{"meta":{"query_hash":"06df2509457d","filters":{"venue":"Applied and Computational Engineering"},"cohort_total":186,"direct_labels_cover":0,"predictions_cover":186,"exported":186,"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/06df2509457d","api":"https://metacan.xera.ac/api/v1/cohort?venue=Applied+and+Computational+Engineering"},"results":[{"id":"W4285815030","doi":"10.54254/ace.2022001","title":"A Report on the Modelling, Design and Fabrication of Polymer Modulators","year":2022,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Photonic and Optical Devices","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":"Fabrication; Materials science; Electron-beam lithography; Wafer; Photolithography; Polymer; Optoelectronics; Lithography; Silicon; Silicon on insulator; Astronomical interferometer; Deposition (geology); Etching (microfabrication); Waveguide; Optics; Nanotechnology; Interferometry; Resist; Composite material; Layer (electronics); Physics","score_opus":0.010175154867706026,"score_gpt":0.1791347259382534,"score_spread":0.16895957107054735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285815030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06769672,0.0026664913,0.9071576,0.00016813925,0.000105349885,0.00013588455,0.00044027893,0.0016631784,0.019966364],"genre_scores_gemma":[0.57269895,0.0070305243,0.40210316,0.00008214711,0.000063156105,0.0003706457,0.0010497026,0.000361162,0.01624052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981576,0.000022096525,0.000008214237,0.000024812394,0.00011104343,0.000018049197],"domain_scores_gemma":[0.9998807,0.000041484956,0.000020915344,0.00002064543,0.000029793138,0.000006529927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027683962,0.00049061066,0.0004200075,0.00016322374,0.000195505,0.00045999885,0.00054455886,0.0004222182,0.0016595758],"category_scores_gemma":[0.00041715725,0.0003099399,0.00042325226,0.00022352759,0.00019006632,0.00043812388,0.00017978252,0.00039199827,0.00088647235],"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":[0.000105982406,0.000081461265,0.002166302,0.0010604647,0.00008719039,0.0002653072,0.00015966638,0.4550525,0.4223721,0.016162446,0.0030680674,0.099418424],"study_design_scores_gemma":[0.00004504843,0.00040618284,0.0026239909,0.00011030895,0.00010951139,0.00045075227,0.000043163134,0.5277481,0.32513112,0.0030888782,0.14018816,0.000054761364],"about_ca_topic_score_codex":0.0006513837,"about_ca_topic_score_gemma":0.0007153923,"teacher_disagreement_score":0.0016595758,"about_ca_system_score_codex":0.00033284345,"about_ca_system_score_gemma":0.00044594536,"threshold_uncertainty_score":0.005551815},"labels":[],"label_agreement":null},{"id":"W4365518837","doi":"10.54254/2755-2721/2/20220540","title":"A Novel Method for Text Classification Dealing with Local Data Structures and High Data Dimensionality","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Curse of dimensionality; Pattern recognition (psychology); Artificial intelligence; k-nearest neighbors algorithm; Feature vector; Correlation; Text categorization; Data mining; Data set; Set (abstract data type); Support vector machine; Mathematics","score_opus":0.060627553714363805,"score_gpt":0.29863529543887635,"score_spread":0.23800774172451256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365518837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052594757,0.0004788558,0.98974484,0.00017463177,0.00032775386,0.00021763016,0.0003651379,0.0020733932,0.0013582946],"genre_scores_gemma":[0.06754955,0.0006322385,0.92076415,0.00020078811,0.00035996255,0.00039686347,0.0013256712,0.00015833149,0.008612383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814403,0.00019410237,0.00015611056,0.00048556956,0.00091359846,0.000106609536],"domain_scores_gemma":[0.9986077,0.00031860315,0.00015172719,0.00022213743,0.00065096153,0.000048826863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091396115,0.001014339,0.0011652107,0.0038251595,0.0010816236,0.0013536692,0.0012681965,0.0010860743,0.002588132],"category_scores_gemma":[0.0027222459,0.00031412966,0.0009903111,0.0044133533,0.0006856423,0.0020556794,0.0007406478,0.0012156016,0.0026570624],"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":[0.000109772125,0.000106758154,0.0013716121,0.00021181523,0.000067849964,0.00010301061,0.00011741551,0.0049568717,0.020757262,0.004952312,0.01144511,0.9558003],"study_design_scores_gemma":[0.000079576836,0.00026753903,0.0056752698,0.00008426273,0.000112673435,0.0016740003,0.00024335303,0.85783523,0.05215184,0.016109562,0.06564753,0.00011927547],"about_ca_topic_score_codex":0.0028360444,"about_ca_topic_score_gemma":0.004402585,"teacher_disagreement_score":0.0038251595,"about_ca_system_score_codex":0.00057153683,"about_ca_system_score_gemma":0.0014180966,"threshold_uncertainty_score":0.008658111},"labels":[],"label_agreement":null},{"id":"W4379986825","doi":"10.54254/2755-2721/3/20230347","title":"The human impact of marine ecosystem imbalance: an analysis of society and ocean management","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Coastal and Marine Management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Overfishing; Marine conservation; Marine ecosystem; Environmental resource management; Sustainable development; Marine pollution; Ecosystem-based management; Environmental planning; Business; Geography; Fishing; Fishery; Ecology; Ecosystem; Environmental science; Pollution","score_opus":0.0036319563368823146,"score_gpt":0.20371785773609524,"score_spread":0.20008590139921292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379986825","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7817146,0.0038681824,0.009792215,0.020768179,0.00011695931,0.000090589536,0.00021958815,0.000028115832,0.18340153],"genre_scores_gemma":[0.99589086,0.0013615035,0.0007540853,0.00017723118,0.00004025155,0.000017536304,0.000028677547,0.000003796099,0.0017260773],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99958366,0.00020912473,0.000010660097,0.000021296957,0.000076573364,0.000098645985],"domain_scores_gemma":[0.99947697,0.00018198745,0.000099922225,0.00002137854,0.000096762364,0.00012292589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048729684,0.00023553701,0.00014659586,0.0018768721,0.0012421191,0.002363641,0.0003017123,0.00049289106,0.0028184152],"category_scores_gemma":[0.00094024657,0.00008873984,0.0005317894,0.001979975,0.0028856832,0.002120849,0.0019278876,0.00050633325,0.00012417526],"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":[0.000048883045,0.00022675608,0.2120437,0.00021841729,0.0001879011,0.0012163504,0.013400304,0.020662302,0.00046716805,0.67594236,0.0091000665,0.06648586],"study_design_scores_gemma":[0.000015879985,0.0002446422,0.4181135,0.0003953263,0.00016104395,0.0005803795,0.09354084,0.05042144,0.00032084555,0.34184873,0.09428078,0.000076486576],"about_ca_topic_score_codex":0.020549074,"about_ca_topic_score_gemma":0.0199266,"teacher_disagreement_score":0.020549074,"about_ca_system_score_codex":0.002915905,"about_ca_system_score_gemma":0.0021040966,"threshold_uncertainty_score":0.040858924},"labels":[],"label_agreement":null},{"id":"W4380029001","doi":"10.54254/2755-2721/3/20230348","title":"Comparative analysis of renewable energy","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Energy Load and Power Forecasting","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 Alberta","funders":"","keywords":"Renewable energy; Wind power; Environmental economics; Climate change; Energy transition; Natural resource economics; Natural resource; Process (computing); Environmental resource management; Business; Environmental planning; Environmental science; Computer science; Engineering; Economics; Political science; Ecology","score_opus":0.01022193923041924,"score_gpt":0.20273463567647046,"score_spread":0.1925126964460512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380029001","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71387815,0.004469617,0.0077787414,0.00067027047,0.00014549367,0.00009823184,0.014729954,0.0001509629,0.25807855],"genre_scores_gemma":[0.98263067,0.0012268506,0.0022024293,0.000057531717,0.000026572689,0.000040450945,0.0073144767,0.00004574003,0.006455277],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992318,0.0001950335,0.00003931323,0.000114147835,0.0002972973,0.00012241102],"domain_scores_gemma":[0.9981901,0.0006828348,0.00015052309,0.0001506068,0.00074675994,0.00007915649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009142543,0.00016764682,0.00034971556,0.0050262124,0.00047291527,0.0015691947,0.00034636998,0.00022613899,0.016588308],"category_scores_gemma":[0.0044995076,0.00007431624,0.0005541119,0.009520691,0.00020015732,0.0010255831,0.0007413702,0.00023823147,0.0011299406],"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":[0.0007117645,0.00016118995,0.3888217,0.001081555,0.0011298229,0.0010568827,0.0022024964,0.020405008,0.0029415474,0.14696021,0.026219608,0.40830818],"study_design_scores_gemma":[0.000026051546,0.00019446366,0.8114876,0.00032661273,0.00030423218,0.0005471576,0.010959298,0.011511059,0.0013613959,0.01809479,0.14513838,0.000049009268],"about_ca_topic_score_codex":0.009891374,"about_ca_topic_score_gemma":0.017643943,"teacher_disagreement_score":0.016588308,"about_ca_system_score_codex":0.001185744,"about_ca_system_score_gemma":0.00055271434,"threshold_uncertainty_score":0.055493414},"labels":[],"label_agreement":null},{"id":"W4383535470","doi":"10.54254/2755-2721/4/20230430","title":"Fast CNN enhancement using channel attention and residual networks for image super-resolution","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Residual; Computer science; Artificial intelligence; Generalization; Similarity (geometry); Channel (broadcasting); Algorithm; Parametric statistics; Image (mathematics); Reset (finance); Deep learning; Pattern recognition (psychology); Activation function; Process (computing); Artificial neural network; Mathematics; Statistics","score_opus":0.013463833685373593,"score_gpt":0.24795767409886124,"score_spread":0.23449384041348764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383535470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08637513,0.0019518726,0.8999181,0.0003775216,0.00021628091,0.00007333969,0.00023578489,0.0047043897,0.006147615],"genre_scores_gemma":[0.7201644,0.0011697634,0.26896498,0.00032702784,0.00007553831,0.000090096364,0.00072379,0.00036761755,0.008116825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998287,0.00001893259,0.000008093502,0.000047190104,0.000059134978,0.00003797828],"domain_scores_gemma":[0.99979264,0.00004999832,0.000023221395,0.00004622039,0.00007465446,0.0000132627865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005616987,0.0010056717,0.0004978924,0.00043321896,0.00019341483,0.00047362287,0.000957761,0.0006222424,0.0019167126],"category_scores_gemma":[0.0009953143,0.00027621372,0.00073389034,0.00042337817,0.00031959155,0.0011283101,0.0006199988,0.0010997793,0.0006550003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023508153,0.00016942869,0.0015071807,0.00020342469,0.00016738483,0.0002948175,0.00009359489,0.57629496,0.07764691,0.006017252,0.0058263973,0.33154356],"study_design_scores_gemma":[0.0000052303503,0.00004359667,0.00029202015,0.000008660304,0.000024489076,0.000047725473,0.0000062181407,0.98398155,0.013477023,0.0008203541,0.001284964,0.000008226032],"about_ca_topic_score_codex":0.007131281,"about_ca_topic_score_gemma":0.0091068875,"teacher_disagreement_score":0.007131281,"about_ca_system_score_codex":0.00058652187,"about_ca_system_score_gemma":0.00067747146,"threshold_uncertainty_score":0.014179528},"labels":[],"label_agreement":null},{"id":"W4383535550","doi":"10.54254/2755-2721/5/20230633","title":"Performance analysis of sentiment classification based neural network","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley; Queen's University","funders":"","keywords":"Computer science; Word2vec; Recurrent neural network; Word embedding; Artificial intelligence; Deep learning; Artificial neural network; Pooling; Convolutional neural network; Softmax function; Encoder; Transformer; Context (archaeology); Language model; Sentiment analysis; Embedding; Machine learning","score_opus":0.01593937030535209,"score_gpt":0.22282005101240202,"score_spread":0.20688068070704993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383535550","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81174684,0.006613637,0.14498064,0.0013634501,0.0011575683,0.00023961376,0.0026991859,0.005982443,0.025216699],"genre_scores_gemma":[0.965996,0.0010675141,0.023605093,0.00015729204,0.00009790385,0.00010895051,0.0038051321,0.00012290556,0.005039176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989863,0.0002338823,0.00009648583,0.00020290262,0.00032182044,0.00015868673],"domain_scores_gemma":[0.99859375,0.00045523574,0.00010932463,0.00009882159,0.00069302844,0.000049832797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001686456,0.0014003728,0.000862801,0.0011766528,0.000443604,0.0008933694,0.00068275566,0.0007548403,0.0023841565],"category_scores_gemma":[0.0039760163,0.0002133474,0.00059967214,0.00081100536,0.00021429498,0.001289676,0.0005755353,0.0006501327,0.0011089774],"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":[0.0022242921,0.000534513,0.025230221,0.00059076364,0.0004845324,0.00021970524,0.00010813308,0.29542536,0.01803169,0.002081214,0.018497346,0.63657224],"study_design_scores_gemma":[0.000013197395,0.00012395601,0.0021312593,0.000013345271,0.000037070804,0.000022005592,0.000024092451,0.99153286,0.0051638368,0.00038164828,0.0005466244,0.000010077922],"about_ca_topic_score_codex":0.008740842,"about_ca_topic_score_gemma":0.004894271,"teacher_disagreement_score":0.008740842,"about_ca_system_score_codex":0.0010582232,"about_ca_system_score_gemma":0.00056536566,"threshold_uncertainty_score":0.01737994},"labels":[],"label_agreement":null},{"id":"W4383535560","doi":"10.54254/2755-2721/5/20230566","title":"Exploration of the feasibility of steering wheelless cars based on Robotaxi operation data","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Autonomous Vehicle Technology and Safety","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":"Steering wheel; Control (management); Automotive engineering; Course (navigation); Computer science; Self driving; Aeronautics; Transport engineering; Engineering; Artificial intelligence","score_opus":0.040728071366712915,"score_gpt":0.23949302465127675,"score_spread":0.19876495328456384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383535560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97688204,0.0009141348,0.011419741,0.00024725683,0.00001130232,0.00006568841,0.0027079806,0.00017994031,0.0075719566],"genre_scores_gemma":[0.9954268,0.00025014667,0.0019478266,0.000013938839,0.0000028604338,0.000016739772,0.0019457026,0.0000090094745,0.00038710565],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990922,0.00020863395,0.000050094168,0.00016616753,0.00037256928,0.00011024853],"domain_scores_gemma":[0.99729425,0.0010740777,0.00035672655,0.00018316648,0.0009976751,0.00009401057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011237593,0.0003051714,0.00025110922,0.0020999238,0.00022665535,0.0008221129,0.0005372986,0.00022220313,0.0011915852],"category_scores_gemma":[0.003113707,0.00015505051,0.0002858009,0.0023676811,0.0002909115,0.0014922218,0.00036512103,0.00021307438,0.00038577538],"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":[0.000566163,0.0002071281,0.7651122,0.0010840395,0.00013788076,0.00052606675,0.0016464941,0.025374075,0.009517375,0.007113883,0.0046394537,0.18407522],"study_design_scores_gemma":[0.000022406537,0.0005647868,0.7896834,0.00020191247,0.00021082813,0.00050843245,0.008975223,0.1623442,0.012242617,0.0040247026,0.021121103,0.000100326],"about_ca_topic_score_codex":0.011513586,"about_ca_topic_score_gemma":0.011042126,"teacher_disagreement_score":0.011513586,"about_ca_system_score_codex":0.00048601767,"about_ca_system_score_gemma":0.00068720017,"threshold_uncertainty_score":0.02289313},"labels":[],"label_agreement":null},{"id":"W4383535562","doi":"10.54254/2755-2721/4/20230425","title":"Research on RGB image optimization technology based on cluster analysis and improved Hibbard algorithm","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Image Fusion Techniques","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":"Demosaicing; Zipper; Diagonal; Artificial intelligence; Interpolation (computer graphics); Cluster analysis; Algorithm; RGB color model; Enhanced Data Rates for GSM Evolution; Computer science; Image (mathematics); Stairstep interpolation; Mathematics; Image gradient; Image processing; Pattern recognition (psychology); Computer vision; Color image; Linear interpolation","score_opus":0.007007596602077992,"score_gpt":0.2582114828595029,"score_spread":0.2512038862574249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383535562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010138289,0.00029216107,0.98677814,0.000110806635,0.000027398384,0.00002262639,0.00001856024,0.0005167631,0.00209516],"genre_scores_gemma":[0.26178142,0.00066815666,0.7314787,0.00012398497,0.000044596676,0.00009926699,0.00011708484,0.00029225566,0.005394547],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961203,0.00007061368,0.000017451344,0.00010614269,0.00015821232,0.000035629004],"domain_scores_gemma":[0.9997453,0.00006236682,0.000023991983,0.00003748862,0.000118254175,0.000012686366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004774427,0.0007105122,0.0008010444,0.0009329578,0.00050448114,0.0007550983,0.0011328706,0.00066317065,0.0017305912],"category_scores_gemma":[0.0008479099,0.00038130098,0.0007465983,0.0014162754,0.00061810925,0.0012913026,0.0005565215,0.0007178228,0.0004076104],"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":[0.00026697546,0.000078271696,0.001994403,0.00026502847,0.00014587113,0.00013313766,0.00034820093,0.5070604,0.073684074,0.07364576,0.0036694007,0.3387085],"study_design_scores_gemma":[0.000009183736,0.000021896583,0.00036186402,0.0000055671167,0.0000114626055,0.000036010413,0.000021066759,0.9827775,0.01121305,0.0037782562,0.001747492,0.000016701564],"about_ca_topic_score_codex":0.008253846,"about_ca_topic_score_gemma":0.0043711495,"teacher_disagreement_score":0.008253846,"about_ca_system_score_codex":0.00090695795,"about_ca_system_score_gemma":0.000808481,"threshold_uncertainty_score":0.016411602},"labels":[],"label_agreement":null},{"id":"W4383535572","doi":"10.54254/2755-2721/5/20230618","title":"Malware detection using different supervised learning methods","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Earl Haig Secondary School","funders":"","keywords":"Machine learning; Computer science; Malware; Artificial intelligence; Decision tree; Supervised learning; Multinomial logistic regression; Naive Bayes classifier; Bayes' theorem; Logistic regression; Bayesian probability; Support vector machine; Artificial neural network; Computer security","score_opus":0.016251198838690036,"score_gpt":0.24977490154873713,"score_spread":0.2335237027100471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383535572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3870172,0.0026622298,0.5940913,0.00076479453,0.00045464296,0.0005454488,0.0010477302,0.007309398,0.006107248],"genre_scores_gemma":[0.75912213,0.0005340792,0.23507339,0.00017265504,0.0001305819,0.00025576912,0.0019887355,0.00015193441,0.0025707146],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964568,0.0012157701,0.0003449159,0.00075893843,0.000996173,0.00022737795],"domain_scores_gemma":[0.9917715,0.0043851873,0.00062873674,0.00079265394,0.0021964307,0.00022548286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003342594,0.0012929763,0.0010415487,0.0031098286,0.0006041575,0.0010680009,0.0011433304,0.0010812073,0.00089633185],"category_scores_gemma":[0.008206705,0.0002922024,0.0013298107,0.0010738723,0.00044893654,0.0014395645,0.00063047535,0.0010367419,0.0006465017],"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":[0.0009375557,0.001409083,0.024065511,0.00053936604,0.0006310821,0.00018388065,0.00023383946,0.19116539,0.008029318,0.0020984847,0.0055697793,0.7651367],"study_design_scores_gemma":[0.000028485603,0.00018156396,0.0027672437,0.000028241242,0.000049202194,0.000095219155,0.00005867116,0.9878997,0.006357399,0.0016877662,0.0008210382,0.000025515003],"about_ca_topic_score_codex":0.0036404273,"about_ca_topic_score_gemma":0.0039157164,"teacher_disagreement_score":0.0036404273,"about_ca_system_score_codex":0.0008129838,"about_ca_system_score_gemma":0.0010855084,"threshold_uncertainty_score":0.017677546},"labels":[],"label_agreement":null},{"id":"W4383560150","doi":"10.54254/2755-2721/4/20230435","title":"Commercial video recognition system for short video (TikTok) based on machine learning","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Random forest; Mobile phone; Principal component analysis; Decision tree; Machine learning; Artificial intelligence; Multimedia; Data mining; Telecommunications","score_opus":0.013860698875399105,"score_gpt":0.20865850634547656,"score_spread":0.19479780747007747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1845553,0.0024379657,0.7647329,0.00041692174,0.00076764135,0.0009378064,0.003075084,0.03220741,0.010868982],"genre_scores_gemma":[0.6852909,0.001286275,0.2932879,0.00025628394,0.00020787625,0.0005175543,0.0067682005,0.00023216782,0.012152782],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956316,0.000035000594,0.000038660786,0.00013703942,0.00017085111,0.000055334884],"domain_scores_gemma":[0.99958545,0.00005493356,0.00005134248,0.00004330312,0.00023075721,0.000034124674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003967951,0.00069683074,0.0007049486,0.0017705011,0.0003216956,0.000603142,0.0007504852,0.00052983366,0.0028863268],"category_scores_gemma":[0.00090962916,0.00015991087,0.0005018086,0.0010022336,0.0001356643,0.0010427933,0.00039679886,0.00053600426,0.0020730165],"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":[0.0005790773,0.00033629235,0.006704106,0.0003342143,0.00011045172,0.000272996,0.00006268,0.0059250393,0.054415364,0.0010165072,0.016788604,0.9134547],"study_design_scores_gemma":[0.000100782694,0.0007122556,0.022022193,0.0000670261,0.00020604873,0.0012579787,0.00012915554,0.84464484,0.11358723,0.0016212469,0.015541706,0.00010945077],"about_ca_topic_score_codex":0.00365058,"about_ca_topic_score_gemma":0.0038715366,"teacher_disagreement_score":0.00365058,"about_ca_system_score_codex":0.0005771891,"about_ca_system_score_gemma":0.0004325628,"threshold_uncertainty_score":0.009655714},"labels":[],"label_agreement":null},{"id":"W4383560183","doi":"10.54254/2755-2721/6/20230807","title":"Orthogonal frequency division multiplexing technology based on MATLAB","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Wireless Communication Techniques","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 Ottawa","funders":"","keywords":"Orthogonal frequency-division multiplexing; Cyclic prefix; Bit error rate; Computer science; Interference (communication); Fading; Channel (broadcasting); Electronic engineering; Telecommunications; Spectral efficiency; Engineering","score_opus":0.006454460859553645,"score_gpt":0.20967146135036835,"score_spread":0.2032170004908147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560183","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034860268,0.00094309024,0.9399169,0.00026552775,0.0003143441,0.00019446055,0.00045015305,0.016390938,0.038038433],"genre_scores_gemma":[0.16509655,0.0025261645,0.778308,0.00031554583,0.00020316574,0.0013180963,0.0012868665,0.0015135556,0.04943205],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991806,0.0001845303,0.000071573195,0.000101956204,0.00040593123,0.000055318556],"domain_scores_gemma":[0.99920076,0.00024335596,0.00007832059,0.00014781769,0.00029924017,0.000030495296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007261404,0.00073579646,0.0005572461,0.0012169704,0.00042986384,0.0013976995,0.0008559488,0.0005188306,0.02825083],"category_scores_gemma":[0.0023138036,0.00028958547,0.00041594735,0.0009364055,0.0004367466,0.0010328292,0.0009225691,0.0011774909,0.01150123],"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":[0.0004768725,0.0002228469,0.0011386917,0.0011312944,0.00009329922,0.00074166915,0.0005835541,0.03647205,0.056007653,0.15882234,0.055732757,0.68857694],"study_design_scores_gemma":[0.00013620824,0.00043994546,0.0013589512,0.00035045098,0.00006520601,0.0018749116,0.00011577706,0.40802044,0.08796755,0.03356475,0.46595535,0.00015047877],"about_ca_topic_score_codex":0.0006040558,"about_ca_topic_score_gemma":0.0005836085,"teacher_disagreement_score":0.02825083,"about_ca_system_score_codex":0.00043461495,"about_ca_system_score_gemma":0.0007195751,"threshold_uncertainty_score":0.09450847},"labels":[],"label_agreement":null},{"id":"W4383560185","doi":"10.54254/2755-2721/4/2023333","title":"Impact