,0.18372652],"study_design_scores_gemma":[0.00054897775,0.00020227676,0.0002990689,0.00018980374,0.00008540388,0.000036617614,0.00014752193,0.11360928,0.86895734,0.00030828328,0.015417948,0.00019748067],"about_ca_topic_score_codex":0.000064068874,"about_ca_topic_score_gemma":0.000017495397,"teacher_disagreement_score":0.83032537,"about_ca_system_score_codex":0.00026380105,"about_ca_system_score_gemma":0.000011840236,"threshold_uncertainty_score":0.6956694},"labels":[],"label_agreement":null},{"id":"W4411007986","doi":"10.38032/scse.2025.2.10","title":"An Intelligent Hybrid Manufacturing System for FDM Surface Defects Monitoring with Industry 4.0","year":2025,"lang":"en","type":"article","venue":"SciEn Conference Series Engineering","topic":"Industrial Vision Systems and Defect Detection","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":"McMaster University","funders":"","keywords":"Industry 4.0; Manufacturing engineering; Surface (topology); Computer science; Engineering; Embedded system; Mathematics","score_opus":0.01671228368939314,"score_gpt":0.234423406411272,"score_spread":0.21771112272187884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411007986","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":[0.8368893,0.00007741244,0.15783906,0.000007974514,0.002796634,0.00047828964,0.000013988167,0.0010156459,0.0008816566],"genre_scores_gemma":[0.997645,0.000005632097,0.0017956323,0.000002132969,0.00023348258,0.00007265971,0.000004869218,0.000046675093,0.00019389093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985779,0.000017840857,0.00032569177,0.00036015848,0.00021906644,0.0004993666],"domain_scores_gemma":[0.9992531,0.00006135272,0.000043333373,0.00037319737,0.00011961281,0.00014939277],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003502105,0.0003112867,0.00032477756,0.00022406012,0.00020327723,0.0003473424,0.000266125,0.0002146832,0.0000063959756],"category_scores_gemma":[0.000025310586,0.0002908942,0.00006156631,0.00032505213,0.000028958832,0.0005567228,0.00003454571,0.0004326616,0.0000056906188],"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":[0.000053169908,0.000008646468,0.00040408713,0.0007404741,0.00007528349,0.000011287852,0.00018622635,0.9220268,0.06878135,0.0013747234,0.00004463765,0.0062932717],"study_design_scores_gemma":[0.00030674253,0.00012844344,0.00033417728,0.00076092355,0.000024426376,0.000028915434,0.0010360054,0.07100974,0.9234641,0.000014268887,0.0025290828,0.0003631247],"about_ca_topic_score_codex":0.000030548465,"about_ca_topic_score_gemma":0.0000066386733,"teacher_disagreement_score":0.8546828,"about_ca_system_score_codex":0.00027112398,"about_ca_system_score_gemma":0.00007687575,"threshold_uncertainty_score":0.99995434},"labels":[],"label_agreement":null},{"id":"W4411064948","doi":"10.1088/1742-6596/3022/1/012001","title":"Deep Learning-driven Blind Spot Detection for Forklifts in Industrial Environments","year":2025,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carbon Engineering (Canada)","funders":"","keywords":"Blind spot; Artificial intelligence; Computer science; Deep learning; Process engineering; Engineering","score_opus":0.03145573844024943,"score_gpt":0.24689926176107868,"score_spread":0.21544352332082925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411064948","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":[0.57751626,0.000057999714,0.41939273,0.00003084971,0.0021600476,0.00030124182,0.000002516529,0.00002593045,0.00051239197],"genre_scores_gemma":[0.9991753,0.000041886535,0.00010184205,0.000004076469,0.00047549434,0.000012073714,0.0000014576564,0.0000121254525,0.00017573628],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915737,0.000043742693,0.00041926414,0.00008364966,0.00014380521,0.0001521662],"domain_scores_gemma":[0.9995995,0.00006326154,0.00016600714,0.000069928195,0.00006612295,0.00003517881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022194925,0.00012100653,0.00026835923,0.00015869098,0.00006673054,0.00006278241,0.00008775566,0.00016457606,0.000008188332],"category_scores_gemma":[0.000079386344,0.00011622966,0.00009886207,0.00020946913,0.000022745087,0.00036412093,0.000014177959,0.00042688526,0.0000028612965],"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":[0.00092746003,0.000056335848,0.001286222,0.00006351618,0.00014323014,0.0000053244285,0.0006425321,0.1177598,0.08381925,0.0010187889,0.00011198236,0.79416555],"study_design_scores_gemma":[0.007839009,0.0013005639,0.0020192396,0.00057736953,0.000099179524,0.000021274807,0.0018076802,0.033487488,0.9051484,0.005972572,0.041283518,0.00044373333],"about_ca_topic_score_codex":0.000006146521,"about_ca_topic_score_gemma":0.000025364781,"teacher_disagreement_score":0.8213291,"about_ca_system_score_codex":0.00011180393,"about_ca_system_score_gemma":0.000045293742,"threshold_uncertainty_score":0.47397083},"labels":[],"label_agreement":null},{"id":"W4411093736","doi":"10.1007/s11760-025-04282-6","title":"Multiscale deformable transformer meets lightweight design: enabling real-time quality detection for automobile body stamping parts under production-line constraints","year":2025,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; Beijing Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Stamping; Production line; Computer science; Automotive industry; Automotive engineering; Quality (philosophy); Transformer; Manufacturing engineering; Embedded system; Mechanical engineering; Engineering; Voltage; Electrical engineering","score_opus":0.02746371083504857,"score_gpt":0.2966369454438943,"score_spread":0.26917323460884574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411093736","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":[0.05878993,0.00059301994,0.9375443,0.00008658227,0.00048234515,0.0010783405,0.0000122333995,0.00050810014,0.0009051465],"genre_scores_gemma":[0.9961205,0.000059539445,0.0026838805,0.000025577818,0.00031446348,0.00018331017,0.000011468691,0.000040510065,0.0005607271],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99821216,0.00010095678,0.0006529921,0.0004182029,0.00018771716,0.00042799654],"domain_scores_gemma":[0.9992433,0.00017333994,0.00011582101,0.00013532664,0.00024025871,0.00009191758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013735243,0.00027605164,0.00038319893,0.00023542035,0.0006489694,0.00029300243,0.00006445009,0.0002175059,0.000029739538],"category_scores_gemma":[0.000088438486,0.00025258382,0.00009678884,0.00038702053,0.0000877191,0.0009618779,0.000011341784,0.0002227155,0.0000064285477],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001408935,0.000029177776,0.0000072475373,0.0006654384,0.00005742673,0.0000014583788,0.00031928063,0.0082814405,0.7975351,0.000015514968,0.00010706387,0.19283994],"study_design_scores_gemma":[0.0009991785,0.00009612081,0.000037731832,0.00055531657,0.000077025616,0.00001752488,0.00031760792,0.15735713,0.83820546,0.0003833512,0.0016220665,0.0003315001],"about_ca_topic_score_codex":0.000053050826,"about_ca_topic_score_gemma":0.000014166926,"teacher_disagreement_score":0.9373306,"about_ca_system_score_codex":0.0001286109,"about_ca_system_score_gemma":0.000099543104,"threshold_uncertainty_score":0.99999267},"labels":[],"label_agreement":null},{"id":"W4411149257","doi":"10.1016/j.compchemeng.2025.109205","title":"A digital twin simulator of a pastillation process with applications to automatic control based on computer vision","year":2025,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Science Foundation","keywords":"Computer science; Process (computing); Computer graphics (images); Simulation; Control (management); Engineering drawing; Artificial intelligence; Engineering; Operating system","score_opus":0.003652105713373932,"score_gpt":0.2102454609912211,"score_spread":0.20659335527784717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411149257","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":[0.12630635,0.0000066532953,0.8725143,0.00001989277,0.00016040885,0.00048735263,0.0000069213474,0.00038405368,0.00011407063],"genre_scores_gemma":[0.995371,3.8839882e-8,0.0043745246,0.000041933064,0.000113816895,0.00006554037,0.0000067612114,0.000023519895,0.0000028878603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991917,0.0000046325235,0.00028510301,0.00018870652,0.00017124678,0.00015862667],"domain_scores_gemma":[0.99945146,0.0001826517,0.000031451902,0.00019004794,0.000064646614,0.000079726175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052906045,0.00016997124,0.00025715624,0.00022204872,0.000020464795,0.000053335476,0.000101401645,0.000090431095,0.000001809371],"category_scores_gemma":[0.000015582258,0.00015458625,0.000055803655,0.0005143633,0.000009155828,0.000073958356,0.000013740357,0.00012512598,0.000005724519],"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":[0.000025955438,0.000021171643,0.000023384797,0.0002005088,0.000024406358,6.648812e-7,0.00002065896,0.9701792,0.005618895,0.000022870492,0.000076331526,0.02378593],"study_design_scores_gemma":[0.0008000664,0.00010068865,0.00010185152,0.0005306907,0.000011695448,0.0000014965343,0.0000026083217,0.9901943,0.0075826137,0.0000030592478,0.0005244363,0.00014649653],"about_ca_topic_score_codex":6.213983e-7,"about_ca_topic_score_gemma":2.846466e-8,"teacher_disagreement_score":0.8690646,"about_ca_system_score_codex":0.00008524355,"about_ca_system_score_gemma":0.00001646294,"threshold_uncertainty_score":0.63038445},"labels":[],"label_agreement":null},{"id":"W4411161447","doi":"10.21203/rs.3.rs-6792823/v1","title":"AI-Powered Defect Prediction: From Code Smells to Failure Forecasting","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wycliffe College","funders":"","keywords":"Code (set theory); Code smell; Computer science; Artificial intelligence; Reliability engineering; Engineering; Programming language; Software quality; Software","score_opus":0.09121409264941353,"score_gpt":0.36095330873979814,"score_spread":0.2697392160903846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411161447","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":[0.63965964,0.00830887,0.1540719,0.0029282968,0.03685742,0.023034368,0.024004953,0.008808834,0.10232574],"genre_scores_gemma":[0.99489045,0.000055391476,0.0004959456,0.000030887117,0.002494139,0.00055501994,0.00029086423,0.00008562473,0.0011016862],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962126,0.00042631425,0.000625563,0.0007985063,0.0011604237,0.00077656884],"domain_scores_gemma":[0.9974365,0.00057031075,0.000053356744,0.000994275,0.00062549365,0.0003200551],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0015455711,0.00039662913,0.00057894376,0.0009227187,0.00030599194,0.00043318744,0.00046937974,0.0010712532,0.00030796893],"category_scores_gemma":[0.0006476768,0.00040449077,0.0003592296,0.0009874235,0.000031670246,0.00010247528,0.00075481215,0.0030953542,0.00030592477],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005547268,0.000110434594,0.0024209935,0.0045682373,0.0011635739,0.0001879604,0.0030890456,0.34397104,0.0046221754,0.00019907736,0.5717707,0.067342],"study_design_scores_gemma":[0.0021130228,0.00076901645,0.0022172162,0.01674835,0.00013464931,0.000020271782,0.0018511907,0.20252083,0.01732076,0.002990247,0.75159407,0.0017203755],"about_ca_topic_score_codex":0.0010574133,"about_ca_topic_score_gemma":0.0004851501,"teacher_disagreement_score":0.3552308,"about_ca_system_score_codex":0.0007298666,"about_ca_system_score_gemma":0.00026645258,"threshold_uncertainty_score":0.9998407},"labels":[],"label_agreement":null},{"id":"W4411347953","doi":"10.18280/mmep.120522","title":"The Improvement Human Resource Performance Through Smart Vision Camera Optimization Based on Artificial Intelligence-Integrated Human Machine Interface Systems","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Human–machine interface; Interface (matter); Computer science; Artificial intelligence; Machine vision; Computer vision; Human–computer interaction; Smart camera; Human interface device; Human–machine system; Operating system","score_opus":0.02589260360813712,"score_gpt":0.24899757953210094,"score_spread":0.22310497592396383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411347953","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":[0.020333273,0.00019589216,0.97577065,0.00004813255,0.0002845098,0.0006286735,0.000002656163,0.00034644292,0.0023897418],"genre_scores_gemma":[0.9977131,0.000027579386,0.0018347674,0.000008239178,0.00005515214,0.000111242625,0.0000088577945,0.000043451906,0.00019759328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853194,0.000029717727,0.0006596787,0.0002518464,0.00021885773,0.000307948],"domain_scores_gemma":[0.9994064,0.00014303948,0.000054979373,0.0002810585,0.000057591962,0.000056918998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052689173,0.00027539177,0.0002833022,0.00012985751,0.0003747195,0.00033713054,0.0001332566,0.00016259044,0.000009238284],"category_scores_gemma":[0.00001538803,0.00019883244,0.000058251328,0.0002732823,0.000031576135,0.00009548271,0.000027787319,0.00041001977,0.000009485338],"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":[0.00000893782,0.000025680401,8.79579e-7,0.00046230867,0.000023596813,2.0653557e-7,0.00010935387,0.9925636,0.0010477506,0.0036176636,0.00004015836,0.002099853],"study_design_scores_gemma":[0.00011379501,0.00019358145,5.582176e-7,0.0013214528,0.000017557777,0.000001399069,0.00009931688,0.99432063,0.0019584184,0.0006531852,0.0011213069,0.00019879233],"about_ca_topic_score_codex":0.000057761583,"about_ca_topic_score_gemma":0.0000012270132,"teacher_disagreement_score":0.97737986,"about_ca_system_score_codex":0.000109954366,"about_ca_system_score_gemma":0.0000073344404,"threshold_uncertainty_score":0.81081516},"labels":[],"label_agreement":null},{"id":"W4411484907","doi":"10.1109/icccbda64898.2025.11030418","title":"Parameter Optimization for Partitioning Algorithms in Object Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Algorithm; Object (grammar); Artificial intelligence","score_opus":0.014853234907157625,"score_gpt":0.2505071242443995,"score_spread":0.2356538893372419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411484907","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":[0.03164919,0.00003329238,0.96291095,0.000010869121,0.0011111608,0.0003572352,0.0000015250381,0.00023354057,0.0036922465],"genre_scores_gemma":[0.99498993,0.0000052227297,0.0044585196,0.000020969921,0.0000796794,0.0001694932,0.00000456449,0.000010167034,0.00026147955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950534,0.000016462722,0.00020554423,0.000104886894,0.000046053814,0.00012170708],"domain_scores_gemma":[0.9997865,0.00008112751,0.000013772222,0.00007378219,0.000030850708,0.000013988732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018034098,0.000069269794,0.00009945601,0.00020120216,0.000051050658,0.000042796986,0.000023382916,0.00010938252,0.000028071941],"category_scores_gemma":[0.00006585135,0.000068522495,0.000041666954,0.0003241376,0.0000040147015,0.000108584,0.0000042966376,0.00007044605,0.0000048625166],"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":[0.000017189397,0.0000070131678,0.00010618734,0.00003319698,0.000012801404,2.6210049e-7,0.000030281646,0.9185676,0.0013798026,0.00010785404,0.00030758043,0.07943025],"study_design_scores_gemma":[0.0004922447,0.000029844947,0.00018517433,0.000037436123,0.0000053596045,8.806712e-7,0.00005579408,0.9617524,0.035161003,0.00018289643,0.002018435,0.00007848531],"about_ca_topic_score_codex":0.000053338415,"about_ca_topic_score_gemma":0.0000730937,"teacher_disagreement_score":0.9633407,"about_ca_system_score_codex":0.00008792024,"about_ca_system_score_gemma":0.000007335025,"threshold_uncertainty_score":0.27942663},"labels":[],"label_agreement":null},{"id":"W4411867027","doi":"10.1109/tase.2025.3584739","title":"Sequential Image Restoration and Segmentation for Interface Detection in Primary Separation Cells","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image segmentation; Artificial intelligence; Computer vision; Segmentation; Computer science; Separation (statistics); Image restoration; Image (mathematics); Image processing; Pattern recognition (psychology); Machine learning","score_opus":0.012157965296798658,"score_gpt":0.2714527770319197,"score_spread":0.2592948117351211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411867027","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":[0.3683679,0.000013801402,0.6303137,0.000012430905,0.0007974138,0.00028918596,0.0000030172257,0.00013913697,0.00006343442],"genre_scores_gemma":[0.998347,0.000021159698,0.0014591676,0.000011158829,0.000021738775,0.000101903555,0.0000011760236,0.000008703563,0.000028014641],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992792,0.000012119464,0.00023973,0.0001838955,0.00015285335,0.00013219872],"domain_scores_gemma":[0.9997438,0.00004794378,0.000025970312,0.00007465234,0.00007484765,0.000032743264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047869398,0.00010289727,0.00010163544,0.0006405757,0.00016086445,0.00015034997,0.000034381264,0.00007791354,0.0000014681699],"category_scores_gemma":[0.00001148453,0.00011405136,0.000017852031,0.00063418644,0.000030301251,0.0008100142,9.565318e-7,0.00010467793,0.0000020522348],"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":[0.000012664973,0.000005042523,7.632642e-7,0.00006587025,0.0000032969708,7.908995e-8,0.00016297663,0.2048208,0.7585759,0.000010497097,0.000011249178,0.03633084],"study_design_scores_gemma":[0.0002780383,0.000030279469,0.00027973618,0.000041812564,0.0000055426412,0.0000015832359,0.000056730558,0.51382554,0.4853204,0.000007670437,0.00008827397,0.000064377484],"about_ca_topic_score_codex":0.000013503142,"about_ca_topic_score_gemma":0.000017288105,"teacher_disagreement_score":0.6299791,"about_ca_system_score_codex":0.00031494623,"about_ca_system_score_gemma":0.000031957734,"threshold_uncertainty_score":0.46508798},"labels":[],"label_agreement":null},{"id":"W4411872481","doi":"10.1364/josaa.561327","title":"Improved mapping between illuminations and sensors for RAW images","year":2025,"lang":"en","type":"article","venue":"Journal of the Optical Society of America A","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Remote sensing; Computer graphics (images); Geography","score_opus":0.012371322133729162,"score_gpt":0.24645648850389784,"score_spread":0.23408516637016868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411872481","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":[0.5173944,0.00034567568,0.47656018,0.0034037514,0.00083499704,0.00038745994,0.000014571883,0.000031568878,0.001027394],"genre_scores_gemma":[0.9861809,0.000041435953,0.013403917,0.000043679338,0.00016529589,0.0000020022192,1.4589209e-7,0.0000067306373,0.00015586738],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945986,0.000016112606,0.00030103218,0.00004341002,0.00009009496,0.00008946317],"domain_scores_gemma":[0.9994142,0.00025862586,0.000117500545,0.00006623367,0.00011200978,0.000031396816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002451034,0.00005930168,0.00021517597,0.000028999413,0.000077160155,0.000017330834,0.000071024755,0.00006702391,0.0000019619201],"category_scores_gemma":[0.00013828301,0.000040237006,0.00026781738,0.00018375255,0.00010618362,0.000050535582,0.000021842365,0.00016486876,1.531723e-7],"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":[0.0000944781,0.000097995064,0.0012756938,0.0009143468,0.0021524942,6.966842e-7,0.004042867,0.010319193,0.58903164,0.00021021684,0.082409084,0.3094513],"study_design_scores_gemma":[0.008754008,0.0014690512,0.043179832,0.002010093,0.0012902666,0.00008467243,0.03455302,0.35663605,0.34645817,0.0024007435,0.20214301,0.0010210846],"about_ca_topic_score_codex":0.000005934578,"about_ca_topic_score_gemma":4.1464485e-8,"teacher_disagreement_score":0.4687865,"about_ca_system_score_codex":0.000039025115,"about_ca_system_score_gemma":0.000018618726,"threshold_uncertainty_score":0.16408175},"labels":[],"label_agreement":null},{"id":"W4411930937","doi":"10.1016/j.patrec.2025.06.022","title":"A novel industrial thermoelectric cooler component defect vision transformer detector based on local and global features fusion","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Fusion; Component (thermodynamics); Detector; Thermoelectric effect; Transformer; Artificial intelligence; Computer vision; Computer science; Materials science; Engineering; Electrical engineering; Physics; Voltage; Telecommunications","score_opus":0.01633668356650146,"score_gpt":0.22650686783887478,"score_spread":0.21017018427237333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411930937","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":[0.62191063,0.000046529403,0.37494436,0.00036608241,0.0011664806,0.0005517366,0.00008663674,0.00022951489,0.00069805206],"genre_scores_gemma":[0.99789494,0.000007196837,0.000035510286,0.0017074001,0.00020664961,0.000059915255,0.000056603436,0.000025976939,0.000005823884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986536,0.00010097652,0.0003298397,0.0003309059,0.0002792306,0.000305486],"domain_scores_gemma":[0.99954855,0.00013428477,0.000050826042,0.00014254518,0.000037842437,0.00008596201],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022567145,0.00028541376,0.00026739255,0.00029149218,0.00014502346,0.000101647194,0.00007325915,0.0002967038,0.000068471716],"category_scores_gemma":[0.00001975308,0.00025776355,0.00015820692,0.00045320776,0.000037900732,0.00009881037,0.000009173072,0.0004051839,0.00004282879],"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":[0.00042449858,0.000073134914,0.00048361556,0.000080127415,0.00007862888,0.00000836328,0.00003049118,0.003104717,0.108293116,0.000001503905,0.004223785,0.883198],"study_design_scores_gemma":[0.05222944,0.0032827232,0.13562156,0.006180709,0.00096211996,0.00026050862,0.00031904763,0.4156097,0.3612684,0.000121787016,0.019561907,0.00458212],"about_ca_topic_score_codex":0.00017545032,"about_ca_topic_score_gemma":0.000045339286,"teacher_disagreement_score":0.8786159,"about_ca_system_score_codex":0.0002691849,"about_ca_system_score_gemma":0.000018972667,"threshold_uncertainty_score":0.9999875},"labels":[],"label_agreement":null},{"id":"W4412043338","doi":"10.5121/csit.2025.151210","title":"Enhancing