of different transaction features on credit card fraud detection by neural networks","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Credit card fraud; Credit card; Database transaction; Feature (linguistics); Computer science; Artificial neural network; Transaction data; Chargeback; ATM card; Card security code; Machine learning; Artificial intelligence; Data mining; Database; Payment; World Wide Web","score_opus":0.006430267088186143,"score_gpt":0.22088885033128416,"score_spread":0.21445858324309802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9736015,0.0009836336,0.022541212,0.0005292535,0.00011340339,0.00003294469,0.00014165492,0.00021417096,0.0018423117],"genre_scores_gemma":[0.9956923,0.00015005968,0.003696465,0.000034123277,0.000016110671,0.0000080051095,0.00012077267,0.0000068245117,0.00027528385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983742,0.000543672,0.0001692561,0.00024210553,0.00044193454,0.00022888408],"domain_scores_gemma":[0.9944036,0.0037189408,0.0005602625,0.000374775,0.0008041156,0.00013840912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039614593,0.0009830719,0.000789108,0.0014092723,0.0005622676,0.0015047787,0.0005829839,0.000846913,0.00051460776],"category_scores_gemma":[0.013766954,0.00028127414,0.0005840846,0.001011193,0.00055158336,0.0022616885,0.0008904485,0.0010085981,0.00018732689],"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":[0.0019351399,0.0008664944,0.11573011,0.00012476204,0.00028960474,0.00034198054,0.00018081811,0.58939725,0.0053402195,0.0010434848,0.0017844014,0.28296575],"study_design_scores_gemma":[0.0000072796333,0.00008062013,0.0056948727,0.000012387089,0.00002942903,0.000031424137,0.000042251293,0.9913374,0.0022525655,0.0003729476,0.00012883918,0.000009969513],"about_ca_topic_score_codex":0.009539942,"about_ca_topic_score_gemma":0.005468974,"teacher_disagreement_score":0.009539942,"about_ca_system_score_codex":0.0012691047,"about_ca_system_score_gemma":0.0006699983,"threshold_uncertainty_score":0.020950437},"labels":[],"label_agreement":null},{"id":"W4383560268","doi":"10.54254/2755-2721/6/20230861","title":"Overview of definition, evaluation, and algorithms of serendipity in recommender systems","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Serendipity; Recommender system; Computer science; Collaborative filtering; Data science; Information retrieval; Epistemology","score_opus":0.06694067093944853,"score_gpt":0.2843659602202711,"score_spread":0.21742528928082258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560268","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036670654,0.11588476,0.86359763,0.0016986717,0.00053679675,0.0003215353,0.00044534582,0.00057146786,0.013276716],"genre_scores_gemma":[0.10856003,0.10488383,0.7765221,0.0008537234,0.0025187773,0.00062099367,0.0011015063,0.00020771769,0.004731383],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9855962,0.005804121,0.0016922856,0.0025332775,0.003991693,0.00038254063],"domain_scores_gemma":[0.97746366,0.013538162,0.0010782359,0.0026927867,0.0048805056,0.00034665476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013955861,0.0019585125,0.0021945594,0.0056267134,0.0012692931,0.0058965306,0.0026332692,0.0029301068,0.0026137624],"category_scores_gemma":[0.035197984,0.0014343256,0.002088809,0.006846086,0.0019352216,0.008041259,0.0021776014,0.004614756,0.0020368823],"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":[0.00016491355,0.00015925177,0.005772279,0.00301273,0.0004180165,0.00015455422,0.00058729533,0.03454052,0.0020535013,0.2908184,0.013912013,0.64840657],"study_design_scores_gemma":[0.00010228087,0.00063127244,0.007476716,0.0029079076,0.00069997617,0.0018771176,0.00036881983,0.31516472,0.0075559155,0.41222245,0.2505592,0.00043365374],"about_ca_topic_score_codex":0.0063629765,"about_ca_topic_score_gemma":0.0029099956,"teacher_disagreement_score":0.013955861,"about_ca_system_score_codex":0.0031604036,"about_ca_system_score_gemma":0.0026159785,"threshold_uncertainty_score":0.073806524},"labels":[],"label_agreement":null},{"id":"W4383560291","doi":"10.54254/2755-2721/6/20230403","title":"Big data in COVID-19 prevention and control: Modeling and analysis report","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"COVID-19 epidemiological studies","field":"Mathematics","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":"Coronavirus disease 2019 (COVID-19); Epidemic model; 2019-20 coronavirus outbreak; Partition (number theory); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); China; Econometrics; Computer science; Epidemic control; Epidemic disease; Operations research; Infectious disease (medical specialty); Geography; Virology; Demography; Mathematics; Medicine; Outbreak; Disease; Sociology; Population","score_opus":0.22087593479801496,"score_gpt":0.39213534234550634,"score_spread":0.1712594075474914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560291","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53004503,0.02852388,0.3399616,0.041873604,0.0021984098,0.00082702393,0.0319264,0.0015271009,0.023116974],"genre_scores_gemma":[0.9328717,0.008121363,0.042802025,0.000515143,0.00048123059,0.00040719192,0.010758268,0.000068273774,0.003974899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985896,0.00060351926,0.00010625064,0.00022360591,0.00032346675,0.00015354439],"domain_scores_gemma":[0.99459076,0.0032930097,0.0005378777,0.00044467163,0.0008953415,0.0002382613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048059104,0.0007403366,0.00077511475,0.0017452767,0.00061736716,0.0015997391,0.0011635432,0.0011169949,0.001747494],"category_scores_gemma":[0.007853169,0.00046688924,0.0013900243,0.002732826,0.00043568743,0.0023365938,0.0012024862,0.0018820668,0.00027298438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027331477,0.00046903052,0.13078243,0.0007446944,0.0006386265,0.0006496058,0.00027578036,0.6700014,0.0006632519,0.07418191,0.037394278,0.08392572],"study_design_scores_gemma":[0.000009418645,0.00003725843,0.008751528,0.000055530887,0.000045048666,0.00004558803,0.00008382875,0.9746348,0.00021970141,0.01197904,0.0041171336,0.000021029804],"about_ca_topic_score_codex":0.029692285,"about_ca_topic_score_gemma":0.01806165,"teacher_disagreement_score":0.029692285,"about_ca_system_score_codex":0.0017335378,"about_ca_system_score_gemma":0.0017680816,"threshold_uncertainty_score":0.059038937},"labels":[],"label_agreement":null},{"id":"W4383560355","doi":"10.54254/2755-2721/5/20230533","title":"A review of the application of CNN-based computer vision in auto-driving","year":2023,"lang":"en","type":"review","venue":"Applied and Computational Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Field (mathematics); Segmentation; Deep learning; Self driving; Computer vision; Machine learning; Engineering","score_opus":0.018005471204747495,"score_gpt":0.28981054722581817,"score_spread":0.2718050760210707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560355","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022580884,0.9935615,0.0014273511,0.0002879558,0.00035868224,0.000010029589,0.00004414624,0.000024680709,0.0040597906],"genre_scores_gemma":[0.001497375,0.99559045,0.000958138,0.00021640758,0.00026500176,0.000011100275,0.000072034156,0.0000063857656,0.0013830663],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99984777,0.000019401054,0.000021195163,0.000036878082,0.00006128078,0.000013445536],"domain_scores_gemma":[0.999466,0.00027032584,0.000047936694,0.00001923485,0.00016848535,0.000028061779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049575896,0.0010306883,0.00072031305,0.00262997,0.00023313986,0.0007232874,0.0008655364,0.00096904405,0.004933149],"category_scores_gemma":[0.0011820687,0.0004299423,0.00047332072,0.003201389,0.00036016744,0.0015330528,0.00053570524,0.00104027,0.0030185576],"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":[0.000033262386,0.000057581423,0.00020008658,0.012868282,0.00007109231,0.000097710086,0.000039777453,0.0010735812,0.001394576,0.0060366057,0.034667395,0.94346005],"study_design_scores_gemma":[0.000006091954,0.0000868431,0.0006740038,0.003934558,0.000094938434,0.0005757544,0.000027905115,0.00063001446,0.00093402574,0.0027264766,0.99028337,0.000026184045],"about_ca_topic_score_codex":0.0018019385,"about_ca_topic_score_gemma":0.0028540604,"teacher_disagreement_score":0.004933149,"about_ca_system_score_codex":0.0004887818,"about_ca_system_score_gemma":0.00095139234,"threshold_uncertainty_score":0.016503036},"labels":[],"label_agreement":null},{"id":"W4383560362","doi":"10.54254/2755-2721/5/20230511","title":"To describe the content of image: The view from image captioning","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Closed captioning; Computer science; Natural language; Field (mathematics); Artificial intelligence; Natural language processing; Task (project management); Image (mathematics); Domain (mathematical analysis); Perspective (graphical); Engineering","score_opus":0.018034436956163823,"score_gpt":0.234893111473301,"score_spread":0.21685867451713717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075828917,0.059769116,0.8747844,0.017920023,0.0030962122,0.00039655747,0.0015879493,0.0030751657,0.031787723],"genre_scores_gemma":[0.101666376,0.0503032,0.8073982,0.0065960907,0.004210678,0.0005382861,0.0043745185,0.0018636347,0.023049038],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99823844,0.00077801873,0.00010337261,0.0003512063,0.00045216925,0.000076743396],"domain_scores_gemma":[0.9936207,0.0031087012,0.00031011534,0.001330308,0.0014058615,0.00022431166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025801002,0.0015613433,0.0008020286,0.0028913154,0.0006989947,0.0049566845,0.0020233565,0.002552517,0.00727852],"category_scores_gemma":[0.01337814,0.0006143539,0.00085083523,0.0021686587,0.0038005637,0.009760413,0.0032419472,0.004668877,0.0045389077],"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":[0.00023655599,0.00006516411,0.0007651845,0.0028806303,0.00008398047,0.00024757895,0.0016612356,0.008724152,0.016451664,0.11803256,0.08857596,0.7622753],"study_design_scores_gemma":[0.00004418105,0.00028018406,0.0021402554,0.0015748375,0.00011473144,0.0028599086,0.0018095316,0.08043665,0.048978757,0.23532261,0.6262411,0.00019720841],"about_ca_topic_score_codex":0.0019479304,"about_ca_topic_score_gemma":0.0018993284,"teacher_disagreement_score":0.00727852,"about_ca_system_score_codex":0.0014832506,"about_ca_system_score_gemma":0.0008972445,"threshold_uncertainty_score":0.024349093},"labels":[],"label_agreement":null},{"id":"W4383560474","doi":"10.54254/2755-2721/6/20230783","title":"Opacity verification in discrete event system","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Petri Nets in System Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Opacity; Automaton; Property (philosophy); Computer science; Information flow; Perspective (graphical); State (computer science); Cellular automaton; Event (particle physics); Theoretical computer science; Algorithm; Artificial intelligence; Physics; Epistemology; Optics","score_opus":0.010604643591237787,"score_gpt":0.21519800653548193,"score_spread":0.20459336294424416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02749814,0.00010656529,0.9686628,0.00027143507,0.000058838934,0.00008875606,0.000081487306,0.0009927613,0.0022392222],"genre_scores_gemma":[0.8798362,0.00017943366,0.1179796,0.000118837175,0.00006126462,0.0001371206,0.00013303649,0.000057701935,0.0014969527],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99511224,0.0013136776,0.00035180972,0.00085676066,0.0019620396,0.00040337068],"domain_scores_gemma":[0.98562074,0.010586031,0.0010330579,0.0013553336,0.0011394131,0.0002653851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034115273,0.00043327865,0.000708444,0.0008805211,0.00087243115,0.0024110647,0.00096942217,0.00096364965,0.0019310804],"category_scores_gemma":[0.018460609,0.0004615579,0.0013389757,0.00054575526,0.0025075327,0.0028843267,0.0020458964,0.002386814,0.00028875287],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052753865,0.00016411951,0.0063722855,0.00045760022,0.00010717833,0.0015187884,0.0013989706,0.34370703,0.02168215,0.5644257,0.0011337368,0.058504917],"study_design_scores_gemma":[0.000034950015,0.000056750578,0.00031846078,0.000030817555,0.000026648528,0.00008962806,0.000041573498,0.87072563,0.008458595,0.118080035,0.0021135025,0.000023431592],"about_ca_topic_score_codex":0.0043235426,"about_ca_topic_score_gemma":0.0021004255,"teacher_disagreement_score":0.0043235426,"about_ca_system_score_codex":0.0014470781,"about_ca_system_score_gemma":0.0017791427,"threshold_uncertainty_score":0.018042147},"labels":[],"label_agreement":null},{"id":"W4383560516","doi":"10.54254/2755-2721/5/20230674","title":"Analysis of China’s aviation network and the key node","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","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":"Centrality; Aviation; Node (physics); Key (lock); Degree distribution; Network analysis; Commercial aviation; Computer science; Network science; Complex network; Aviation engineering; Transport engineering; Engineering; Computer security; Civil aviation; Mathematics; Aerospace engineering","score_opus":0.004192663045602268,"score_gpt":0.20203170233698092,"score_spread":0.19783903929137867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560516","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97130704,0.00048851117,0.009683255,0.0009057202,0.000031250565,0.00007477364,0.0028410174,0.000109358916,0.014559033],"genre_scores_gemma":[0.9933403,0.00025120832,0.0023082297,0.000026464613,0.000009438894,0.000033629254,0.0017044573,0.000009802082,0.002316284],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997805,0.000049988797,0.000007129197,0.000038393082,0.00005839658,0.00006557888],"domain_scores_gemma":[0.9991013,0.00029070384,0.00014700842,0.000056916262,0.00030855884,0.00009550187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005003144,0.00024848702,0.00016050572,0.002760976,0.00067403377,0.0005750917,0.0003527934,0.00028026736,0.0031067345],"category_scores_gemma":[0.0019106705,0.00009686864,0.0003596588,0.0031874597,0.00028353336,0.00081385154,0.00049803074,0.0002540423,0.00018474001],"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":[0.00026338187,0.00012077749,0.43587184,0.0005543264,0.00019435263,0.0027865572,0.0032394717,0.32223517,0.004587794,0.10610886,0.027935266,0.09610215],"study_design_scores_gemma":[0.000013930874,0.000050692757,0.31045437,0.00007780992,0.00007745477,0.00036980578,0.0027106775,0.6528539,0.0010981663,0.013399855,0.018857023,0.000036291276],"about_ca_topic_score_codex":0.104723446,"about_ca_topic_score_gemma":0.07379019,"teacher_disagreement_score":0.104723446,"about_ca_system_score_codex":0.0022386122,"about_ca_system_score_gemma":0.0010596021,"threshold_uncertainty_score":0.20822781},"labels":[],"label_agreement":null},{"id":"W4383560524","doi":"10.54254/2755-2721/4/20230490","title":"PreSoramimiset: Establishing dataset for Chinese misheard lyrics generation","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Lyrics; Computer science; Transformer; Python (programming language); Conversation; Natural language processing; Focus (optics); Artificial intelligence; Annotation; Information retrieval; Linguistics","score_opus":0.018236629116905493,"score_gpt":0.23837246363212353,"score_spread":0.22013583451521804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560524","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13952024,0.0012963014,0.038382765,0.0008880037,0.0016814652,0.0024740817,0.76791024,0.02414127,0.023705553],"genre_scores_gemma":[0.037706207,0.00018417895,0.021920422,0.00013519035,0.00009615475,0.0011551484,0.9331468,0.00039176864,0.005264112],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985273,0.0002410264,0.00015041504,0.00043937162,0.0004367687,0.0002051304],"domain_scores_gemma":[0.99843913,0.0002013001,0.000080460515,0.00044918936,0.0006078523,0.00022202518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010591879,0.0018483863,0.0006774121,0.0026541045,0.0013448892,0.0011643764,0.0021156934,0.0014555264,0.011239456],"category_scores_gemma":[0.0034317756,0.0003293671,0.0010498321,0.0020775453,0.0006190092,0.0012943354,0.0020599635,0.0018263428,0.010063004],"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":[0.0011353669,0.0009817113,0.014066835,0.0017262719,0.00015003052,0.0011002381,0.0009723555,0.005767068,0.028500117,0.0032421944,0.74116546,0.20119242],"study_design_scores_gemma":[0.00071041833,0.000920519,0.100043364,0.0005458724,0.00019064186,0.001742881,0.0023101517,0.06737956,0.055439435,0.002883555,0.76740265,0.0004310068],"about_ca_topic_score_codex":0.016727377,"about_ca_topic_score_gemma":0.03334082,"teacher_disagreement_score":0.016727377,"about_ca_system_score_codex":0.0012299481,"about_ca_system_score_gemma":0.0019638995,"threshold_uncertainty_score":0.037599742},"labels":[],"label_agreement":null},{"id":"W4383560525","doi":"10.54254/2755-2721/6/20230836","title":"Comparing the effect of CNN and linear regression on facial expression recognition","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kamloops Art Gallery","funders":"","keywords":"Convolutional neural network; Computer science; Facial expression; Facial expression recognition; Artificial intelligence; Expression (computer science); Pattern recognition (psychology); Linear regression; Regression; Facial recognition system; Linear model; Machine learning; Mathematics; Statistics","score_opus":0.014842560057323937,"score_gpt":0.22474121916694875,"score_spread":0.20989865910962482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74843615,0.025048938,0.18355441,0.0020482715,0.0022624005,0.00029834066,0.0020204256,0.0072912388,0.029039888],"genre_scores_gemma":[0.9260758,0.005029045,0.05593533,0.00038674314,0.00025053046,0.00009370705,0.002634565,0.0004010204,0.009193167],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99826825,0.0004337013,0.00008285417,0.0004398256,0.0005063342,0.00026904827],"domain_scores_gemma":[0.9983936,0.00087217346,0.00008805666,0.00018541385,0.00040813367,0.000052571813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002492019,0.0015789504,0.00092309486,0.0010009526,0.00023220568,0.0007908692,0.0007075557,0.0007865248,0.0029841098],"category_scores_gemma":[0.007899201,0.00030733316,0.00077925617,0.00070274976,0.00030109304,0.0017891848,0.00065431395,0.0008040859,0.0011480739],"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":[0.0038073203,0.00053455285,0.01161496,0.00077541877,0.00071404374,0.00018490803,0.00008133723,0.09371167,0.027871525,0.0012810471,0.009098785,0.85032445],"study_design_scores_gemma":[0.00008019607,0.0013551026,0.015196532,0.00007113738,0.0003685552,0.00020030547,0.00014905282,0.93801564,0.03902536,0.0012170345,0.0042525753,0.00006851145],"about_ca_topic_score_codex":0.010289593,"about_ca_topic_score_gemma":0.009557427,"teacher_disagreement_score":0.010289593,"about_ca_system_score_codex":0.0008104839,"about_ca_system_score_gemma":0.0005709896,"threshold_uncertainty_score":0.020459354},"labels":[],"label_agreement":null},{"id":"W4383560526","doi":"10.54254/2755-2721/4/2023429","title":"Using sequence-to-sequence LSTM to predict RNA virus mutations","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Influenza Virus Research Studies","field":"Medicine","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":"Sequence (biology); Mutation; Virus; Construct (python library); Sequence analysis; RNA; Computational biology; Virology; Biology; Computer science; RNA virus; Artificial intelligence; Bioinformatics; Genetics; Gene","score_opus":0.1189012455409579,"score_gpt":0.3701671773105058,"score_spread":0.2512659317695479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63374865,0.0031840664,0.32544512,0.0030519525,0.0025201396,0.00030374576,0.002002407,0.0123137,0.017430188],"genre_scores_gemma":[0.95960855,0.0003843895,0.03569238,0.00039555752,0.00006349293,0.00007311087,0.000950198,0.0000646412,0.0027677445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971336,0.000038635928,0.00002313918,0.00009642833,0.000059668277,0.000068735295],"domain_scores_gemma":[0.9995772,0.00018222262,0.000039272025,0.000030822233,0.00013797439,0.00003244774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005061967,0.0012875814,0.0005768575,0.00041639345,0.00041552665,0.0005775688,0.0007483567,0.0010815134,0.0024011198],"category_scores_gemma":[0.0017397932,0.00031765745,0.0006059611,0.00035042173,0.0002550266,0.0018312033,0.0005011547,0.0018092876,0.00066519843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012809378,0.0008850258,0.0099355765,0.00046498043,0.00025804192,0.0007970251,0.00026486485,0.5045966,0.062337246,0.0029280623,0.0144374855,0.4018142],"study_design_scores_gemma":[0.000013275494,0.00010312481,0.000425499,0.000009306297,0.000017184404,0.000034467066,0.000020661855,0.9909117,0.0070318906,0.0009273394,0.0004954633,0.0000100311945],"about_ca_topic_score_codex":0.007928012,"about_ca_topic_score_gemma":0.0072010835,"teacher_disagreement_score":0.007928012,"about_ca_system_score_codex":0.0006930468,"about_ca_system_score_gemma":0.0009435487,"threshold_uncertainty_score":0.01576376},"labels":[],"label_agreement":null},{"id":"W4383560595","doi":"10.54254/2755-2721/5/20230668","title":"Applications of deep reinforcement learning — Alphago","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Reinforcement learning; Artificial intelligence; Deep learning; Field (mathematics); Computer science; Engineering","score_opus":0.006777116084209396,"score_gpt":0.21245335243257704,"score_spread":0.20567623634836765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04408945,0.0051656333,0.9195495,0.0022114674,0.00035182154,0.00009151436,0.00008393426,0.001144718,0.027311953],"genre_scores_gemma":[0.9204552,0.0017666366,0.06936482,0.0005865912,0.00012434293,0.000083200604,0.000082141305,0.00007191436,0.007465166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996131,0.0001377913,0.000019962306,0.000071204784,0.00010795958,0.000049992825],"domain_scores_gemma":[0.9990989,0.0005397359,0.00007763125,0.00007039164,0.00014795605,0.0000653697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008334296,0.0006112957,0.00057012687,0.00042924203,0.00031358656,0.00072980474,0.0008817288,0.00092731207,0.0028610197],"category_scores_gemma":[0.0031618073,0.00023533721,0.00031009567,0.0003355175,0.0008390572,0.00089653255,0.0013972484,0.0014345024,0.00037617664],"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":[0.00019668406,0.0002053152,0.002580043,0.000226947,0.00011313362,0.00016622049,0.00011954022,0.7108754,0.0033402748,0.062187046,0.0049193157,0.21507008],"study_design_scores_gemma":[0.000016901236,0.00007464547,0.00017247023,0.000028014698,0.0000114066925,0.00003917562,0.00001378396,0.9729926,0.00080183777,0.02253458,0.003306523,0.000008094259],"about_ca_topic_score_codex":0.0032509982,"about_ca_topic_score_gemma":0.0032735053,"teacher_disagreement_score":0.0032509982,"about_ca_system_score_codex":0.0006822174,"about_ca_system_score_gemma":0.00085760484,"threshold_uncertainty_score":0.009571075},"labels":[],"label_agreement":null},{"id":"W4383560704","doi":"10.54254/2755-2721/4/20230478","title":"The current situation and potential development of face recognition","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Face recognition and analysis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Three-dimensional face recognition; Facial recognition system; Biometrics; Computer science; Face (sociological concept); Identification (biology); Face Recognition Grand Challenge; Artificial intelligence; Pattern recognition (psychology); Computer vision; Face detection; Object-class detection; Feature extraction","score_opus":0.012586521992187604,"score_gpt":0.213141001419751,"score_spread":0.2005544794275634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560704","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009357683,0.83078694,0.039285224,0.044717558,0.003608921,0.00008919699,0.00044968948,0.00045091624,0.07125389],"genre_scores_gemma":[0.12379506,0.76488787,0.051328167,0.013520585,0.007233277,0.00022404332,0.0013313247,0.00012984143,0.03754986],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99740076,0.00056852156,0.00013873733,0.0007052094,0.0009845914,0.00020218242],"domain_scores_gemma":[0.9921119,0.0030373763,0.00039991082,0.00039200438,0.0036052112,0.00045367444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004517776,0.00081776525,0.0008426383,0.0018947781,0.0006993035,0.002256198,0.0027056425,0.003080741,0.016476873],"category_scores_gemma":[0.0060911095,0.0003611725,0.0007201857,0.0017596252,0.0016182776,0.006039487,0.0014204788,0.0023684504,0.007915285],"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":[0.00009669929,0.000070373375,0.002071246,0.0012818903,0.00001809585,0.00012643119,0.00015274077,0.00065532455,0.0018069217,0.03500152,0.034092583,0.9246263],"study_design_scores_gemma":[0.000014625008,0.00023273399,0.0053106663,0.002119696,0.000059949834,0.0019709186,0.0009124749,0.0057002334,0.003096373,0.035523668,0.94497657,0.0000821005],"about_ca_topic_score_codex":0.0024954646,"about_ca_topic_score_gemma":0.001342966,"teacher_disagreement_score":0.016476873,"about_ca_system_score_codex":0.0014912431,"about_ca_system_score_gemma":0.002087848,"threshold_uncertainty_score":0.055120647},"labels":[],"label_agreement":null},{"id":"W4383560750","doi":"10.54254/2755-2721/5/20230517","title":"A novel treatment program for adolescents with post traumatic stress disorder with virtual reality technology","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Posttraumatic Stress Disorder Research","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Virtual reality; Modalities; Treatment modality; Population; Posttraumatic stress; Psychology; Clinical psychology; Virtual Reality Exposure Therapy; Psychotherapist; Medicine; Computer science; Human–computer interaction","score_opus":0.0305542507821761,"score_gpt":0.3200130010985734,"score_spread":0.2894587503163973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9309497,0.0010284061,0.04671236,0.0016589727,0.00033069454,0.003255504,0.00023222555,0.0007282372,0.015103829],"genre_scores_gemma":[0.8837274,0.001492297,0.10531356,0.0004503157,0.00011611435,0.002704517,0.00022031144,0.00003672116,0.005938705],"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","domain_scores_codex":[0.9997521,0.0001060234,0.000014720487,0.00003683168,0.00004238403,0.00004783997],"domain_scores_gemma":[0.99980074,0.000061078885,0.000021308795,0.000018648892,0.000014978163,0.00008316786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031337465,0.0003115655,0.0002325185,0.00048792505,0.00054405245,0.0003042531,0.0005559431,0.0003921223,0.00401189],"category_scores_gemma":[0.0007507366,0.00010491989,0.00052347896,0.00020154766,0.00029235956,0.00035605245,0.0010700241,0.00056082645,0.00039289822],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010407869,0.02821939,0.008342533,0.00067976647,0.00008907705,0.0015375409,0.0046584704,0.002581273,0.015681572,0.0025987334,0.006690793,0.92788005],"study_design_scores_gemma":[0.012562782,0.12523867,0.4716103,0.0020315822,0.001396455,0.021728339,0.030304264,0.05044143,0.042769145,0.013877775,0.22751911,0.0005201175],"about_ca_topic_score_codex":0.0006551045,"about_ca_topic_score_gemma":0.0016169484,"teacher_disagreement_score":0.00401189,"about_ca_system_score_codex":0.00018610951,"about_ca_system_score_gemma":0.0007962909,"threshold_uncertainty_score":0.013421118},"labels":[],"label_agreement":null},{"id":"W4383560769","doi":"10.54254/2755-2721/5/20230694","title":"Analysis of sentiment analysis model based on deep learning","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sentiment analysis; Computer science; Artificial intelligence; Deep learning; Convolutional neural network; Recurrent neural network; Machine learning; Natural language processing; Artificial neural network","score_opus":0.008983315016342275,"score_gpt":0.22281215350672154,"score_spread":0.21382883849037926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31662765,0.0011637611,0.6659751,0.0010301546,0.0002671095,0.0001371432,0.0012161669,0.00210307,0.011479816],"genre_scores_gemma":[0.9589916,0.0005098518,0.03363714,0.00011820976,0.000053905267,0.00007275881,0.001378961,0.00007681441,0.005160756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9998031,0.000033283486,0.000011265872,0.000038615522,0.00007067003,0.000043013006],"domain_scores_gemma":[0.99962556,0.00010269657,0.000033097203,0.00002176967,0.00020102659,0.000015837079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061014784,0.00064276083,0.000480234,0.0007143336,0.00026589035,0.0006027843,0.00042229428,0.00031525502,0.0029926298],"category_scores_gemma":[0.0013527987,0.00016996135,0.0007297067,0.00038388127,0.00014800673,0.0007781854,0.00025580148,0.00069102267,0.0006942453],"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":[0.00061456993,0.00034657714,0.021142257,0.00031855493,0.00037959014,0.00046925232,0.00015599265,0.55692667,0.042969894,0.021419542,0.020110272,0.33514684],"study_design_scores_gemma":[0.0000030119993,0.000014573782,0.0007650024,0.0000031335105,0.0000106093885,0.000011066972,0.0000065642844,0.9961683,0.0013415503,0.0013516145,0.00032145015,0.000003058378],"about_ca_topic_score_codex":0.0054212403,"about_ca_topic_score_gemma":0.0039036348,"teacher_disagreement_score":0.0054212403,"about_ca_system_score_codex":0.0006735996,"about_ca_system_score_gemma":0.0006049616,"threshold_uncertainty_score":0.010779381},"labels":[],"label_agreement":null},{"id":"W4383561026","doi":"10.54254/2755-2721/6/20230789","title":"Overweight and overload implementation in Apollo systems","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Distributed systems and fault tolerance","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Apollo; Computer science; Architecture; Software engineering; Software deployment; Sequence diagram; Component (thermodynamics); Process (computing); Concurrency; Data flow diagram; Systems engineering; Distributed computing; Programming language; Engineering; Software; Unified Modeling Language; Database","score_opus":0.005029931727855745,"score_gpt":0.21712529831149233,"score_spread":0.2120953665836366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383561026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25617027,0.00030127406,0.65623105,0.0014358828,0.00020801186,0.00039554056,0.0003733972,0.033724703,0.051159915],"genre_scores_gemma":[0.7536334,0.00020743255,0.22337426,0.00031316004,0.00006960502,0.00029042087,0.000627919,0.0024754107,0.019008337],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979075,0.00045981328,0.00012315555,0.00033877196,0.0009103396,0.0002605088],"domain_scores_gemma":[0.9952348,0.0019878696,0.00041541536,0.0012440487,0.0009315938,0.00018635075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020957014,0.00055754016,0.00036706883,0.0013462526,0.0014322842,0.0024749117,0.0017453134,0.0010255601,0.006732702],"category_scores_gemma":[0.009007093,0.0005458505,0.0005757405,0.0008809071,0.0016707773,0.0032619208,0.0023770074,0.0011421602,0.0011488638],"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":[0.0012282698,0.00069876923,0.036631986,0.0012119749,0.00014172535,0.0041750297,0.01754638,0.12414813,0.07002617,0.24923915,0.034120396,0.46083212],"study_design_scores_gemma":[0.0002252238,0.00045990237,0.013755788,0.00039867603,0.00016734195,0.0015089617,0.002623159,0.636157,0.057001222,0.0919236,0.19560388,0.00017527991],"about_ca_topic_score_codex":0.0056791687,"about_ca_topic_score_gemma":0.007370635,"teacher_disagreement_score":0.006732702,"about_ca_system_score_codex":0.0019877686,"about_ca_system_score_gemma":0.0021745595,"threshold_uncertainty_score":0.022523105},"labels":[],"label_agreement":null},{"id":"W4385421783","doi":"10.54254/2755-2721/8/20230272","title":"The Exploration of AR on Intelligent Packaging","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"QR Code Applications and Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Augmented reality; Variety (cybernetics); Usability; Entertainment; Human–computer interaction; Computer science; Product (mathematics); Multimedia; Field (mathematics); Packaging and labeling; Engineering; Manufacturing engineering; Mechanical engineering; Artificial intelligence","score_opus":0.019817841118844902,"score_gpt":0.22323386750820265,"score_spread":0.20341602638935774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385421783","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05528488,0.07377172,0.26361597,0.010517271,0.0030673924,0.00011663467,0.000086812775,0.0008168218,0.5927225],"genre_scores_gemma":[0.67126393,0.06720617,0.14912166,0.002795875,0.0014645484,0.00011062267,0.00007988951,0.00031182673,0.10764557],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990299,0.00045160338,0.000034773173,0.00009918594,0.0003216399,0.00006287476],"domain_scores_gemma":[0.9992279,0.00047302534,0.0000457314,0.00009937026,0.00012800859,0.00002589814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080996926,0.000607266,0.0003532843,0.00074558903,0.0007357667,0.003798822,0.00053045264,0.0014954032,0.0042122565],"category_scores_gemma":[0.0015656389,0.00033522546,0.000661975,0.0007408158,0.0025093083,0.0045415433,0.0015980806,0.0011468972,0.0010373781],"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":[0.000085610634,0.000044434633,0.00052923895,0.00079829973,0.000026001728,0.0015746229,0.009515723,0.0040565836,0.018461706,0.73064697,0.012633165,0.2216277],"study_design_scores_gemma":[0.000018965702,0.00030373494,0.0017161748,0.0010225542,0.000048258,0.0055275126,0.0039851335,0.013460919,0.011725666,0.09761672,0.86445516,0.000119285905],"about_ca_topic_score_codex":0.00048259643,"about_ca_topic_score_gemma":0.0004813126,"teacher_disagreement_score":0.0042122565,"about_ca_system_score_codex":0.0005838174,"about_ca_system_score_gemma":0.00040429507,"threshold_uncertainty_score":0.014091432},"labels":[],"label_agreement":null},{"id":"W4385421790","doi":"10.54254/2755-2721/8/20230130","title":"The Impact of Color on Players in Human-machine Interaction in Games","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Color perception and design","field":"Psychology","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":"Popularity; Human–computer interaction; Computer science; Vitality; Perception; Video game; Variety (cybernetics); Game mechanics; Visualization; Multimedia; Psychology; Artificial intelligence; Social psychology","score_opus":0.021243334045565235,"score_gpt":0.3332688277511333,"score_spread":0.3120254937055681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385421790","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9731802,0.00027598042,0.00610909,0.0001951943,0.000038222235,0.000051697767,0.000019429994,0.000061587365,0.020068549],"genre_scores_gemma":[0.9974757,0.000075270924,0.0012592394,0.000039257764,0.0000043179234,0.000013890127,0.0000067700366,0.000018797104,0.0011068496],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997775,0.0013626589,0.000058248996,0.00017408075,0.00041783144,0.00021210582],"domain_scores_gemma":[0.99252677,0.0050457683,0.00075304357,0.00029086805,0.00075971993,0.0006238136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014629888,0.00058784964,0.0001899205,0.0005217919,0.00078690675,0.0026519794,0.00040518193,0.00044702322,0.0038961656],"category_scores_gemma":[0.011156363,0.0002108111,0.00028773825,0.00020141568,0.0012740648,0.0011046492,0.0009768347,0.00052054727,0.00033171446],"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":[0.0071273204,0.0027106744,0.3902501,0.0014817208,0.00050256855,0.0022441677,0.0696893,0.004472775,0.23780416,0.018710457,0.0034861858,0.2615207],"study_design_scores_gemma":[0.00016518672,0.0044904747,0.8232361,0.00030676433,0.00064367097,0.001965852,0.06912715,0.015696406,0.051310737,0.00903407,0.023793839,0.00022977649],"about_ca_topic_score_codex":0.0013731277,"about_ca_topic_score_gemma":0.0015077299,"teacher_disagreement_score":0.0038961656,"about_ca_system_score_codex":0.00063803594,"about_ca_system_score_gemma":0.0003082399,"threshold_uncertainty_score":0.013033986},"labels":[],"label_agreement":null},{"id":"W4385720957","doi":"10.54254/2755-2721/8/20230252","title":"Machine Learning in Stock Price Analysis","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Stock (firearms); Computer science; Stock price; Stock market; Machine learning; Artificial intelligence; Econometrics; Economics; Engineering; Series (stratigraphy)","score_opus":0.04706359813045594,"score_gpt":0.331612044878229,"score_spread":0.284548446747773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385720957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038219165,0.12428595,0.78896916,0.019021675,0.0018771137,0.00014089931,0.00054474274,0.00062514545,0.026316155],"genre_scores_gemma":[0.673626,0.06071179,0.250391,0.0018782899,0.0038720728,0.00024146061,0.0008046987,0.0001436834,0.008330951],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980977,0.000876936,0.00012642947,0.0002753366,0.00055326096,0.00007024328],"domain_scores_gemma":[0.99487454,0.004101908,0.00034395696,0.00021675556,0.00039533965,0.000067547815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033312456,0.0007403366,0.0010640367,0.0019943682,0.00040089953,0.00214375,0.0008448799,0.0014526602,0.001753317],"category_scores_gemma":[0.012315434,0.00032005744,0.0005662056,0.0040439307,0.001300719,0.0025436117,0.0007734947,0.0025864474,0.00075519114],"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":[0.00007665917,0.00016681997,0.0143736405,0.0007397457,0.0002841413,0.0002596175,0.00029700156,0.19487478,0.0007206547,0.26855096,0.018500157,0.50115585],"study_design_scores_gemma":[0.000017094038,0.00005154967,0.0044656047,0.000292791,0.000037242113,0.00011865784,0.00010679109,0.61088574,0.0008268219,0.36224908,0.020893529,0.000055057913],"about_ca_topic_score_codex":0.003766267,"about_ca_topic_score_gemma":0.0019423899,"teacher_disagreement_score":0.003766267,"about_ca_system_score_codex":0.00104793,"about_ca_system_score_gemma":0.0007155825,"threshold_uncertainty_score":0.017617524},"labels":[],"label_agreement":null},{"id":"W4385842033","doi":"10.54254/2755-2721/7/20230527","title":"Analysis on the catalytic performance of catalysts and cost-effectiveness and selectivity of methanol carbonylation","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Carbon dioxide utilization in catalysis","field":"Chemical 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":"Insight Design Labs (Canada)","funders":"","keywords":"Methanol; Catalysis; Carbonylation; Raw material; Chemistry; Chloromethane; Organic chemistry; Selectivity; Syngas; Environmentally friendly; Carbon monoxide","score_opus":0.011764385220700575,"score_gpt":0.22934179894223378,"score_spread":0.2175774137215332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385842033","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59630215,0.36450619,0.009016257,0.00047731967,0.000116153,0.000181541,0.0026693277,0.00008232492,0.026648829],"genre_scores_gemma":[0.82482827,0.1612294,0.0066686375,0.00008621624,0.00005580672,0.00012155539,0.002084478,0.000026071733,0.004899582],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992187,0.00011801854,0.00008735077,0.00009935826,0.00042433123,0.000052305324],"domain_scores_gemma":[0.9991136,0.00038319113,0.00012825384,0.000030289057,0.00033591114,0.000008790082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011216054,0.0003974328,0.0006437178,0.0035624972,0.00020488826,0.0007328263,0.0005519126,0.000564732,0.0008557661],"category_scores_gemma":[0.001963691,0.00018174811,0.00072679046,0.0046154987,0.00013573522,0.0005795026,0.00014511621,0.00019392652,0.00030113556],"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":[0.0008537313,0.0005077397,0.033024646,0.038138386,0.0019425249,0.001759312,0.0004865695,0.02062277,0.29483357,0.010075943,0.006583753,0.5911711],"study_design_scores_gemma":[0.00003525922,0.0020888345,0.05044401,0.002047138,0.0033907152,0.002136417,0.000816183,0.022373218,0.80668086,0.0017683539,0.10812144,0.000097453034],"about_ca_topic_score_codex":0.0015332571,"about_ca_topic_score_gemma":0.0030562384,"teacher_disagreement_score":0.0035624972,"about_ca_system_score_codex":0.00068837055,"about_ca_system_score_gemma":0.0003981795,"threshold_uncertainty_score":0.005931735},"labels":[],"label_agreement":null},{"id":"W4386712828","doi":"10.54254/2755-2721/6/20230751","title":"A channel attention and feature manipulation network for facial expression recognition","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Interpretability; Feature (linguistics); Computer science; Artificial intelligence; Facial expression; Pattern recognition (psychology); Confusion matrix; Residual; Network architecture; Machine learning","score_opus":0.029876016971868764,"score_gpt":0.26081759781164693,"score_spread":0.23094158083977817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386712828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11504252,0.00092187466,0.86903733,0.0004980227,0.00036172158,0.00015401782,0.0003106306,0.003817961,0.009855965],"genre_scores_gemma":[0.86317855,0.00041873727,0.119976714,0.00032018858,0.00012473408,0.0002226749,0.0008017574,0.00009893972,0.014857712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998312,0.00002438889,0.0000057035654,0.000060700157,0.000031874155,0.000046107965],"domain_scores_gemma":[0.9998579,0.00003571551,0.000011152106,0.000016860215,0.000064836335,0.0000134287575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039130228,0.00072458026,0.00036298865,0.0003612262,0.00032312027,0.0003406425,0.0009791614,0.00045704248,0.0026905022],"category_scores_gemma":[0.0005318282,0.00020270188,0.00057316327,0.00025671747,0.00032698055,0.00063917035,0.0005396391,0.0007358225,0.00070263364],"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":[0.00070276164,0.0006016203,0.003473588,0.00011790639,0.00016691016,0.00023953874,0.00012630981,0.1588528,0.09034643,0.0071515553,0.013418605,0.7248019],"study_design_scores_gemma":[0.000013725672,0.00016336981,0.00124265,0.000008034131,0.000055395863,0.000064343236,0.000016154265,0.97989804,0.0147301955,0.0017627509,0.0020297265,0.000015614365],"about_ca_topic_score_codex":0.0072679776,"about_ca_topic_score_gemma":0.009229166,"teacher_disagreement_score":0.0072679776,"about_ca_system_score_codex":0.0006008329,"about_ca_system_score_gemma":0.00064727396,"threshold_uncertainty_score":0.014451385},"labels":[],"label_agreement":null},{"id":"W4386937233","doi":"10.54254/2755-2721/6/20230930","title":"Multi-treatment casual analysis using improved meta learner and uplift tree","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Causal Inference Techniques","field":"Mathematics","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":"Causal inference; Causality (physics); Computer science; Inference; Machine learning; Artificial intelligence; Focus (optics); Casual; Tree (set theory); Causal model; Process (computing); Econometrics; Mathematics; Statistics","score_opus":0.14686260789798092,"score_gpt":0.3694765840489,"score_spread":0.22261397615091907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386937233","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009619313,0.00069871196,0.9872321,0.0006943633,0.00008064517,0.00015958624,0.00024410652,0.00042736443,0.00084386027],"genre_scores_gemma":[0.33535933,0.00092051574,0.65672755,0.0008434586,0.0002523777,0.0009298844,0.001020948,0.00030864688,0.003637372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9875894,0.008694973,0.00050788955,0.001528763,0.0012796618,0.0003993476],"domain_scores_gemma":[0.9474444,0.04381417,0.0018767529,0.0036598975,0.0025682843,0.0006365691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029098673,0.0011429759,0.0033520944,0.003436384,0.001388526,0.0023025079,0.0031382411,0.0020853975,0.0069587124],"category_scores_gemma":[0.061770733,0.0007669141,0.0043975944,0.0028116887,0.0011889022,0.004271261,0.002632584,0.0042734803,0.00063921366],"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":[0.0011026509,0.00062937854,0.026452718,0.0008789808,0.0024194792,0.0008520219,0.0012582043,0.337717,0.0013108596,0.20155974,0.010501013,0.41531783],"study_design_scores_gemma":[0.00011487297,0.00014994339,0.0012119595,0.00008179584,0.0004282854,0.00015081736,0.000094160445,0.8573931,0.00056876795,0.13701622,0.0027500622,0.00004007811],"about_ca_topic_score_codex":0.004945214,"about_ca_topic_score_gemma":0.00520387,"teacher_disagreement_score":0.029098673,"about_ca_system_score_codex":0.0018968324,"about_ca_system_score_gemma":0.003541509,"threshold_uncertainty_score":0.15389031},"labels":[],"label_agreement":null},{"id":"W4386961221","doi":"10.54254/2755-2721/11/20230223","title":"Diverse sustainable methods for future jet engine","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Hybrid Renewable Energy Systems","field":"Energy","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":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Jet fuel; Fossil fuel; Aviation biofuel; Environmental science; Biofuel; Renewable fuels; Aviation fuel; Renewable energy; Hydrogen fuel; Aviation; Greenhouse gas; Waste management; Combustion; Engineering; Bioenergy; Aerospace engineering; Fuel cells; Chemistry; Ecology","score_opus":0.008737146010734876,"score_gpt":0.2524917954453507,"score_spread":0.24375464943461583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386961221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010118304,0.061415844,0.651578,0.014746126,0.00424096,0.0003125132,0.00080220506,0.0023791068,0.25440693],"genre_scores_gemma":[0.2111832,0.0838784,0.55601776,0.0032564453,0.0014950321,0.00089335715,0.0020813015,0.0014813074,0.1397132],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992567,0.00010766789,0.00003539003,0.00012464824,0.00040649713,0.00006922776],"domain_scores_gemma":[0.9995338,0.000074708056,0.000027548163,0.00009887002,0.00021420297,0.000050902127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014740524,0.00092975114,0.00056323415,0.001227232,0.0009841964,0.002723555,0.001939461,0.001833963,0.01898451],"category_scores_gemma":[0.0013745956,0.0004992787,0.0009877698,0.0011435661,0.0010875101,0.0036138732,0.0022216411,0.0023103512,0.0074637337],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049238457,0.00013898767,0.0006376855,0.0010203639,0.000059779108,0.00014633952,0.00020918064,0.03715648,0.013717787,0.5651176,0.034878295,0.34686825],"study_design_scores_gemma":[0.000023751527,0.00010170227,0.00034059063,0.00047264434,0.00003236402,0.00017908624,0.00025200087,0.04474082,0.00914543,0.2735649,0.67107356,0.00007318218],"about_ca_topic_score_codex":0.0012483306,"about_ca_topic_score_gemma":0.002430448,"teacher_disagreement_score":0.01898451,"about_ca_system_score_codex":0.00149192,"about_ca_system_score_gemma":0.0017486529,"threshold_uncertainty_score":0.063509464},"labels":[],"label_agreement":null},{"id":"W4387099761","doi":"10.54254/2755-2721/6/20230359","title":"Impact of mouse DPI on wrist fatigue","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Wrist; Muscle fatigue; Connection (principal bundle); Physical medicine and rehabilitation; SIGNAL (programming language); Structural engineering; Medicine; Computer