Frame Detection with Retrieval Augmented Generation","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Compute Canada","keywords":"Computer science; Frame (networking); Information retrieval; Artificial intelligence; Computer vision; Computer network","score_opus":0.010415230041706035,"score_gpt":0.21811132161873714,"score_spread":0.20769609157703112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412043338","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":[0.5031992,0.000027950762,0.48901567,0.000007431433,0.0009136271,0.00013539474,3.5738714e-7,0.00034328777,0.0063570538],"genre_scores_gemma":[0.99830806,0.0000030232475,0.00022083163,0.000025299098,0.00021296786,0.000008201952,0.0000022927711,0.000010356865,0.0012089538],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994976,0.00001792263,0.0001640247,0.00010997105,0.00010335941,0.000107129556],"domain_scores_gemma":[0.9997899,0.000015598958,0.000015722138,0.000109222696,0.000047453028,0.000022127424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013414166,0.00008300586,0.00009155346,0.00012978417,0.000083800376,0.000051256993,0.000024103585,0.00011288231,0.00004498402],"category_scores_gemma":[0.00001906588,0.000067817164,0.000023621864,0.00038685245,0.0000050019767,0.00009824157,0.000005090517,0.00012039472,0.00002683088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034652392,0.000004063132,0.000033192715,0.000017342514,0.00003068495,7.5287625e-7,0.00002693069,0.005546274,0.9850396,0.00007717207,0.0003513914,0.00883797],"study_design_scores_gemma":[0.000331956,0.00006172917,0.00017148245,0.00003198251,0.000010399117,0.0000034755312,0.000055446766,0.05684126,0.9384265,0.000010545628,0.0039707003,0.000084550804],"about_ca_topic_score_codex":0.000041941817,"about_ca_topic_score_gemma":0.00016110869,"teacher_disagreement_score":0.49510884,"about_ca_system_score_codex":0.00010364299,"about_ca_system_score_gemma":0.0000126493715,"threshold_uncertainty_score":0.27655038},"labels":[],"label_agreement":null},{"id":"W4412084016","doi":"10.1115/1.4069097","title":"A Feature-Engineering Approach to Support Vector Machine-Based Damage Detection in Lead Zirconate Titanate Ceramics Via Point-Contact Wavefield Measurement","year":2025,"lang":"en","type":"article","venue":"Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Support vector machine; Feature (linguistics); Ceramic; Point (geometry); Materials science; Multi point; Computer science; Pattern recognition (psychology); Artificial intelligence; Acoustics; Composite material; Mathematics; Physics; Geometry","score_opus":0.020973134698972027,"score_gpt":0.24444226440998496,"score_spread":0.22346912971101293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412084016","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":[0.42372474,0.0016201371,0.5686676,0.00004173394,0.004090876,0.0015113944,0.00003306189,0.00006471675,0.0002457498],"genre_scores_gemma":[0.9972944,0.0000441806,0.0023658527,0.000007264461,0.00017814901,0.00005956943,0.0000059499034,0.000040938565,0.000003650882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779797,0.00009332851,0.00089021755,0.00020701432,0.000745787,0.00026568607],"domain_scores_gemma":[0.9979113,0.00050819473,0.000270885,0.00018593794,0.0010052829,0.00011839629],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023889656,0.0003000873,0.0005795173,0.0010341842,0.000039893835,0.000094985495,0.00013977416,0.00024637187,0.0000015068498],"category_scores_gemma":[0.0024002325,0.00029976416,0.00010917981,0.00064745476,0.0000113282495,0.0001885716,0.000023027484,0.000521681,7.8842436e-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":[0.00006841303,0.000053357584,0.0009580917,0.0006829177,0.00017447904,0.000008711758,0.00013482558,0.97326624,0.01994045,0.00032594803,0.000102409,0.004284156],"study_design_scores_gemma":[0.0015152032,0.00047562417,0.012654131,0.0010716511,0.00016862163,0.000042406762,0.00009484311,0.9732057,0.010208583,0.000028320086,0.0002588182,0.00027610702],"about_ca_topic_score_codex":0.000036387108,"about_ca_topic_score_gemma":0.0000104033115,"teacher_disagreement_score":0.5735697,"about_ca_system_score_codex":0.00049577205,"about_ca_system_score_gemma":0.00013038462,"threshold_uncertainty_score":0.99994546},"labels":[],"label_agreement":null},{"id":"W4412138270","doi":"10.21203/rs.3.rs-7008217/v1","title":"No-reference Quality Assessment of Contrast-distorted Images using Contrast-enhanced Pseudo Reference","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Contrast (vision); Quality (philosophy); Computer science; Artificial intelligence; Physics","score_opus":0.17700891407783922,"score_gpt":0.45267381941201607,"score_spread":0.27566490533417687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412138270","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":[0.735387,0.0012565145,0.117145635,0.000028588265,0.0038383799,0.0050705043,0.0035856634,0.00084539806,0.13284229],"genre_scores_gemma":[0.9974083,0.00023185149,0.00085413246,0.0000033971037,0.0002807153,0.00021445034,0.00014067757,0.000043695374,0.0008228203],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.994475,0.0011943345,0.0012436446,0.00074947026,0.0015639901,0.0007735726],"domain_scores_gemma":[0.99509394,0.0008314923,0.00027990225,0.0011123273,0.0024765322,0.00020580098],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0033214728,0.00045559823,0.001147017,0.0006473404,0.00022197614,0.00018835976,0.0005667013,0.00095475005,0.00025378636],"category_scores_gemma":[0.0008127092,0.00044721618,0.00022671133,0.00061935507,0.00016228297,0.00013863538,0.00048539083,0.0030395489,0.000028305098],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000598306,0.00047133377,0.002508152,0.019464426,0.001063286,0.000036055673,0.00043845808,0.049013216,0.8945179,0.0035239726,0.004522309,0.023842596],"study_design_scores_gemma":[0.012891296,0.0022809296,0.13475738,0.044265766,0.00046896437,0.00001968302,0.0031951217,0.33648047,0.43382406,0.0033734427,0.022026794,0.006416089],"about_ca_topic_score_codex":0.0040560914,"about_ca_topic_score_gemma":0.00015494728,"teacher_disagreement_score":0.46069384,"about_ca_system_score_codex":0.0010437325,"about_ca_system_score_gemma":0.0009413881,"threshold_uncertainty_score":0.99979794},"labels":[],"label_agreement":null},{"id":"W4412394752","doi":"10.1007/978-981-96-9949-0_38","title":"MCT-Net: Multiscale Convolution-Transformer Network for Defect Image Generation Using Segmentation Maps","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Convolution (computer science); Transformer; Artificial intelligence; Segmentation; Computer vision; Pattern recognition (psychology); Electrical engineering; Engineering; Artificial neural network; Voltage","score_opus":0.045916379604135846,"score_gpt":0.2915973830434393,"score_spread":0.24568100343930344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412394752","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":[0.0003310632,0.0005333848,0.9571772,0.000038853344,0.0013227784,0.0012431344,0.00011081451,0.00011498048,0.039127793],"genre_scores_gemma":[0.24614942,0.0070821573,0.7342677,0.0009783568,0.0020479807,0.0007200911,0.00353834,0.0001310753,0.005084889],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998712,0.000022670496,0.00069002475,0.00016355583,0.00020992172,0.0002018214],"domain_scores_gemma":[0.9988172,0.00012809149,0.00015305757,0.0005463008,0.00030538262,0.00004994493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008025643,0.00019750607,0.00022818244,0.00056161877,0.00056606863,0.0003112689,0.0003487731,0.00020105277,0.0000067223564],"category_scores_gemma":[0.000019538593,0.0002125745,0.00007930564,0.0002902532,0.00019978837,0.0025097239,0.000092907634,0.00025094443,0.0000115187995],"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":[0.00003326055,0.000024543611,0.00007822419,0.0005325526,0.00009746492,2.575978e-7,0.002312406,0.23752578,0.0025372829,0.095679455,0.016956728,0.6442221],"study_design_scores_gemma":[0.000450067,0.000030146419,0.000042936208,0.00021741382,0.000021723565,0.000005709413,0.00001902317,0.9108215,0.00021410058,0.0004975278,0.08744852,0.00023134226],"about_ca_topic_score_codex":0.00002307352,"about_ca_topic_score_gemma":0.000031506475,"teacher_disagreement_score":0.67329574,"about_ca_system_score_codex":0.00029593534,"about_ca_system_score_gemma":0.00011766404,"threshold_uncertainty_score":0.86685365},"labels":[],"label_agreement":null},{"id":"W4412445297","doi":"10.1109/sustained63638.2024.11073942","title":"Sustainable Textile Defect Management Using IoT and SVM-Based Smart Inspection","year":2024,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Internet of Things; Textile; Support vector machine; Computer science; Embedded system; Artificial intelligence; Materials science","score_opus":0.01216580989208886,"score_gpt":0.22748107789508568,"score_spread":0.21531526800299683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412445297","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":[0.8162602,0.0009654707,0.09964492,0.00002096411,0.0018898688,0.00065191585,0.0000022476977,0.0026169934,0.07794742],"genre_scores_gemma":[0.9970716,0.0000065791805,0.00029415893,0.0000110904,0.00014142516,0.000015430633,0.0000012178004,0.000027427111,0.0024310374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937826,0.000017532719,0.00014324771,0.00016369109,0.000106022184,0.00019122218],"domain_scores_gemma":[0.99980307,0.000023612303,0.000007604233,0.00010403156,0.000019477875,0.000042203483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021950892,0.00010769185,0.00009783483,0.00025184892,0.00010889384,0.00017929923,0.000023184557,0.0000795941,0.00007525154],"category_scores_gemma":[0.0000048565958,0.00009682341,0.000056268607,0.00035159444,0.000009521828,0.00009314061,0.000016377502,0.00010514856,0.000039828396],"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":[0.00019365651,0.00014630299,0.0017167343,0.011579317,0.0012937314,0.0012447909,0.0012800756,0.56500596,0.036938556,0.06761807,0.06284832,0.25013447],"study_design_scores_gemma":[0.00045227114,0.00009198207,0.00033363578,0.00016483541,0.0000676883,0.000036408608,0.00096112845,0.8787202,0.0074484586,0.0002325303,0.11119684,0.0002940078],"about_ca_topic_score_codex":0.00025549042,"about_ca_topic_score_gemma":0.00001279103,"teacher_disagreement_score":0.31371427,"about_ca_system_score_codex":0.00017725218,"about_ca_system_score_gemma":0.000010300404,"threshold_uncertainty_score":0.3948344},"labels":[],"label_agreement":null},{"id":"W4412561528","doi":"10.1007/s11222-025-10680-0","title":"Penalty Learning for Optimal Partitioning using Multilayer Perceptron","year":2025,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Perceptron; Artificial intelligence; Machine learning; Mathematical optimization; Pattern recognition (psychology); Mathematics; Artificial neural network","score_opus":0.01909557214336142,"score_gpt":0.2874322873045626,"score_spread":0.26833671516120117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412561528","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":[0.29163852,0.000072722796,0.70751053,0.0000018936416,0.00037017398,0.00009408179,0.000007221147,0.00006614981,0.00023869822],"genre_scores_gemma":[0.9624909,0.0000032814373,0.037261497,0.000010470707,0.00013718216,0.0000029402802,0.0000073485517,0.0000120207715,0.00007433037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994722,0.000018577695,0.00018406287,0.000113142945,0.000051308347,0.00016068647],"domain_scores_gemma":[0.9997162,0.00013553769,0.000029361765,0.000040534578,0.000051503368,0.000026864649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018055235,0.00008222202,0.00012237071,0.000059908936,0.00031475068,0.00008686106,0.000021838116,0.00005028223,0.0000074203326],"category_scores_gemma":[0.0000545865,0.000087144326,0.000021963326,0.00007281609,0.000011160044,0.000030241052,0.000019428659,0.00012528061,0.0000013668786],"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":[0.0000142318495,0.0000050461967,0.000927132,0.00016247765,0.00003585494,0.0000015004681,0.0003738018,0.9004124,0.00417445,0.0024109387,0.0007995546,0.0906826],"study_design_scores_gemma":[0.00030047985,0.00002862437,0.00069954793,0.00008860081,0.000016519833,0.000002934695,0.0002789691,0.9955226,0.00036854093,0.00010562144,0.0024958467,0.00009169433],"about_ca_topic_score_codex":0.000029753914,"about_ca_topic_score_gemma":0.0000027326969,"teacher_disagreement_score":0.67085236,"about_ca_system_score_codex":0.000033917982,"about_ca_system_score_gemma":0.000011701469,"threshold_uncertainty_score":0.35536426},"labels":[],"label_agreement":null},{"id":"W4412611237","doi":"10.1016/j.atech.2025.101198","title":"Pig weight estimation using a movable top-mounted depth camera with improved EfficientNetV2","year":2025,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Industrial Vision Systems and Defect Detection","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 Guelph","funders":"National Major Science and Technology Projects of China; Beijing Academy of Agricultural and Forestry Sciences","keywords":"Estimation; Computer vision; Computer science; Artificial intelligence; Computer graphics (images); Geology; Engineering","score_opus":0.005676391774957016,"score_gpt":0.2202186436253514,"score_spread":0.21454225185039438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412611237","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":[0.98014414,0.00018666631,0.014134125,0.00014660733,0.00086989155,0.00047972272,0.0000031496309,0.0012069908,0.0028287275],"genre_scores_gemma":[0.9973766,0.0000042116535,0.0019658257,0.000016345744,0.00006084937,0.000049846727,0.000012659972,0.0000132364885,0.0005003891],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990506,0.000017323273,0.00027196592,0.00024092736,0.00010464876,0.0003145184],"domain_scores_gemma":[0.9995637,0.000018884439,0.000058906524,0.00020696715,0.000120205594,0.00003136545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007093679,0.0002086635,0.00025993952,0.00032806606,0.0001654243,0.000052111638,0.00012367705,0.00036585928,0.000010758073],"category_scores_gemma":[0.000029346336,0.00013410771,0.000048656766,0.0015784414,0.000043076096,0.00012500274,0.000037295304,0.000307394,0.000021704363],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084636085,0.00006599258,0.0019303827,0.0001377328,0.0002814086,0.000015585105,0.00011744897,0.053263582,0.9071394,0.0022606156,0.0037298072,0.030973386],"study_design_scores_gemma":[0.0024715953,0.00037139413,0.005082471,0.00033646225,0.0001463158,0.00026658693,0.0009793155,0.4465924,0.52459484,0.00028656397,0.018051075,0.0008209905],"about_ca_topic_score_codex":0.00014671934,"about_ca_topic_score_gemma":0.000103359045,"teacher_disagreement_score":0.39332882,"about_ca_system_score_codex":0.0002107457,"about_ca_system_score_gemma":0.000018978322,"threshold_uncertainty_score":0.5468754},"labels":[],"label_agreement":null},{"id":"W4412691073","doi":"10.22260/isarc2025/0128","title":"Transformer-based Multi-resolution Fast 3D Reconstruction for Structural Damage Detection","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transformer; Artificial intelligence; Engineering; Voltage; Electrical engineering","score_opus":0.01349201726820299,"score_gpt":0.23372793920843835,"score_spread":0.22023592194023536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412691073","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":[0.94531775,0.000052336785,0.04806557,0.0000604947,0.0026868081,0.0009857921,0.000023507748,0.00023785947,0.0025698657],"genre_scores_gemma":[0.99796593,0.0000027387887,0.001666445,0.00000982795,0.00012681614,0.000065604785,0.0000012590996,0.000018154897,0.0001432176],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920344,0.0000056599433,0.00032203912,0.00015716071,0.00012748591,0.00018420341],"domain_scores_gemma":[0.9995958,0.000028099914,0.0000911171,0.000073948075,0.00018618678,0.000024803698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022661292,0.00014127982,0.0001771838,0.00017712916,0.00020452988,0.000042440188,0.00012438046,0.00017042941,0.000005684244],"category_scores_gemma":[0.00007383467,0.00011248241,0.0001526219,0.0003814294,0.00004390245,0.00020766788,0.000008209595,0.00017289192,0.000001250448],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024161722,0.000015855983,0.000908159,0.00073449657,0.00007543056,2.7285658e-8,0.00021960677,0.011758444,0.8378095,0.00029720383,0.00042886453,0.14751077],"study_design_scores_gemma":[0.0012512456,0.00006913718,0.0017111402,0.00019748085,0.000051278737,0.0000051395614,0.00028832632,0.39469874,0.5997574,0.00022058575,0.0016104152,0.0001391257],"about_ca_topic_score_codex":0.000039168706,"about_ca_topic_score_gemma":0.000023682847,"teacher_disagreement_score":0.3829403,"about_ca_system_score_codex":0.0001387376,"about_ca_system_score_gemma":0.000017754253,"threshold_uncertainty_score":0.45868996},"labels":[],"label_agreement":null},{"id":"W4412768237","doi":"10.18280/jesa.580611","title":"Automated Surface Quality Control via Deep Visual Inspection in Manufacturing: A ResNet50 Implementation Case Study","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Visual inspection; Quality (philosophy); Control (management); Computer science; Artificial intelligence; Surface (topology); Computer vision; Mathematics; Physics","score_opus":0.021349766555675546,"score_gpt":0.33036572807495995,"score_spread":0.3090159615192844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412768237","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":[0.9698897,0.00021375652,0.025094626,0.00001477743,0.0017291969,0.0009880778,0.0000063976895,0.0016980377,0.00036544644],"genre_scores_gemma":[0.99953884,0.000012588415,0.0001419416,0.000017628814,0.00016034085,0.000027038144,0.0000023918528,0.000047346617,0.000051862648],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.996308,0.001047564,0.0015061335,0.00028438965,0.00041812047,0.00043576406],"domain_scores_gemma":[0.9989864,0.00020279702,0.00031399255,0.00023014062,0.00014299394,0.00012367881],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018953489,0.00032282714,0.00056093483,0.00060251675,0.0004447835,0.0003614562,0.0001322062,0.00016741129,0.000054012984],"category_scores_gemma":[0.000084627645,0.00030565725,0.0001322661,0.00069009827,0.00002889239,0.0004764869,0.000035352776,0.0005663836,0.00003305813],"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":[0.00039019575,0.0006596125,0.07981777,0.0007436345,0.0011892361,0.0077172043,0.007113718,0.2693849,0.015356398,0.000055552307,0.0023707454,0.61520106],"study_design_scores_gemma":[0.0051784264,0.00041571542,0.5460962,0.0001929849,0.00008463548,0.0038293519,0.0068695135,0.4347721,0.0019476912,0.000105395506,0.00013208692,0.00037585135],"about_ca_topic_score_codex":0.002340863,"about_ca_topic_score_gemma":0.0023413855,"teacher_disagreement_score":0.6148252,"about_ca_system_score_codex":0.0009989763,"about_ca_system_score_gemma":0.00006432441,"threshold_uncertainty_score":0.99993956},"labels":[],"label_agreement":null},{"id":"W4412962459","doi":"10.1007/978-3-031-93691-3","title":"Computer Vision and Image Processing","year":2025,"lang":"en","type":"book","venue":"Communications in computer and information science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute for Materials Science; University at Albany; University of Texas MD Anderson Cancer Center; University of California, Santa Barbara; Samsung; International Institute of Information Technology, Hyderabad; Dr B R Ambedkar National Institute of Technology Jalandhar; Malaviya National Institute of Technology, Jaipur; Motilal Nehru National Institute of Technology Allahabad; University of Mysore; National Institute of Technology, Patna; Universitat Autònoma de Barcelona; National Institute of Technology Rourkela; Indian Institute of Technology Delhi; College of Engineering, Michigan State University; National Taipei University of Technology; Southeast University; Indian Institute of Technology, Patna; Birla Institute of Technology and Science, Pilani; Indian Institute of Technology Jodhpur; South China University of Technology; Indian Institute of Technology Roorkee; Zhejiang University; Università degli Studi di Pavia; Akademie Věd České Republiky; National Institute of Technology Hamirpur; Indian Institute of Technology Indore; Khalifa University of Science, Technology and Research; Indian Institute of Technology Madras; Visvesvaraya National Institute of Technology; Rijksuniversiteit Groningen; Westfälische Wilhelms-Universität Münster; Amity University; Indian Institute of Technology Kanpur; Indian Institute of Technology Tirupati; Vellore Institute of Technology, Chennai; Universitetet i Tromsø; University of Alberta; Florida Atlantic University; Indian Institute of Technology Palakkad; University of Pittsburgh; Jawaharlal Nehru University; Indian Institute of Technology Gandhinagar; National Institute of Technology Warangal; King Mongkut's Institute of Technology Ladkrabang; Universidad del Atlántico; Indian Space Research Organisation; National Institute of Technology Sikkim; Concordia University; Trinity College Dublin; Indian National Science Academy; Jadavpur University; National Institute of Technology Calicut; Università degli Studi di Milano; University of Salford Manchester; Case Western Reserve University; University of Windsor; Indian Institute of Technology (BHU) Varanasi; Indian Institute of Technology Bombay; University of South Florida; National Institute of Technology, Raipur; Michigan State University; National Institute of Technology Delhi; Indian Institute of Technology Kharagpur; Indian Institute of Science; University at Buffalo; University of Rochester; Universidade do Porto; University of Central Florida; Maulana Azad National Institute of Technology; Indian Institute of Technology Guwahati; Johns Hopkins University; Lehigh University; Netaji Subhas University of Technology; Cleveland State University","keywords":"Computer science; Computer vision; Image processing; Artificial intelligence; Information retrieval; Computer graphics (images); Image (mathematics)","score_opus":0.019486899296515748,"score_gpt":0.28422884439111173,"score_spread":0.264741945094596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412962459","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":[0.0006269541,0.0015675382,0.5510581,0.00020231468,0.0013145208,0.0008585232,0.000024634133,0.00038449504,0.4439629],"genre_scores_gemma":[0.5410645,0.018975396,0.4145321,0.0028569216,0.0016130861,0.000319441,0.0006593624,0.00013688649,0.019842355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990322,0.00002249962,0.00047384342,0.00013406195,0.00019921469,0.00013819066],"domain_scores_gemma":[0.9990008,0.000092411865,0.0000949179,0.000571905,0.00018852553,0.000051466934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006414636,0.0001546347,0.00020347374,0.00081353646,0.00032829694,0.000624777,0.00046741118,0.0001482958,0.0000020439945],"category_scores_gemma":[0.000013214985,0.00014675206,0.000020841127,0.00046045796,0.00033173602,0.003222004,0.0005495667,0.00036748557,0.000012240962],"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":[0.0000021276894,0.000004802781,0.0000073112733,0.00021471526,0.0000033026347,1.8139934e-7,0.000750127,0.0007958103,0.000012201257,0.0025127027,0.006351873,0.98934484],"study_design_scores_gemma":[0.00020743479,0.000030980165,0.0003989804,0.0006145119,0.0000041296116,0.000014164951,0.000014688361,0.77922773,0.000010678459,0.00025735263,0.21905103,0.0001683352],"about_ca_topic_score_codex":0.0000047450494,"about_ca_topic_score_gemma":0.0000026430669,"teacher_disagreement_score":0.9891765,"about_ca_system_score_codex":0.00014662305,"about_ca_system_score_gemma":0.00016675548,"threshold_uncertainty_score":0.6024739},"labels":[],"label_agreement":null},{"id":"W4413049812","doi":"10.1016/j.ins.2025.122561","title":"A context-aware multi-stream attentive convolutional neural network for surface defect segmentation","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"University of British Columbia, Okanagan Campus","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Computer science; Context (archaeology); Segmentation; Artificial intelligence; Surface (topology); Artificial neural network; Pattern recognition (psychology); Machine learning; Geology; Mathematics","score_opus":0.036288707917104986,"score_gpt":0.293265785071727,"score_spread":0.25697707715462204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413049812","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":[0.47535148,0.00015918055,0.5175972,0.00009541306,0.0034493047,0.0009741376,0.00008331659,0.0002998595,0.0019900738],"genre_scores_gemma":[0.9987848,0.0000030446372,0.00084008306,0.00013043966,0.00006371616,0.000047460777,0.00003519244,0.0000025088166,0.00009277494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920595,0.000024916508,0.00032460334,0.0000851719,0.00017686721,0.00018249066],"domain_scores_gemma":[0.99955297,0.00014000916,0.000078550125,0.000057753852,0.00014303689,0.000027684635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004725352,0.0000932303,0.00010999038,0.00010678063,0.0003506293,0.00016677685,0.00008852186,0.00007143611,0.000013980641],"category_scores_gemma":[0.0000435228,0.0000821565,0.00007867647,0.0004724772,0.000057754314,0.0010791963,0.0000128541815,0.00006366292,0.000033493707],"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":[0.000036423666,0.000008513537,0.004956266,0.00006780418,0.000046859703,1.03222945e-7,0.0006412859,0.93401337,0.00045374568,0.0013499168,0.01608669,0.04233901],"study_design_scores_gemma":[0.0009791323,0.00006844798,0.004534824,0.000057173784,0.000013270623,0.000002619295,0.0018799774,0.97657883,0.0019236665,0.000084006104,0.013734615,0.00014342814],"about_ca_topic_score_codex":0.00006433534,"about_ca_topic_score_gemma":0.000039447157,"teacher_disagreement_score":0.52343327,"about_ca_system_score_codex":0.000077289835,"about_ca_system_score_gemma":0.000048644066,"threshold_uncertainty_score":0.33502448},"labels":[],"label_agreement":null},{"id":"W4413089419","doi":"10.23977/acss.2025.090302","title":"ELF-CandyGAN: A Candy Color Coloring Method for Image Local Feature Enhancement","year":2025,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Artificial intelligence; Computer vision; Image (mathematics); Computer science; Color image; Pattern recognition (psychology); Mathematics; Image processing; Linguistics","score_opus":0.010382018815201557,"score_gpt":0.28769265865248833,"score_spread":0.27731063983728677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413089419","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":[0.0086002285,0.008855481,0.9770059,0.00002840645,0.003842907,0.00090894825,0.000012373701,0.000088681314,0.00065709656],"genre_scores_gemma":[0.9838837,0.00031539952,0.014301441,0.000060445942,0.00058412965,0.00048742048,0.0000054307275,0.000023426594,0.00033862275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988861,0.00007909375,0.00038096897,0.00027544014,0.00011656534,0.0002618261],"domain_scores_gemma":[0.99939275,0.00029540822,0.00005870193,0.0001339484,0.00006939534,0.000049782346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056947937,0.00018482428,0.00042824878,0.0001495085,0.00009404957,0.00011427623,0.00008785781,0.00013864612,0.0000021672636],"category_scores_gemma":[0.000008823615,0.00016240266,0.000055866938,0.00024091228,0.000018069448,0.00019373796,0.000030672243,0.00014423723,0.0000015790929],"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":[0.00025593783,0.00005916241,0.00028118072,0.0026628356,0.00020415446,0.000030747833,0.00058410777,0.5106888,0.013059941,0.0026024997,0.009039828,0.46053085],"study_design_scores_gemma":[0.0012010719,0.00020420583,0.00002870701,0.0006196042,0.000014750066,0.000013552567,0.00016819495,0.8399868,0.0061566485,0.00010154655,0.15128815,0.00021677991],"about_ca_topic_score_codex":0.00007368162,"about_ca_topic_score_gemma":0.00005221624,"teacher_disagreement_score":0.97528344,"about_ca_system_score_codex":0.00011724015,"about_ca_system_score_gemma":0.00002117303,"threshold_uncertainty_score":0.66225886},"labels":[],"label_agreement":null},{"id":"W4413144558","doi":"10.1109/cvpr52734.2025.02130","title":"AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"University of Toronto","funders":"Canada Foundation for Innovation","keywords":"Computer science; Computer graphics (images); Computer vision; Transformer; Artificial intelligence; Engineering; Electrical engineering; Voltage","score_opus":0.004147331809868267,"score_gpt":0.20390892937152216,"score_spread":0.1997615975616539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413144558","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":[0.828075,0.000369175,0.156793,0.000050713683,0.00053750677,0.00031879215,0.000002160003,0.00017936574,0.0136743225],"genre_scores_gemma":[0.99965465,0.000027690949,0.0000493249,0.000045413326,0.000036450867,0.000012450509,5.675821e-7,0.000008335688,0.00016510946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994154,0.000019639492,0.00022739868,0.00012232277,0.000059721024,0.0001555201],"domain_scores_gemma":[0.99982524,0.000056141907,0.000012079022,0.000063051666,0.000012100322,0.00003136591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000201558,0.00009476,0.00017389952,0.00023792418,0.000050203253,0.00004138295,0.000026451553,0.00010723139,0.000021074364],"category_scores_gemma":[0.000018230441,0.000082232495,0.00003305267,0.00025787862,0.000007993236,0.00010990494,0.000005935931,0.00014768363,0.0000028804307],"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":[0.000069336726,0.000013404872,0.0077596535,0.00016358244,0.0000488079,0.0000068901627,0.00030285845,0.0037262856,0.34857374,0.00016023885,0.00024031942,0.63893485],"study_design_scores_gemma":[0.007782771,0.00011904941,0.011243091,0.00036569356,0.00006940678,0.000012779474,0.001143199,0.94677573,0.02097981,0.00006549472,0.010878301,0.000564693],"about_ca_topic_score_codex":0.000691721,"about_ca_topic_score_gemma":0.00020850053,"teacher_disagreement_score":0.94304943,"about_ca_system_score_codex":0.000060633887,"about_ca_system_score_gemma":0.000009846097,"threshold_uncertainty_score":0.3353344},"labels":[],"label_agreement":null},{"id":"W4413145141","doi":"10.1109/cvpr52734.2025.01312","title":"Insightful Instance Features for 3D Instance Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Kootenay Association for Science & Technology","funders":"Korea University","keywords":"Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.009723518938488768,"score_gpt":0.2474587054135016,"score_spread":0.23773518647501282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413145141","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":[0.08390492,0.000859357,0.7569399,0.00007542092,0.0039545707,0.00095593266,0.000014461838,0.00072803104,0.1525674],"genre_scores_gemma":[0.9894677,0.00002332411,0.004224249,0.00012948168,0.00013505241,0.000102518025,0.000006468004,0.000012744929,0.0058984635],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995302,0.000009418803,0.00016587062,0.00011078997,0.00007091618,0.00011278104],"domain_scores_gemma":[0.9997667,0.000042846794,0.000017714998,0.0001087998,0.00004551028,0.0000183952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000082160244,0.0000862685,0.0001078618,0.00009844893,0.000072623996,0.000041172378,0.000044160788,0.00009804927,0.00001363091],"category_scores_gemma":[0.000021951111,0.000076811164,0.00003773032,0.00024672103,0.000006327258,0.00012806147,0.000005826541,0.00007545153,0.000009731191],"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":[0.00024591983,0.000044320503,0.00041949115,0.00055809354,0.00018571287,0.0000026594553,0.0007375815,0.01833124,0.09178454,0.0401385,0.2430495,0.60450244],"study_design_scores_gemma":[0.002728368,0.00010970473,0.0019722031,0.00022596156,0.000028025554,0.000003552605,0.00058245973,0.023646072,0.25628108,0.0011884056,0.7127979,0.00043628152],"about_ca_topic_score_codex":0.000014345945,"about_ca_topic_score_gemma":0.000059861748,"teacher_disagreement_score":0.90556276,"about_ca_system_score_codex":0.00008261296,"about_ca_system_score_gemma":0.000013754146,"threshold_uncertainty_score":0.31322685},"labels":[],"label_agreement":null},{"id":"W4413157151","doi":"10.1109/tim.2025.3598394","title":"Dual Feature-Integration Network for Faster and More Pragmatic Few-Shot Strip Steel Surface Defect Classification","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Shot (pellet); Dual (grammatical number); Feature (linguistics); One shot; Computer science; Surface (topology); Feature extraction; Artificial intelligence; Pattern recognition (psychology); Structural engineering; Materials science; Engineering; Mechanical engineering; Geometry; Mathematics; Metallurgy","score_opus":0.04806248038570873,"score_gpt":0.2759812045294622,"score_spread":0.22791872414375344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413157151","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":[0.19668563,0.00014665053,0.79864615,0.0002882026,0.0019926084,0.0014485844,0.000038513557,0.00016554348,0.0005881279],"genre_scores_gemma":[0.99846977,0.00006478929,0.00082693045,0.000083037296,0.0000622932,0.00019624851,0.000016007087,0.000018093247,0.00026282851],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989416,0.00006820261,0.00031219053,0.00023373774,0.00026982976,0.0001744367],"domain_scores_gemma":[0.9995659,0.00005736552,0.00006262675,0.00012805901,0.00012492017,0.0000611018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045789347,0.00018879763,0.00018267664,0.00013113832,0.00028605727,0.00012147433,0.000028052811,0.00014653763,0.000007836448],"category_scores_gemma":[0.000008121651,0.0001786302,0.00007307406,0.0002238632,0.000023759085,0.00018166802,6.026357e-7,0.00016959313,0.000002634383],"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":[0.0007963755,0.00022385617,0.00025894816,0.00081640726,0.00076414284,8.438525e-7,0.0015876765,0.17558977,0.21355513,0.0011201734,0.008391191,0.5968955],"study_design_scores_gemma":[0.018162241,0.0015998291,0.018559013,0.0023262457,0.0013116978,0.000045656965,0.010832993,0.5822748,0.3386792,0.00054572156,0.023907058,0.0017555767],"about_ca_topic_score_codex":0.000018248316,"about_ca_topic_score_gemma":0.00011960099,"teacher_disagreement_score":0.80178416,"about_ca_system_score_codex":0.00022897022,"about_ca_system_score_gemma":0.00003351035,"threshold_uncertainty_score":0.72843283},"labels":[],"label_agreement":null},{"id":"W4413206572","doi":"10.3389/fcomp.2025.1664990","title":"Editorial: Deep learning for industrial applications","year":2025,"lang":"en","type":"editorial","venue":"Frontiers in Computer Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Data science","score_opus":0.009369413274543392,"score_gpt":0.24199022301067327,"score_spread":0.2326208097361299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413206572","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000014163668,0.00011283076,0.4813569,0.000003225198,0.51775426,0.0005125422,0.000009467168,0.00015081499,0.00009856208],"genre_scores_gemma":[0.00015295716,0.00004277269,0.0114813065,0.0000028271425,0.98770547,0.00039749822,0.000044773806,0.00003026213,0.00014212957],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975888,0.000045206354,0.0004995799,0.00060816645,0.00078333233,0.00047491986],"domain_scores_gemma":[0.9987226,0.00038001154,0.00011625875,0.00036523532,0.00032159817,0.00009426781],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014129342,0.00027642876,0.0004772449,0.00084813667,0.00029539815,0.00033378994,0.00077106553,0.0011489212,0.0000011497636],"category_scores_gemma":[0.0004054092,0.0002997655,0.00011192064,0.0014253272,0.00011170295,0.00023111423,0.00012184789,0.0013454778,0.0000048065817],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010668158,0.0000056229296,0.000015507985,0.000040276944,0.000010169886,2.2086648e-7,0.000029414377,0.02815511,0.0000036573012,0.000004281645,0.85098827,0.12073679],"study_design_scores_gemma":[0.00059190724,0.000056830522,8.89995e-7,0.00012250694,0.000011364002,1.4703856e-7,0.000008895861,0.12961778,0.00003491813,0.000105878134,0.8692028,0.00024609597],"about_ca_topic_score_codex":0.00002109524,"about_ca_topic_score_gemma":0.000004535478,"teacher_disagreement_score":0.4699512,"about_ca_system_score_codex":0.00064157543,"about_ca_system_score_gemma":0.00048803695,"threshold_uncertainty_score":0.99994546},"labels":[],"label_agreement":null},{"id":"W4413248274","doi":"10.1504/ijqet.2024.147893","title":"Harnessing deep learning for quality engineering and technology: innovations in process optimisation, defect detection, and predictive quality control","year":2024,"lang":"en","type":"article","venue":"International Journal of Quality Engineering and Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Quality (philosophy); Process (computing); Model predictive control; Engineering; Control (management); Process control; Manufacturing engineering; Risk analysis (engineering); Computer science; Artificial intelligence; Business","score_opus":0.01624148549262381,"score_gpt":0.3021479659991376,"score_spread":0.2859064805065138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413248274","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":[0.6999597,0.0018595018,0.29643667,0.0004980497,0.00074443984,0.00017280078,0.000012230517,0.0003092331,0.000007386822],"genre_scores_gemma":[0.9987672,0.000105903426,0.00084984483,0.0000062737554,0.00018345483,0.000049355698,0.000003222156,0.000029781471,0.0000049450537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984086,0.00004163859,0.0009090003,0.00022954142,0.00021124099,0.00019999118],"domain_scores_gemma":[0.9987033,0.00046940186,0.00017205706,0.0000826417,0.000522567,0.00005003401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014935259,0.00019657181,0.0003948701,0.0016710799,0.00006998604,0.00013072953,0.000112126,0.00036279688,0.0000013243841],"category_scores_gemma":[0.0019460899,0.00020020297,0.000052621046,0.0007534939,0.00006110113,0.0003275788,0.000029870644,0.00073384907,2.6270493e-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":[0.00028376962,0.0000778693,0.02053576,0.0018234943,0.0015000718,0.000041807943,0.0017904666,0.52900064,0.201078,0.021427842,0.000013665001,0.22242664],"study_design_scores_gemma":[0.0037328417,0.00040461094,0.016297042,0.0012958184,0.00010012885,0.0007608421,0.0019367555,0.9480813,0.01897902,0.0046057296,0.0030675072,0.00073838275],"about_ca_topic_score_codex":0.000014812492,"about_ca_topic_score_gemma":0.000011082757,"teacher_disagreement_score":0.4190807,"about_ca_system_score_codex":0.00015639834,"about_ca_system_score_gemma":0.000028517708,"threshold_uncertainty_score":0.816404},"labels":[],"label_agreement":null},{"id":"W4413678661","doi":"10.36227/techrxiv.175616901.10303345/v1","title":"Object Detection Survey for Industrial Applications with Focus on Quality Control","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Universität Salzburg","keywords":"Quality (philosophy); Focus (optics); Computer science; Control (management); Object (grammar); Artificial intelligence; Optics","score_opus":0.06914397182363716,"score_gpt":0.3044149923085498,"score_spread":0.23527102048491264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413678661","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":[0.011757001,0.000039512383,0.96864176,0.000023113369,0.0028980672,0.005469674,0.0012379292,0.00089123787,0.009041712],"genre_scores_gemma":[0.9952671,0.000004277579,0.00008637464,0.000018421753,0.0009902521,0.0029278854,0.00009577852,0.000038330785,0.00057161244],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99819076,0.00024360027,0.00059188687,0.00047313818,0.00024733564,0.00025325431],"domain_scores_gemma":[0.9980446,0.00092309236,0.00015940229,0.00057025393,0.00022850749,0.000074157404],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013301916,0.000349084,0.000583529,0.00026570214,0.00014189171,0.00011877659,0.00015822404,0.0009920603,0.000015347636],"category_scores_gemma":[0.00017433344,0.00029459115,0.00018380188,0.0002962197,0.000016731816,0.00003738872,0.000033665303,0.0008355738,0.00001532473],"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":[0.0064687342,0.0002796971,0.0023092635,0.0012247568,0.0022375698,0.000001478527,0.00015648156,0.38648257,0.0016665629,0.0021071576,0.011404033,0.5856617],"study_design_scores_gemma":[0.09775569,0.008249589,0.040741634,0.0046363114,0.0025666691,0.000033697743,0.0008936112,0.21641313,0.31398484,0.01185075,0.28784993,0.015024153],"about_ca_topic_score_codex":0.0023654772,"about_ca_topic_score_gemma":0.0025997614,"teacher_disagreement_score":0.9835101,"about_ca_system_score_codex":0.00029675092,"about_ca_system_score_gemma":0.00013856737,"threshold_uncertainty_score":0.99995065},"labels":[],"label_agreement":null},{"id":"W4413679607","doi":"10.1109/compsac65507.2025.00076","title":"Tactile Texture Recognition On Uneven Surfaces Using Self-Attention Based Neural Networks","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; Lakehead University","funders":"","keywords":"Computer science; Texture (cosmology); Artificial intelligence; Artificial neural network; Computer vision; Pattern recognition (psychology); Image (mathematics)","score_opus":0.018714148642426102,"score_gpt":0.23846622994938554,"score_spread":0.21975208130695945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413679607","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":[0.9069109,0.00005678004,0.07922208,0.00003791377,0.0031417268,0.0003532926,0.000005526275,0.0008746332,0.009397125],"genre_scores_gemma":[0.99929035,0.0000024892468,0.00020412492,0.00007950219,0.00019348928,0.000008474097,0.000016646478,0.000016636179,0.00018830464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932176,0.000057007794,0.00020401645,0.00014951962,0.00010600712,0.0001617051],"domain_scores_gemma":[0.999706,0.00006179769,0.000032250162,0.00012277666,0.000046714358,0.000030484696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015452385,0.00012831688,0.00013365939,0.00014854626,0.0001083131,0.00007230285,0.00003989227,0.0002054503,0.000081610764],"category_scores_gemma":[0.000012353353,0.0001160239,0.000072746465,0.00036437498,0.0000042388465,0.00012045602,0.0000065759787,0.00021284941,0.000022151507],"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":[0.00004127594,0.000028612176,0.00047429587,0.00004630323,0.000043953434,0.000002640541,0.000010598009,0.9555512,0.0049533225,0.00001725391,0.0040912325,0.034739323],"study_design_scores_gemma":[0.00039451176,0.00003832112,0.00082996895,0.00009042919,0.000023969918,0.0000017454829,0.000039861607,0.993218,0.003659595,0.000016384993,0.0015600763,0.00012714622],"about_ca_topic_score_codex":0.00008067223,"about_ca_topic_score_gemma":0.000013815944,"teacher_disagreement_score":0.09237941,"about_ca_system_score_codex":0.00010764015,"about_ca_system_score_gemma":0.000010542406,"threshold_uncertainty_score":0.47313172},"labels":[],"label_agreement":null},{"id":"W4413681759","doi":"10.1016/j.patrec.2025.08.021","title":"An asymmetric teacher-student network based industrial vision model for abnormal grain detection of semiconductor cooling devices","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Semiconductor; Computer science; Artificial intelligence; Computer vision; Materials science; Optoelectronics","score_opus":0.034090522011541856,"score_gpt":0.27472553806725125,"score_spread":0.2406350160557094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413681759","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":[0.56149125,0.000023425933,0.43633893,0.000027583576,0.0014030683,0.00049459096,0.00003981958,0.00014385067,0.000037483886],"genre_scores_gemma":[0.9980985,0.000002720974,0.00038578137,0.00040919037,0.00081822375,0.00015183855,0.00008799961,0.000038684182,0.000007084155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984373,0.0001319144,0.0006038312,0.00028987424,0.00022584057,0.00031123217],"domain_scores_gemma":[0.99930114,0.00016798281,0.00016586717,0.00018457144,0.00011115143,0.0000692986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070560863,0.00022647102,0.00030793896,0.0005107764,0.00014359603,0.000083440274,0.00012653615,0.00028780146,0.000015079118],"category_scores_gemma":[0.000055595003,0.00023921726,0.00015737631,0.00057823997,0.000018904326,0.00028343848,0.000013923412,0.00030483396,0.000006695132],"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":[0.00027930198,0.00010649267,0.0049289363,0.00024688643,0.00014246597,0.0000015491614,0.00017185163,0.32731393,0.14811306,9.474559e-7,0.0023115713,0.516383],"study_design_scores_gemma":[0.0022638454,0.00016404351,0.0015965123,0.000330974,0.00009704444,0.0000018223492,0.00009624928,0.94523585,0.049564715,0.000019857944,0.00031925546,0.0003098579],"about_ca_topic_score_codex":0.00006725371,"about_ca_topic_score_gemma":0.000050685187,"teacher_disagreement_score":0.6179219,"about_ca_system_score_codex":0.00015109724,"about_ca_system_score_gemma":0.000024310559,"threshold_uncertainty_score":0.9754997},"labels":[],"label_agreement":null},{"id":"W4413803607","doi":"10.1007/s00170-025-16382-1","title":"Defect detection in 3D printing: a review of image processing and machine vision techniques","year":2025,"lang":"en","type":"review","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Machine vision; Computer vision; Image processing; Artificial intelligence; Computer science; 3D printing; Image (mathematics); Engineering drawing; Engineering; Mechanical engineering","score_opus":0.012419002059083042,"score_gpt":0.30933496065714355,"score_spread":0.2969159585980605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413803607","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":[0.00050198747,0.99329233,0.0047546593,0.000105006045,0.0005693663,0.0004954717,0.0000049052865,0.00011111134,0.00016517375],"genre_scores_gemma":[0.0033496653,0.9946589,0.0018287699,0.000019713223,0.000073582996,0.000029726683,0.0000016157625,0.000027867374,0.000010187085],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99817485,0.00006642462,0.0011817643,0.00016408505,0.0002728398,0.00014003742],"domain_scores_gemma":[0.998538,0.00016021096,0.0009068604,0.00019734628,0.00017905497,0.000018536457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076289434,0.0002559475,0.0010178846,0.001340942,0.000033561697,0.000028136792,0.0005160637,0.00025823395,0.000004528148],"category_scores_gemma":[0.00031837923,0.00017411115,0.0002417078,0.0003263788,0.000057199355,0.00017187027,0.00016437822,0.0009734661,6.949605e-7],"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":[0.000012933922,0.000008967528,3.008491e-7,0.01487638,0.00006802411,0.0000136771505,0.0000051106795,0.000024083776,0.0005215192,0.000005501399,0.000020497695,0.984443],"study_design_scores_gemma":[0.00032503408,0.00014687213,0.0000033824647,0.28834322,0.00020331258,0.0010827718,0.000016870305,0.000057841356,0.070009165,0.00038275274,0.63918924,0.00023952445],"about_ca_topic_score_codex":0.000004902413,"about_ca_topic_score_gemma":0.0000060437615,"teacher_disagreement_score":0.98420346,"about_ca_system_score_codex":0.00024177851,"about_ca_system_score_gemma":0.00005146976,"threshold_uncertainty_score":0.7100046},"labels":[],"label_agreement":null},{"id":"W4413863034","doi":"10.35490/ec3.2025.311","title":"Graph Deviation Network for Fault Detection and Diagnosis Using Building Automation System Data","year":2025,"lang":"en","type":"article","venue":"Computing in construction","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Automation; Graph; Fault detection and isolation; Fault (geology); Data mining; Real-time computing; Reliability engineering; Artificial intelligence; Theoretical computer science; Engineering; Geology; Seismology","score_opus":0.030983613521759098,"score_gpt":0.2794476059040237,"score_spread":0.24846399238226458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413863034","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":[0.46338248,0.00014371457,0.5333044,0.0000029649457,0.002582419,0.00030587005,0.000003890791,0.00024325779,0.000031006784],"genre_scores_gemma":[0.9828534,0.000009825669,0.016679093,0.0000036341557,0.00040665932,0.000020043131,0.00001253923,0.000014058083,7.1219876e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998987,0.00006588379,0.00042334225,0.00026307756,0.00008198457,0.00017866891],"domain_scores_gemma":[0.99940705,0.00020429403,0.00010704294,0.00019666269,0.00006347896,0.000021466649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067272235,0.00012291962,0.00019072424,0.00030860014,0.00026168142,0.00010350911,0.00007087843,0.00017310781,3.6792463e-7],"category_scores_gemma":[0.00009937227,0.00014330396,0.000028170163,0.0006181989,0.000018254379,0.00024234368,0.000044375953,0.0001286852,3.6202923e-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":[0.000016026237,0.0000035652918,0.008952804,0.0004146938,0.000034677134,3.66311e-7,0.000041577216,0.67143214,0.002364345,0.0008379698,0.0000737478,0.31582808],"study_design_scores_gemma":[0.00045369848,0.0000124909775,0.0030185655,0.00066794676,0.0000318246,0.000025862964,0.00015776491,0.99312836,0.0018270707,0.00020261829,0.00035336256,0.000120423516],"about_ca_topic_score_codex":0.0000984546,"about_ca_topic_score_gemma":0.000039515136,"teacher_disagreement_score":0.51947093,"about_ca_system_score_codex":0.00025006355,"about_ca_system_score_gemma":0.000017053093,"threshold_uncertainty_score":0.5843766},"labels":[],"label_agreement":null},{"id":"W4414015517","doi":"10.11159/mvml25.121","title":"Fine-Tuning YOLOv8 for Vehicle Detection: A Deep Learning Approach to Traffic Congestion Monitoring","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Centre National pour la Recherche Scientifique et Technique","keywords":"Computer science; Deep learning; Traffic congestion; Real-time computing; Artificial intelligence; Transport engineering; Engineering","score_opus":0.009692609428053306,"score_gpt":0.21190443270571252,"score_spread":0.20221182327765921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015517","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":[0.9627076,0.00040781364,0.033243895,0.000021994541,0.0026898468,0.0004909666,4.2397502e-7,0.00020241986,0.00023505278],"genre_scores_gemma":[0.99914545,0.0000041490375,0.00039188797,0.0000036750646,0.00022377723,0.00006962086,4.642424e-8,0.000010771479,0.0001506058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905795,0.0000052244195,0.00023364737,0.0002644795,0.00018557247,0.00025310437],"domain_scores_gemma":[0.999579,0.000093157454,0.000048345726,0.000061253195,0.00013780918,0.000080409634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044208945,0.00014244602,0.00021991969,0.00038957104,0.00031980954,0.00028301368,0.00016706642,0.000058155983,3.999629e-8],"category_scores_gemma":[0.00007579716,0.00011334416,0.000037426726,0.0012096495,0.000040583822,0.00011642491,0.000047804893,0.00023182546,1.1914062e-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":[0.000031572145,0.000013380243,0.00038215477,0.00033575748,0.00002625914,1.04137946e-7,0.00014293873,0.88580275,0.027030785,0.004243554,0.000041262807,0.08194948],"study_design_scores_gemma":[0.00024400222,0.00011448068,0.0011860706,0.00035249462,0.000010361059,0.000008174565,0.000023461558,0.9882098,0.008688888,0.0000032997489,0.0010373539,0.00012165527],"about_ca_topic_score_codex":0.000005486321,"about_ca_topic_score_gemma":5.0077387e-7,"teacher_disagreement_score":0.10240701,"about_ca_system_score_codex":0.00006754875,"about_ca_system_score_gemma":0.000010582224,"threshold_uncertainty_score":0.46220407},"labels":[],"label_agreement":null},{"id":"W4414015747","doi":"10.11159/mvml25.128","title":"Contrastive Self-Supervised Learning for Packaging Artwork Layer Classification with Text and Image Features","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Layer (electronics); Artificial intelligence; Contextual image classification; Image (mathematics); Natural language processing; Pattern recognition (psychology); Materials science; Nanotechnology","score_opus":0.00637281257876713,"score_gpt":0.2059374558583182,"score_spread":0.1995646432795511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015747","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":[0.96430033,0.001172547,0.03033881,0.00016427727,0.0015873322,0.0010510378,0.0000018560166,0.0003467419,0.0010370357],"genre_scores_gemma":[0.99915576,0.000017840635,0.0005035652,0.000009674867,0.00009903705,0.000036177902,9.080753e-8,0.000009491916,0.00016837563],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992309,0.0000050090457,0.00016903102,0.0002369752,0.00015182821,0.00020625694],"domain_scores_gemma":[0.999556,0.00013567359,0.000053246433,0.00005008401,0.00014779018,0.000057188692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003240033,0.00013741753,0.00021320739,0.00022548027,0.00022420786,0.00027409496,0.00010338105,0.00004418978,6.718309e-8],"category_scores_gemma":[0.000035339173,0.00009212422,0.000021023101,0.0006705748,0.00006277717,0.00014148105,0.000030857736,0.0002052678,5.5432732e-8],"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":[0.00068312156,0.00015883433,0.02658534,0.005383424,0.00073047733,0.0000034345712,0.0020420307,0.09869438,0.35242033,0.29076457,0.0060527134,0.21648136],"study_design_scores_gemma":[0.00043555503,0.00011543384,0.0069337664,0.0005551166,0.000019271138,0.000013493035,0.00004316948,0.9859856,0.004798366,0.000012021642,0.0009595754,0.00012863259],"about_ca_topic_score_codex":0.000005320699,"about_ca_topic_score_gemma":5.4022075e-7,"teacher_disagreement_score":0.88729125,"about_ca_system_score_codex":0.000034456894,"about_ca_system_score_gemma":0.000013433839,"threshold_uncertainty_score":0.37567165},"labels":[],"label_agreement":null},{"id":"W4414054539","doi":"10.1109/icmi65310.2025.11141215","title":"Deep Learning Based Solar Panel Structural and Electrical Damage Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"Université du Québec en Abitibi-Témiscamingue","funders":"Université du Québec à Rimouski","keywords":"Classifier (UML); Deep learning; Solar energy; Feature (linguistics); Feature extraction","score_opus":0.010095140296768185,"score_gpt":0.21601247651086158,"score_spread":0.2059173362140934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414054539","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":[0.75564957,0.00010014909,0.23980871,0.0000071221743,0.00049962016,0.00012549224,2.1220328e-7,0.0004506093,0.0033585406],"genre_scores_gemma":[0.9994778,0.0000034539776,0.00011072431,0.000018920678,0.00007303804,0.000006438327,0.0000012475386,0.000010220194,0.00029814872],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947244,0.00004223528,0.0001435145,0.00011996838,0.00007583749,0.00014602662],"domain_scores_gemma":[0.99979645,0.000059780792,0.000013218797,0.00007192074,0.00002329159,0.000035361292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011887115,0.00009348436,0.000113140144,0.00015232131,0.00013449318,0.00006296433,0.000028199798,0.00013335755,0.000036228434],"category_scores_gemma":[0.00005431404,0.00008330851,0.000032632077,0.0002767214,0.000008095794,0.00007391458,0.000009096832,0.00026829747,0.00000824706],"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":[0.00009528605,0.000007642059,0.0055393064,0.000117601616,0.0000807684,0.000008687824,0.00010675538,0.15065229,0.123736896,0.00032713305,0.0002458986,0.71908176],"study_design_scores_gemma":[0.00039187682,0.00005227405,0.008786998,0.000010088296,0.000010331563,0.0000042749716,0.0000416116,0.9517933,0.036172386,0.0000419763,0.0025940237,0.00010084859],"about_ca_topic_score_codex":0.000051566476,"about_ca_topic_score_gemma":0.000028307986,"teacher_disagreement_score":0.801141,"about_ca_system_score_codex":0.00005189029,"about_ca_system_score_gemma":0.000006570458,"threshold_uncertainty_score":0.33972222},"labels":[],"label_agreement":null},{"id":"W4414109843","doi":"10.1051/0004-6361/202554780","title":"Three-dimensional stacking as a line intensity mapping statistic","year":2025,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"National Research Foundation of Korea; Keck Institute for Space Studies; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Intensity mapping; Galaxy; Stacking; Halo; Pipeline (software); Line (geometry); Sensitivity (control systems); Simple (philosophy); Population","score_opus":0.012166807990001123,"score_gpt":0.21539510472377546,"score_spread":0.20322829673377435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414109843","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":[0.42010802,0.000042127627,0.5790632,0.000011775077,0.00035768573,0.000083015155,0.000008625533,0.000073532974,0.00025200332],"genre_scores_gemma":[0.98682,9.768809e-7,0.012877369,0.000021239288,0.00020019745,0.0000075563125,0.000012223472,0.0000114773375,0.000049001013],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999394,0.0000094883735,0.00018508977,0.00015298538,0.000078193465,0.00018024372],"domain_scores_gemma":[0.99972314,0.000037919923,0.000030293171,0.00011347517,0.000048179118,0.000046996338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006481625,0.00013334639,0.00018827348,0.00007192995,0.00012321124,0.000052551502,0.000040830804,0.0000512852,0.000014244052],"category_scores_gemma":[0.000008712054,0.00013448922,0.00004117536,0.00014905984,0.00002347435,0.00009502189,0.00004302307,0.00020142981,0.00002460806],"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":[0.000054349108,0.000024105715,0.00061705604,0.00005384598,0.00014711711,0.00001092349,0.00005615824,0.022294857,0.0057109855,0.001709384,0.0007007286,0.9686205],"study_design_scores_gemma":[0.009185097,0.0015314292,0.053779908,0.0027524605,0.00040061763,0.0001205196,0.0025308223,0.42395893,0.052357294,0.018200917,0.43221414,0.0029678547],"about_ca_topic_score_codex":0.00007819419,"about_ca_topic_score_gemma":0.0000057043317,"teacher_disagreement_score":0.96565264,"about_ca_system_score_codex":0.000037551818,"about_ca_system_score_gemma":0.000025086081,"threshold_uncertainty_score":0.54843116},"labels":[],"label_agreement":null},{"id":"W4414166025","doi":"10.1109/jsteap.2025.3607855","title":"6G ISAC Enables Environment Object Reconstruction","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Electromagnetics Antennas and Propagation","topic":"Industrial Vision Systems and Defect Detection","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":"Huawei Technologies (Canada)","funders":"","keywords":"Grid; Object (grammar); Field (mathematics); Projection (relational algebra); 3D reconstruction; Standardization; Range (aeronautics); Key (lock)","score_opus":0.006202544579827969,"score_gpt":0.19984673383163387,"score_spread":0.1936441892518059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414166025","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":[0.99034333,0.0015985485,0.005959055,0.000105216706,0.0009141878,0.00017668252,8.831861e-7,0.000023758857,0.00087835727],"genre_scores_gemma":[0.99690765,0.0021261782,0.0004694372,0.000012491147,0.00026531663,0.000003947895,9.326978e-7,0.000009708587,0.00020434984],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990403,0.00006020096,0.0004976557,0.00009790665,0.00013512759,0.00016878558],"domain_scores_gemma":[0.99962646,0.00003302437,0.000121161786,0.0000707098,0.000116261515,0.000032398733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028761066,0.000114488175,0.00020702207,0.00034648433,0.000055314344,0.00005048638,0.000052692936,0.00014241361,0.0000103988195],"category_scores_gemma":[0.00004233044,0.000104756946,0.000035048546,0.000386533,0.000022358387,0.000103720944,0.000004734152,0.00034298736,7.868008e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010303902,0.000043465945,0.0016780819,0.00011260243,0.000070907285,0.000012149212,0.000173471,0.0028863614,0.7365903,0.00029553828,0.0004188401,0.2576152],"study_design_scores_gemma":[0.0047571324,0.0043323156,0.04214969,0.0011976927,0.00020474817,0.0011223921,0.0002991585,0.109675206,0.80911785,0.0075454353,0.018739538,0.0008588461],"about_ca_topic_score_codex":0.000010723606,"about_ca_topic_score_gemma":0.000014289885,"teacher_disagreement_score":0.25675637,"about_ca_system_score_codex":0.0001306925,"about_ca_system_score_gemma":0.000052548745,"threshold_uncertainty_score":0.4271864},"labels":[],"label_agreement":null},{"id":"W4414271693","doi":"10.1109/tmech.2025.3602812","title":"Systematic Error Correction in Robotic-OCT Inspection of Hard-to-Reach Industrial Parts","year":2025,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Liaoning Revitalization Talents Program","keywords":"Completeness (order theory); Distortion (music); Systematic error; Error detection and correction; Rotation (mathematics); Point cloud; Coherence (philosophical gambling strategy); Point (geometry)","score_opus":0.02745429770535035,"score_gpt":0.25268462377629936,"score_spread":0.225230326070949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414271693","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":[0.1751908,0.00012912742,0.79857427,0.00007534171,0.022029657,0.0021628141,0.000023535149,0.00095238583,0.0008620719],"genre_scores_gemma":[0.99886054,0.000023082861,0.00018356628,0.0000175559,0.000116311734,0.00023902691,0.0000038205903,0.00004170379,0.0005143828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977969,0.00019634646,0.0009814363,0.00033215573,0.0003433128,0.00034983453],"domain_scores_gemma":[0.99909717,0.0001696525,0.00011786131,0.00042920036,0.00009675572,0.00008938448],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070373266,0.0002886568,0.00065279927,0.0011388363,0.00014719224,0.000050283736,0.00014316235,0.00046431948,0.000023604794],"category_scores_gemma":[0.000060138987,0.0003003848,0.00021741875,0.0015600286,0.000018610948,0.00018791322,0.0000024336948,0.00073782116,0.000072817726],"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":[0.00013097991,0.00012814497,0.000023721519,0.0008757389,0.00013274369,0.0000016141792,0.00023178883,0.98952514,0.0043910383,0.00016391181,0.0009699408,0.0034252144],"study_design_scores_gemma":[0.0052424213,0.0011695932,0.00025541903,0.0130853625,0.0005556211,0.000039240706,0.0024312576,0.79045594,0.18389452,0.00016697192,0.0015884263,0.0011152481],"about_ca_topic_score_codex":0.00026727834,"about_ca_topic_score_gemma":0.00044739348,"teacher_disagreement_score":0.82366973,"about_ca_system_score_codex":0.00080458034,"about_ca_system_score_gemma":0.00010212197,"threshold_uncertainty_score":0.9999448},"labels":[],"label_agreement":null},{"id":"W4414567853","doi":"10.1016/j.ifacol.2025.09.473","title":"Hybrid Machine Learning Chain for Manufacturing Time Estimation Across Multiple Product Families","year":2025,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Agence Nationale de la Recherche","keywords":"Reliability (semiconductor); Artificial neural network; Process (computing); Product (mathematics); Categorization; Lead time; Chain (unit)","score_opus":0.009734394785823018,"score_gpt":0.2449529303444177,"score_spread":0.23521853555859468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414567853","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":[0.932764,0.00046820476,0.06256849,0.00018498527,0.001303877,0.00097299856,0.00019007252,0.0009622577,0.0005851074],"genre_scores_gemma":[0.97642547,0.000022469414,0.019023892,0.000037791928,0.00043497313,0.00008837878,0.00022215645,0.00004985122,0.0036950253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883425,0.00003451185,0.00034499104,0.00029567664,0.00014319722,0.00034739196],"domain_scores_gemma":[0.9994817,0.0001611996,0.000059743816,0.00020062283,0.000049083992,0.000047693924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000390752,0.00023429164,0.00029778728,0.00013623487,0.00029256116,0.000081875834,0.00010129137,0.00009653722,0.000024169627],"category_scores_gemma":[0.00028854547,0.00022234129,0.00011704584,0.00013512946,0.000019715126,0.00015484302,0.000032953438,0.00027782138,0.000058281457],"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":[0.00018693005,0.000048922262,0.00038090977,0.00052071904,0.00016356666,0.0000064053083,0.0007162241,0.39012754,0.0827435,0.000014538314,0.00019476452,0.52489597],"study_design_scores_gemma":[0.0011246643,0.000059836177,0.00034949466,0.000113757225,0.000019765406,0.000008432446,0.00013220165,0.82385224,0.15707035,0.000019258154,0.01700788,0.00024213035],"about_ca_topic_score_codex":0.000118378106,"about_ca_topic_score_gemma":0.000031422787,"teacher_disagreement_score":0.52465385,"about_ca_system_score_codex":0.00011231962,"about_ca_system_score_gemma":0.00001525879,"threshold_uncertainty_score":0.9066814},"labels":[],"label_agreement":null},{"id":"W4414601430","doi":"10.1038/s44296-025-00075-7","title":"Author Correction: Improving process granularity of life cycle inventories for battery grade nickel","year":2025,"lang":"en","type":"article","venue":"npj Materials Sustainability","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Granularity; Process (computing); Battery (electricity); Nickel; Work (physics)","score_opus":0.01240206775647906,"score_gpt":0.2747230341932737,"score_spread":0.26232096643679464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414601430","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":[0.9619764,0.00006877338,0.029017905,0.00016142176,0.0070481454,0.0012961217,0.00004460296,0.00030153076,0.000085079984],"genre_scores_gemma":[0.99899924,8.4097337e-7,0.000054666893,0.000021911677,0.00027698642,0.00032223683,0.000012484856,0.000022511469,0.00028914827],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983508,0.00018196093,0.00072672474,0.00028061014,0.00015522986,0.00030469897],"domain_scores_gemma":[0.9987545,0.00018253701,0.00012838781,0.00033749745,0.0005299683,0.00006711345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018171816,0.00019632143,0.00047186186,0.00014095817,0.00017205854,0.00009445207,0.00012877709,0.00026309126,0.00003441249],"category_scores_gemma":[0.0023339111,0.00019370831,0.00010833695,0.00032609404,0.00006534493,0.0002005575,0.000038734313,0.00011835781,9.57061e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006375593,0.0016713564,0.05159268,0.15369791,0.0010294529,0.000013861413,0.008086604,0.06324761,0.53474814,0.008199222,0.05687762,0.11445995],"study_design_scores_gemma":[0.002247141,0.0003843346,0.008074863,0.00025845785,0.00012428247,0.0000039605493,0.0057288385,0.01634889,0.9371882,0.022095717,0.006815801,0.00072951417],"about_ca_topic_score_codex":0.00047303503,"about_ca_topic_score_gemma":0.00002620941,"teacher_disagreement_score":0.40244007,"about_ca_system_score_codex":0.00033071215,"about_ca_system_score_gemma":0.00025688845,"threshold_uncertainty_score":0.7899196},"labels":[],"label_agreement":null},{"id":"W4414681828","doi":"10.1007/978-3-032-00986-9_7","title":"Robot Vision System for Retail Shelf Monitoring","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Robot; Mobile robot; Object (grammar); Machine vision; Off the shelf; Object detection","score_opus":0.042969259975963715,"score_gpt":0.28625693338982633,"score_spread":0.24328767341386262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414681828","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":[0.00017928485,0.0011854717,0.5194548,0.000096468415,0.004378717,0.0014248604,0.000048123296,0.0005116427,0.47272065],"genre_scores_gemma":[0.90860265,0.0044817855,0.0743183,0.00011430675,0.0007490872,0.00036587735,0.00016172537,0.00006516719,0.011141074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889195,0.000010864483,0.0006055834,0.00013134131,0.00021479435,0.00014549044],"domain_scores_gemma":[0.9985567,0.00015531744,0.00012096211,0.0008529851,0.00026550944,0.00004857721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008019672,0.00015900322,0.00022500091,0.0007867679,0.00036747995,0.0003213229,0.0006268665,0.00018815404,0.0000017597221],"category_scores_gemma":[0.000023403432,0.00016171433,0.000049330763,0.00023968189,0.00012666028,0.0018272828,0.00029987367,0.0002816536,0.00001645085],"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":[0.000007792664,0.0000042308043,0.000017202488,0.0004822481,0.000014385398,1.2900406e-7,0.0006389319,0.009197307,0.000056581706,0.0716219,0.0008584573,0.91710085],"study_design_scores_gemma":[0.0003956506,0.00005180984,0.0001657555,0.0016774349,0.000011901548,0.000008762512,0.00007430727,0.59931535,0.00019542743,0.00018796293,0.39763087,0.0002847791],"about_ca_topic_score_codex":0.000005666075,"about_ca_topic_score_gemma":0.0000015271434,"teacher_disagreement_score":0.91681606,"about_ca_system_score_codex":0.00028304008,"about_ca_system_score_gemma":0.00007507141,"threshold_uncertainty_score":0.6594519},"labels":[],"label_agreement":null},{"id":"W4414772063","doi":"10.1007/978-3-031-95455-9_10","title":"The Effect of Acquisition Time on Thermographic Monitoring and Identification of LRM Process Dynamics","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Western University","funders":"","keywords":"Data acquisition; Identifiability; Observability; Process (computing); Identification (biology); Quality (philosophy); Control (management)","score_opus":0.003523790354606748,"score_gpt":0.20777835546767112,"score_spread":0.20425456511306436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414772063","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":[0.4003343,0.008766611,0.56707084,0.00008342535,0.009597208,0.0054141176,0.00020427458,0.0012330586,0.0072961366],"genre_scores_gemma":[0.9995493,0.00009995222,0.000024295632,6.620157e-7,0.00013195825,0.000024365576,0.000007812367,0.000048405902,0.00011319027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988662,0.00002146556,0.00053136196,0.00020027247,0.00023405423,0.0001466738],"domain_scores_gemma":[0.9987922,0.0007606262,0.00012718524,0.00024850358,0.000045162273,0.000026368092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004807996,0.00026908368,0.00043137095,0.00029043888,0.000035619338,0.000022372475,0.00012556728,0.0005305367,0.0000030739945],"category_scores_gemma":[0.00018937717,0.0002098958,0.000112495625,0.00015347109,0.000016872244,0.000031586344,0.00002214039,0.000552193,0.0000011427006],"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":[0.0002779469,0.000010960872,0.000036662845,0.0035023468,0.00037355188,0.000006100618,0.00010149632,0.58542794,0.20756786,0.0072237407,0.0000037708035,0.19546764],"study_design_scores_gemma":[0.000531103,0.00041012344,0.000067050445,0.0033393868,0.00011571365,0.000005557476,0.0000020870016,0.42868102,0.56395257,0.0024520117,0.000058153582,0.00038526155],"about_ca_topic_score_codex":0.0000031690954,"about_ca_topic_score_gemma":0.0000014872772,"teacher_disagreement_score":0.5992151,"about_ca_system_score_codex":0.000096074626,"about_ca_system_score_gemma":0.000007145254,"threshold_uncertainty_score":0.85593027},"labels":[],"label_agreement":null},{"id":"W4414812179","doi":"","title":"Validation of the DESI DR2 Ly$\\alpha$ BAO analysis using synthetic datasets","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Regional Municipality of Waterloo; Perimeter Institute; University of Waterloo","funders":"","keywords":"Set (abstract data type); Quasar; Dark energy; Spectral density; Redshift; Data set; Spectral line; Oscillation (cell signaling); Energy (signal processing)","score_opus":0.05994800265446518,"score_gpt":0.285528685362799,"score_spread":0.22558068270833384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414812179","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":[0.9847456,0.00017543767,0.011646599,0.000010998466,0.0020946478,0.00036315463,0.00036115714,0.000107709006,0.00049467426],"genre_scores_gemma":[0.9994344,0.000019195217,0.00009361592,0.000008513558,0.00017127383,0.000017209682,0.0001387172,0.000020965794,0.00009608092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984813,0.00016978002,0.0005732184,0.0003269443,0.00026481177,0.00018395965],"domain_scores_gemma":[0.99851483,0.00009299547,0.00023116755,0.0010385397,0.00008300492,0.000039455208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005118939,0.00024076752,0.00048499028,0.00036493773,0.00010943145,0.000049844533,0.0003242652,0.00038671918,0.00006302694],"category_scores_gemma":[0.00014149884,0.00019688673,0.00037105873,0.0008634493,0.000027283451,0.00007219378,0.00027267242,0.0004521509,0.000015850417],"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":[0.000017969614,0.00003485937,0.108783826,0.0005379431,0.0017730095,0.0000032576993,0.00022955902,0.87261534,0.013316535,0.000019940202,0.0006241521,0.0020435816],"study_design_scores_gemma":[0.00089268136,0.00004395481,0.11806911,0.0023139438,0.008089154,0.000012260259,0.00023888084,0.28320253,0.5796097,0.00017777449,0.005953335,0.0013966585],"about_ca_topic_score_codex":0.00045200041,"about_ca_topic_score_gemma":0.000017528148,"teacher_disagreement_score":0.5894128,"about_ca_system_score_codex":0.00016198211,"about_ca_system_score_gemma":0.0000658134,"threshold_uncertainty_score":0.80288076},"labels":[],"label_agreement":null},{"id":"W4415071192","doi":"10.1016/j.mlwa.2025.100754","title":"Predictive modeling for quality prediction in multi-stage manufacturing systems using artificial intelligence","year":2025,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Industrial Vision Systems and Defect Detection","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":"Polytechnique Montréal","funders":"","keywords":"Process (computing); Quality (philosophy); Artificial neural network; Principal component analysis; Component (thermodynamics); Support vector machine; Product (mathematics); Basis (linear algebra); Partial least squares regression","score_opus":0.0921453006494709,"score_gpt":0.3419330885212035,"score_spread":0.24978778787173261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415071192","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":[0.0942718,0.00012386084,0.9033969,0.000008431024,0.00018807822,0.0013424294,0.000047723246,0.00035542678,0.00026534614],"genre_scores_gemma":[0.9956579,0.000004554788,0.0031292369,0.0000030991635,0.000101567355,0.00088777486,0.000038791575,0.000027795983,0.00014927422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883157,0.00006760857,0.00049632794,0.00027470622,0.00011977356,0.00021003203],"domain_scores_gemma":[0.9995218,0.00010833396,0.000077977085,0.0001863845,0.000067079345,0.000038434806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051887135,0.00015582089,0.00021470581,0.0002712456,0.00026292363,0.000076892684,0.00008518854,0.00013086155,0.0000031352508],"category_scores_gemma":[0.000041453153,0.00015029132,0.00004313314,0.00034866683,0.000015717897,0.00010865744,0.00001775611,0.00039300876,0.0000038356975],"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":[0.000047796973,0.000021543356,0.00093255995,0.00014153756,0.000023998753,1.6964843e-7,0.00010136038,0.9918865,0.0009824918,0.0014662178,0.0000010287284,0.004394807],"study_design_scores_gemma":[0.00021125504,0.000032666776,0.00019391307,0.00012163048,0.000019004152,0.0000015206693,0.00058360974,0.9959686,0.0017692362,0.00013018363,0.0008410621,0.00012734922],"about_ca_topic_score_codex":0.0007358561,"about_ca_topic_score_gemma":0.00012696045,"teacher_disagreement_score":0.9013861,"about_ca_system_score_codex":0.00022288272,"about_ca_system_score_gemma":0.00002864757,"threshold_uncertainty_score":0.6128702},"labels":[],"label_agreement":null},{"id":"W4415390017","doi":"10.1016/j.cag.2025.104458","title":"Evaluating graphical perception capabilities of Vision Transformers","year":2025,"lang":"en","type":"article","venue":"Computers & Graphics","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Universität Ulm","keywords":"Perception; Visualization; Visual perception; Convolutional neural network; Benchmark (surveying); Transformer; Human visual system model; Graphical model","score_opus":0.023345486953306644,"score_gpt":0.3018931063226519,"score_spread":0.2785476193693453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415390017","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":[0.88893455,0.00014250878,0.10773188,0.00005057291,0.0017994858,0.00024230537,0.0000039823785,0.00020830378,0.0008863813],"genre_scores_gemma":[0.9991965,0.00005223726,0.00060777954,0.000036868667,0.00006564284,0.000009880329,0.000005017527,0.000012381737,0.000013661304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989765,0.000071931136,0.000395988,0.00016320987,0.00022810334,0.00016421505],"domain_scores_gemma":[0.99953884,0.00013380885,0.00003424292,0.0001592429,0.00009544983,0.00003842446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004736698,0.00013552402,0.00023223719,0.0004221602,0.00009311214,0.000027541115,0.00009192582,0.00018356805,0.000009772264],"category_scores_gemma":[0.00002118656,0.0001317064,0.00018193261,0.00068417954,0.000081072416,0.00010623658,0.000013334319,0.00022423758,0.0000021560975],"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":[0.00024180704,0.00016988629,0.004136019,0.002119976,0.00040724213,0.0000039746083,0.004577608,0.07003271,0.13633457,0.020033633,0.008946154,0.75299644],"study_design_scores_gemma":[0.0024228215,0.00092176994,0.03166691,0.0011771544,0.0001379505,0.000012493678,0.0014516987,0.94395226,0.0060683885,0.0051510055,0.006387851,0.0006497049],"about_ca_topic_score_codex":0.000041723128,"about_ca_topic_score_gemma":0.00000960944,"teacher_disagreement_score":0.87391955,"about_ca_system_score_codex":0.000042734886,"about_ca_system_score_gemma":0.000020429105,"threshold_uncertainty_score":0.5370831},"labels":[],"label_agreement":null},{"id":"W4415400302","doi":"10.1109/miucc66482.2025.11196864","title":"Optimized Neural Networks for Fashion-MNIST: A High-Accuracy Approach Using Augmentation and Tuning","year":2025,"lang":"","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Benchmark (surveying); Artificial neural network; Residual; Robotics; Hyperparameter; Deep learning; Image (mathematics); Pattern recognition (psychology)","score_opus":0.031723522545306936,"score_gpt":0.2799091107858077,"score_spread":0.24818558824050077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415400302","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":[0.087071106,0.0004740293,0.90513384,0.000038568072,0.003603442,0.0017504905,0.000009011498,0.00018407809,0.0017354126],"genre_scores_gemma":[0.97143716,0.000053151576,0.02715636,0.000060415332,0.00059683784,0.00010258239,0.000032011467,0.000052012096,0.00050949375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977975,0.0001654156,0.00085998885,0.00051831634,0.0001687457,0.0004900218],"domain_scores_gemma":[0.9987579,0.00054793636,0.00019107794,0.00025872304,0.00012760404,0.000116779214],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007838473,0.0003946715,0.00058122363,0.00032880178,0.0006086275,0.00053606916,0.00010137938,0.00047097637,0.000041010837],"category_scores_gemma":[0.00018028574,0.00040289864,0.00017316818,0.0006121929,0.00003902472,0.0004658152,0.000078426514,0.00035870294,9.258106e-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":[0.00030278662,0.000026602735,0.000032067,0.00026784214,0.00016304584,0.000001090598,0.00020238737,0.94730276,0.0019442784,0.0016622046,0.00024103475,0.04785392],"study_design_scores_gemma":[0.004264549,0.00009283596,0.0000580778,0.0001666817,0.00021107575,0.00001427842,0.0010063522,0.9930473,0.00032941008,0.000034165983,0.0004094241,0.00036587665],"about_ca_topic_score_codex":0.00023663529,"about_ca_topic_score_gemma":0.0000031592474,"teacher_disagreement_score":0.88436604,"about_ca_system_score_codex":0.0002400236,"about_ca_system_score_gemma":0.00004841294,"threshold_uncertainty_score":0.9998423},"labels":[],"label_agreement":null},{"id":"W4415427388","doi":"10.3233/faia251482","title":"Application of Multi-Output Regression and Feature Selection Methods in Semiconductor Manufacturing","year":2025,"lang":"","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Infineon Technologies (Canada)","funders":"","keywords":"Feature selection; Robustness (evolution); Boosting (machine learning); Semiconductor device fabrication; Gradient boosting; Regression; Support vector machine; Regression analysis; Feature (linguistics)","score_opus":0.047902512005915616,"score_gpt":0.3294058487456396,"score_spread":0.281503336739724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415427388","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":[0.001402423,0.004297177,0.9897158,0.00006018626,0.0006840044,0.0022525256,0.000044898494,0.000056974863,0.001485995],"genre_scores_gemma":[0.76851165,0.013855988,0.20529386,0.000044298624,0.0010036144,0.0017002624,0.00013104676,0.00019531693,0.009263947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973777,0.00010187814,0.0012323563,0.00080637337,0.00017612828,0.0003055895],"domain_scores_gemma":[0.9988628,0.00016946837,0.00040057488,0.00036367748,0.00010397929,0.00009951799],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007220355,0.00047974562,0.0008066066,0.0011731832,0.00019071015,0.00006970836,0.00017250578,0.0010870163,0.000012151318],"category_scores_gemma":[0.000047406327,0.0005116318,0.00010822253,0.00043151996,0.00017325071,0.00017090696,0.00008656035,0.0010421681,0.0000041853987],"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":[0.00006216002,0.000048463477,0.00035475928,0.00036428365,0.00004210155,5.723125e-7,0.00027790962,0.0032544388,0.008942836,0.008829114,0.00008761246,0.97773576],"study_design_scores_gemma":[0.00038408075,0.00015272688,0.0006654578,0.0027678167,0.00022740004,0.000020088033,0.0025135963,0.49221262,0.36194223,0.04457364,0.093053505,0.0014868365],"about_ca_topic_score_codex":0.00027417304,"about_ca_topic_score_gemma":0.00014317114,"teacher_disagreement_score":0.9762489,"about_ca_system_score_codex":0.00023200763,"about_ca_system_score_gemma":0.000050991915,"threshold_uncertainty_score":0.9997335},"labels":[],"label_agreement":null},{"id":"W4415465301","doi":"10.1017/psy.2026.10103","title":"High-Dimensional Perception with the Double Machine Learning Lens Model","year":2025,"lang":"en","type":"article","venue":"Psychometrika","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Perception; Artifact (error); Class (philosophy); Through-the-lens metering; Perceptual learning","score_opus":0.019858006349132238,"score_gpt":0.23864724957172964,"score_spread":0.2187892432225974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415465301","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":[0.92534083,0.000422568,0.06107014,0.0003761016,0.001252043,0.00034139355,0.000006225899,0.00046593585,0.010724771],"genre_scores_gemma":[0.99638,0.000025180809,0.00029862797,0.00007262336,0.00013075341,0.000027018466,0.000008254997,0.000020676533,0.0030368727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992638,0.000025875153,0.00015918886,0.00016395275,0.00021280991,0.00017434183],"domain_scores_gemma":[0.9996369,0.00006771406,0.00002798979,0.00018682222,0.00005293214,0.000027665612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027825497,0.00013100785,0.00014790172,0.00035138495,0.00019553766,0.00005610693,0.00008382582,0.00009977913,0.00007876443],"category_scores_gemma":[0.000015016688,0.000083711115,0.000052523053,0.0010854525,0.000017870414,0.00009904667,0.000013402913,0.00036555526,0.00007132153],"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":[0.0002560258,0.000027350536,0.0007121268,0.000031017025,0.0000968172,0.0000010679606,0.00014965906,0.9487626,0.008964688,0.0006555152,0.013788053,0.026555099],"study_design_scores_gemma":[0.004936145,0.00024215633,0.0068954765,0.00014428285,0.00007735622,0.000019339857,0.00020018418,0.9081155,0.0022582014,0.00013660183,0.07649513,0.00047962405],"about_ca_topic_score_codex":0.00008960405,"about_ca_topic_score_gemma":0.00002457578,"teacher_disagreement_score":0.07103917,"about_ca_system_score_codex":0.00007221481,"about_ca_system_score_gemma":0.000012774006,"threshold_uncertainty_score":0.34136403},"labels":[],"label_agreement":null},{"id":"W4415524297","doi":"10.1109/ubmk67458.2025.11206948","title":"YOLOv11-Fiducial: Real Time Detection of Fiducial Marker Points on Printed Circuit Boards for Electronic Manufacturing Services","year":2025,"lang":"","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Fiducial marker; Printed circuit board; Intersection (aeronautics); Surface-mount