science; Electromyography; Anatomy; Engineering","score_opus":0.011818190662530308,"score_gpt":0.22769544482226944,"score_spread":0.21587725415973913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387099761","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.969231,0.0011408154,0.021804666,0.00026930243,0.0003103071,0.000117711694,0.00067067804,0.0018137404,0.004641807],"genre_scores_gemma":[0.99279976,0.00029430242,0.0034525015,0.00015056388,0.000022781249,0.00005277719,0.00036551105,0.000104631974,0.0027571942],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99941206,0.00011895437,0.000044032724,0.000106189786,0.00021717738,0.00010157643],"domain_scores_gemma":[0.99762005,0.0011223909,0.00019311812,0.00035781635,0.00046380472,0.00024281764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053427817,0.000603219,0.0004789015,0.00053757755,0.00024422794,0.0005584618,0.00045211604,0.000998852,0.0082825925],"category_scores_gemma":[0.0042556357,0.00019578947,0.0005963484,0.0001846618,0.00031796287,0.0007952944,0.00050516636,0.00053328223,0.0012934937],"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":[0.007048696,0.0013240444,0.018166449,0.0011376094,0.00019344679,0.0013766885,0.00019377656,0.021340538,0.81471926,0.0006499147,0.0027956604,0.13105378],"study_design_scores_gemma":[0.00055686425,0.039606526,0.24902755,0.0004927685,0.0006938442,0.0035240601,0.00064205664,0.1524094,0.5395933,0.0018024371,0.011470798,0.0001803897],"about_ca_topic_score_codex":0.00063693384,"about_ca_topic_score_gemma":0.0004663595,"teacher_disagreement_score":0.0082825925,"about_ca_system_score_codex":0.00014869124,"about_ca_system_score_gemma":0.00015819604,"threshold_uncertainty_score":0.027708054},"labels":[],"label_agreement":null},{"id":"W4387099797","doi":"10.54254/2755-2721/6/20230334","title":"Feature selection in text classification: Identifying spurious words with causal inference methods","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Spurious relationship; Computer science; Inference; Causal inference; Artificial intelligence; Weighting; Feature selection; Feature (linguistics); Machine learning; Propensity score matching; Matching (statistics); Selection (genetic algorithm); Model selection; Selection bias; Pattern recognition (psychology); Data mining; Statistics; Mathematics","score_opus":0.023988210522339418,"score_gpt":0.3065342753834491,"score_spread":0.28254606486110967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387099797","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02512864,0.0006808767,0.9721273,0.00060916226,0.00007989365,0.00010696245,0.00013707163,0.0008314564,0.00029853292],"genre_scores_gemma":[0.69330645,0.00053380657,0.30262738,0.00059034,0.00040237443,0.0004027253,0.0009721573,0.00014179954,0.0010230561],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9909156,0.0052195867,0.0006386349,0.0016494329,0.0012547985,0.00032197704],"domain_scores_gemma":[0.96676385,0.02392734,0.002600528,0.004510113,0.0018442132,0.00035392604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016197568,0.0013264196,0.0016574993,0.0035106842,0.0012002256,0.0019667007,0.0021404058,0.0015780966,0.0015848979],"category_scores_gemma":[0.04581133,0.00047146258,0.0014884986,0.0029872453,0.0014691862,0.0036205947,0.0021782564,0.0030692825,0.00068300974],"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":[0.0007139641,0.0007318093,0.042286012,0.00049967656,0.00063254137,0.0005730531,0.0008109833,0.105633445,0.007108908,0.041454602,0.008191219,0.7913637],"study_design_scores_gemma":[0.00009673784,0.00012692346,0.003842483,0.00006918857,0.00011076302,0.00013748379,0.00010191276,0.89302003,0.004092844,0.09591518,0.002445032,0.000041313204],"about_ca_topic_score_codex":0.0015421506,"about_ca_topic_score_gemma":0.0019386931,"teacher_disagreement_score":0.016197568,"about_ca_system_score_codex":0.0009037137,"about_ca_system_score_gemma":0.0017482525,"threshold_uncertainty_score":0.08566195},"labels":[],"label_agreement":null},{"id":"W4387099847","doi":"10.54254/2755-2721/6/20230831","title":"Sentiment analysis of Amazon product reviews","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Random forest; Naive Bayes classifier; Computer science; Sentiment analysis; Amazon rainforest; Support vector machine; Product (mathematics); Recall; Machine learning; Artificial intelligence; tf–idf; Security token; Precision and recall; Bayes' theorem; Data science; Data mining; Term (time); Information retrieval; Bayesian probability; Psychology; Mathematics; Computer security; Cognitive psychology","score_opus":0.015808690560455342,"score_gpt":0.2420592292557835,"score_spread":0.22625053869532816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387099847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97926563,0.0009719868,0.007332849,0.00036121233,0.00016024755,0.0001235742,0.004763301,0.00037255333,0.006648715],"genre_scores_gemma":[0.9837763,0.00032587248,0.009119447,0.000066053966,0.00009158701,0.000058140846,0.0044768834,0.000031342657,0.0020544112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989741,0.00023540489,0.00010554105,0.00011869762,0.00048639992,0.00007992301],"domain_scores_gemma":[0.99731135,0.00057860475,0.00032516287,0.00008146208,0.0016464747,0.000056976365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008411402,0.00032476263,0.000343712,0.0013266387,0.00021365884,0.000482301,0.00014899312,0.00018529022,0.0007733496],"category_scores_gemma":[0.004366255,0.000091466,0.00039957365,0.0010699781,0.00009223466,0.0003582648,0.00014751994,0.00018361451,0.0005190344],"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":[0.0022488632,0.00035287795,0.27939162,0.0014502363,0.00055662263,0.0012031853,0.0013372172,0.0091105765,0.069029704,0.0017331203,0.058274124,0.57531196],"study_design_scores_gemma":[0.000057776324,0.0005958781,0.6854237,0.00012827214,0.00027958307,0.001262577,0.001345958,0.24451584,0.038610455,0.0009977187,0.02668495,0.000097299846],"about_ca_topic_score_codex":0.004786539,"about_ca_topic_score_gemma":0.005865875,"teacher_disagreement_score":0.004786539,"about_ca_system_score_codex":0.00038220247,"about_ca_system_score_gemma":0.0002706556,"threshold_uncertainty_score":0.009517372},"labels":[],"label_agreement":null},{"id":"W4387588881","doi":"10.54254/2755-2721/1/2022001","title":"A Report on the Modelling, Design and Fabrication of Polymer Modulators","year":2022,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Photonic and Optical Devices","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":"","keywords":"Fabrication; Materials science; Electron-beam lithography; Wafer; Polymer; Photolithography; Silicon; Optoelectronics; Lithography; Astronomical interferometer; Silicon on insulator; Etching (microfabrication); Deposition (geology); Waveguide; Optics; Interferometry; Nanotechnology; Resist; Layer (electronics); Composite material; Physics","score_opus":0.010175154867706026,"score_gpt":0.1791347259382534,"score_spread":0.16895957107054735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387588881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07539658,0.0028913089,0.8985177,0.00017160262,0.000108513144,0.00014315473,0.000460794,0.0016820644,0.020628251],"genre_scores_gemma":[0.5885499,0.007068413,0.3867792,0.000078079705,0.000060197443,0.0003627955,0.0010238929,0.00033830354,0.015739104],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998191,0.000020761003,0.000007952826,0.000024496498,0.000109797,0.000017885704],"domain_scores_gemma":[0.9998845,0.000039643743,0.000020668991,0.000020155028,0.000028674443,0.000006358261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026750576,0.00047659152,0.00041039658,0.00016053265,0.0001942037,0.00045277659,0.00052276545,0.00040780203,0.0015831196],"category_scores_gemma":[0.00039486008,0.0002991274,0.00041648,0.00022200619,0.00018755262,0.00042977367,0.00017619845,0.00038604205,0.00086212956],"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":[0.000108591084,0.000081822014,0.0022077234,0.0010725991,0.00008462891,0.0002677566,0.00016081255,0.41110942,0.46932164,0.015917346,0.002912578,0.096755035],"study_design_scores_gemma":[0.00004431918,0.00042203296,0.0027491557,0.000110612826,0.00010882318,0.00047915685,0.000043373067,0.48840922,0.36403465,0.002958551,0.14058523,0.000054848933],"about_ca_topic_score_codex":0.0006179299,"about_ca_topic_score_gemma":0.00066561665,"teacher_disagreement_score":0.0015831196,"about_ca_system_score_codex":0.00033696406,"about_ca_system_score_gemma":0.0004384521,"threshold_uncertainty_score":0.005296111},"labels":[],"label_agreement":null},{"id":"W4387895505","doi":"10.54254/2755-2721/21/20231143","title":"An overview of big data mining and data privacy protection technologies","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"La Cité Collégiale","funders":"","keywords":"Computer science; Information privacy; Privacy protection; Big data; Data Protection Act 1998; Privacy by Design; Privacy software; Computer security; Personally identifiable information; Association rule learning; Internet privacy; Data anonymization; Data science; Data mining","score_opus":0.14510471718940618,"score_gpt":0.31281759825455346,"score_spread":0.16771288106514728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387895505","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029154825,0.49698007,0.44450197,0.013021814,0.0017435651,0.0005560959,0.0009617726,0.0010168006,0.038302504],"genre_scores_gemma":[0.03851508,0.62848073,0.31010836,0.005499011,0.005832085,0.0009676164,0.0019876156,0.00019375152,0.008415688],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99359727,0.0016469941,0.0007964575,0.0007126233,0.0030004864,0.00024617597],"domain_scores_gemma":[0.99274856,0.004300317,0.0006545466,0.00091459276,0.0011878521,0.00019418652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006124,0.0013178318,0.0013635677,0.0062914426,0.0015355333,0.0057203686,0.00250408,0.0033673327,0.0028929573],"category_scores_gemma":[0.007994867,0.0011931361,0.001899909,0.010872483,0.0018331943,0.009797606,0.0028489802,0.0042113946,0.0020155052],"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":[0.00010217494,0.00017295437,0.0025070182,0.007244277,0.00020204957,0.00060944975,0.0005888448,0.0072785136,0.0025121758,0.25046316,0.037898038,0.69042134],"study_design_scores_gemma":[0.00002037364,0.00015061894,0.0020716614,0.0035988858,0.00012399376,0.0028672204,0.00034863534,0.01709843,0.0040912246,0.19920065,0.77029514,0.0001331768],"about_ca_topic_score_codex":0.0009493424,"about_ca_topic_score_gemma":0.0006289033,"teacher_disagreement_score":0.0062914426,"about_ca_system_score_codex":0.0020245623,"about_ca_system_score_gemma":0.0028305412,"threshold_uncertainty_score":0.032387197},"labels":[],"label_agreement":null},{"id":"W4387896026","doi":"10.54254/2755-2721/19/20231029","title":"Review of Adversarial Attacks in Object Detection","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversarial system; Computer science; Object (grammar); Exploit; Object detection; Computer security; Artificial intelligence; Deep learning; Masking (illustration); Adversarial machine learning; Risk analysis (engineering); Data science; Pattern recognition (psychology); Business","score_opus":0.00712527573519359,"score_gpt":0.23422256478764214,"score_spread":0.22709728905244855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387896026","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007694655,0.9531455,0.02863907,0.002295832,0.0012236766,0.000033719007,0.00007253803,0.00009223183,0.013727924],"genre_scores_gemma":[0.012607652,0.9721395,0.007121809,0.0013740154,0.0022846607,0.000035874087,0.00014761227,0.000039381724,0.004249545],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99904174,0.00022908102,0.000099710545,0.00016742513,0.0003981754,0.00006395689],"domain_scores_gemma":[0.9969035,0.0021803523,0.00019423717,0.00017092822,0.0004897855,0.00006127947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018191237,0.0012959637,0.0010654766,0.0018125975,0.00054760167,0.0016790361,0.0015328323,0.0018702976,0.0035042062],"category_scores_gemma":[0.00402205,0.00066778687,0.00071968045,0.0021007492,0.0012700362,0.0029134178,0.0010133712,0.0023104204,0.0020618946],"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":[0.00007337577,0.00008978654,0.0007328028,0.008328674,0.0001952076,0.00027281546,0.0001476561,0.015912598,0.0016718404,0.0949286,0.060458023,0.81718856],"study_design_scores_gemma":[0.000012330577,0.00019345486,0.0011404692,0.0045600757,0.00013664289,0.0013419774,0.00009404476,0.010034828,0.0021903464,0.04971879,0.9305059,0.00007129402],"about_ca_topic_score_codex":0.00139652,"about_ca_topic_score_gemma":0.0011053028,"teacher_disagreement_score":0.0035042062,"about_ca_system_score_codex":0.0010798989,"about_ca_system_score_gemma":0.0012403481,"threshold_uncertainty_score":0.0117227435},"labels":[],"label_agreement":null},{"id":"W4387896033","doi":"10.54254/2755-2721/18/20230994","title":"The prediction and feature importance analysis of stroke based on the machine learning algorithm","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Acute Ischemic Stroke Management","field":"Medicine","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":"Maple Leaf Foods","funders":"","keywords":"Logistic regression; Computer science; Stroke (engine); Preprocessor; Feature (linguistics); Machine learning; Artificial intelligence; Data pre-processing; Predictive modelling; Engineering","score_opus":0.004614449575472712,"score_gpt":0.19587194882098072,"score_spread":0.191257499245508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387896033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48730215,0.0021741884,0.50455004,0.0011029767,0.00024252487,0.00020807098,0.00092747365,0.00091532816,0.002577274],"genre_scores_gemma":[0.93722314,0.00056331226,0.060392078,0.00006310347,0.00008334379,0.000098219745,0.0008012797,0.000018055687,0.0007575501],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992563,0.00020331994,0.00008702742,0.00014972854,0.00021911977,0.00008452655],"domain_scores_gemma":[0.99801123,0.0012627325,0.00016153992,0.000087362096,0.00043162188,0.000045503024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012791815,0.0005729088,0.0007713748,0.001963581,0.0003022922,0.00076103315,0.0004933402,0.0004997296,0.00076976116],"category_scores_gemma":[0.006272241,0.00015355244,0.0006849216,0.0011998444,0.00018087937,0.000736914,0.00035249643,0.0008416385,0.00026547594],"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":[0.00065736246,0.000735261,0.14405146,0.00023621395,0.00036609088,0.00076033856,0.00016332908,0.23275809,0.0076253633,0.0029724927,0.00585407,0.60382],"study_design_scores_gemma":[0.000012236837,0.000118491735,0.01619337,0.000016325486,0.000038856815,0.00014408132,0.000026228678,0.9799321,0.0017160348,0.0013495792,0.00043758674,0.000015110001],"about_ca_topic_score_codex":0.003565154,"about_ca_topic_score_gemma":0.0021573224,"teacher_disagreement_score":0.003565154,"about_ca_system_score_codex":0.0003550018,"about_ca_system_score_gemma":0.0008237848,"threshold_uncertainty_score":0.00708884},"labels":[],"label_agreement":null},{"id":"W4387896041","doi":"10.54254/2755-2721/15/20230804","title":"Balance of rights in the protection of users' data interests in the era of big data","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Big data; Data Protection Act 1998; Balance (ability); Internet privacy; Order (exchange); Cloud computing; Value (mathematics); Key (lock); Fundamental rights; Computer security; Computer science; Business; Political science; Human rights; Law; Psychology; Data mining","score_opus":0.06035683638239013,"score_gpt":0.26575683791263033,"score_spread":0.20540000153024018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387896041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18847202,0.08254109,0.27956977,0.19032659,0.0014904272,0.0002711536,0.00023331818,0.00014136713,0.25695425],"genre_scores_gemma":[0.9577668,0.013352551,0.018771572,0.0047466857,0.0010559821,0.000104431616,0.000057464982,0.000036661466,0.0041078813],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9723528,0.01503959,0.0012656937,0.0022018373,0.007318933,0.0018212671],"domain_scores_gemma":[0.92183435,0.055906557,0.0073340856,0.007798595,0.00531174,0.0018146607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032250628,0.00041259057,0.0008432994,0.0026068352,0.0034285386,0.012589601,0.0018474408,0.0040974068,0.0025332335],"category_scores_gemma":[0.055962913,0.00047305843,0.00089595956,0.003239456,0.016097149,0.037906546,0.006974269,0.006129037,0.0005041665],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060394443,0.000028072689,0.0026246412,0.0002705179,0.00002479614,0.00022903993,0.0047754366,0.0007994683,0.0006737628,0.9416982,0.0016936668,0.047122043],"study_design_scores_gemma":[0.0000165373,0.0000822217,0.0028224965,0.0008828237,0.00004537752,0.0007031443,0.0062138666,0.0023329374,0.0015784451,0.91839176,0.06687588,0.000054428416],"about_ca_topic_score_codex":0.0010872066,"about_ca_topic_score_gemma":0.00089739496,"teacher_disagreement_score":0.032250628,"about_ca_system_score_codex":0.0035952386,"about_ca_system_score_gemma":0.0046033827,"threshold_uncertainty_score":0.17055964},"labels":[],"label_agreement":null},{"id":"W4387896177","doi":"10.54254/2755-2721/13/20230734","title":"The development and advance of machine translation","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Machine translation; Computer science; Translation (biology); Example-based machine translation; Artificial intelligence; Evaluation of machine translation; Natural language processing; Key (lock); Machine translation software usability; Machine learning; Transfer-based machine translation; Encoder","score_opus":0.006754346068365035,"score_gpt":0.21969552781179624,"score_spread":0.2129411817434312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387896177","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01974192,0.22381434,0.6390841,0.018644169,0.0051847575,0.00033909915,0.0009066892,0.0022060007,0.09007895],"genre_scores_gemma":[0.22472635,0.16929631,0.5574814,0.0047409455,0.007745252,0.0005008559,0.002370638,0.0007549396,0.03238332],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966613,0.0013269503,0.0002580632,0.00060094666,0.0010152631,0.00013751129],"domain_scores_gemma":[0.9944159,0.0025864209,0.00027291264,0.0009900222,0.0015675277,0.00016729145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043648183,0.0008796075,0.00089882227,0.002256695,0.0007772716,0.0029901993,0.0014052197,0.0021495433,0.006454417],"category_scores_gemma":[0.011936817,0.00055874937,0.0008511348,0.003302631,0.0019917968,0.0073409597,0.0023374578,0.0027352064,0.005037215],"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":[0.0001422367,0.0000715544,0.0009264085,0.002111295,0.00005892757,0.00024138491,0.0005491889,0.0053630914,0.009198815,0.22043811,0.01856886,0.7423302],"study_design_scores_gemma":[0.000037038255,0.0003256314,0.0023741801,0.0010243878,0.000064825304,0.0012652305,0.00045256029,0.053881314,0.012769684,0.22332428,0.70436513,0.00011568558],"about_ca_topic_score_codex":0.0013240599,"about_ca_topic_score_gemma":0.0006236212,"teacher_disagreement_score":0.006454417,"about_ca_system_score_codex":0.001456054,"about_ca_system_score_gemma":0.00247749,"threshold_uncertainty_score":0.023083627},"labels":[],"label_agreement":null},{"id":"W4387896262","doi":"10.54254/2755-2721/13/20230728","title":"The comparison of top-down and bottom-up methods in multi-person pose estimation","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Top-down and bottom-up design; Pose; Estimation; Computer science; Focus (optics); Process (computing); Artificial intelligence; Computer vision; Engineering","score_opus":0.03536998904615377,"score_gpt":0.3277623599768753,"score_spread":0.2923923709307215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387896262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01580457,0.0049260603,0.97261626,0.00019616006,0.0004223196,0.00009431558,0.00018594462,0.0012976404,0.0044567827],"genre_scores_gemma":[0.25077504,0.005950449,0.7323619,0.00034718149,0.00033894725,0.00012259664,0.0011441419,0.00039396773,0.00856577],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970193,0.00060721254,0.00015979126,0.0005886484,0.0013871439,0.00023803378],"domain_scores_gemma":[0.9971215,0.0011541978,0.00012597343,0.00037180848,0.0011275233,0.00009890842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021900139,0.0014396008,0.0011008447,0.0020622576,0.000506389,0.0013636377,0.0010572689,0.0011063463,0.004438276],"category_scores_gemma":[0.005460393,0.0004931333,0.0012634372,0.0014001683,0.00038515596,0.0017910738,0.0011429454,0.0008151326,0.0023568259],"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":[0.00036839154,0.00009242256,0.003513536,0.00031501157,0.00023766891,0.00008984209,0.0001312241,0.022473568,0.011545607,0.0016162681,0.0028672654,0.95674926],"study_design_scores_gemma":[0.00006813627,0.00069608376,0.030095765,0.00020655981,0.00046798994,0.0016964407,0.000483133,0.8988468,0.03988633,0.00505365,0.022294149,0.00020505088],"about_ca_topic_score_codex":0.0060882405,"about_ca_topic_score_gemma":0.0063222046,"teacher_disagreement_score":0.0060882405,"about_ca_system_score_codex":0.0002881962,"about_ca_system_score_gemma":0.00073459075,"threshold_uncertainty_score":0.014847517},"labels":[],"label_agreement":null},{"id":"W4387896300","doi":"10.54254/2755-2721/15/20230839","title":"Brain tumour MRI detection and classification based on the convolutional neural network","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Preprocessor; Pattern recognition (psychology); Magnetic resonance imaging; Deep learning; Computer vision; Radiology; Medicine","score_opus":0.023551766489491154,"score_gpt":0.21930357400930175,"score_spread":0.1957518075198106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387896300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26096323,0.0021673667,0.7266562,0.0005106434,0.00018107738,0.00019092872,0.0005396031,0.0030997545,0.005691195],"genre_scores_gemma":[0.85772544,0.001043555,0.1342333,0.00016604984,0.000059155536,0.00008517105,0.001014586,0.00006723252,0.0056053987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977356,0.000023953611,0.000012796195,0.000060286777,0.00008001459,0.000049540253],"domain_scores_gemma":[0.9997482,0.00006538635,0.000040347113,0.000026465937,0.00010365846,0.000015899708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039617275,0.0006719832,0.00041281563,0.0009707415,0.00020337364,0.00039976148,0.0005720355,0.00049396435,0.00071299996],"category_scores_gemma":[0.0008363788,0.00026453857,0.0004907828,0.0005140856,0.0002144573,0.0004739998,0.00035306075,0.00041590727,0.00033490828],"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":[0.00042204402,0.00027792668,0.012156026,0.00016182421,0.00021548336,0.000368506,0.00006968559,0.21273091,0.130004,0.0031021186,0.005513715,0.63497776],"study_design_scores_gemma":[0.000004224452,0.000035551187,0.0036093567,0.000007990665,0.000023900078,0.00010131667,0.000005906016,0.9784615,0.016478855,0.00043852575,0.0008210996,0.000011702716],"about_ca_topic_score_codex":0.015583187,"about_ca_topic_score_gemma":0.017478961,"teacher_disagreement_score":0.015583187,"about_ca_system_score_codex":0.0007966537,"about_ca_system_score_gemma":0.0006715556,"threshold_uncertainty_score":0.030984938},"labels":[],"label_agreement":null},{"id":"W4387896315","doi":"10.54254/2755-2721/13/20230739","title":"Ethnic minorities' mentality and homosexuality psychology in literature: A text emotion analysis with NRC lexicon","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","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":"Lexicon; Ethnic group; Character (mathematics); Linguistics; Natural (archaeology); Homosexuality; Psychology; Pragmatics; Artificial intelligence; Computer science; Sociology; History; Anthropology; Psychoanalysis; Philosophy","score_opus":0.017784160912183836,"score_gpt":0.27706158207172066,"score_spread":0.25927742115953684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387896315","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8779218,0.0011099377,0.076869145,0.0010064198,0.00022710439,0.001099995,0.015788604,0.0037541424,0.022222701],"genre_scores_gemma":[0.88187486,0.00042941753,0.09214602,0.00017015771,0.00006991455,0.0007944188,0.01962863,0.00023854391,0.0046480354],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99954563,0.00012006997,0.00007145637,0.00009858252,0.00012119139,0.000042961634],"domain_scores_gemma":[0.99867046,0.00059091044,0.0001249983,0.00008877225,0.0004594023,0.00006549227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053823006,0.00037771714,0.00025594566,0.0034806232,0.0007435555,0.0012853808,0.00029799252,0.0003467972,0.0031435366],"category_scores_gemma":[0.0028786573,0.00014327544,0.00046764375,0.002144401,0.0004097637,0.0008836603,0.0007687053,0.0003822888,0.0015064598],"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":[0.0009742186,0.00069765607,0.11196635,0.0024253132,0.00021688192,0.0023377945,0.011942695,0.0022401428,0.16399594,0.006619678,0.05227953,0.6443038],"study_design_scores_gemma":[0.00020948537,0.00050801237,0.59090275,0.00061689573,0.00079372094,0.0034984658,0.022651209,0.15570721,0.07106823,0.0069795805,0.14679135,0.00027313447],"about_ca_topic_score_codex":0.011154523,"about_ca_topic_score_gemma":0.015156657,"teacher_disagreement_score":0.011154523,"about_ca_system_score_codex":0.00082832103,"about_ca_system_score_gemma":0.0011248726,"threshold_uncertainty_score":0.022179186},"labels":[],"label_agreement":null},{"id":"W4388416067","doi":"10.54254/2755-2721/23/20230613","title":"Application of nanotechnology in lithium titanate batteries","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advancements in Battery Materials","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":"Context (archaeology); Nanotechnology; Lithium (medication); Materials science; Lithium titanate; Energy storage; Titanate; New energy; Lithium-ion battery; Battery (electricity); Engineering; Mechanical engineering; Ceramic; Composite material; Power (physics); Physics","score_opus":0.005222980663660168,"score_gpt":0.20289203236212425,"score_spread":0.19766905169846408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388416067","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58882415,0.080038406,0.12764983,0.00454296,0.0011999817,0.0002176288,0.0004272817,0.00043946886,0.19666028],"genre_scores_gemma":[0.8985672,0.03260537,0.057641692,0.00029746362,0.00009792364,0.00015245062,0.000135076,0.0000393403,0.010463579],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987864,0.000022017013,0.000005761727,0.00003308393,0.000049848215,0.000010732814],"domain_scores_gemma":[0.99993217,0.000025390053,0.0000072583084,0.000009355026,0.000021883126,0.0000038300577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001495053,0.00019119798,0.00022994874,0.00025131687,0.00044553066,0.0006522652,0.0002192555,0.00040345232,0.0014303528],"category_scores_gemma":[0.0002005145,0.000113825685,0.00020816317,0.00037706847,0.00042743867,0.0007724006,0.0005081468,0.0003202644,0.00030458948],"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":[0.000120081466,0.00017948894,0.0019721156,0.0015496777,0.000036432437,0.00043249613,0.00045852162,0.00876826,0.75872666,0.121999964,0.0018300557,0.10392616],"study_design_scores_gemma":[0.000045704594,0.0007074212,0.004463246,0.00038727533,0.000062566345,0.0010641286,0.00055008393,0.038384933,0.7842245,0.05394337,0.11609004,0.0000767104],"about_ca_topic_score_codex":0.0002810452,"about_ca_topic_score_gemma":0.00039473042,"teacher_disagreement_score":0.0014303528,"about_ca_system_score_codex":0.0004361849,"about_ca_system_score_gemma":0.000346031,"threshold_uncertainty_score":0.004785061},"labels":[],"label_agreement":null},{"id":"W4389482633","doi":"10.54254/2755-2721/27/20230133","title":"Features of realized volatility analysis and return predicting based on LGBM and RNN model","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Volatility (finance); Econometrics; Market liquidity; Computer science; Financial market; Stochastic volatility; Volatility swap; Monte Carlo method; Implied volatility; Economics; Finance; Mathematics; Statistics","score_opus":0.041300035129197187,"score_gpt":0.3281966808681954,"score_spread":0.28689664573899826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389482633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37624502,0.0018085631,0.6125496,0.00052478374,0.00012674455,0.00006299948,0.001302894,0.0018214657,0.0055579753],"genre_scores_gemma":[0.9650436,0.00041646243,0.03146711,0.000056700006,0.000044916964,0.00004836677,0.0009785488,0.000044841465,0.0018994978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976677,0.00003674009,0.000016420032,0.00006055153,0.000080338934,0.00003911261],"domain_scores_gemma":[0.99967694,0.000113910435,0.00005183347,0.00003320336,0.00009935749,0.000024677516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057357043,0.000624105,0.0005286605,0.0011801724,0.00022979114,0.00072638405,0.00063873956,0.0004503772,0.0011270805],"category_scores_gemma":[0.001837841,0.00018485611,0.00048363995,0.0009353123,0.00019396258,0.000872225,0.00046862994,0.00059604866,0.00035755255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003470107,0.00015360228,0.033568967,0.00012569262,0.00014714013,0.00042098443,0.00011660608,0.5204961,0.017274054,0.008948449,0.0048795734,0.4135218],"study_design_scores_gemma":[0.0000028152838,0.000010812731,0.0028766685,0.0000053652775,0.000009589183,0.00003154856,0.000006968293,0.9945462,0.00089813117,0.0013476466,0.00025678275,0.000007614704],"about_ca_topic_score_codex":0.008914509,"about_ca_topic_score_gemma":0.005615942,"teacher_disagreement_score":0.008914509,"about_ca_system_score_codex":0.00044637092,"about_ca_system_score_gemma":0.0005783155,"threshold_uncertainty_score":0.01772523},"labels":[],"label_agreement":null},{"id":"W4389482670","doi":"10.54254/2755-2721/27/20230119","title":"Predicting consumer acceptance of automobiles based on deep learning and traditional machine learning algorithms","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Diverse Interdisciplinary Research Innovations","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Machine learning; Support vector machine; Artificial neural network; Computer science; Random forest; Construct (python library); Deep learning; Online machine learning; Algorithm","score_opus":0.05796686332196537,"score_gpt":0.329352946750497,"score_spread":0.2713860834285316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389482670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9601595,0.00044949155,0.03615696,0.0003940142,0.000032539298,0.000037592345,0.00051695347,0.00015815817,0.002094849],"genre_scores_gemma":[0.9891685,0.00016167724,0.009176999,0.000051757215,0.000018446563,0.000021328422,0.0006619978,0.000006481512,0.00073290773],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941766,0.00015979786,0.0000479802,0.00009791191,0.0001809951,0.000095712305],"domain_scores_gemma":[0.99822885,0.0009772851,0.00020476307,0.00008240183,0.0004300731,0.000076572025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093945814,0.0006308589,0.0003696966,0.0016727996,0.00016797132,0.00058516365,0.0004773965,0.0005645857,0.0010687475],"category_scores_gemma":[0.0030062874,0.00017085666,0.0006741982,0.001003597,0.0002314941,0.00093963405,0.00034334417,0.0006934934,0.00023132774],"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":[0.0007371919,0.0014502838,0.4488109,0.00020975003,0.00032743718,0.00020109165,0.00018439758,0.25464797,0.003710491,0.0026774635,0.0036760797,0.2833669],"study_design_scores_gemma":[0.000009621428,0.00013449536,0.036691654,0.00001314662,0.00002436817,0.000022704233,0.000080733356,0.9605996,0.0011202914,0.0010197286,0.00026975333,0.0000139207605],"about_ca_topic_score_codex":0.011255541,"about_ca_topic_score_gemma":0.012949324,"teacher_disagreement_score":0.011255541,"about_ca_system_score_codex":0.0006812964,"about_ca_system_score_gemma":0.0004052472,"threshold_uncertainty_score":0.022380054},"labels":[],"label_agreement":null},{"id":"W4389895081","doi":"10.54254/2755-2721/27/ojs/20230119","title":"NA","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Embodied and Extended Cognition","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Chemistry","score_opus":0.017993656396421035,"score_gpt":0.22105624545874378,"score_spread":0.20306258906232275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389895081","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014106382,0.0018710479,0.0037424562,0.008310123,0.005848321,0.00013418755,0.0023772442,0.0008699488,0.975436],"genre_scores_gemma":[0.01074607,0.0014832122,0.0023041202,0.0032079683,0.0009715962,0.000116922965,0.0016199395,0.00040670816,0.9791434],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982974,0.00021754073,0.0001361082,0.00044762,0.0007046273,0.00019664865],"domain_scores_gemma":[0.99581856,0.00028320352,0.0002535995,0.0006656188,0.0024452386,0.0005338369],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018687317,0.0005167309,0.0005815147,0.0015410725,0.0018808104,0.006884391,0.001420468,0.0016476254,0.536318],"category_scores_gemma":[0.006711002,0.0003506931,0.0004784418,0.0013776263,0.0012650298,0.003760371,0.003250932,0.0018900981,0.5093042],"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":[0.00009834969,0.00007417201,0.0013341306,0.0004034291,0.000019663823,0.00018873175,0.00040366326,0.00011643405,0.001994831,0.14079332,0.6171236,0.23744966],"study_design_scores_gemma":[0.0000027559315,0.0000033537171,0.00024381625,0.000048397887,0.0000025800377,0.00008692823,0.000055005,0.00003478935,0.00019293191,0.0024992558,0.99682546,0.0000047247972],"about_ca_topic_score_codex":0.004148936,"about_ca_topic_score_gemma":0.005389138,"teacher_disagreement_score":0.463682,"about_ca_system_score_codex":0.0026368531,"about_ca_system_score_gemma":0.004696221,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4391357281","doi":"10.54254/2755-2721/31/20230160","title":"Analysis of concrete architecture of Bitcoin Core","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Architecture; Cryptocurrency; Core (optical fiber); Computer security; Blockchain; Robustness (evolution); Telecommunications","score_opus":0.005651434332423655,"score_gpt":0.20629070021220547,"score_spread":0.20063926587978181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391357281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45326504,0.0010549189,0.44042018,0.0012665704,0.000044438948,0.00034408175,0.00025350184,0.0004280058,0.10292326],"genre_scores_gemma":[0.94315296,0.00062559784,0.049105838,0.000042488286,0.000012715998,0.000117495285,0.00015402956,0.00005372682,0.00673514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995484,0.00010415088,0.000023187667,0.00007421978,0.00015661582,0.00009341583],"domain_scores_gemma":[0.99900335,0.00029138837,0.00014331419,0.00018702036,0.00028773968,0.000087213004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006656398,0.00031389765,0.00026413734,0.0009720386,0.0010960329,0.002274485,0.00070971774,0.0009127903,0.005178681],"category_scores_gemma":[0.0023296787,0.00036094312,0.00040990475,0.0008597927,0.001888777,0.003046275,0.0010917713,0.0009421486,0.00067189033],"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":[0.000044276087,0.0000359259,0.0040749684,0.0001313397,0.000013729611,0.0005182121,0.00076452445,0.07876261,0.0094868215,0.8927588,0.0006699937,0.01273888],"study_design_scores_gemma":[0.000017611494,0.00008313073,0.005476829,0.0001291632,0.00003648104,0.0005949894,0.0010072225,0.5369726,0.006864627,0.4282284,0.020543383,0.000045608653],"about_ca_topic_score_codex":0.0035946185,"about_ca_topic_score_gemma":0.0035845253,"teacher_disagreement_score":0.005178681,"about_ca_system_score_codex":0.0016939653,"about_ca_system_score_gemma":0.001911158,"threshold_uncertainty_score":0.017324448},"labels":[],"label_agreement":null},{"id":"W4391477578","doi":"10.54254/2755-2721/34/20230301","title":"Research on real-time fire detection and locating for automotive firefighting robot in factories based on Convolutional Neural Network","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Fire Detection and Safety Systems","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":"Firefighting; Convolutional neural network; Automotive industry; Robot; Computer science; Artificial intelligence; Computer vision; Patrolling; RGB color model; Factory (object-oriented programming); Fire detection; Engineering; Architectural engineering","score_opus":0.015205076487066222,"score_gpt":0.24608612909627223,"score_spread":0.230881052609206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391477578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2860166,0.0033006726,0.70184,0.000412552,0.00017337289,0.00007010705,0.00012093271,0.0017538603,0.0063119526],"genre_scores_gemma":[0.9214347,0.001714428,0.071155354,0.00009222292,0.00003610744,0.000035349003,0.00017311928,0.000034146833,0.0053245225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998747,0.000009470192,0.000007179864,0.000043226923,0.00003758623,0.000027921807],"domain_scores_gemma":[0.9998628,0.00003119247,0.000019682566,0.000013373637,0.000062247156,0.000010680752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002455024,0.0005301238,0.00029949838,0.00036952813,0.00021323188,0.00040243304,0.00069657515,0.00044299016,0.0007184781],"category_scores_gemma":[0.00037705607,0.00022694381,0.0004113202,0.0003630802,0.00023631351,0.00062486826,0.00018974167,0.0004365917,0.00014281482],"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":[0.0003548942,0.00024047043,0.009372835,0.00028130348,0.00020902965,0.0002461932,0.00013696673,0.34961045,0.09801066,0.0041547483,0.0023676339,0.5350147],"study_design_scores_gemma":[0.0000055926894,0.00008337983,0.0022455028,0.000006752256,0.000036668258,0.000040177296,0.000015979535,0.98047423,0.015745042,0.00034414695,0.0009939928,0.0000084623725],"about_ca_topic_score_codex":0.019074826,"about_ca_topic_score_gemma":0.013742638,"teacher_disagreement_score":0.019074826,"about_ca_system_score_codex":0.00067313924,"about_ca_system_score_gemma":0.0007768373,"threshold_uncertainty_score":0.037927628},"labels":[],"label_agreement":null},{"id":"W4391483815","doi":"10.54254/2755-2721/34/20230297","title":"FPGA accelerator for wireless AR/VR display","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Analog and Mixed-Signal Circuit Design","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; Headset; Virtual reality; Frame rate; Wireless; Computer hardware; Field-programmable gate array; Bandwidth (computing); Augmented reality; Latency (audio); Computer graphics (images); Artificial intelligence; Computer network; Telecommunications","score_opus":0.007686264187866796,"score_gpt":0.19356285176139384,"score_spread":0.18587658757352704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391483815","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17711312,0.0026990767,0.7077216,0.0012759528,0.0012376516,0.00065719144,0.0026371374,0.032978825,0.07367938],"genre_scores_gemma":[0.8478588,0.0005687522,0.09961875,0.0005411755,0.0000997583,0.0002805234,0.0012658654,0.0004015769,0.04936478],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997893,0.000020461652,0.000014172034,0.00003498355,0.00010035308,0.000040692896],"domain_scores_gemma":[0.99979526,0.000032773813,0.000032412252,0.000028582417,0.00009047974,0.000020539705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014554747,0.00046288455,0.00026632877,0.0004144807,0.0001753171,0.00040480145,0.0008180825,0.0002832124,0.02145181],"category_scores_gemma":[0.00043375636,0.00017681695,0.00018546681,0.00033117726,0.00009089717,0.00046041122,0.00026273634,0.00046661604,0.003954761],"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":[0.0018481718,0.0003214575,0.008012608,0.0016052617,0.00017473735,0.0013025209,0.00039758487,0.028466906,0.468902,0.018632794,0.090553135,0.3797828],"study_design_scores_gemma":[0.00038935817,0.0025405278,0.009070536,0.00018470283,0.00018525394,0.002230615,0.00014672469,0.30455142,0.40615764,0.0022194672,0.27217263,0.00015112835],"about_ca_topic_score_codex":0.0009785057,"about_ca_topic_score_gemma":0.001077067,"teacher_disagreement_score":0.02145181,"about_ca_system_score_codex":0.0004070173,"about_ca_system_score_gemma":0.00048277611,"threshold_uncertainty_score":0.071763396},"labels":[],"label_agreement":null},{"id":"W4391505669","doi":"10.54254/2755-2721/33/20230273","title":"Human-centric artificial intelligence","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sociology and Cultural Identity Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Productivity; Computer science; Automation; Human intelligence; Deep learning; Artificial general intelligence; Independence (probability theory); Robot; Data science; Engineering","score_opus":0.030290360518644064,"score_gpt":0.30378110833635114,"score_spread":0.27349074781770705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391505669","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009407704,0.051281724,0.045559067,0.12410673,0.00830768,0.00008183783,0.00015566272,0.00026376013,0.76083577],"genre_scores_gemma":[0.63596964,0.047137417,0.026801376,0.033603445,0.010623076,0.00024408399,0.00037509197,0.00025719774,0.24498871],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99811953,0.00093338126,0.000063851614,0.00020933703,0.00054619403,0.00012771461],"domain_scores_gemma":[0.99774784,0.0012683324,0.00015343398,0.00040693858,0.00025826617,0.00016522968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020311621,0.00049840024,0.00035233464,0.00087089755,0.0011737109,0.0066835284,0.0008006239,0.001999395,0.005257679],"category_scores_gemma":[0.00423036,0.00020087294,0.0003881436,0.0009405714,0.009090782,0.0045403778,0.0019036849,0.0027161373,0.0014747011],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011356557,0.00002019604,0.0003267203,0.00015144848,0.000018188512,0.000047353027,0.00090755866,0.0008148864,0.00018957962,0.9117062,0.055351477,0.030454975],"study_design_scores_gemma":[0.0000060051716,0.00002672387,0.0005298507,0.00021435392,0.000008347229,0.00009744126,0.00063320826,0.0012641306,0.0002503856,0.47728884,0.519672,0.000008766904],"about_ca_topic_score_codex":0.0012588773,"about_ca_topic_score_gemma":0.0015447012,"teacher_disagreement_score":0.0066835284,"about_ca_system_score_codex":0.002949298,"about_ca_system_score_gemma":0.0023167315,"threshold_uncertainty_score":0.021398723},"labels":[],"label_agreement":null},{"id":"W4391572494","doi":"10.54254/2755-2721/37/20230503","title":"Parallel implementation of Wiener's attack on RSA: Algorithm design and performance evaluation","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Cryptographic Implementations and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cryptosystem; Public-key cryptography; Robustness (evolution); Computer science; Algorithm; Thread (computing); Key (lock); Laptop; Adversary; Key generation; Mathematics; Theoretical computer science; Cryptography; Computer security; Encryption; Operating system","score_opus":0.02736191178095903,"score_gpt":0.29259532597155047,"score_spread":0.26523341419059143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391572494","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6401974,0.0006212732,0.3440982,0.00029767578,0.00010802511,0.00044974333,0.00017163214,0.0046765124,0.009379537],"genre_scores_gemma":[0.7851597,0.00026837236,0.21174942,0.000037990147,0.000024127563,0.00021424788,0.00018450907,0.00015798012,0.0022035995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843115,0.00029920996,0.00012871229,0.00023233262,0.00066875335,0.00023988145],"domain_scores_gemma":[0.9968443,0.0011057623,0.00035013264,0.00078280095,0.0008083571,0.00010876571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016325737,0.0007816565,0.00087573304,0.000839236,0.000590133,0.0008072412,0.0011649249,0.00065316906,0.0018527014],"category_scores_gemma":[0.0045816605,0.00037383626,0.00043931557,0.0009814444,0.0006991799,0.0014034477,0.00079216796,0.0007326681,0.00055520626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00425069,0.0010730517,0.008420497,0.000454059,0.00024967708,0.00038781945,0.00038459158,0.54400975,0.107100815,0.017687969,0.0035139685,0.3124671],"study_design_scores_gemma":[0.00020870766,0.00074231904,0.0009396285,0.000012150584,0.000043348027,0.00019663965,0.00004682732,0.93728155,0.056858834,0.002104651,0.0015405695,0.000024803529],"about_ca_topic_score_codex":0.0023286154,"about_ca_topic_score_gemma":0.0014154804,"teacher_disagreement_score":0.0023286154,"about_ca_system_score_codex":0.0011448277,"about_ca_system_score_gemma":0.0018744423,"threshold_uncertainty_score":0.008633971},"labels":[],"label_agreement":null},{"id":"W4391572539","doi":"10.54254/2755-2721/38/20230559","title":"Exploration of movie evaluation analysis and data preprocessing impact based on RNN technology","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Punctuation; Recurrent neural network; Preprocessor; Artificial intelligence; Word embedding; Lexical analysis; Machine learning; Data pre-processing; Natural language processing; Artificial neural network; Embedding","score_opus":0.03480916320377801,"score_gpt":0.32080088080215646,"score_spread":0.28599171759837844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391572539","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.547441,0.0018061243,0.4330051,0.0010948855,0.00010471829,0.00042781717,0.0010552743,0.002411764,0.012653211],"genre_scores_gemma":[0.85190403,0.0005748391,0.14437905,0.000060644536,0.000033227327,0.00013411278,0.0010269118,0.0001289172,0.001758216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899286,0.00045737065,0.00005664233,0.0001491749,0.00028467938,0.000059242342],"domain_scores_gemma":[0.99810493,0.0011092951,0.0001220948,0.000109364315,0.0005090599,0.000045291337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00199572,0.0008451194,0.00040469534,0.0014425259,0.00023722395,0.0010651721,0.0004187392,0.00027093652,0.0013638864],"category_scores_gemma":[0.00652372,0.00018094612,0.00051380316,0.0009234899,0.00016869635,0.0015506883,0.00035531446,0.000556328,0.0003624634],"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":[0.0009847961,0.00036077623,0.03214989,0.00082779,0.00025331596,0.0005361031,0.00057477306,0.079539984,0.057401236,0.006255621,0.004638009,0.8164777],"study_design_scores_gemma":[0.000017342794,0.00021884317,0.019053549,0.000045973495,0.000065400985,0.00010984864,0.00030854152,0.9547095,0.020184368,0.0026734392,0.002586515,0.000026776492],"about_ca_topic_score_codex":0.0047508962,"about_ca_topic_score_gemma":0.006899188,"teacher_disagreement_score":0.0047508962,"about_ca_system_score_codex":0.00075173035,"about_ca_system_score_gemma":0.00050780404,"threshold_uncertainty_score":0.010554552},"labels":[],"label_agreement":null},{"id":"W4391580152","doi":"10.54254/2755-2721/38/20230534","title":"Dense-connected Stacked Hourglass Networks for Human Pose Estimation","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Hourglass; Pose; Computer science; Artificial intelligence; Feature (linguistics); Architecture; Computer vision; Network architecture; Geography; Computer network","score_opus":0.00867375794573755,"score_gpt":0.22578530807020253,"score_spread":0.21711155012446498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391580152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03457938,0.00095031894,0.95951647,0.00026541372,0.00016760726,0.00006252582,0.0005524139,0.0017839717,0.0021219503],"genre_scores_gemma":[0.7777253,0.0012926053,0.20730068,0.00027673854,0.00027361567,0.0001782444,0.0026622938,0.00031544582,0.0099751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967444,0.00008162045,0.000009715837,0.0001173247,0.00005909457,0.00005772917],"domain_scores_gemma":[0.99943393,0.00021186381,0.000064358785,0.00012811046,0.00012616554,0.00003553491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005248589,0.0013788437,0.00073434715,0.0008205539,0.00037698544,0.0007679833,0.0015683501,0.0008831287,0.0037180327],"category_scores_gemma":[0.0017761354,0.00068613805,0.00078184163,0.00095032156,0.0005876057,0.0015407313,0.0008668842,0.0010956847,0.0010942651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000296047,0.000072708266,0.001475888,0.000096708194,0.00018336088,0.000102377766,0.00010222421,0.78029597,0.0058235354,0.006108175,0.0040816474,0.20136128],"study_design_scores_gemma":[0.000003155528,0.000023614903,0.00056585186,0.000009028622,0.000016092648,0.000018894749,0.000015927295,0.9923254,0.0013588511,0.0047160517,0.0009389508,0.000008241958],"about_ca_topic_score_codex":0.018214349,"about_ca_topic_score_gemma":0.02155306,"teacher_disagreement_score":0.018214349,"about_ca_system_score_codex":0.00092985266,"about_ca_system_score_gemma":0.00057030754,"threshold_uncertainty_score":0.036216676},"labels":[],"label_agreement":null},{"id":"W4391580154","doi":"10.54254/2755-2721/36/20230442","title":"A