technology; Object (grammar); Skew; Point (geometry); Automated optical inspection","score_opus":0.009122119098219304,"score_gpt":0.23771612467246406,"score_spread":0.22859400557424475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415524297","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":[0.9377368,0.00014194268,0.019002223,0.00007143533,0.005752269,0.0027583768,0.00005857264,0.0005493819,0.033929013],"genre_scores_gemma":[0.99284804,0.00009261157,0.00004154198,0.00004761335,0.0009698551,0.00012411622,0.000017264481,0.00010163556,0.005757316],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9959774,0.00026430594,0.0014241134,0.00085140154,0.0004808141,0.001001945],"domain_scores_gemma":[0.9981429,0.0004219973,0.00037190286,0.0006771629,0.0002620737,0.00012397909],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017226129,0.00063566276,0.00094353093,0.0008685531,0.00033151702,0.00014637984,0.00032137343,0.0009796594,0.0004938688],"category_scores_gemma":[0.00012110542,0.00065883924,0.0005679259,0.00059762486,0.000039086524,0.00026596908,0.00009207707,0.0006902109,0.00014672878],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010559619,0.0005653574,0.0001970678,0.0056243693,0.0030142663,0.0000075939224,0.0009932441,0.009905648,0.5795918,0.0047202115,0.0009342723,0.38388655],"study_design_scores_gemma":[0.0036064468,0.001366818,0.0048916256,0.0011480768,0.00034284504,0.000009383818,0.00023434717,0.034075987,0.9241674,0.0011558698,0.028201422,0.00079976214],"about_ca_topic_score_codex":0.0009728409,"about_ca_topic_score_gemma":0.00030027723,"teacher_disagreement_score":0.3830868,"about_ca_system_score_codex":0.0009684715,"about_ca_system_score_gemma":0.00020744352,"threshold_uncertainty_score":0.9995863},"labels":[],"label_agreement":null},{"id":"W4415530757","doi":"10.1016/j.neunet.2025.108232","title":"Internal-external boundary attention fusion for glass surface segmentation","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Information Technology Research Centre; Ministry of Science and ICT, South Korea; Institute for Information and Communications Technology Promotion; Electronics and Telecommunications Research Institute; National Natural Science Foundation of China","keywords":"Boundary (topology); Benchmark (surveying); Surface (topology); Fusion; Task (project management); Segmentation; Exploit","score_opus":0.011660020933705006,"score_gpt":0.2523567421843928,"score_spread":0.2406967212506878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415530757","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":[0.5740778,0.0003275835,0.41894293,0.000030600586,0.005503456,0.00033425936,0.0000033107908,0.00018323262,0.00059686333],"genre_scores_gemma":[0.9981176,0.000025030442,0.00022429772,0.00006190405,0.0005349934,0.000024219407,0.000018942374,0.0000177274,0.0009752878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992722,0.000032911088,0.00025747195,0.00015073478,0.00010194102,0.00018471768],"domain_scores_gemma":[0.99971056,0.000060417296,0.000043224853,0.00010626133,0.00004677689,0.000032737844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016592006,0.000119653225,0.00013288866,0.000059241986,0.00013034434,0.000111086745,0.000068084446,0.00013832579,0.000017047743],"category_scores_gemma":[0.0000085715865,0.00011487176,0.000099439996,0.00016783766,0.000010315479,0.00014397695,0.000020299849,0.00017876408,0.000006152413],"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":[0.00021818603,0.000016560578,0.0028983902,0.00008649309,0.000049699425,0.0000031709767,0.000034157158,0.7141524,0.05195977,0.00009224738,0.022211296,0.20827764],"study_design_scores_gemma":[0.00076970394,0.00006846087,0.002679048,0.00012087828,0.000019438516,0.0000053194835,0.00002853295,0.9868759,0.0026827818,0.00007338357,0.0065600816,0.0001164751],"about_ca_topic_score_codex":0.000033859673,"about_ca_topic_score_gemma":0.000016446058,"teacher_disagreement_score":0.42403984,"about_ca_system_score_codex":0.000093947994,"about_ca_system_score_gemma":0.0000054829543,"threshold_uncertainty_score":0.46843344},"labels":[],"label_agreement":null},{"id":"W4415719947","doi":"10.18280/ts.420524","title":"Contour Extraction and Size Estimation of Garment Images Based on Edge Detection and Morphological Analysis","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Morphological analysis; Edge detection; Pattern recognition (psychology); Morphological gradient; Mathematical morphology; Enhanced Data Rates for GSM Evolution; Feature extraction; Shape analysis (program analysis)","score_opus":0.013139675960039109,"score_gpt":0.25678928781692284,"score_spread":0.24364961185688372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415719947","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":[0.6375584,0.00021699631,0.36041313,0.00007483445,0.00041630116,0.00062856404,0.00003022508,0.000062382955,0.0005991728],"genre_scores_gemma":[0.9992117,0.000051362407,0.0004311622,0.000039093557,0.00011204888,0.00005611118,0.000008009275,0.000013753214,0.000076740784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997921,0.00022618272,0.0008158638,0.00044484463,0.0003512239,0.00024089997],"domain_scores_gemma":[0.998862,0.0005202527,0.00024422898,0.00017385629,0.00010418817,0.00009543905],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009013293,0.00031527516,0.00055856374,0.00050037453,0.00019990867,0.00012425028,0.000050627143,0.0002999732,0.00033802786],"category_scores_gemma":[0.00011189638,0.00030344774,0.00018526915,0.000742392,0.00007254834,0.00017022926,0.000021333377,0.00027320476,0.0000029834112],"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":[0.0013213097,0.00038387047,0.0014949573,0.00046741383,0.0010593613,0.000015995085,0.00017546986,0.45774773,0.28480858,0.00009379838,0.0001615364,0.25226998],"study_design_scores_gemma":[0.0025946395,0.00095257285,0.08081222,0.0002124657,0.0012750595,0.0000061257483,0.0001866442,0.8197556,0.09370633,0.000049143382,0.00019184743,0.00025733712],"about_ca_topic_score_codex":0.00014506419,"about_ca_topic_score_gemma":0.00001397196,"teacher_disagreement_score":0.3620079,"about_ca_system_score_codex":0.00019244483,"about_ca_system_score_gemma":0.000027747561,"threshold_uncertainty_score":0.99994177},"labels":[],"label_agreement":null},{"id":"W4415764193","doi":"10.29173/mocs306","title":"Comparative Study of YOLO Architectures for Automated Wood Defect Detection","year":2025,"lang":"","type":"article","venue":"Modular and Offsite Construction (MOC) Summit Proceedings","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Feature (linguistics); Quality (philosophy); Trim; Key (lock); Engineered wood; Automated X-ray inspection; Principal (computer security)","score_opus":0.01591524834611261,"score_gpt":0.2626792971433867,"score_spread":0.24676404879727412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415764193","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":[0.97989136,0.00090899447,0.011370174,0.000016972766,0.0027726314,0.0035244075,0.000051788586,0.0006632534,0.00080041436],"genre_scores_gemma":[0.9985649,0.000027131775,0.00063447043,0.000010449039,0.00031016712,0.00024327569,0.000006434956,0.00005166005,0.00015150686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99676603,0.00008870085,0.0013030736,0.000904652,0.00039739627,0.00054014334],"domain_scores_gemma":[0.9981289,0.00015507784,0.00051368284,0.00024110616,0.0008068308,0.00015438219],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066428643,0.00066578144,0.0012735389,0.0012360653,0.0007070284,0.00027792822,0.00017807452,0.0006220828,0.000015314201],"category_scores_gemma":[0.00014471963,0.00068768905,0.00037295636,0.001413894,0.00021989405,0.00022186848,0.00009630217,0.00059388706,0.0000038744915],"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":[0.008265453,0.0019432957,0.045642365,0.009548844,0.010397653,0.000010838932,0.02461236,0.051154286,0.39580026,0.0022582936,0.0021305084,0.44823584],"study_design_scores_gemma":[0.017864073,0.006381567,0.020853676,0.0017849501,0.0027034727,0.00014158519,0.047470108,0.54226947,0.3511795,0.0009679176,0.006361325,0.0020223663],"about_ca_topic_score_codex":0.00022380496,"about_ca_topic_score_gemma":0.00013369681,"teacher_disagreement_score":0.49111515,"about_ca_system_score_codex":0.00020566273,"about_ca_system_score_gemma":0.00006511718,"threshold_uncertainty_score":0.99955744},"labels":[],"label_agreement":null},{"id":"W4416177399","doi":"10.1108/ijcst-10-2024-0213","title":"A novel plaid fabric image search method based on handcrafted features","year":2025,"lang":"en","type":"article","venue":"International Journal of Clothing Science and Technology","topic":"Industrial Vision Systems and Defect Detection","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Feature (linguistics); Factory (object-oriented programming); Image (mathematics); Pattern recognition (psychology); Image retrieval; Local binary patterns; Feature extraction; Feature vector","score_opus":0.00943822373839669,"score_gpt":0.30966102250847,"score_spread":0.3002227987700733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416177399","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":[0.3819788,0.00021660769,0.6007794,0.0063408664,0.0043022726,0.00015450796,0.000007051311,0.00019233073,0.006028162],"genre_scores_gemma":[0.9923959,0.000007942663,0.007309474,0.00014461522,0.00008063747,0.0000016270731,1.4125526e-7,0.000004624094,0.00005503416],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990315,0.00001309473,0.00023081207,0.00011111846,0.00048521472,0.00012824891],"domain_scores_gemma":[0.99905676,0.00010996433,0.000062515144,0.00008272638,0.0006564029,0.00003162409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010511861,0.000071834926,0.00013404965,0.0018592553,0.00009492029,0.00013446192,0.00036874058,0.00010838272,0.00000481323],"category_scores_gemma":[0.0005129241,0.00005790965,0.000031254276,0.0008818662,0.00013440449,0.00019599443,0.000039336475,0.0003876223,0.0000017111477],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095373376,0.00004484059,0.00026972085,0.0000140714355,0.000065839275,0.000045061865,0.00011092314,0.009220414,0.8220019,0.007826876,0.0011362027,0.1591688],"study_design_scores_gemma":[0.0027016678,0.00042678698,0.002389533,0.00067987683,0.0000237228,0.00077116874,0.00046414713,0.21157543,0.76417637,0.0021838953,0.014366764,0.00024064774],"about_ca_topic_score_codex":0.000017187731,"about_ca_topic_score_gemma":0.0000019661115,"teacher_disagreement_score":0.6104171,"about_ca_system_score_codex":0.00013656876,"about_ca_system_score_gemma":0.00012211505,"threshold_uncertainty_score":0.23614869},"labels":[],"label_agreement":null},{"id":"W4416833453","doi":"10.18280/jesa.581008","title":"YMaskNet: A Deep Learning Based Handloom Fabric Defect Detection Technique","year":2025,"lang":"","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deep learning; Artificial neural network; Feature (linguistics); Pattern recognition (psychology); Noise (video)","score_opus":0.01271732017605982,"score_gpt":0.24283994545752643,"score_spread":0.23012262528146663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416833453","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":[0.05079053,0.008925802,0.9238552,0.000051341405,0.0065657906,0.0016718127,0.000008918097,0.0014503927,0.006680224],"genre_scores_gemma":[0.9942001,0.00040159075,0.0017522268,0.000050257564,0.0010974334,0.00015971465,0.000003971546,0.00023064711,0.0021040493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99230194,0.0021750682,0.0024905587,0.00076988863,0.000978423,0.0012841116],"domain_scores_gemma":[0.9964048,0.00061501155,0.00105462,0.00064607686,0.0008279267,0.00045153586],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0033626202,0.0010380661,0.0013713393,0.0024464256,0.0022270791,0.0017191367,0.0005443554,0.0010749019,0.00037035142],"category_scores_gemma":[0.0016567025,0.0010322824,0.001239068,0.0032123837,0.00015515523,0.0007642625,0.00014229101,0.003334642,0.00028633347],"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":[0.00016469369,0.00009148021,0.0005799786,0.0013470802,0.00062293984,0.00022637619,0.0003224664,0.13746628,0.02878933,0.000052169496,0.00040147512,0.8299357],"study_design_scores_gemma":[0.004220959,0.0018401323,0.03720635,0.008481833,0.0010231364,0.0034460141,0.0005606437,0.86483335,0.04440109,0.0013620337,0.030847687,0.001776739],"about_ca_topic_score_codex":0.00020103273,"about_ca_topic_score_gemma":0.00007303293,"teacher_disagreement_score":0.94340956,"about_ca_system_score_codex":0.0026788926,"about_ca_system_score_gemma":0.0004838947,"threshold_uncertainty_score":0.99931717},"labels":[],"label_agreement":null},{"id":"W4416862965","doi":"10.23977/jaip.2025.080318","title":"A Review of the Applications of Machine Vision in Industrial Surface Defect Detection","year":2025,"lang":"","type":"review","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Machine vision; Field (mathematics); Software deployment; Sorting; Key (lock); Object detection; Reflection (computer programming)","score_opus":0.06706877689005532,"score_gpt":0.3773870532628263,"score_spread":0.310318276372771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416862965","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":[0.00009696431,0.92349374,0.0678776,0.00019697857,0.0045919637,0.0030691063,0.000043614113,0.000010641939,0.00061940687],"genre_scores_gemma":[0.026341643,0.9724852,0.00031020847,0.000039770544,0.00071394414,0.0000311642,0.0000021317726,0.00004390035,0.000032034783],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.988125,0.0027515728,0.007311753,0.00037831918,0.0010903709,0.00034296082],"domain_scores_gemma":[0.9835316,0.0050058668,0.008668219,0.0008072452,0.0018696332,0.00011745579],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01108953,0.0005596668,0.0027394907,0.0008223347,0.00015654901,0.000074211704,0.00087405456,0.0009362842,0.00011888718],"category_scores_gemma":[0.014552333,0.0004152284,0.001862477,0.005483371,0.00013219424,0.00070647465,0.00017387119,0.0034429792,0.000021718166],"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":[0.00029948534,0.00033423706,0.0000030836227,0.036567014,0.0002967287,0.0000044614276,0.00010249241,0.0050637377,0.0005419933,0.00012637296,0.00011964096,0.95654076],"study_design_scores_gemma":[0.00021734106,0.0010138383,0.0000018265678,0.38476095,0.003905923,0.00044245785,0.0005570113,0.0017314097,0.016435713,0.00026500723,0.59014225,0.0005263003],"about_ca_topic_score_codex":0.00046684372,"about_ca_topic_score_gemma":0.00009027118,"teacher_disagreement_score":0.95601445,"about_ca_system_score_codex":0.0005781729,"about_ca_system_score_gemma":0.0012025565,"threshold_uncertainty_score":0.99982995},"labels":[],"label_agreement":null},{"id":"W4416883163","doi":"10.37665/cltrnnc93944","title":"Rinsing Study Based on Inline Cleaning Processes","year":2018,"lang":"","type":"article","venue":"Cleaning and Coating Conference","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"","score_opus":0.045747158659973,"score_gpt":0.2771505718126047,"score_spread":0.23140341315263172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416883163","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":[0.96472824,0.00009254623,0.021796856,0.00003314195,0.0015255994,0.00060140825,0.000006677902,0.00043280766,0.010782752],"genre_scores_gemma":[0.9978717,0.000011679775,0.00036038636,0.0000791638,0.0014354974,0.000016062528,0.0000030599497,0.00008303182,0.00013943328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972873,0.0002296694,0.000785796,0.0006522368,0.00047071744,0.00057429174],"domain_scores_gemma":[0.9980277,0.00046387356,0.0003015891,0.00042203054,0.00058988907,0.00019491362],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010589664,0.00050414813,0.0005619221,0.00027832936,0.0010957083,0.0007302991,0.00019403879,0.00026492157,0.0000845873],"category_scores_gemma":[0.001124966,0.0005117635,0.00005761933,0.0007338808,0.0001397895,0.0002243996,0.00010276731,0.00073052855,0.000075147815],"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":[0.0010128347,0.00086704554,0.023567906,0.0018925712,0.0002694325,0.00008904014,0.048217017,0.017037237,0.015003002,0.00084878475,0.00032892407,0.8908662],"study_design_scores_gemma":[0.003515236,0.0076303305,0.0041126227,0.006865053,0.00017164311,0.0000276346,0.034986727,0.93065274,0.009219765,0.00006709246,0.0014203476,0.0013308383],"about_ca_topic_score_codex":0.00022491987,"about_ca_topic_score_gemma":0.000108265005,"teacher_disagreement_score":0.91361547,"about_ca_system_score_codex":0.00008942953,"about_ca_system_score_gemma":0.00020995784,"threshold_uncertainty_score":0.9997334},"labels":[],"label_agreement":null},{"id":"W567418933","doi":"","title":"Object recognition via multi-view inspection using saturation-weighted distributive hue histograms and depth information","year":2010,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hue; Histogram; Artificial intelligence; Computer vision; Distributive property; Object (grammar); Computer science; Pattern recognition (psychology); Saturation (graph theory); Mathematics; Image (mathematics)","score_opus":0.016184344812387792,"score_gpt":0.21445564437953954,"score_spread":0.19827129956715175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W567418933","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":[0.9496744,0.00020369438,0.038476836,0.000003868331,0.004750774,0.0012049939,0.00032306593,0.00093256665,0.0044297725],"genre_scores_gemma":[0.992564,0.00022698462,0.0005757781,0.000008708749,0.00024572315,0.0000058956934,0.0059966263,0.000052472475,0.00032378442],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986747,0.00008667673,0.00041428703,0.00030253502,0.0002583295,0.000263489],"domain_scores_gemma":[0.9989503,0.00004175877,0.00030484537,0.0002097555,0.0003625308,0.00013078141],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014228825,0.0004010447,0.00039027588,0.0008408319,0.0004282322,0.00014385369,0.0001120866,0.0010643094,0.000040123603],"category_scores_gemma":[0.000041445805,0.00046878174,0.00014220565,0.0008917256,0.00003572339,0.0013168887,0.000017669316,0.00089432416,0.000056107707],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018021291,0.00031573957,0.023961185,0.0028779146,0.0012088069,0.00012338451,0.0015403768,0.0011500866,0.004586306,0.00019866566,0.007930209,0.9543052],"study_design_scores_gemma":[0.01562669,0.0010301844,0.024650417,0.0037600854,0.003032805,0.000002404866,0.083813,0.17514108,0.08950465,0.0004142458,0.59492874,0.008095696],"about_ca_topic_score_codex":0.00083138695,"about_ca_topic_score_gemma":0.10023039,"teacher_disagreement_score":0.9462095,"about_ca_system_score_codex":0.0005952315,"about_ca_system_score_gemma":0.000071391536,"threshold_uncertainty_score":0.99977636},"labels":[],"label_agreement":null},{"id":"W61878324","doi":"","title":"Conception d'un préamplificateur de charges pour un scanner TEP/TDM sur technologie CMOS 0,18 [micro]m","year":2005,"lang":"fr","type":"article","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Physics; Scanner; Optics","score_opus":0.021092102575587683,"score_gpt":0.20791481046504662,"score_spread":0.18682270788945893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W61878324","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":[0.8594303,0.021837786,0.10477764,0.0010126786,0.0021029927,0.0006758093,0.00006173539,0.00023812328,0.009862944],"genre_scores_gemma":[0.9855801,0.00110863,0.0066839997,0.000011026277,0.0004877503,0.000001472444,0.0000072767684,0.000026849035,0.006092907],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99827665,0.0001593875,0.00046655216,0.00035663854,0.0003155912,0.0004251618],"domain_scores_gemma":[0.9989024,0.00007522391,0.00018455501,0.00042910944,0.00028655975,0.00012214929],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004011544,0.0003301358,0.00044443508,0.0002318895,0.0005517804,0.000027877853,0.0006602862,0.00066077727,0.00033971152],"category_scores_gemma":[0.00009946241,0.00033526847,0.00040175908,0.0005236018,0.0006239293,0.000381155,0.0002201037,0.00050928217,0.00031071075],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020136763,0.00063040387,0.003844518,0.0021168417,0.00047917463,0.000017571014,0.0024687133,0.1587874,0.6963463,0.0073324614,0.011708548,0.11606675],"study_design_scores_gemma":[0.0019186399,0.00011894331,0.017430034,0.0032571466,0.0002778758,0.00012742484,0.00040322042,0.03054153,0.77722776,0.00021629973,0.16803178,0.00044933104],"about_ca_topic_score_codex":0.001481329,"about_ca_topic_score_gemma":0.0013235692,"teacher_disagreement_score":0.15632324,"about_ca_system_score_codex":0.0024180717,"about_ca_system_score_gemma":0.00025607803,"threshold_uncertainty_score":0.99990994},"labels":[],"label_agreement":null},{"id":"W6892754855","doi":"10.5281/zenodo.11860462","title":"The bible american pie pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Faith; Griffin; New Testament; Old