parallel Breadth-First Search using shared memory level-synchronization","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Speedup; Graph traversal; Parallel computing; Graph; Tree traversal; Benchmark (surveying); Overhead (engineering); Multiprocessing; Distributed memory; Relation (database); Breadth-first search; Distributed computing; Shared memory; Theoretical computer science; Algorithm; Data mining","score_opus":0.019793538495116104,"score_gpt":0.2238018173652416,"score_spread":0.2040082788701255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391580154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04371993,0.00041096882,0.94710076,0.00030067234,0.000050993083,0.00011892338,0.00020440426,0.00167642,0.0064169136],"genre_scores_gemma":[0.2857211,0.00022162331,0.7089827,0.00011760742,0.00002263159,0.00017172743,0.0003463822,0.00023258795,0.0041837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963856,0.00007258064,0.000021602393,0.000089350324,0.00011762611,0.000060291168],"domain_scores_gemma":[0.99948764,0.00021681839,0.000044628407,0.00010711824,0.000107266475,0.000036485733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004831241,0.00046496702,0.0005906902,0.0008005568,0.0007542152,0.00090492127,0.0014730814,0.0007463052,0.0038449466],"category_scores_gemma":[0.0015459779,0.0003334288,0.0005187525,0.0012784104,0.00055789045,0.0012553163,0.0011494582,0.0005357152,0.000759991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050723774,0.00026765684,0.0030443827,0.0005171094,0.00014035976,0.0003497306,0.00044278463,0.58424515,0.040124696,0.072503954,0.011070482,0.28678647],"study_design_scores_gemma":[0.00008650407,0.000119711134,0.00025361485,0.000018786268,0.00002124425,0.0001041157,0.000058432754,0.9684839,0.005939957,0.02038799,0.004510991,0.000014742375],"about_ca_topic_score_codex":0.005544346,"about_ca_topic_score_gemma":0.009625025,"teacher_disagreement_score":0.005544346,"about_ca_system_score_codex":0.0008482323,"about_ca_system_score_gemma":0.0022835298,"threshold_uncertainty_score":0.012862623},"labels":[],"label_agreement":null},{"id":"W4392014762","doi":"10.54254/2755-2721/41/20230759","title":"Cryogenic survival: Analysis and development of 'lost with frost' a 2D Python-based RPG game","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Lawrence College","funders":"","keywords":"Game design; Computer science; Adventure; Video game development; Game art design; Game Developer; Adversary; Game design document; Multimedia; Artificial intelligence; Computer security","score_opus":0.007714221398816967,"score_gpt":0.20783349383848998,"score_spread":0.200119272439673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392014762","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75242704,0.00029734775,0.19371329,0.0005697891,0.0002316354,0.0014930362,0.0023282773,0.0059435996,0.04299599],"genre_scores_gemma":[0.7686857,0.00026316472,0.20527847,0.00024734842,0.000015804451,0.0011371053,0.003589372,0.0012938211,0.019489182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994123,0.00018380598,0.000024330315,0.0000801377,0.00020098084,0.00009844151],"domain_scores_gemma":[0.99920577,0.00036446948,0.000042886,0.00006756657,0.0001617993,0.00015752396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010045556,0.00089399784,0.0002894535,0.000548039,0.00043518894,0.0015418835,0.0015760402,0.0005451667,0.0044905264],"category_scores_gemma":[0.00311465,0.00027029277,0.0004445045,0.00015983982,0.0008594512,0.000811975,0.001651813,0.0006950758,0.0009989723],"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":[0.002854834,0.0034757245,0.04932412,0.0031955186,0.00031260288,0.006609619,0.0232075,0.26546195,0.14303042,0.054898877,0.07750641,0.3701224],"study_design_scores_gemma":[0.00033502586,0.0029398145,0.038783494,0.0005672656,0.00014683843,0.00248383,0.00688235,0.7011336,0.057805218,0.012194783,0.17640191,0.0003259232],"about_ca_topic_score_codex":0.003934071,"about_ca_topic_score_gemma":0.0077402173,"teacher_disagreement_score":0.0044905264,"about_ca_system_score_codex":0.0006755267,"about_ca_system_score_gemma":0.0007612616,"threshold_uncertainty_score":0.015022278},"labels":[],"label_agreement":null},{"id":"W4392107315","doi":"10.54254/2755-2721/43/20230829","title":"Gameful interaction: How principles of game design can be applied to enhance user experiences in non-game applications","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Educational Games and Gamification","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Variety (cybernetics); Computer science; Scope (computer science); Game design; Human–computer interaction; Focus (optics); User experience design; Multimedia; Artificial intelligence","score_opus":0.023467359971420916,"score_gpt":0.30197703775604406,"score_spread":0.27850967778462316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392107315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016754655,0.0025888209,0.92492366,0.003614978,0.00028995145,0.0009423222,0.000069459784,0.0008398605,0.04997627],"genre_scores_gemma":[0.25234425,0.0037397454,0.7286527,0.0013851232,0.00009074621,0.0020727895,0.000100884,0.00042249472,0.011191207],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966486,0.0021010546,0.00014794155,0.0003176247,0.00060084555,0.00018392074],"domain_scores_gemma":[0.99631786,0.0028309408,0.00011559669,0.00034889005,0.00022904195,0.00015771292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053479513,0.0015931057,0.0005974663,0.0019272555,0.0013337574,0.0075014476,0.0022346876,0.001973071,0.00426956],"category_scores_gemma":[0.0106833,0.00077498506,0.0011156698,0.0008178989,0.008097092,0.0065118815,0.005582892,0.0025091472,0.0010400732],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001675572,0.00037297627,0.0024779355,0.00288229,0.0001368577,0.00058463134,0.028120596,0.014222678,0.014273902,0.7168395,0.008567621,0.21135348],"study_design_scores_gemma":[0.00013799194,0.0007191205,0.0024110684,0.0025178082,0.0001236236,0.0017585618,0.0068383478,0.04457352,0.009993411,0.64499,0.28572667,0.00020986047],"about_ca_topic_score_codex":0.0016029258,"about_ca_topic_score_gemma":0.002345462,"teacher_disagreement_score":0.0075014476,"about_ca_system_score_codex":0.0011853329,"about_ca_system_score_gemma":0.001443167,"threshold_uncertainty_score":0.028283},"labels":[],"label_agreement":null},{"id":"W4392135525","doi":"10.54254/2755-2721/42/20230776","title":"Event shape engineering via Glauber MC model","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"High-Energy Particle Collisions Research","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Glauber; Event (particle physics); Anisotropy; Plot (graphics); Yield (engineering); Physics; Particle physics; Plasma; Statistical physics; Geometry; Mathematics; Nuclear physics; Statistics; Optics; Astrophysics; Thermodynamics","score_opus":0.006337435640876099,"score_gpt":0.23115703131646378,"score_spread":0.22481959567558768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392135525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48996308,0.0019171488,0.38629052,0.0021377725,0.0004774835,0.0004626583,0.0042879037,0.0024223237,0.11204116],"genre_scores_gemma":[0.93863255,0.0004276756,0.05142856,0.00050494634,0.00011971448,0.00041438304,0.0012184815,0.0005979645,0.0066557224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951947,0.00015551885,0.000013295073,0.0000640792,0.00013956503,0.000108091524],"domain_scores_gemma":[0.9987149,0.00064406364,0.00014838405,0.00014029547,0.00023212133,0.00012028638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008939442,0.000762514,0.0010259892,0.00093165104,0.0012649195,0.0016154827,0.0027881174,0.0016653951,0.0065191444],"category_scores_gemma":[0.0030951486,0.0005245241,0.0010931513,0.0010263328,0.00094571227,0.0011211669,0.0010625336,0.0013506975,0.0007757701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007297353,0.0000393364,0.0013962346,0.00004719445,0.00002761181,0.00024084104,0.00006301376,0.9558715,0.0006750362,0.038272176,0.0015384074,0.0017556292],"study_design_scores_gemma":[0.000012964076,0.000007841379,0.00012342322,0.000004549517,0.000006267762,0.000019365,0.000011213846,0.99362695,0.00016773539,0.00545488,0.00055745116,0.000007463082],"about_ca_topic_score_codex":0.018888628,"about_ca_topic_score_gemma":0.011540241,"teacher_disagreement_score":0.018888628,"about_ca_system_score_codex":0.0015315047,"about_ca_system_score_gemma":0.0016232522,"threshold_uncertainty_score":0.037557364},"labels":[],"label_agreement":null},{"id":"W4392370758","doi":"10.54254/2755-2721/44/20230050","title":"Achieving fairness in team-based FPS games: A skill-based matchmaking solution","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Outcome (game theory); Process (computing); Fairness measure; Measure (data warehouse); Degree (music); Human–computer interaction; Data mining; Programming language; Operating system","score_opus":0.009748049752763676,"score_gpt":0.23703693600915074,"score_spread":0.22728888625638707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392370758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060457096,0.00011349469,0.9234203,0.0004032989,0.000114113114,0.0003777853,0.00007251962,0.00042551523,0.014615822],"genre_scores_gemma":[0.6273531,0.00011767895,0.36472803,0.00016729029,0.00006156261,0.00039003193,0.00012397695,0.000085534826,0.006972843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975859,0.00088352413,0.00011755407,0.00039792992,0.00062066835,0.00039435265],"domain_scores_gemma":[0.9985415,0.00049688306,0.0001699211,0.0001782168,0.0003039215,0.00030971225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025756864,0.0011807999,0.000871923,0.0009126216,0.00142143,0.0017693427,0.0019026452,0.001229301,0.0040537757],"category_scores_gemma":[0.005748802,0.00028739418,0.00077990734,0.0005523249,0.0009128381,0.001814912,0.003686264,0.0014602544,0.0006765414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000905455,0.001221036,0.0064155166,0.00044152577,0.0002227267,0.0004812574,0.0020041878,0.38463458,0.015743118,0.21853887,0.0077207237,0.361671],"study_design_scores_gemma":[0.000101720565,0.000416536,0.0015997628,0.00006584971,0.000068935115,0.0002177699,0.000536256,0.87143415,0.0043931366,0.11329533,0.007818673,0.000051979652],"about_ca_topic_score_codex":0.0034954636,"about_ca_topic_score_gemma":0.0033033846,"teacher_disagreement_score":0.0040537757,"about_ca_system_score_codex":0.0011147854,"about_ca_system_score_gemma":0.0020290243,"threshold_uncertainty_score":0.013621688},"labels":[],"label_agreement":null},{"id":"W4392370839","doi":"10.54254/2755-2721/44/20230078","title":"Predictive model on detecting ChatGPT responses against human responses","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","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":"Natural language processing; Computer science; Psychology; Artificial intelligence","score_opus":0.07326105859028954,"score_gpt":0.3644849911620548,"score_spread":0.29122393257176526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392370839","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78588307,0.0011121699,0.19854923,0.0022097046,0.0003449629,0.00037900766,0.0038019456,0.0025291226,0.0051907543],"genre_scores_gemma":[0.98481154,0.00012511514,0.01082708,0.00020694283,0.00011213761,0.00011975481,0.0019634299,0.000046640555,0.0017873603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976235,0.0011256175,0.00011556332,0.00065290445,0.00029583194,0.00018649419],"domain_scores_gemma":[0.969535,0.026188053,0.0013156938,0.00090285856,0.0015661069,0.0004922786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060422933,0.0013058287,0.0008573626,0.002266609,0.00047346175,0.0013017512,0.0013410533,0.0015732133,0.0028510278],"category_scores_gemma":[0.022049624,0.00028496017,0.00055080873,0.0009136708,0.0007303064,0.0011992725,0.0010557882,0.0023009498,0.0016027289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031632052,0.0016168541,0.2204592,0.0005982397,0.0005848139,0.0014336485,0.0014718494,0.4360647,0.009972613,0.0070246845,0.019284725,0.29832554],"study_design_scores_gemma":[0.000023565064,0.00011463997,0.006899485,0.000025504512,0.00003305446,0.00014617816,0.00008504585,0.9888421,0.0009975127,0.002411627,0.00040445238,0.000016827818],"about_ca_topic_score_codex":0.006287232,"about_ca_topic_score_gemma":0.0038666332,"teacher_disagreement_score":0.006287232,"about_ca_system_score_codex":0.0007407841,"about_ca_system_score_gemma":0.00082891714,"threshold_uncertainty_score":0.031955063},"labels":[],"label_agreement":null},{"id":"W4392370875","doi":"10.54254/2755-2721/44/20230093","title":"Applying self-attention model to learn both Empirical Risk Minimization and Invariant Risk Minimization for multimedia recommendation","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Recommender system; Computer science; Invariant (physics); Minification; Preference; Empirical risk minimization; Machine learning; Artificial intelligence; Empirical research; World Wide Web; Mathematics; Statistics","score_opus":0.017324423018050164,"score_gpt":0.253662561341842,"score_spread":0.23633813832379186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392370875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038885146,0.0006494414,0.9580633,0.00034142114,0.000034451772,0.000042951473,0.00006056632,0.00051047356,0.0014123328],"genre_scores_gemma":[0.78147787,0.00053203537,0.21063595,0.00045281483,0.00014887414,0.00014327494,0.0003933193,0.00013111775,0.0060847215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991078,0.0003254511,0.00005153472,0.00025236054,0.00018626968,0.00007650319],"domain_scores_gemma":[0.998387,0.0008621057,0.00015125089,0.00023565952,0.000291004,0.00007303365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019649663,0.00085423084,0.0011357989,0.0006666769,0.00027775145,0.0007210603,0.0015728972,0.0012192085,0.0013613235],"category_scores_gemma":[0.0051817056,0.0005128762,0.0009446695,0.0006419824,0.00055438373,0.0016531921,0.000981998,0.0018039863,0.0004659655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015787434,0.0002508045,0.0050696214,0.00012164289,0.00021074004,0.00012048041,0.00015191911,0.7588076,0.004449994,0.019099787,0.0037021446,0.20785746],"study_design_scores_gemma":[0.0000040859004,0.00002390007,0.00021693371,0.0000029399398,0.000008252038,0.0000115312705,0.000002969187,0.9972174,0.00024765774,0.0021026907,0.00015706333,0.0000045774227],"about_ca_topic_score_codex":0.009212255,"about_ca_topic_score_gemma":0.010320128,"teacher_disagreement_score":0.009212255,"about_ca_system_score_codex":0.0009383511,"about_ca_system_score_gemma":0.0007202489,"threshold_uncertainty_score":0.018317282},"labels":[],"label_agreement":null},{"id":"W4392370905","doi":"10.54254/2755-2721/44/20230280","title":"Parkinson’s disease diagnosis through electroencephalographic signal processing and neural network classification","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Electroencephalography; Parkinson's disease; Disease; Artificial neural network; Computer science; MATLAB; Artificial intelligence; Pattern recognition (psychology); Machine learning; Medicine; Psychology; Neuroscience; Pathology","score_opus":0.01709484707471265,"score_gpt":0.2351626795758444,"score_spread":0.21806783250113176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392370905","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6684302,0.0044005727,0.31150326,0.0010225605,0.00041404914,0.00038488721,0.003527785,0.0023084867,0.008008263],"genre_scores_gemma":[0.9357166,0.0012948582,0.056974072,0.00009316756,0.00009528742,0.00013109007,0.0030024822,0.000023936218,0.0026684834],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996729,0.000060776205,0.000043344255,0.000098838806,0.000088925786,0.000035181278],"domain_scores_gemma":[0.99971,0.000106991574,0.00003896044,0.000028422117,0.0001039976,0.000011508975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003896488,0.00074535393,0.000459263,0.0012858044,0.00020320888,0.0005952791,0.00031889122,0.0005612037,0.00087653904],"category_scores_gemma":[0.0013111482,0.00013875807,0.00045074054,0.0007630996,0.00014989913,0.00046260867,0.00030056966,0.0004744032,0.00043161318],"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":[0.00059093075,0.0006022755,0.03475665,0.0002482577,0.00027962695,0.00069432775,0.00009015702,0.1017369,0.017695343,0.00065784727,0.007170143,0.8354775],"study_design_scores_gemma":[0.000018261282,0.0001552197,0.027049191,0.000042213473,0.00006561462,0.00032550358,0.00005881522,0.9631684,0.006192263,0.0011458982,0.0017595818,0.000019049035],"about_ca_topic_score_codex":0.0055749984,"about_ca_topic_score_gemma":0.0048215347,"teacher_disagreement_score":0.0055749984,"about_ca_system_score_codex":0.0003410007,"about_ca_system_score_gemma":0.00026283605,"threshold_uncertainty_score":0.011085093},"labels":[],"label_agreement":null},{"id":"W4392373677","doi":"10.54254/2755-2721/44/20230249","title":"Distributionally Robust Optimization methods on robust medical diagnosis systems","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robust optimization; Computer science; Robustness (evolution); Outlier; Machine learning; Artificial intelligence; Domain (mathematical analysis); Partition (number theory); Optimization problem; Data mining; Mathematical optimization; Algorithm; Mathematics","score_opus":0.047100498649426246,"score_gpt":0.3401294191023163,"score_spread":0.2930289204528901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392373677","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004416805,0.00034247182,0.99395484,0.0003695579,0.000026370602,0.000023393024,0.000049835915,0.00017831581,0.0006383165],"genre_scores_gemma":[0.57704294,0.0011596461,0.4149385,0.0006982638,0.00029314938,0.0002394862,0.0005846447,0.00030317722,0.0047401334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979292,0.000902386,0.0001594353,0.00047692328,0.0004055609,0.00012652065],"domain_scores_gemma":[0.9956052,0.0028698754,0.0004559718,0.0003423161,0.0005927827,0.00013375447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046346406,0.0014017252,0.0015724547,0.0011171683,0.00045744213,0.0014080395,0.0013976217,0.0016469557,0.0018890233],"category_scores_gemma":[0.012914392,0.00071447657,0.0011441697,0.0009390885,0.0014790489,0.00154478,0.002052334,0.0023469375,0.00051366485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044372915,0.000030576564,0.0006078809,0.00005782955,0.00006147159,0.000046303318,0.00003110256,0.9473485,0.0008388764,0.018187517,0.0008259921,0.031919636],"study_design_scores_gemma":[0.0000033263516,0.000011432933,0.00007815809,0.000004756062,0.0000034599009,0.000010734523,0.0000031244963,0.9927458,0.00020717153,0.006703405,0.00022363356,0.0000050818026],"about_ca_topic_score_codex":0.0041305968,"about_ca_topic_score_gemma":0.0028225055,"teacher_disagreement_score":0.0046346406,"about_ca_system_score_codex":0.0013428823,"about_ca_system_score_gemma":0.0012525516,"threshold_uncertainty_score":0.024510562},"labels":[],"label_agreement":null},{"id":"W4392851625","doi":"10.54254/2755-2721/47/20241357","title":"Prediction of patient breast cancer probability","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Breast cancer; Cancer; Medicine; Internal medicine","score_opus":0.007037028037619113,"score_gpt":0.18677232085494655,"score_spread":0.17973529281732745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392851625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7764216,0.0021442804,0.13870876,0.012853654,0.00041845092,0.0004655713,0.047658555,0.0030907087,0.018238513],"genre_scores_gemma":[0.974495,0.00057819474,0.012754991,0.00056193565,0.00016854855,0.00012625821,0.009348332,0.000051147865,0.0019154679],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929166,0.00022358884,0.00006416725,0.00018791329,0.00013436476,0.00009828424],"domain_scores_gemma":[0.99214536,0.0060488847,0.0005562143,0.00029390934,0.0006128037,0.00034279135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016828377,0.0004033083,0.0006165845,0.001243243,0.0002479569,0.0010815304,0.00060160935,0.0008451085,0.00730682],"category_scores_gemma":[0.019097185,0.00024008988,0.00066113326,0.0010100227,0.00028310387,0.0008122761,0.00067590433,0.0012559257,0.0019932834],"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":[0.0007253553,0.00031476837,0.77607536,0.00016655518,0.00014910715,0.00047249423,0.00015684089,0.0754631,0.00047301585,0.0045297737,0.033449277,0.10802435],"study_design_scores_gemma":[0.000068545414,0.00017089231,0.10942819,0.00008928879,0.00008104168,0.00080570026,0.0001689052,0.8688587,0.0010081276,0.012109813,0.0071698646,0.00004088695],"about_ca_topic_score_codex":0.007194728,"about_ca_topic_score_gemma":0.007014193,"teacher_disagreement_score":0.00730682,"about_ca_system_score_codex":0.0006952404,"about_ca_system_score_gemma":0.00086572696,"threshold_uncertainty_score":0.024443746},"labels":[],"label_agreement":null},{"id":"W4392863972","doi":"10.54254/2755-2721/47/20241101","title":"Exploring correlations between economic indicators with natural and societal factors based on linear regression model","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Global Trade and Competitiveness","field":"Business, Management and Accounting","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":"Natural (archaeology); Econometrics; Linear regression; Regression analysis; Regression; Cross-sectional regression; Proper linear model; Statistics; Bayesian multivariate linear regression; Economics; Mathematics; Geography","score_opus":0.02599188073876835,"score_gpt":0.20642577166407805,"score_spread":0.1804338909253097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392863972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76806223,0.0015668249,0.21093884,0.0017423441,0.00022855824,0.000611327,0.00299279,0.0008752196,0.012981851],"genre_scores_gemma":[0.9714386,0.0008541776,0.021463647,0.000072676376,0.000058067897,0.00043357912,0.0018650308,0.00006019655,0.003754133],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99604744,0.0018354695,0.00026308,0.00078282785,0.0006782286,0.00039288498],"domain_scores_gemma":[0.99001324,0.00729397,0.0009178238,0.0003633669,0.0012153788,0.0001962199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048672766,0.0017605184,0.0009839779,0.00395673,0.00059893646,0.002570774,0.001355041,0.00077205175,0.0056919595],"category_scores_gemma":[0.01604382,0.0004965433,0.0023286918,0.0048865974,0.0006938137,0.0018854953,0.0015960186,0.0018012759,0.0012920888],"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":[0.00026350401,0.0006454233,0.7415149,0.0005293233,0.0017911457,0.0010862945,0.001599904,0.14471596,0.0011853023,0.014811299,0.0054063136,0.086450584],"study_design_scores_gemma":[0.000034445005,0.00040676713,0.16175942,0.00020938236,0.0006505683,0.00025215303,0.0016708446,0.8198705,0.0008796415,0.008934811,0.0052126157,0.00011886729],"about_ca_topic_score_codex":0.021352705,"about_ca_topic_score_gemma":0.011376474,"teacher_disagreement_score":0.021352705,"about_ca_system_score_codex":0.0011273307,"about_ca_system_score_gemma":0.0026295735,"threshold_uncertainty_score":0.042456865},"labels":[],"label_agreement":null},{"id":"W4392914277","doi":"10.54254/2755-2721/48/20241332","title":"Harnessing AI and machine learning for enhanced credit risk analysis: A comprehensive exploration of computational techniques in the financial realm","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","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":"Transformative learning; Artificial intelligence; Financial services; Expansive; Computer science; Machine learning; Big data; Data science; Finance; Economics; Sociology; Data mining","score_opus":0.011139069127943695,"score_gpt":0.21931872034823532,"score_spread":0.20817965122029164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392914277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00686448,0.018777518,0.9509702,0.0079113245,0.00015503571,0.000037910217,0.000052370207,0.0002336045,0.014997557],"genre_scores_gemma":[0.3381585,0.043140505,0.61050045,0.0012990668,0.0009354856,0.00014382081,0.00015227724,0.00014362714,0.0055262838],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998417,0.0007931585,0.00008646348,0.0001364,0.00050736364,0.000059662896],"domain_scores_gemma":[0.9931359,0.005642814,0.0002970979,0.00043629264,0.00039610596,0.00009181448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030601665,0.00074542745,0.00074418954,0.001910156,0.0005117252,0.0037670399,0.0010391062,0.0013316674,0.0014386312],"category_scores_gemma":[0.008537595,0.0003821447,0.00082299626,0.0019186767,0.0027660131,0.004815789,0.0021090766,0.0027818084,0.00055001496],"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":[0.000034693636,0.00009069156,0.001518419,0.00060876226,0.00010984862,0.00012288227,0.0003327762,0.10650494,0.0015929802,0.639112,0.0040640794,0.24590798],"study_design_scores_gemma":[0.0000071080226,0.000035134337,0.00047064084,0.0002888866,0.000017204542,0.00009045408,0.00012050973,0.3344069,0.00092312804,0.64125293,0.0223515,0.000035626344],"about_ca_topic_score_codex":0.0017034335,"about_ca_topic_score_gemma":0.001516288,"teacher_disagreement_score":0.0037670399,"about_ca_system_score_codex":0.0011734371,"about_ca_system_score_gemma":0.0015766842,"threshold_uncertainty_score":0.016183853},"labels":[],"label_agreement":null},{"id":"W4393054557","doi":"10.54254/2755-2721/49/20241085","title":"Investigation the influence related to parameters configuration of Generative Adversarial Networks in face image generation","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Discriminator; Computer science; Autoencoder; Generative grammar; Artificial intelligence; Generator (circuit theory); Pace; Face (sociological concept); Function (biology); Convergence (economics); Adversarial system; Encoder; Image (mathematics); Machine learning; Deep learning; Pattern recognition (psychology)","score_opus":0.009122380945071642,"score_gpt":0.20669426095353507,"score_spread":0.19757188000846343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393054557","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83515275,0.0015522138,0.15360287,0.00046439128,0.00010385309,0.00013595948,0.0002211083,0.0004184094,0.008348572],"genre_scores_gemma":[0.9878035,0.0002293354,0.011220682,0.000044335484,0.000008446498,0.00002973559,0.000082036066,0.00004625292,0.0005357071],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994155,0.0002427242,0.000024859768,0.00010752686,0.00011307807,0.00009632682],"domain_scores_gemma":[0.99354666,0.0051845564,0.0003919649,0.0003965255,0.0003857475,0.00009454943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018901972,0.0006584407,0.00035933664,0.00048068605,0.0002519104,0.0006507827,0.00051649526,0.0006904543,0.0010098616],"category_scores_gemma":[0.015646923,0.0002743367,0.00023899678,0.00029657313,0.00051135855,0.0008931937,0.0005481735,0.00091524917,0.00021757741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038899446,0.0001538128,0.006137735,0.0001518878,0.00006948256,0.00020061889,0.00011394763,0.9351311,0.022500893,0.0034467108,0.0005817598,0.031123161],"study_design_scores_gemma":[0.000010153453,0.00026116197,0.0015787032,0.00003275291,0.000028776198,0.000089016095,0.000050449264,0.9712007,0.025150817,0.001113738,0.00046581184,0.000017990194],"about_ca_topic_score_codex":0.0023280005,"about_ca_topic_score_gemma":0.0020279544,"teacher_disagreement_score":0.0023280005,"about_ca_system_score_codex":0.000500016,"about_ca_system_score_gemma":0.00036013778,"threshold_uncertainty_score":0.009996414},"labels":[],"label_agreement":null},{"id":"W4393089565","doi":"10.54254/2755-2721/51/20241187","title":"AI in education: Enhancing learning experiences and student outcomes","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":119,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lambton College","funders":"","keywords":"Outcome (game theory); Augmented reality; Computer science; Artificial intelligence; Mathematics education; Psychology; Human–computer interaction","score_opus":0.005380135143455498,"score_gpt":0.2469985170923898,"score_spread":0.24161838194893429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393089565","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9875673,0.00027205885,0.0015654255,0.000349774,0.00003527107,0.0000792507,0.000024920268,0.000064875705,0.010041036],"genre_scores_gemma":[0.99661213,0.00019491957,0.0013468955,0.000045819867,0.000016472357,0.000047651254,0.000029503428,0.000007120749,0.0016994842],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99780077,0.0009877557,0.00011628094,0.00016632333,0.00058216375,0.00034668692],"domain_scores_gemma":[0.9948618,0.0017090226,0.0006656544,0.0002246469,0.0005049952,0.0020338818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023990658,0.00032588633,0.00042489512,0.0008793113,0.00078147405,0.0036128364,0.0005536099,0.0006334737,0.0030926366],"category_scores_gemma":[0.008257442,0.00009185854,0.00033833872,0.0004570161,0.000536452,0.0009615422,0.0025174408,0.0008128246,0.00063396624],"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":[0.0011223004,0.030919448,0.16128613,0.0012290046,0.00021417534,0.00092664507,0.04146297,0.0023625393,0.021350218,0.005600662,0.003614335,0.72991157],"study_design_scores_gemma":[0.00039388123,0.026298603,0.77513874,0.0010522694,0.0005608393,0.0024734454,0.060700484,0.007359597,0.050333984,0.015865719,0.05955072,0.00027174773],"about_ca_topic_score_codex":0.00022352867,"about_ca_topic_score_gemma":0.00028441733,"teacher_disagreement_score":0.0036128364,"about_ca_system_score_codex":0.00040254567,"about_ca_system_score_gemma":0.0006340623,"threshold_uncertainty_score":0.012687683},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0105283195,0.0005348725,0.98693556,0.00025766072,0.000022706223,0.00001295563,0.00006182743,0.00065579225,0.0009902195],"genre_scores_gemma":[0.507288,0.0012968178,0.4847365,0.00038499368,0.000064727115,0.00006419646,0.0005857662,0.00035813442,0.005220908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997812,0.000052233547,0.000008419245,0.000055164674,0.00007740717,0.000025667245],"domain_scores_gemma":[0.9996935,0.000118411335,0.000034836783,0.00006619187,0.00007193088,0.000015141033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057659746,0.0006380684,0.00046163113,0.00031382192,0.00013745512,0.00064516056,0.0010260376,0.00074488844,0.0010916117],"category_scores_gemma":[0.0016668878,0.0003241001,0.0003961943,0.0004629651,0.0006123226,0.0012101694,0.0006855812,0.0014140778,0.0005176875],"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":[0.00007549076,0.00006441063,0.0008454004,0.00015283286,0.0000591803,0.00005851694,0.000053173477,0.74097896,0.017894518,0.01390277,0.0040319553,0.22188267],"study_design_scores_gemma":[0.00000205712,0.000013367355,0.00008842299,0.000006386188,0.000002729936,0.0000148105855,0.0000046648706,0.99200165,0.0033600177,0.0035641794,0.00093863235,0.0000030391564],"about_ca_topic_score_codex":0.0029824276,"about_ca_topic_score_gemma":0.0038432172,"teacher_disagreement_score":0.0029824276,"about_ca_system_score_codex":0.0005896962,"about_ca_system_score_gemma":0.0006438957,"threshold_uncertainty_score":0.0059301853},"labels":[],"label_agreement":null},{"id":"W4393227855","doi":"10.54254/2755-2721/53/20241290","title":"Application of machine learning in lung cancer prediction","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"Machine learning; Artificial intelligence; Computer science; Lung cancer; Medicine; Pathology","score_opus":0.0030012133303715443,"score_gpt":0.24007181952708387,"score_spread":0.23707060619671233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393227855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0502072,0.07915395,0.8507223,0.00499401,0.00090892747,0.00013594655,0.00070366537,0.0012299864,0.011943947],"genre_scores_gemma":[0.782642,0.042380255,0.16710705,0.00079032336,0.0010872078,0.00016574818,0.0010783398,0.00009597215,0.0046531954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990939,0.0003543115,0.00006531217,0.00017050725,0.00025713042,0.00005888472],"domain_scores_gemma":[0.99760723,0.0017397472,0.00013906561,0.00009411364,0.00037530402,0.000044573517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001641397,0.0007403077,0.0009564939,0.0017089596,0.0002832602,0.0013235386,0.000828807,0.0010521344,0.0010602252],"category_scores_gemma":[0.0058736354,0.00028816322,0.0008934601,0.001739274,0.0004209952,0.00089412736,0.0005195373,0.0013837182,0.00054937345],"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":[0.000098644334,0.00017694719,0.024045782,0.00079603464,0.00042332895,0.00030950858,0.0001030787,0.39500424,0.0015669918,0.015440138,0.00948378,0.55255157],"study_design_scores_gemma":[0.000008362292,0.000077878816,0.0037046417,0.00018034417,0.00007397937,0.00016132834,0.000042665077,0.96193844,0.0017282875,0.023413174,0.008637322,0.0000335837],"about_ca_topic_score_codex":0.0034765785,"about_ca_topic_score_gemma":0.001979274,"teacher_disagreement_score":0.0034765785,"about_ca_system_score_codex":0.000662904,"about_ca_system_score_gemma":0.0008416958,"threshold_uncertainty_score":0.008680642},"labels":[],"label_agreement":null},{"id":"W4393269028","doi":"10.54254/2755-2721/54/20241407","title":"CO2 emissions prediction based on regression, neural network and SVM","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pace; Artificial neural network; Support vector machine; Greenhouse gas; Process (computing); Machine learning; Computer science; Artificial intelligence; Regression analysis; Predictive modelling","score_opus":0.0031384682749874726,"score_gpt":0.1714708825789278,"score_spread":0.16833241430394033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393269028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43991393,0.0073095416,0.5419138,0.0013090209,0.00027901886,0.00006586362,0.00045313552,0.0013245863,0.0074311066],"genre_scores_gemma":[0.97065365,0.0013645657,0.025554415,0.000061194005,0.00009074376,0.000035820012,0.00027289375,0.000036288315,0.0019305407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995105,0.00014877542,0.000028427043,0.00013887603,0.000111656555,0.00006172738],"domain_scores_gemma":[0.9990213,0.00062367704,0.00010818076,0.00003814896,0.00018370272,0.000025040135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009377117,0.0007794957,0.00090132694,0.0010707006,0.00028320806,0.00079007866,0.00069696986,0.00074335036,0.0007426586],"category_scores_gemma":[0.0036006267,0.00023103721,0.0006232641,0.0012328059,0.00030424757,0.0014681778,0.0003915508,0.00079861993,0.00019854147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019868958,0.00013715471,0.011071583,0.0001612635,0.0001032293,0.00010560096,0.000039003146,0.8833243,0.0020630646,0.0031859698,0.0014463834,0.098163776],"study_design_scores_gemma":[0.0000010379348,0.000007599768,0.000422713,0.0000023167643,0.0000041055655,0.0000049466744,0.0000032833627,0.99898094,0.00019932044,0.00030726288,0.00006386748,0.0000026880314],"about_ca_topic_score_codex":0.014807916,"about_ca_topic_score_gemma":0.0061278758,"teacher_disagreement_score":0.014807916,"about_ca_system_score_codex":0.0005789257,"about_ca_system_score_gemma":0.00055198494,"threshold_uncertainty_score":0.029443443},"labels":[],"label_agreement":null},{"id":"W4393276984","doi":"10.54254/2755-2721/54/20241598","title":"Comparison of stock price prediction models for linear models, random forest and LSTM","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Random forest; Computer science; Predictive modelling; Stock (firearms); Econometrics; Stock price; Machine learning; Stock market; Linear model; Data mining; Scope (computer science); Artificial intelligence; Mathematics; Engineering","score_opus":0.09409090818649549,"score_gpt":0.3666108364807489,"score_spread":0.2725199282942534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393276984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37934363,0.012491688,0.5874367,0.003171374,0.0008331074,0.0002156094,0.0017277768,0.0030558256,0.011724286],"genre_scores_gemma":[0.8789181,0.004161379,0.11142429,0.00032786684,0.00019843022,0.00022609804,0.0016585728,0.00013718655,0.0029480704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991929,0.00029541506,0.00006353551,0.00015560361,0.00019119833,0.000101393125],"domain_scores_gemma":[0.9964887,0.0025320058,0.00015599726,0.00010848754,0.00064959633,0.00006529438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032417774,0.0008941192,0.00078173546,0.0014157224,0.00032711524,0.0009537003,0.0010935158,0.00084258174,0.0017629594],"category_scores_gemma":[0.008850295,0.0002719663,0.0011372903,0.0015282604,0.00027671296,0.002381937,0.00041629918,0.0010750787,0.00063419825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008295927,0.00023607466,0.016660273,0.0006599799,0.00045371696,0.00015593352,0.00023855885,0.53326327,0.0016582196,0.007518033,0.0061706416,0.43215564],"study_design_scores_gemma":[0.000019661726,0.00009508602,0.0026006626,0.00005026988,0.00006410513,0.000036962378,0.000048909365,0.99225134,0.00067749625,0.003399256,0.0007321657,0.000024149811],"about_ca_topic_score_codex":0.017445615,"about_ca_topic_score_gemma":0.015721282,"teacher_disagreement_score":0.017445615,"about_ca_system_score_codex":0.0009137235,"about_ca_system_score_gemma":0.001281159,"threshold_uncertainty_score":0.034688175},"labels":[],"label_agreement":null},{"id":"W4393277074","doi":"10.54254/2755-2721/54/20241586","title":"Assessing the robustness of Multi-Armed Bandit algorithms against biased initialization","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Regret; Initialization; Robustness (evolution); Adaptability; Computer science; Recommender system; Thompson sampling; Greedy algorithm; Upper and lower bounds; Commit; Machine learning; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.101806615478968,"score_gpt":0.405559755997657,"score_spread":0.30375314051868896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393277074","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7543615,0.0018231223,0.23378399,0.0015483061,0.00018115043,0.0002831206,0.00031724985,0.00039584443,0.0073056556],"genre_scores_gemma":[0.97434676,0.00031037309,0.024343919,0.00015875575,0.00003616298,0.00011295516,0.00015994535,0.000037187998,0.0004939403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926341,0.0045844615,0.0004125736,0.0008705436,0.001018929,0.00047928985],"domain_scores_gemma":[0.90339756,0.07846125,0.00665335,0.0071818805,0.0032799053,0.0010261539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018616948,0.0009819345,0.0011951437,0.0011803978,0.0010822843,0.002688934,0.0014740911,0.0019589635,0.0009896467],"category_scores_gemma":[0.11542984,0.00043941152,0.00065009895,0.0011099956,0.0016269494,0.0024152582,0.0017387393,0.0020747979,0.0003376411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005553152,0.00015791006,0.01691798,0.0001544315,0.00032503728,0.00009126173,0.00022561188,0.9425053,0.0012803671,0.012450789,0.0007491506,0.024586927],"study_design_scores_gemma":[0.000050379043,0.00044487213,0.0034699703,0.00006782164,0.000062931365,0.00007214634,0.0001875103,0.9828471,0.0016615139,0.010536523,0.00056478626,0.000034482026],"about_ca_topic_score_codex":0.0071801515,"about_ca_topic_score_gemma":0.0043815253,"teacher_disagreement_score":0.018616948,"about_ca_system_score_codex":0.0014979737,"about_ca_system_score_gemma":0.0021007713,"threshold_uncertainty_score":0.09845698},"labels":[],"label_agreement":null},{"id":"W4396220737","doi":"10.54254/2755-2721/57/20241325","title":"Advancements and challenges in AI-driven language technologies: From natural language processing to language acquisition","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Natural Language Processing Techniques","field":"Computer Science","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":"Thompson Rivers University","funders":"","keywords":"Computer science; Interpretability; Artificial intelligence; Machine translation; Natural language processing; Natural language; Language acquisition; Language model; Universal Networking Language; Language technology; Computational linguistics; Language industry; Comprehension approach; Linguistics","score_opus":0.0071218990408329925,"score_gpt":0.247057581249163,"score_spread":0.23993568220833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396220737","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039193545,0.17863534,0.3799558,0.25835714,0.0019474596,0.00010507676,0.0003145465,0.0011318135,0.14035936],"genre_scores_gemma":[0.53367025,0.18258686,0.23428817,0.01734195,0.0046328786,0.0002800516,0.00039815018,0.000616366,0.026185254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966024,0.0016481202,0.00019834626,0.00037498644,0.0010070554,0.00016900907],"domain_scores_gemma":[0.9837219,0.01319586,0.0004450569,0.0010594367,0.0012052679,0.00037240816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070213107,0.0004634689,0.0005510729,0.0018596204,0.0011477071,0.0075199953,0.001458534,0.0024181353,0.00415897],"category_scores_gemma":[0.013850011,0.0003593493,0.00036338717,0.0017613214,0.00642954,0.018073194,0.0036639227,0.004323984,0.0021526467],"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":[0.000047279947,0.000090662135,0.0015033936,0.0007877411,0.00001952468,0.00014407122,0.0024037694,0.0031342246,0.0017690596,0.6549178,0.00853458,0.3266479],"study_design_scores_gemma":[0.000008957111,0.00007553084,0.000785857,0.0007963418,0.000013712793,0.0004873437,0.0027787017,0.01840238,0.0036003576,0.74624354,0.22674187,0.000065387954],"about_ca_topic_score_codex":0.0011985644,"about_ca_topic_score_gemma":0.0010263924,"teacher_disagreement_score":0.0075199953,"about_ca_system_score_codex":0.0021650419,"about_ca_system_score_gemma":0.0022109593,"threshold_uncertainty_score":0.03713268},"labels":[],"label_agreement":null},{"id":"W4396223310","doi":"10.54254/2755-2721/58/20240734","title":"SUDS: New solution for urban flooding","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Smart Cities and Technologies","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":"Flooding (psychology); Environmental science; Water resource management; Hydrology (agriculture); Geology; Psychology; Geotechnical engineering; Psychotherapist","score_opus":0.007969619664108802,"score_gpt":0.18257973618382953,"score_spread":0.17461011651972072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396223310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062088203,0.008589285,0.7573375,0.008051626,0.0022738979,0.000617697,0.0029792413,0.016273707,0.14178888],"genre_scores_gemma":[0.43232912,0.009744681,0.45529854,0.002245395,0.0006091887,0.00071577454,0.005718854,0.0008713215,0.09246709],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99980253,0.000033952194,0.000015742837,0.00003689457,0.00006982023,0.000041061634],"domain_scores_gemma":[0.9999019,0.000015512256,0.000009687845,0.000016735134,0.000031524367,0.00002473295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016692425,0.0005273113,0.00039375594,0.0005877155,0.0007241029,0.00081096706,0.0009363497,0.0006891021,0.012941111],"category_scores_gemma":[0.0004144769,0.00015948327,0.00060521765,0.00070551824,0.00032043207,0.001352988,0.002218618,0.0007509399,0.002673353],"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":[0.00021254618,0.00021747868,0.002590313,0.0011897811,0.00007850998,0.00070439145,0.0006148146,0.026457245,0.017431365,0.083219856,0.12958185,0.73770195],"study_design_scores_gemma":[0.00015743698,0.00025790854,0.0021172545,0.000214782,0.00011807373,0.0010620345,0.0010520737,0.17039317,0.009222308,0.06828972,0.7470306,0.00008457653],"about_ca_topic_score_codex":0.0015313165,"about_ca_topic_score_gemma":0.0028723478,"teacher_disagreement_score":0.012941111,"about_ca_system_score_codex":0.00036230986,"about_ca_system_score_gemma":0.00085581583,"threshold_uncertainty_score":0.043292344},"labels":[],"label_agreement":null},{"id":"W4396223353","doi":"10.54254/2755-2721/58/20240703","title":"The recovery of the Antarctic ozone layer and suggestions for addressing the global warming","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","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":"Ozone depletion; Ozone layer; Environmental science; Global warming; Ozone; Layer (electronics); Climatology; Atmospheric sciences; Climate change; Oceanography; Meteorology; Geography; Geology; Chemistry","score_opus":0.015466665854672126,"score_gpt":0.22381440707090897,"score_spread":0.20834774121623684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396223353","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026986856,0.35349867,0.023821825,0.5064145,0.028897703,0.000067650595,0.00023572342,0.00032213947,0.05975499],"genre_scores_gemma":[0.39617077,0.4817863,0.035076726,0.020683726,0.010849902,0.00017692658,0.00039571003,0.00014327116,0.05471665],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995065,0.00024503967,0.0000225133,0.00006338917,0.00009724869,0.000065336026],"domain_scores_gemma":[0.9990006,0.00036070342,0.00010214872,0.000061368744,0.000372922,0.000102291735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017115902,0.00043806582,0.00029441967,0.00084594236,0.0008758286,0.0019514182,0.0011107736,0.0016938442,0.0026647472],"category_scores_gemma":[0.003887533,0.00014927424,0.0005442695,0.0009942079,0.001724111,0.0027488153,0.0008984263,0.0018753542,0.00075174996],"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":[0.0001405736,0.00016561145,0.008378777,0.0023965898,0.00007137694,0.0007898033,0.0024406116,0.012192278,0.0022672527,0.43420655,0.32892612,0.2080244],"study_design_scores_gemma":[0.000018944904,0.000099085824,0.007396159,0.001630935,0.00007594392,0.00031105886,0.008294072,0.011109164,0.0019486424,0.31157985,0.6574362,0.00009984363],"about_ca_topic_score_codex":0.008005008,"about_ca_topic_score_gemma":0.009022991,"teacher_disagreement_score":0.008005008,"about_ca_system_score_codex":0.0017803067,"about_ca_system_score_gemma":0.0039988174,"threshold_uncertainty_score":0.015916884},"labels":[],"label_agreement":null},{"id":"W4396228320","doi":"10.54254/2755-2721/58/20240699","title":"Comparative analysis of greywater recycling and rainwater harvesting as supplementary water sources for conventional urban and tourist resort water supplies","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Wastewater Treatment and Reuse","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Rainwater harvesting; Greywater; Water scarcity; Tourism; Water supply; Population; Resource (disambiguation); Water resource management; Water conservation; Environmental science; Water resources; Urbanization; Environmental engineering; Business; Natural resource economics; Environmental planning; Geography; Wastewater; Ecology; Computer science; Economics","score_opus":0.007800774745075118,"score_gpt":0.21699351971825984,"score_spread":0.2091927449731847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396228320","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99439716,0.0005808328,0.00089512416,0.00005550785,0.000006051491,0.000031674444,0.0001076094,0.000008809041,0.0039172317],"genre_scores_gemma":[0.9957431,0.0008330013,0.0016703401,0.000019755356,0.000004903995,0.000023917208,0.00014440273,0.000008113677,0.0015525542],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99946517,0.00020185685,0.000026546048,0.000058011432,0.00016674398,0.00008173787],"domain_scores_gemma":[0.999413,0.00023518242,0.00007730807,0.00003455387,0.00016393267,0.00007601784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066240254,0.00022016613,0.00035806728,0.00096665946,0.0003061908,0.0008867266,0.0003304906,0.00022463707,0.0020523171],"category_scores_gemma":[0.0010062112,0.00008382504,0.0004892892,0.0015156355,0.00032562888,0.0007703027,0.00057820306,0.00015068892,0.0001427228],"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":[0.0052769533,0.0012929387,0.22101499,0.006135119,0.00077686924,0.002111927,0.0041622715,0.040130034,0.122557364,0.011012541,0.0028298746,0.58269906],"study_design_scores_gemma":[0.00010506628,0.007032303,0.832424,0.00037579387,0.0010121504,0.00054731604,0.01874609,0.05593215,0.047767926,0.004235294,0.03168423,0.0001377059],"about_ca_topic_score_codex":0.0056430106,"about_ca_topic_score_gemma":0.02011251,"teacher_disagreement_score":0.0056430106,"about_ca_system_score_codex":0.0008802158,"about_ca_system_score_gemma":0.0008575035,"threshold_uncertainty_score":0.011220336},"labels":[],"label_agreement":null},{"id":"W4396238840","doi":"10.54254/2755-2721/58/20240701","title":"Research