Testament; Comics; SAINT; Gospel","score_opus":0.02170331883351289,"score_gpt":0.22996591191616159,"score_spread":0.2082625930826487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892754855","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000026695678,0.0007459915,0.0009697345,0.000077852164,0.000971086,0.00034588404,0.00011496173,0.0029485566,0.9937992],"genre_scores_gemma":[0.010688325,0.0008162399,0.000034534736,0.0000283704,0.0016472007,1.3505907e-7,0.0003255568,0.015373675,0.97108597],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998812,0.00012728458,0.00020291911,0.00026685977,0.00029294865,0.00029795908],"domain_scores_gemma":[0.999295,0.000015545678,0.00006843171,0.00043672175,0.00008538498,0.000098919794],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00040210443,0.00017911555,0.00017176413,0.00042724484,0.0006937402,0.0010734685,0.00052375917,0.000120935976,0.02229633],"category_scores_gemma":[0.00011393509,0.00014528555,0.000072484465,0.00091322913,0.00011373041,0.00004079615,0.00028745833,0.00044002716,0.16995758],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048925604,0.0000046736354,2.4807818e-8,0.000059064503,0.00006300408,0.0000053258254,0.000050765655,0.00002463928,0.00014820315,0.00028720647,0.91099674,0.08835545],"study_design_scores_gemma":[0.000100204314,0.00007823837,0.0000020398854,0.00009582021,0.000015040254,0.000035889952,0.00011781274,0.000271739,0.000059647027,0.00003314884,0.9990256,0.00016482579],"about_ca_topic_score_codex":0.00006282721,"about_ca_topic_score_gemma":0.0000020167483,"teacher_disagreement_score":0.14766124,"about_ca_system_score_codex":0.00011814791,"about_ca_system_score_gemma":0.0000017548408,"threshold_uncertainty_score":0.9999635},"labels":[],"label_agreement":null},{"id":"W6931094392","doi":"10.5281/zenodo.4629194","title":"pytroll/pyresample: Version 1.18.1 (2021/03/22)","year":2021,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Resampling; Context (archaeology); Aliasing; Term (time)","score_opus":0.03509439429549108,"score_gpt":0.23561242372587182,"score_spread":0.20051802943038075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931094392","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001390439,0.0008464495,0.002434398,0.000031858417,0.001263912,0.00047605942,0.00041229828,0.0023801671,0.99201584],"genre_scores_gemma":[0.04327604,0.0020245083,0.0004929162,0.00012222021,0.0069337734,4.086913e-7,0.014861367,0.04606139,0.88622737],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981248,0.00025415426,0.00030148774,0.00045632964,0.0004661106,0.00039712316],"domain_scores_gemma":[0.99884915,0.000018646764,0.00010536211,0.0006147245,0.00022811892,0.0001840019],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00039700535,0.0002826185,0.00032621025,0.0004927157,0.0007103151,0.0006322716,0.0005459032,0.00038919252,0.25995705],"category_scores_gemma":[0.00024853417,0.00030995882,0.00011680514,0.0006968646,0.000052669955,0.00008108257,0.00045645272,0.00053683284,0.021396268],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015506432,0.000021627846,2.6710433e-7,0.00013394226,0.000082588274,0.000023124592,0.000056387507,0.00012793932,0.0015341921,0.0000969207,0.96674937,0.031158144],"study_design_scores_gemma":[0.00055027026,0.00009021828,0.000012762772,0.0002294854,0.0000213199,0.00005690178,0.00010190317,0.00038552517,0.00034903202,0.000008261106,0.99788886,0.00030546577],"about_ca_topic_score_codex":0.00006285414,"about_ca_topic_score_gemma":0.0000011227149,"teacher_disagreement_score":0.23856078,"about_ca_system_score_codex":0.00023087276,"about_ca_system_score_gemma":0.0000049613027,"threshold_uncertainty_score":0.99993527},"labels":[],"label_agreement":null},{"id":"W6964538168","doi":"10.25549/wpacards-c8-149752","title":"WPA block face card for household census (block 1911) in Los Angeles","year":2014,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Census; Block (permutation group theory); Face (sociological concept); Quarter (Canadian coin); Population","score_opus":0.012399123182297042,"score_gpt":0.16474336377384227,"score_spread":0.15234424059154522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6964538168","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005740409,0.00009066685,0.000053066833,0.0000074943455,0.00017227858,0.0003831444,0.99291205,0.0002085387,0.00043235868],"genre_scores_gemma":[0.01681891,0.00002052273,0.000030777694,0.000013007749,0.000314628,0.0000010725703,0.98247206,0.000080170226,0.00024884398],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988841,0.000028877814,0.00030251747,0.00028893843,0.00020837286,0.00028720216],"domain_scores_gemma":[0.9992838,0.00009911191,0.00014174293,0.0003329021,0.000021258771,0.00012119407],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000671195,0.00031513555,0.000518581,0.0003067906,0.00006361626,0.000082537226,0.00032858254,0.0005447881,0.00001007135],"category_scores_gemma":[0.000018492052,0.00034271297,0.00025648397,0.00019966094,0.000056546483,0.00015826966,0.000113845315,0.00032481083,0.00022602208],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097482225,0.00002645787,0.00010579392,0.00032894567,0.000049740007,0.000020108411,0.000009232245,0.0018388586,0.0000060117595,3.3892525e-7,0.9971471,0.0003699714],"study_design_scores_gemma":[0.00066721503,0.00004423821,0.0000035855464,0.00023148555,0.00003942921,0.0000066154616,0.00040283945,0.00029020727,0.00006346419,0.000012543529,0.9978901,0.00034827608],"about_ca_topic_score_codex":0.00009441188,"about_ca_topic_score_gemma":0.000031352134,"teacher_disagreement_score":0.011078503,"about_ca_system_score_codex":0.000061572566,"about_ca_system_score_gemma":0.00004065837,"threshold_uncertainty_score":0.9999025},"labels":[],"label_agreement":null},{"id":"W6986302464","doi":"","title":"Pediatric Pulmonary Hypertension in the Netherlands Epidemiology and Characterization During the Period 1991 to 2005","year":2011,"lang":"en","type":"article","venue":"Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Pediatric Oncology Group","funders":"","keywords":"Epidemiology; Period (music); Pulmonary hypertension; Incidence (geometry); MEDLINE","score_opus":0.020432476812171745,"score_gpt":0.1915075109054716,"score_spread":0.17107503409329985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6986302464","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":[0.9935871,0.00011152613,0.0002003072,0.00010653949,0.00036353536,0.00043111332,0.000026961205,0.00008869922,0.005084237],"genre_scores_gemma":[0.99849725,0.00013172401,0.000015412586,0.000048520546,0.0002005559,9.670897e-7,0.000018404122,0.000019869824,0.001067288],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99843144,0.00023627756,0.00042018763,0.0003404638,0.00020021255,0.0003714376],"domain_scores_gemma":[0.9991532,0.0001507853,0.00018842319,0.00032244017,0.000056249744,0.00012891486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000387095,0.00024854264,0.00036131387,0.00043421608,0.000297308,0.000028107064,0.0004621523,0.00033141946,0.000008062433],"category_scores_gemma":[0.000029507559,0.00019602636,0.00011606546,0.00052532414,0.0001228171,0.0007916194,0.00017993119,0.00053344923,0.000011889972],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033254514,0.00040662196,0.78960276,0.0008169936,0.0003996409,0.0022164793,0.05525336,0.0027071172,0.12529832,0.0004084514,0.0021484338,0.017416358],"study_design_scores_gemma":[0.0018293546,0.0003922971,0.95308155,0.00024040842,0.00015029992,0.0012587985,0.014666001,0.0024595025,0.001492008,0.000032799344,0.023559866,0.0008370895],"about_ca_topic_score_codex":0.000087890716,"about_ca_topic_score_gemma":0.0000063633643,"teacher_disagreement_score":0.1634788,"about_ca_system_score_codex":0.00014725346,"about_ca_system_score_gemma":0.00001697783,"threshold_uncertainty_score":0.7993723},"labels":[],"label_agreement":null},{"id":"W7021991813","doi":"","title":"Preface","year":2016,"lang":"en","type":"book-chapter","venue":"Research Publications (Maastricht University)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Council on Foreign Relations","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Emperipolesis; Triacetin; Durvalumab","score_opus":0.09478128205684025,"score_gpt":0.27603550532404686,"score_spread":0.1812542232672066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7021991813","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000027523358,0.00018761231,0.0034090374,0.00022861363,0.0002727343,0.0005460071,0.00014428844,0.00069373404,0.99449044],"genre_scores_gemma":[0.006917621,0.00043634448,0.000047048117,0.0000020214775,0.00041661324,0.000004827381,0.00003089956,0.0000681769,0.99207646],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998227,0.00006763873,0.00022516436,0.00037475544,0.0006604671,0.00044498415],"domain_scores_gemma":[0.9979312,0.00026357296,0.0000640856,0.0007201597,0.0007576848,0.0002633254],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00051496853,0.00022478469,0.00024030193,0.0031166696,0.0003405325,0.000120566205,0.0005381565,0.00058960594,0.0010522432],"category_scores_gemma":[0.00009429844,0.00022220892,0.00014025516,0.000771439,0.00012293398,0.0003518682,0.00015740214,0.0009037523,0.0023562089],"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":[0.000014944183,0.000013655509,0.000007870812,0.000048419515,0.00018210095,0.000017413658,0.000024004063,0.00006819276,0.00026708475,0.6857999,0.2744837,0.039072704],"study_design_scores_gemma":[0.00025971644,0.00004325894,0.000013973822,0.00009288148,0.000015484975,0.0000058237315,0.000020776004,0.00007460115,0.00006818984,0.0008883936,0.9982948,0.00022213414],"about_ca_topic_score_codex":0.000008542187,"about_ca_topic_score_gemma":0.000008691452,"teacher_disagreement_score":0.72381103,"about_ca_system_score_codex":0.0008996074,"about_ca_system_score_gemma":0.00025556915,"threshold_uncertainty_score":0.99986094},"labels":[],"label_agreement":null},{"id":"W7033957093","doi":"","title":"SNL: Alberta regulators approve ATCO's purchase of Elk Point REA distribution system","year":2013,"lang":"en","type":"other","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Point (geometry); Distribution (mathematics); Point system; Point of sale; Term (time)","score_opus":0.005933176184157031,"score_gpt":0.18239201977204073,"score_spread":0.1764588435878837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033957093","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00095190643,0.00032298692,0.025741534,0.00001081393,0.0033099789,0.0014033279,0.0002090646,0.0011618482,0.96688855],"genre_scores_gemma":[0.2752847,0.000015778638,0.000090636226,0.000003055545,0.0011060975,0.00011233514,0.00027543577,0.0005948932,0.7225171],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99850404,0.000049560218,0.0005693433,0.0002938797,0.00031478802,0.00026836107],"domain_scores_gemma":[0.9989675,0.000036626356,0.00023649077,0.0005938562,0.000052706393,0.000112820686],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020638312,0.00035895762,0.00061987387,0.00019456104,0.000027739914,0.000039888393,0.00014767451,0.000857142,0.00097506127],"category_scores_gemma":[0.00003356601,0.0002987008,0.00019736544,0.0002296307,0.000026922733,0.00006059748,0.000035838413,0.00022967566,0.0004808165],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000118298185,0.00001663043,0.0000069509992,0.0010326006,0.00018275478,0.0000032070573,0.000033505592,0.00020542569,0.00053117244,0.005814028,0.9889418,0.0032200788],"study_design_scores_gemma":[0.0007420562,0.000102824124,0.000012922915,0.0012733707,0.0000820737,0.000043773856,0.00028275742,0.009180483,0.005608693,0.000010467772,0.98202443,0.00063611777],"about_ca_topic_score_codex":0.008244723,"about_ca_topic_score_gemma":0.00011986993,"teacher_disagreement_score":0.27433282,"about_ca_system_score_codex":0.00022411006,"about_ca_system_score_gemma":0.000018641145,"threshold_uncertainty_score":0.99994653},"labels":[],"label_agreement":null},{"id":"W7043426325","doi":"","title":"Seguindo os passos do exército romano : uma proposta metodológica para a deteção de assentamentos militares romanos no Noroeste Peninsular.","year":2017,"lang":"en","type":"book-chapter","venue":"Durham Research Online (Durham University)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Middle Ages; Human settlement; Period (music); Aerial survey","score_opus":0.08481907481974352,"score_gpt":0.3127419867593187,"score_spread":0.22792291193957517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043426325","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12740438,0.0021788261,0.00069323,0.0005676742,0.003087715,0.009103524,0.003078527,0.0021146133,0.85177153],"genre_scores_gemma":[0.19595195,0.0019974934,0.00087345205,0.000024118142,0.002734146,0.00003310165,0.000537639,0.0004884021,0.7973597],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932507,0.0005083814,0.0008866108,0.0014313589,0.0021882157,0.0017347203],"domain_scores_gemma":[0.9947812,0.00040813265,0.00039481273,0.0022313294,0.0012669518,0.0009175768],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity","insufficient_payload"],"category_scores_codex":[0.0015098092,0.0011141435,0.0013927539,0.0025148743,0.0013234854,0.000535274,0.0018342615,0.0018287785,0.0010613439],"category_scores_gemma":[0.000503005,0.0011887321,0.00076448807,0.0003444129,0.0004388841,0.0006708688,0.00090186583,0.0034627065,0.0009252296],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013332121,0.0038297703,0.0031041107,0.013494556,0.019221054,0.07992862,0.0074446313,0.0057880706,0.14633991,0.034714293,0.4871959,0.18560696],"study_design_scores_gemma":[0.0029382177,0.0010476959,0.0005132498,0.001405435,0.00022365515,0.00013693044,0.00045451924,0.0027961477,0.0010817975,0.0003833275,0.98753256,0.0014864915],"about_ca_topic_score_codex":0.0010222228,"about_ca_topic_score_gemma":0.0012194837,"teacher_disagreement_score":0.50033665,"about_ca_system_score_codex":0.0021982992,"about_ca_system_score_gemma":0.00077097217,"threshold_uncertainty_score":0.99997663},"labels":[],"label_agreement":null},{"id":"W7099187296","doi":"","title":"STATE RECOGNITION OF FAMILY VIOLENCE SERVICES DELIVERED BY WOMEN'S SHELTERS IN SASKATCHEWAN By","year":2001,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Domestic violence; State (computer science); Social Welfare; Social policy; Social assistance; Welfare; Social work; Public policy","score_opus":0.03418673170039021,"score_gpt":0.2544335567821501,"score_spread":0.2202468250817599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099187296","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":[0.99585587,0.00017708854,0.0022042033,0.000003360091,0.00023632504,0.00017359831,0.00007281147,0.0001334088,0.0011433522],"genre_scores_gemma":[0.99922204,0.0002452146,0.00008059568,0.000049532544,0.000022652126,0.000024204397,0.000039278024,0.000018945377,0.00029753835],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990459,0.000037894704,0.00036298714,0.00015571016,0.0001582923,0.00023920018],"domain_scores_gemma":[0.9996847,0.00002884656,0.00005219496,0.00012535755,0.000044445755,0.0000644911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021650581,0.00013094067,0.00019549958,0.00010665713,0.000021907803,0.000023673345,0.000074968404,0.00010888851,0.00012215441],"category_scores_gemma":[0.0000036993317,0.00012792133,0.000031953557,0.0003051022,0.000010456522,0.0001858122,0.000011330607,0.0001176216,0.000069447626],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019540997,0.00007284041,0.0012960562,0.00019097322,0.00004867772,0.000004293228,0.0039307983,0.0026465582,0.63381726,3.079625e-7,0.004645411,0.3531514],"study_design_scores_gemma":[0.015176212,0.0015935571,0.005708269,0.0037944594,0.00006345562,0.000060466373,0.11318265,0.040395707,0.7083528,0.0013740311,0.1063535,0.003944904],"about_ca_topic_score_codex":0.00467251,"about_ca_topic_score_gemma":0.0009022097,"teacher_disagreement_score":0.34920648,"about_ca_system_score_codex":0.00011827457,"about_ca_system_score_gemma":0.000009033088,"threshold_uncertainty_score":0.70634687},"labels":[],"label_agreement":null},{"id":"W7117537085","doi":"10.18280/ts.420632","title":"Image Processing Algorithms Combining Transfer Learning and Semi-Supervised Learning for Industrial Part Defect Detection","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transfer of learning; Image processing; Pattern recognition (psychology); Transfer (computing); Image (mathematics)","score_opus":0.026185051180955123,"score_gpt":0.2535943361905159,"score_spread":0.22740928500956079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117537085","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":[0.38230956,0.0013815691,0.6093724,0.00006624496,0.0025578185,0.0025865715,0.000019657344,0.00058261433,0.0011235861],"genre_scores_gemma":[0.99679756,0.000103069025,0.00031751354,0.00002729949,0.0017603893,0.00037482116,0.000039763298,0.00012809082,0.00045146295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99561596,0.00052376505,0.0014543771,0.0009321945,0.0004954819,0.0009782197],"domain_scores_gemma":[0.9986476,0.0005224289,0.00018168992,0.00014615098,0.0002701436,0.00023203665],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0023928415,0.000759046,0.0009339385,0.00063633296,0.0016647285,0.000838137,0.00015454486,0.00086134305,0.00022916074],"category_scores_gemma":[0.000229428,0.0008446331,0.00047050108,0.0009200492,0.00010348596,0.0006282104,0.00005713913,0.0018050132,0.000010528151],"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":[0.0011625219,0.00013305385,0.0008743528,0.0013118058,0.0006251994,0.000011141572,0.0021437253,0.036019575,0.12226201,0.00003903632,0.00018776885,0.8352298],"study_design_scores_gemma":[0.014379055,0.0019876852,0.00013217323,0.002339448,0.0008471273,0.000027112754,0.0033583865,0.8882328,0.0429145,0.000039593466,0.044623453,0.0011186686],"about_ca_topic_score_codex":0.00007196681,"about_ca_topic_score_gemma":0.000012375099,"teacher_disagreement_score":0.8522132,"about_ca_system_score_codex":0.00035029443,"about_ca_system_score_gemma":0.0001750669,"threshold_uncertainty_score":0.999635},"labels":[],"label_agreement":null},{"id":"W7125579881","doi":"10.1109/iccect64621.2025.11339644","title":"Research on the Quality Improvement Strategy of Inspection and Testing Institutions Based on AI Vision Systems","year":2025,"lang":"","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"123 Certification (Canada)","funders":"","keywords":"Field (mathematics); Convolutional neural network; Quality (philosophy); Key (lock); Machine vision; Plan (archaeology); Service (business)","score_opus":0.20395155219188887,"score_gpt":0.42917611051758153,"score_spread":0.22522455832569266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125579881","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":[0.82278055,0.00024783963,0.026098859,0.00061611616,0.0038486456,0.003322288,0.000043361913,0.00028957395,0.14275277],"genre_scores_gemma":[0.99913484,0.000016376887,0.000013424478,0.00005584069,0.00021132841,0.00009541148,0.000002177889,0.000016956521,0.00045361824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996317,0.0008692768,0.0010990934,0.00047871275,0.00084977114,0.00038613743],"domain_scores_gemma":[0.99634516,0.00210727,0.00016111317,0.0006398614,0.00066194154,0.00008467955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063070473,0.00026916125,0.00041108901,0.00080338935,0.0010632584,0.00035379533,0.00014811316,0.00037036018,0.000026844156],"category_scores_gemma":[0.0008620277,0.00019048052,0.00009002473,0.0021006013,0.00017024214,0.0001480888,0.00006883658,0.0011287149,0.000019115589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039149,0.00039069905,0.00024582853,0.0012335754,0.00010999805,0.0000023023858,0.00009695979,0.6737834,0.042151205,0.22546345,0.0025199843,0.05361111],"study_design_scores_gemma":[0.0018655686,0.0038036145,0.0066365344,0.00425267,0.00004084062,0.0000017912116,0.00487764,0.9375232,0.03771626,0.00029488094,0.0026339253,0.00035308424],"about_ca_topic_score_codex":0.0066343676,"about_ca_topic_score_gemma":0.00009661332,"teacher_disagreement_score":0.2637398,"about_ca_system_score_codex":0.0005647629,"about_ca_system_score_gemma":0.00036330166,"threshold_uncertainty_score":0.99998057},"labels":[],"label_agreement":null},{"id":"W7125651753","doi":"10.1109/metroxraine66377.2025.11340257","title":"Artificial Intelligence-Based Vision Systems for Automated Footwear Disassembly","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","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":"Canadian University Press; Ministero dell'Università e della Ricerca; Elon University","keywords":"Machine vision; Process (computing); Robot; Image stitching; Scope (computer science); Workcell; Image processing; Monocular vision; Line (geometry)","score_opus":0.02884500899960828,"score_gpt":0.29942564541298866,"score_spread":0.27058063641338037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125651753","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":[0.038890716,0.00015487331,0.9448226,0.00005692679,0.0049915137,0.00095735537,0.000012621013,0.002950722,0.0071626487],"genre_scores_gemma":[0.99899393,0.0000024420474,0.00027934503,0.000016798549,0.00018576036,0.00011030241,0.000009044579,0.000020507834,0.00038186868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990503,0.000030366276,0.0004195163,0.00016984215,0.0001210176,0.0002089414],"domain_scores_gemma":[0.99950004,0.00016291263,0.000028333161,0.00018719485,0.00007800382,0.000043540622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031862245,0.00013630638,0.00020590518,0.00021033251,0.00010744655,0.00013843602,0.00008212801,0.0001919706,0.000013192418],"category_scores_gemma":[0.00005680074,0.00011692569,0.000097537275,0.00038685455,0.000010924934,0.00006886936,0.000009183635,0.00007733047,0.000056929865],"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":[0.00020192449,0.00008262755,0.00003796687,0.0006560605,0.00011803879,0.0000027063686,0.000062108906,0.77684087,0.043728974,0.028457468,0.053073462,0.0967378],"study_design_scores_gemma":[0.000101326616,0.000091172435,0.00001929772,0.00012315133,0.0000130944245,5.9505453e-7,0.00012713175,0.92809564,0.05497286,0.00017425854,0.016151257,0.00013022071],"about_ca_topic_score_codex":0.000075117445,"about_ca_topic_score_gemma":0.000013758501,"teacher_disagreement_score":0.9601032,"about_ca_system_score_codex":0.00009586583,"about_ca_system_score_gemma":0.00003091338,"threshold_uncertainty_score":0.4768091},"labels":[],"label_agreement":null},{"id":"W7132527051","doi":"","title":"Advances in a next generation measurement & inspection system for automated fibre placement","year":2019,"lang":"en","type":"article","venue":"NPARC","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Automated optical inspection; Automated X-ray inspection; Visual inspection; Process (computing); Overhead (engineering); Fidelity; Overlay; Optical fiber; Sensitivity (control systems)","score_opus":0.041039981571248864,"score_gpt":0.24447229459054323,"score_spread":0.20343231301929438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132527051","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":[0.9373074,0.00049998984,0.027168965,0.000023770624,0.0067174365,0.0026285918,0.000010137406,0.0036789773,0.021964738],"genre_scores_gemma":[0.9992829,0.000013002273,0.00024310865,0.000003283078,0.00025464548,0.0001585298,0.0000067326764,0.000016811362,0.000020989473],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992445,0.000026489413,0.00024466804,0.00013563657,0.00021042417,0.00013826413],"domain_scores_gemma":[0.9997573,0.000011049665,0.00003735221,0.000106775144,0.000063464395,0.00002407263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034553555,0.000092245646,0.0001375158,0.00010774109,0.000041594485,0.000040594397,0.000031501804,0.0000845087,0.000014429633],"category_scores_gemma":[0.000012030064,0.000088278684,0.00003590727,0.00014419247,0.000002226301,0.00021913314,0.000004587772,0.000059297417,0.00005331027],"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":[0.000059774335,0.000014555663,0.00011251073,0.0003727543,0.000014603798,6.303858e-7,0.00015718584,0.16845787,0.8129274,0.00031742707,0.0035799136,0.013985369],"study_design_scores_gemma":[0.0010226597,0.000131945,0.000079006866,0.0001686084,0.0000050187855,0.000003465835,0.00028946696,0.94127154,0.0466724,0.000007378247,0.010225742,0.00012274871],"about_ca_topic_score_codex":0.000008874961,"about_ca_topic_score_gemma":0.00003745614,"teacher_disagreement_score":0.7728137,"about_ca_system_score_codex":0.0005209493,"about_ca_system_score_gemma":0.000014517493,"threshold_uncertainty_score":0.35999003},"labels":[],"label_agreement":null},{"id":"W7154593260","doi":"10.1109/imc-ssgp67001.2025.11473987","title":"Vision Transformer-Assisted Defect Classification and YOLOv8-Based Localization in Industrial Quality Control","year":2025,"lang":"","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep de Rimouski; Université du Québec à Rimouski","funders":"","keywords":"Quality (philosophy); Control (management); Control system; Automation; Machine vision","score_opus":0.046677582563570734,"score_gpt":0.31065781795686537,"score_spread":0.2639802353932946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7154593260","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":[0.2460243,0.0006707938,0.734948,0.0008539482,0.0039495057,0.0031688337,0.000054215252,0.00035498544,0.009975463],"genre_scores_gemma":[0.9992016,0.000048210295,0.00004279887,0.00017635764,0.00018783062,0.00009201301,0.00003813276,0.000036536505,0.0001765282],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99554634,0.0009785332,0.0018580816,0.00070222677,0.00042197973,0.00049281743],"domain_scores_gemma":[0.99831825,0.0007170866,0.00022785534,0.00041059678,0.00017909017,0.00014709092],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002545433,0.0004757161,0.00083889434,0.0009699037,0.00026194865,0.00027269754,0.00012549949,0.0014620055,0.00013108947],"category_scores_gemma":[0.00047742343,0.00047445126,0.00026037567,0.0018944021,0.000093569026,0.00036470447,0.000011165521,0.00073231396,0.000021219012],"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":[0.005586512,0.0009333675,0.058509555,0.0014344499,0.0004639178,0.000010522604,0.00039720512,0.09585819,0.073040284,0.0086860005,0.003269855,0.75181013],"study_design_scores_gemma":[0.027992304,0.0006826983,0.07306467,0.001511414,0.0002737177,0.0000036018014,0.0005762291,0.87284595,0.009038813,0.00020695725,0.012904629,0.00089902064],"about_ca_topic_score_codex":0.0013525955,"about_ca_topic_score_gemma":0.0007093956,"teacher_disagreement_score":0.77698773,"about_ca_system_score_codex":0.0006274144,"about_ca_system_score_gemma":0.0002789065,"threshold_uncertainty_score":0.9998343},"labels":[],"label_agreement":null},{"id":"W7164051824","doi":"10.1109/iipem65914.2025.11548217","title":"AI-Powered Smart Defect Detection System for Printed Circuit Boards","year":2025,"lang":"","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Printed circuit board; Noise (video); Circuit design; Signal processing; Integrated circuit","score_opus":0.01744003844745053,"score_gpt":0.251496320887233,"score_spread":0.23405628243978246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7164051824","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":[0.018712064,0.0006886489,0.8907783,0.000070436916,0.026929477,0.0037808677,0.0000490574,0.0015933546,0.05739781],"genre_scores_gemma":[0.99309105,0.000014950669,0.000100248086,0.000073411284,0.0007975548,0.00049212785,0.000009047596,0.00010355395,0.0053180587],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962269,0.0002201904,0.0014687809,0.0008505318,0.00038787292,0.000845722],"domain_scores_gemma":[0.997946,0.00031709016,0.00020473973,0.0008074441,0.0005268946,0.00019781193],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013880169,0.0006468675,0.0009471498,0.00086245843,0.00056623435,0.0004241536,0.00025184551,0.0011566624,0.00013023746],"category_scores_gemma":[0.00031065312,0.0006642366,0.0010197236,0.0013873365,0.000036624566,0.0002806712,0.00007654018,0.0006628966,0.0002233945],"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":[0.0025216674,0.00034166712,0.00062432315,0.016279142,0.0056009907,0.000031396667,0.0007481314,0.009271978,0.37833408,0.035383776,0.012611592,0.5382512],"study_design_scores_gemma":[0.007806318,0.001062256,0.0005900997,0.0028726005,0.0009962807,0.00008618886,0.001697125,0.11833558,0.59783876,0.00039584457,0.2667493,0.0015696815],"about_ca_topic_score_codex":0.00055023324,"about_ca_topic_score_gemma":0.0001942395,"teacher_disagreement_score":0.974379,"about_ca_system_score_codex":0.0015383209,"about_ca_system_score_gemma":0.000198493,"threshold_uncertainty_score":0.99958086},"labels":[],"label_agreement":null},{"id":"W791250446","doi":"","title":"A Comparison of Two Automated Tests of Refractive Error","year":2014,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Refractive error; Computer science; Optometry; Ophthalmology; Medicine","score_opus":0.06918542538702831,"score_gpt":0.3987916459203465,"score_spread":0.3296062205333182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W791250446","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":[0.99484193,0.000019540923,0.000057328412,0.000008788153,0.00049991737,0.00018383708,0.0000047439544,0.0001627174,0.00422122],"genre_scores_gemma":[0.99920434,1.3662036e-7,0.0007059423,0.000005921301,0.000043982443,0.000011900856,9.941048e-7,0.000012021711,0.000014746054],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984643,0.00017110142,0.00048870425,0.0002616168,0.00034459314,0.0002697131],"domain_scores_gemma":[0.9988637,0.00027302458,0.00026292566,0.00019296222,0.00027727926,0.00013009855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009033087,0.00015191388,0.0004356448,0.00025934976,0.00010441262,0.000012074153,0.00024409757,0.000117528085,0.000026514632],"category_scores_gemma":[0.00080073864,0.00013524698,0.000047849382,0.0011654162,0.0015780289,0.00022685167,0.000059748192,0.00020353866,0.000021930744],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010232246,0.000040830288,0.020113213,0.000020882422,0.000012245051,0.000002217354,0.0006897621,0.0039167805,0.9746672,0.00013001738,0.000045637076,0.0003509899],"study_design_scores_gemma":[0.0002902325,0.0006719825,0.067089036,0.00007174722,0.000008711852,0.00003291653,0.00019376553,0.11324658,0.8177047,0.0005474105,0.000019132154,0.00012378031],"about_ca_topic_score_codex":0.0001744717,"about_ca_topic_score_gemma":0.0000033184806,"teacher_disagreement_score":0.15696248,"about_ca_system_score_codex":0.00007472599,"about_ca_system_score_gemma":0.00009369627,"threshold_uncertainty_score":0.5814313},"labels":[],"label_agreement":null},{"id":"W81251885","doi":"10.1007/978-3-319-10404-1_7","title":"A Probabilistic Approach to Quantification of Melanin and Hemoglobin Content in Dermoscopy Images","year":2014,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Probabilistic logic; Artificial intelligence; Melanin; Pattern recognition (psychology); Content (measure theory); Mathematics; Chemistry","score_opus":0.027623896056373057,"score_gpt":0.2397442734450706,"score_spread":0.21212037738869755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W81251885","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":[0.3720672,0.000023429035,0.62746423,0.00002287536,0.00020131121,0.00017237455,4.2791737e-7,0.00001974411,0.000028379827],"genre_scores_gemma":[0.97106034,7.1632553e-7,0.028855277,0.0000311969,0.000035287696,0.000011979214,3.5393322e-7,0.000004593106,2.6311488e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999243,0.00003657373,0.00020569602,0.00022722901,0.00014309706,0.00014436073],"domain_scores_gemma":[0.9996272,0.00010486332,0.000025922434,0.00016198921,0.000042022846,0.000038008686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063365966,0.00007778832,0.0001477449,0.00020254479,0.00002536544,0.00004749559,0.00012410141,0.000049842383,4.2877062e-7],"category_scores_gemma":[0.00017584066,0.00006605428,0.000011017592,0.0006315354,0.000060185346,0.00006411089,0.00003363912,0.00009304076,0.0000014156761],"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":[0.000010606111,0.000027991044,0.0015673756,0.00007649124,0.0000014036331,5.059436e-7,0.00078480435,0.7255937,0.09148988,0.00016451774,0.00000869798,0.18027404],"study_design_scores_gemma":[0.00022517396,0.000075507094,0.010544976,0.000078120305,9.750187e-7,0.00000948425,0.0000025144923,0.89816606,0.09036677,0.00041505342,0.000018988547,0.00009638336],"about_ca_topic_score_codex":0.00012733109,"about_ca_topic_score_gemma":0.000026658397,"teacher_disagreement_score":0.5989931,"about_ca_system_score_codex":0.00005215062,"about_ca_system_score_gemma":0.000013633599,"threshold_uncertainty_score":0.26936153},"labels":[],"label_agreement":null},{"id":"W821352721","doi":"","title":"BEHAVIOURAL VALIDATION OF A TRANSGENIC MOUSE MODELING SPECIFIC IMPAIRMENT OF DETAILED VISION","year":2015,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Genetically modified mouse; Transgene; Neuroscience; Computer science; Psychology; Biology; Genetics","score_opus":0.10045402394211866,"score_gpt":0.3279034177916204,"score_spread":0.22744939384950175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W821352721","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":[0.9982175,0.00004747626,0.0005995006,0.000006878957,0.00045827765,0.00030104295,0.0000074418704,0.000051835657,0.0003100211],"genre_scores_gemma":[0.99941576,0.000001763432,0.00050231116,0.0000017320269,0.00003457155,0.0000147182,0.0000034428783,0.000014256238,0.00001143688],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998313,0.00011830265,0.0005405456,0.00026691053,0.00049812207,0.00026313818],"domain_scores_gemma":[0.99912924,0.00003599819,0.00013665868,0.00018167513,0.00031603826,0.00020038015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011869663,0.00015703359,0.00030113236,0.0002861771,0.000074940835,0.000019624204,0.00021313291,0.00012863024,0.000021539838],"category_scores_gemma":[0.0000725144,0.00014131857,0.00007289306,0.00082853786,0.00063372246,0.00037656433,0.00004006283,0.00015606447,0.000010906297],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032146367,0.000047224526,0.004149954,0.000013270165,0.000007732394,0.0000028366126,0.0010095529,0.038965218,0.9556185,0.000014032504,0.0000086282425,0.00013088857],"study_design_scores_gemma":[0.0004907732,0.0008579814,0.0015937479,0.00004654336,0.000010190046,0.000027483442,0.00041751433,0.062356785,0.93374866,0.00031692052,0.0000018373426,0.00013158274],"about_ca_topic_score_codex":0.0001114902,"about_ca_topic_score_gemma":0.0000012288693,"teacher_disagreement_score":0.023391569,"about_ca_system_score_codex":0.00016050352,"about_ca_system_score_gemma":0.00014026553,"threshold_uncertainty_score":0.57628036},"labels":[],"label_agreement":null},{"id":"W970684003","doi":"","title":"New Attended Field of View (AFOV) test","year":2009,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Test (biology); Field (mathematics); Computer science; Mathematics; Geology","score_opus":0.038321966170290586,"score_gpt":0.32797972301017986,"score_spread":0.28965775683988926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W970684003","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":[0.988075,0.0000996536,0.00012946581,0.00015254915,0.0005611729,0.0001780884,0.000002018207,0.00009156481,0.010710487],"genre_scores_gemma":[0.9988778,0.0000026069717,0.00070952537,0.000105028936,0.00013316142,0.0000038505673,5.820436e-7,0.0000073594374,0.00016007121],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99874353,0.000042093936,0.0003347696,0.00027311267,0.00028814477,0.0003183191],"domain_scores_gemma":[0.9992078,0.0001905916,0.000086499604,0.00018697164,0.000107320106,0.00022081743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004370054,0.00015699095,0.00027049406,0.00018103869,0.00011753416,0.000027570602,0.0002744304,0.0001429975,0.00014304013],"category_scores_gemma":[0.00062408106,0.0001392018,0.00005413517,0.0011253372,0.0003699341,0.00025977308,0.00003284691,0.00021711143,0.00005722898],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005563796,0.000019614467,0.0018075225,0.000008735566,0.0000050403823,0.00002120546,0.00020363416,0.00013942122,0.98867464,0.00016309202,0.0006496182,0.008301898],"study_design_scores_gemma":[0.00024044499,0.0012688137,0.02534745,0.00008172736,0.0000071904915,0.00015319811,0.00006016364,0.0009201167,0.9683013,0.0031624588,0.00026906945,0.00018805309],"about_ca_topic_score_codex":0.000095948024,"about_ca_topic_score_gemma":0.0000010674182,"teacher_disagreement_score":0.023539929,"about_ca_system_score_codex":0.00005908283,"about_ca_system_score_gemma":0.00015454166,"threshold_uncertainty_score":0.5676485},"labels":[],"label_agreement":null},{"id":"W983086246","doi":"","title":"SEGMENTACIÓN DE IMÁGENES DE COLOR EMPLEANDO EL ESPACIO DE ESCALA GAUSSIANO","year":2006,"lang":"es","type":"article","venue":"Universidad Industrial de Santander","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Humanities; Art; Philosophy","score_opus":0.013244778019065349,"score_gpt":0.24056652192742586,"score_spread":0.22732174390836052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W983086246","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":[0.95903724,0.0003833714,0.011147122,0.00026443446,0.0017585566,0.00079119386,0.00018716465,0.00039793528,0.026032997],"genre_scores_gemma":[0.99237734,0.00014661784,0.00030140285,0.000086136766,0.0043511703,0.000013901481,0.0000381151,0.000121645375,0.002563669],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963125,0.00053373934,0.0006687468,0.0005458469,0.00045258016,0.001486597],"domain_scores_gemma":[0.99846643,0.0002854836,0.00025112726,0.0004216318,0.000097456024,0.0004778445],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0010635128,0.000617458,0.00068959023,0.0005331674,0.00051598565,0.00040404973,0.0003467825,0.0018578529,0.0007085259],"category_scores_gemma":[0.00013141865,0.0006832231,0.0003994246,0.00078074314,0.000119063094,0.00035844438,0.00010118466,0.0011353807,0.00011785961],"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":[0.005265135,0.00074758625,0.29700908,0.00038987136,0.0017764546,0.002286474,0.0027392302,0.0899349,0.28857005,0.020937491,0.27201805,0.018325675],"study_design_scores_gemma":[0.03932089,0.0017800559,0.038753886,0.001977663,0.0018909118,0.0015235735,0.009150999,0.027491283,0.3083996,0.001093404,0.5644708,0.004146912],"about_ca_topic_score_codex":0.004829306,"about_ca_topic_score_gemma":0.0001063253,"teacher_disagreement_score":0.29245275,"about_ca_system_score_codex":0.0036304041,"about_ca_system_score_gemma":0.0009627328,"threshold_uncertainty_score":0.9995619},"labels":[],"label_agreement":null},{"id":"W989593260","doi":"","title":"Distinguishing profile deviations from a part's deformation using the maximum normed residual test","year":2012,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","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":"École de Technologie Supérieure","funders":"","keywords":"Residual; Distortion (music); Displacement (psychology); Identification (biology); Sorting; Task (project management); Deformation (meteorology); Computer science; Process (computing); Algorithm; Mathematics; Engineering; Materials science","score_opus":0.029856218525095336,"score_gpt":0.2503662137620685,"score_spread":0.22050999523697318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W989593260","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":[0.97924876,0.0002283689,0.013146475,0.00011590629,0.0024440133,0.00045898248,0.00007123581,0.00036549175,0.0039207446],"genre_scores_gemma":[0.9973741,0.000003024948,0.0006321548,0.000021552722,0.0017319774,0.000035911344,0.00003599406,0.00003256433,0.00013273073],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989583,0.000069967624,0.0002841779,0.0001116512,0.0002444539,0.0003314471],"domain_scores_gemma":[0.9992345,0.00026670977,0.00010547672,0.00025155535,0.000057653193,0.00008412769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047093275,0.0001512877,0.00014689576,0.000071203925,0.00035443046,0.00015609799,0.00009578676,0.00013531961,0.00009188818],"category_scores_gemma":[0.0005644224,0.000114841736,0.000052512616,0.00028687078,0.000016386512,0.0004983767,0.000033666653,0.00024943225,0.0001697858],"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":[0.00031139294,0.0006788416,0.28218392,0.0007310063,0.000972476,0.00003160578,0.109289624,0.15545917,0.17962936,0.0030172647,0.16271427,0.104981095],"study_design_scores_gemma":[0.0033944238,0.00019760817,0.10984294,0.0011509302,0.00046164653,0.00013972736,0.017116657,0.3629697,0.084980905,0.00048457965,0.41668385,0.002577021],"about_ca_topic_score_codex":0.0003327718,"about_ca_topic_score_gemma":0.00014243987,"teacher_disagreement_score":0.25396958,"about_ca_system_score_codex":0.00015667155,"about_ca_system_score_gemma":0.000023014705,"threshold_uncertainty_score":0.46831098},"labels":[],"label_agreement":null},{"id":"W999465353","doi":"","title":"Adaptation to Near Addition Lens","year":2006,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adaptation (eye); Lens (geology); Optometry; Through-the-lens metering; Optics; Physics; Medicine","score_opus":0.0468575267562669,"score_gpt":0.2965717876925691,"score_spread":0.2497142609363022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W999465353","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":[0.9912833,0.00001649601,0.0003192435,0.000052484073,0.00086787815,0.00025348918,0.000010285902,0.00018070094,0.0070161335],"genre_scores_gemma":[0.99840987,2.77165e-7,0.0011311148,0.00004984145,0.00023237191,0.00004521142,0.000007708598,0.000014623131,0.00010895848],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987136,0.000062293126,0.00025382568,0.0003126038,0.00031292718,0.00034472716],"domain_scores_gemma":[0.9994851,0.000059339945,0.00004954585,0.00012825662,0.00014672265,0.0001310685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044967374,0.00014505387,0.00015137508,0.00019987361,0.00034521607,0.000093133516,0.00014776082,0.00011067757,0.000095749645],"category_scores_gemma":[0.00016565161,0.00014141113,0.000033958564,0.0012198017,0.00058972434,0.0004566353,0.000033624303,0.00015338526,0.00038307352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000903446,0.000019440902,0.00060285564,0.0000060607986,0.0000043744767,0.00002790023,0.0004557419,0.040153515,0.9560354,0.00029930833,0.00107197,0.0013144232],"study_design_scores_gemma":[0.00042814636,0.00089513534,0.06746224,0.000082307335,0.000011740141,0.0002612385,0.00039389764,0.07019732,0.8514254,0.0038279013,0.0044316137,0.0005830486],"about_ca_topic_score_codex":0.0004098473,"about_ca_topic_score_gemma":0.000010378172,"teacher_disagreement_score":0.104609974,"about_ca_system_score_codex":0.00017352468,"about_ca_system_score_gemma":0.000095491705,"threshold_uncertainty_score":0.57665783},"labels":[],"label_agreement":null}]}