on impact of flooding on urban transportation safety","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Safety and Risk Management","field":"Business, Management and Accounting","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":"Flooding (psychology); Transport engineering; Business; Environmental planning; Environmental science; Engineering; Psychology","score_opus":0.020030776904263206,"score_gpt":0.2721273039422498,"score_spread":0.25209652703798663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396238840","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49471077,0.27107832,0.012332217,0.02113623,0.0008034321,0.00015343782,0.00090900843,0.00008251105,0.19879413],"genre_scores_gemma":[0.8593807,0.13534898,0.0009870306,0.00040136,0.0002485467,0.000018838628,0.00019260532,0.0000062752465,0.0034155888],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99949896,0.00015509843,0.00003483525,0.000058344744,0.00019189635,0.000060832313],"domain_scores_gemma":[0.99782515,0.0009787395,0.00036948427,0.00004153944,0.0007165851,0.00006855851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006216875,0.0002630233,0.00018875714,0.0020468954,0.00041163416,0.0011284638,0.00032692816,0.00034255508,0.0027407112],"category_scores_gemma":[0.002927457,0.000109242734,0.0002904019,0.003192172,0.00053591054,0.0016329412,0.00050918973,0.0003814515,0.00017776564],"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":[0.00014958519,0.00017159779,0.11693802,0.0125499265,0.0005858899,0.0020215313,0.008074147,0.028236704,0.0041447296,0.12993564,0.020827698,0.6763645],"study_design_scores_gemma":[0.00003478208,0.00095124973,0.2885461,0.008316523,0.0011323848,0.001253847,0.058829766,0.020028595,0.009115556,0.078825906,0.53279585,0.00016950066],"about_ca_topic_score_codex":0.011903361,"about_ca_topic_score_gemma":0.0103793135,"teacher_disagreement_score":0.011903361,"about_ca_system_score_codex":0.0016100906,"about_ca_system_score_gemma":0.002053766,"threshold_uncertainty_score":0.02366817},"labels":[],"label_agreement":null},{"id":"W4396242707","doi":"10.54254/2755-2721/58/20240723","title":"The significance of materials informatics on material science","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Manufacturing Process and Optimization","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":"Informatics; Materials informatics; Computer science; Engineering; Health informatics; Political science; Engineering informatics","score_opus":0.0037603137809486924,"score_gpt":0.1807009440541171,"score_spread":0.17694063027316842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396242707","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010304085,0.32089403,0.16754763,0.23963651,0.01742419,0.00019384135,0.0007482182,0.0010820767,0.24216938],"genre_scores_gemma":[0.233425,0.38082054,0.26102632,0.05205376,0.028178433,0.0005717507,0.0011624148,0.0011836757,0.041578062],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99439526,0.0025881412,0.00036830237,0.00071913894,0.0016797149,0.00024945528],"domain_scores_gemma":[0.9798723,0.014992187,0.0005430462,0.00190366,0.0021000258,0.00058884965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009079236,0.001147424,0.0013087764,0.004970332,0.0031657456,0.010295141,0.0017941549,0.004237267,0.007831139],"category_scores_gemma":[0.012056131,0.0008753839,0.0011583742,0.0050142435,0.014669608,0.021688567,0.0053573395,0.009917345,0.002913803],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022407165,0.00005067923,0.00034055737,0.0005101134,0.000016720813,0.00006523199,0.00031518072,0.00085206074,0.00036307154,0.9040826,0.020520817,0.07286063],"study_design_scores_gemma":[0.000009585987,0.000043140168,0.00042790142,0.00071154506,0.000011316983,0.00016699891,0.00034869518,0.0018250897,0.00088023656,0.5881355,0.40739065,0.000049336406],"about_ca_topic_score_codex":0.0021604246,"about_ca_topic_score_gemma":0.0015595581,"teacher_disagreement_score":0.010295141,"about_ca_system_score_codex":0.0050670635,"about_ca_system_score_gemma":0.0048376373,"threshold_uncertainty_score":0.04801613},"labels":[],"label_agreement":null},{"id":"W4396242884","doi":"10.54254/2755-2721/57/20241307","title":"Enhancing efficiency and user-centricity in architectural remodeling: A comprehensive system design for structural renovation","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Cultural Heritage Management and Preservation","field":"Arts and Humanities","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":"Architectural engineering; Architectural design; Business; Computer science; Process management; Engineering; Systems engineering; Architecture; Geography","score_opus":0.04376798714496556,"score_gpt":0.21785755558111183,"score_spread":0.17408956843614626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396242884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070883706,0.00015861176,0.918925,0.00018505451,0.000032434727,0.00031563966,0.00007696834,0.0049619502,0.0044606435],"genre_scores_gemma":[0.5813768,0.00017737201,0.41307306,0.0001063059,0.000015587546,0.00036353234,0.00025642835,0.00033835214,0.004292651],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913895,0.00032939945,0.00007495502,0.00017748987,0.00020244344,0.000076693315],"domain_scores_gemma":[0.99909616,0.00027766617,0.00008118395,0.00023761624,0.00022865804,0.00007862242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014103289,0.0005174547,0.00045541165,0.00044754107,0.0005681092,0.0016558132,0.0010808392,0.00068820553,0.0039102966],"category_scores_gemma":[0.0027923312,0.00028358877,0.00048498914,0.000309672,0.0005070862,0.0017838097,0.0018409727,0.0005111887,0.00079992163],"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":[0.0008515678,0.0006669625,0.010538203,0.0010992306,0.00018847435,0.0006123703,0.0043460825,0.11336514,0.24592341,0.02925272,0.0076493714,0.5855063],"study_design_scores_gemma":[0.00016655121,0.0016083904,0.010091992,0.00018810625,0.00033932584,0.000776368,0.0011503092,0.7965362,0.098875105,0.0140418485,0.076078616,0.00014730789],"about_ca_topic_score_codex":0.00081647706,"about_ca_topic_score_gemma":0.0012768152,"teacher_disagreement_score":0.0039102966,"about_ca_system_score_codex":0.0003681073,"about_ca_system_score_gemma":0.0007369045,"threshold_uncertainty_score":0.013081253},"labels":[],"label_agreement":null},{"id":"W4396664877","doi":"10.54254/2755-2721/59/20240779","title":"Necessity and limitations of transportation electrification","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Electrification; Prioritization; Energy security; Risk analysis (engineering); Rural electrification; Environmental economics; Business; Computer science; Economics; Electricity; Engineering; Management science; Renewable energy","score_opus":0.014887214372833274,"score_gpt":0.2214629525837199,"score_spread":0.20657573821088662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396664877","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25573882,0.07355011,0.04468992,0.17777142,0.0024578779,0.000092156755,0.0007607444,0.00018056242,0.44475836],"genre_scores_gemma":[0.9582741,0.024496138,0.0050547067,0.0023253215,0.0005385222,0.0000619406,0.00017974424,0.000042636602,0.0090268925],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980848,0.00057146885,0.00013980745,0.00025480372,0.0006936278,0.00025550573],"domain_scores_gemma":[0.9936458,0.0035441623,0.0006132614,0.00033044472,0.0015681215,0.0002983034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002747289,0.00025633836,0.00027917634,0.0007643586,0.00080153695,0.0026477578,0.001210551,0.001460925,0.005496078],"category_scores_gemma":[0.0092974035,0.00015953954,0.00034239088,0.0009427426,0.0029974352,0.0054322532,0.0023542643,0.0019002995,0.0005727103],"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":[0.00010522756,0.000042288757,0.011142508,0.0016210745,0.00005728854,0.0006755453,0.001747667,0.0076284595,0.0019836964,0.79763794,0.016209776,0.16114855],"study_design_scores_gemma":[0.000016292983,0.00011826722,0.01660225,0.0029940975,0.00007196744,0.0019997642,0.017461915,0.0093748635,0.002742818,0.4388138,0.5096837,0.000120266755],"about_ca_topic_score_codex":0.0031858026,"about_ca_topic_score_gemma":0.0040230644,"teacher_disagreement_score":0.005496078,"about_ca_system_score_codex":0.0017498344,"about_ca_system_score_gemma":0.0023012431,"threshold_uncertainty_score":0.018386245},"labels":[],"label_agreement":null},{"id":"W4396672111","doi":"10.54254/2755-2721/59/20240807","title":"Application of antimicrobial nanocoatings on biological implants","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Bone Tissue Engineering Materials","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":"Antimicrobial; Nanotechnology; Materials science; Coating; Nanomaterials; Biofilm; Biocompatibility; Surface modification; Antibiotics; Implant; Biomedical engineering; Medicine; Microbiology; Bacteria; Engineering; Biology; Surgery; Mechanical engineering","score_opus":0.006053839881123392,"score_gpt":0.1930002061535518,"score_spread":0.1869463662724284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396672111","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94860715,0.019017832,0.022358956,0.00037594582,0.00044653146,0.00014923811,0.00014628068,0.0002593393,0.00863859],"genre_scores_gemma":[0.9708346,0.0040098326,0.022687526,0.00017859222,0.0000472805,0.000056489323,0.00005305082,0.000028439907,0.0021041268],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998573,0.000016938013,0.000011241196,0.000034828605,0.000055304517,0.000024375833],"domain_scores_gemma":[0.99989724,0.00002529759,0.000027527605,0.000011974204,0.000026507421,0.000011421888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021494635,0.0003974775,0.00018720333,0.00038021238,0.00015586831,0.00032815442,0.00020849529,0.00048249707,0.00056209456],"category_scores_gemma":[0.00027086627,0.00013751295,0.00026884582,0.0001352226,0.00023538795,0.00019748024,0.00023979614,0.00017135247,0.00017071416],"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":[0.000023801153,0.000012871327,0.00007228556,0.0000894246,0.0000033816725,0.000052436273,0.000012944783,0.00018000447,0.99547774,0.00009084487,0.000033508615,0.0039507267],"study_design_scores_gemma":[0.00000511349,0.00015128746,0.0004634215,0.0000100112975,0.0000099626095,0.00012272551,0.000011941469,0.00086526986,0.99590355,0.00003883853,0.0024128265,0.0000050212384],"about_ca_topic_score_codex":0.00042964157,"about_ca_topic_score_gemma":0.0005520936,"teacher_disagreement_score":0.00056209456,"about_ca_system_score_codex":0.00034527786,"about_ca_system_score_gemma":0.00014716391,"threshold_uncertainty_score":0.0025051832},"labels":[],"label_agreement":null},{"id":"W4396672131","doi":"10.54254/2755-2721/59/20240782","title":"Research on the critical role of clean energy for dual carbon targets","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Global Energy and Sustainability Research","field":"Energy","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":"Greenhouse gas; Climate change mitigation; Climate change; Renewable energy; Global warming; Environmental economics; Dual (grammatical number); Environmental science; Sustainable development; Efficient energy use; Clean technology; Environmental resource management; Natural resource economics; Business; Engineering; Political science; Economics; Ecology","score_opus":0.016272359365359285,"score_gpt":0.29188948413465776,"score_spread":0.27561712476929845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396672131","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17485534,0.08848497,0.097671755,0.14924186,0.0020262047,0.0005150975,0.0008612709,0.000077008815,0.48626652],"genre_scores_gemma":[0.90240425,0.060045592,0.022425136,0.006556507,0.0004775962,0.00040981808,0.0002930911,0.00006616803,0.0073218644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9935627,0.0033731484,0.0002533328,0.0006745615,0.0016848242,0.00045146255],"domain_scores_gemma":[0.91589177,0.07044684,0.0046253973,0.0019304967,0.006219329,0.00088607636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011440526,0.0005790656,0.00062633713,0.003595647,0.002383542,0.009552977,0.0011243016,0.0021901065,0.01006043],"category_scores_gemma":[0.036484733,0.00031861407,0.0007797145,0.00701417,0.007528933,0.015297726,0.0023015628,0.0034692988,0.0008049253],"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":[0.0000287117,0.00009236758,0.0062252176,0.0013458837,0.000047743913,0.0000755723,0.0029693684,0.0013471339,0.00032149727,0.9400373,0.003067649,0.0444416],"study_design_scores_gemma":[0.00001783006,0.00016952364,0.020522755,0.0078053162,0.00013488383,0.0002477255,0.038498584,0.004914917,0.0025376282,0.7060056,0.21906309,0.000082204955],"about_ca_topic_score_codex":0.005499264,"about_ca_topic_score_gemma":0.007633528,"teacher_disagreement_score":0.011440526,"about_ca_system_score_codex":0.008647083,"about_ca_system_score_gemma":0.013227894,"threshold_uncertainty_score":0.06273925},"labels":[],"label_agreement":null},{"id":"W4396675382","doi":"10.54254/2755-2721/60/20240884","title":"A comprehensive review of superconductivity in heterostructures and superlattices comprising 2D materials","year":2024,"lang":"en","type":"review","venue":"Applied and Computational Engineering","topic":"Graphene research and applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Graphene; Superlattice; Heterojunction; Materials science; Moiré pattern; Superconductivity; Nanotechnology; Bilayer graphene; Condensed matter physics; Physics; Optoelectronics; Optics","score_opus":0.03417597708716272,"score_gpt":0.3214393761555365,"score_spread":0.28726339906837384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396675382","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015403211,0.99791986,0.0001595074,0.00016571679,0.00020300754,0.0000057097127,0.0000349849,0.000008961581,0.0013482211],"genre_scores_gemma":[0.00068216305,0.99806017,0.00032278476,0.0001267896,0.00011854471,0.000008183243,0.000044358683,0.0000021719636,0.00063477893],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99977356,0.00003968795,0.000041617972,0.000042587566,0.000081238686,0.000021294647],"domain_scores_gemma":[0.9996592,0.00016566529,0.000044397602,0.000013748464,0.0000922725,0.000024760597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051832054,0.0009577678,0.0010991817,0.0034557597,0.00039572548,0.00082007586,0.0006546779,0.0008165829,0.004114786],"category_scores_gemma":[0.0008356081,0.00043149907,0.000624692,0.0036071576,0.00030939875,0.0014770207,0.0006098517,0.0010948328,0.0015350342],"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":[0.00007293555,0.000095321135,0.00020524161,0.07115378,0.00015443446,0.000291644,0.00019940943,0.00079960044,0.0051679485,0.012834631,0.060328845,0.8486962],"study_design_scores_gemma":[0.0000059244694,0.00007102285,0.00044732698,0.005430604,0.0000974013,0.0005158836,0.00004895254,0.000074728254,0.00066959596,0.0014672709,0.99115163,0.000019616986],"about_ca_topic_score_codex":0.0015329097,"about_ca_topic_score_gemma":0.0025815167,"teacher_disagreement_score":0.004114786,"about_ca_system_score_codex":0.00059427234,"about_ca_system_score_gemma":0.0017504384,"threshold_uncertainty_score":0.0137652755},"labels":[],"label_agreement":null},{"id":"W4396675458","doi":"10.54254/2755-2721/60/20240841","title":"Permanent magnet motor drive technology for mitigating greenhouse gas emissions","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Wireless Power Transfer Systems","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":"Ste. Anne's Hospital","funders":"","keywords":"Greenhouse gas; Automotive industry; Climate change; Climate change mitigation; Environmental science; Global warming; Natural resource economics; Business; Engineering; Economics; Ecology; Aerospace engineering","score_opus":0.004427367714719454,"score_gpt":0.19002754219905862,"score_spread":0.18560017448433916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396675458","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0668895,0.6834769,0.16262983,0.005260909,0.0028488766,0.00016822452,0.0005005316,0.0010322037,0.07719301],"genre_scores_gemma":[0.5625544,0.37473238,0.035020705,0.0009979971,0.0009350216,0.00016400697,0.00043692504,0.00009273979,0.025065808],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997923,0.000034522145,0.000015215661,0.000039092385,0.000096847834,0.000021995844],"domain_scores_gemma":[0.9998466,0.000059183203,0.000027821878,0.000009113776,0.000049554645,0.000007794794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033406034,0.00036070985,0.0004296571,0.0006967693,0.00021176136,0.00071749656,0.0006423022,0.000859934,0.0032129714],"category_scores_gemma":[0.0004607326,0.000155194,0.00041499405,0.0005688805,0.00034506086,0.001274909,0.00042699417,0.00066882215,0.0011662869],"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":[0.00032966564,0.000113688446,0.0015792992,0.01804089,0.00018978321,0.000805406,0.00019157,0.011740329,0.19880128,0.0376099,0.021847755,0.70875037],"study_design_scores_gemma":[0.00006465696,0.0010333401,0.0035543821,0.0018213732,0.0002718891,0.002165758,0.0002661976,0.01800747,0.13261154,0.018711362,0.8214026,0.00008935613],"about_ca_topic_score_codex":0.00030852508,"about_ca_topic_score_gemma":0.00049036,"teacher_disagreement_score":0.0032129714,"about_ca_system_score_codex":0.00029514782,"about_ca_system_score_gemma":0.00036891236,"threshold_uncertainty_score":0.010748446},"labels":[],"label_agreement":null},{"id":"W4396699257","doi":"10.54254/2755-2721/61/20240954","title":"Urban Green-Space: Environmental Justice &amp; Green Gentrification","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gentrification; Environmental justice; Economic Justice; Inequality; Economic growth; Dilemma; Geography; Political science; Development economics; Sociology; Economics","score_opus":0.008548873871352117,"score_gpt":0.20512778277705768,"score_spread":0.19657890890570556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396699257","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030736338,0.03526551,0.062260304,0.2031733,0.0041972543,0.000113516995,0.00036680867,0.00036302273,0.6635239],"genre_scores_gemma":[0.8622385,0.017607395,0.017167581,0.011456279,0.0016119714,0.00015955717,0.0001690088,0.0002716194,0.08931805],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99870944,0.00058847055,0.000032247233,0.0001849444,0.00029420145,0.00019068054],"domain_scores_gemma":[0.9985852,0.0004404905,0.00016605199,0.00013963685,0.00032917422,0.00033944362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014564818,0.000528566,0.00038254927,0.0013434375,0.005176726,0.008536364,0.0011644191,0.0033509347,0.01585265],"category_scores_gemma":[0.0026299525,0.00020995433,0.0003832388,0.0024796794,0.018139517,0.006629958,0.009315881,0.0033082722,0.0012595598],"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":[0.0000087932685,0.000020232841,0.0008977153,0.00012199032,0.000006259521,0.000120785575,0.0024421008,0.00070315565,0.00009216247,0.9343554,0.028414644,0.032816783],"study_design_scores_gemma":[0.0000029364983,0.000013687141,0.0012699328,0.00033177453,0.0000074415343,0.00021589773,0.008444528,0.0012501198,0.0002045909,0.638115,0.3501233,0.00002077292],"about_ca_topic_score_codex":0.014256394,"about_ca_topic_score_gemma":0.033703048,"teacher_disagreement_score":0.01585265,"about_ca_system_score_codex":0.006839336,"about_ca_system_score_gemma":0.0055866973,"threshold_uncertainty_score":0.053032458},"labels":[],"label_agreement":null},{"id":"W4396699569","doi":"10.54254/2755-2721/61/20240922","title":"Exploring attributes of global CCS projects and the key factors to their accomplishment based on the CCUS project database","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Comparability; Greenhouse gas; Carbon capture and storage (timeline); Scale (ratio); Deforestation (computer science); Variety (cybernetics); Environmental resource management; Global warming; Production (economics); Environmental science; Business; Environmental planning; Environmental economics; Climate change; Computer science; Geography; Ecology","score_opus":0.06123363528982902,"score_gpt":0.2444933237493054,"score_spread":0.18325968845947638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396699569","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6107296,0.0017559276,0.005833124,0.0006107467,0.000064078595,0.0004291807,0.34511822,0.0010593674,0.03439972],"genre_scores_gemma":[0.5754884,0.002285823,0.015122371,0.00009558629,0.00004144624,0.00088392716,0.40042746,0.0003138961,0.005341111],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99598926,0.0005798524,0.00070096203,0.0004958945,0.0018078971,0.00042609932],"domain_scores_gemma":[0.98005575,0.0060947645,0.0043491013,0.0012493682,0.0067211483,0.0015299772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033953746,0.0006550505,0.00043176115,0.017279766,0.00067176775,0.0021111036,0.0008452511,0.0003598267,0.0048020603],"category_scores_gemma":[0.01955952,0.00020811711,0.00047846895,0.02742193,0.0003697972,0.002138431,0.0017644941,0.0005127117,0.0018321581],"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":[0.00042270223,0.00020528413,0.73633075,0.0023776072,0.00018340022,0.0008555109,0.0019958415,0.011193869,0.0022746788,0.0124575915,0.087179095,0.14452372],"study_design_scores_gemma":[0.000033036904,0.00017409466,0.68614656,0.00059765164,0.00010391533,0.0007835153,0.0071622995,0.017140152,0.0030858605,0.0034325093,0.28121677,0.00012368397],"about_ca_topic_score_codex":0.017106976,"about_ca_topic_score_gemma":0.0122126015,"teacher_disagreement_score":0.017279766,"about_ca_system_score_codex":0.0013260703,"about_ca_system_score_gemma":0.002281486,"threshold_uncertainty_score":0.03401482},"labels":[],"label_agreement":null},{"id":"W4396745895","doi":"10.54254/2755-2721/63/20240992","title":"Medium office energy consumption optimization using EnergyPlus","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Building Energy and Comfort Optimization","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 Toronto","funders":"","keywords":"HVAC; Setpoint; Energy consumption; Environmental science; Building envelope; Occupancy; Scheduling (production processes); Thermal conductivity; Baseline (sea); Thermal mass; Automotive engineering; Computer science; Thermal; Engineering; Materials science; Meteorology; Architectural engineering; Air conditioning; Mechanical engineering; Electrical engineering; Composite material; Operations management; Geography","score_opus":0.006821912456709024,"score_gpt":0.18450008071586432,"score_spread":0.1776781682591553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396745895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72308016,0.00027411434,0.1847665,0.0002843384,0.0000692844,0.00016095498,0.0017595956,0.0011231701,0.08848187],"genre_scores_gemma":[0.96763,0.00007884831,0.02351279,0.000031425065,0.000005727839,0.000072206865,0.000530556,0.00008867995,0.00804979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99991906,0.00001786751,0.0000022781117,0.000012712978,0.000029872352,0.000018144332],"domain_scores_gemma":[0.9998797,0.00007326096,0.000009444355,0.000008317844,0.000020878417,0.000008409177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018016882,0.00043066154,0.0004482609,0.0002896396,0.00024773617,0.00064629724,0.00039537612,0.00029904445,0.004986588],"category_scores_gemma":[0.0005486638,0.00022231693,0.0003829609,0.00043121818,0.0001699539,0.00030401794,0.00034229262,0.00039071633,0.00032820826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003714983,0.000028052196,0.00038811006,0.000011381186,0.0000073245833,0.000014077846,0.0000043646205,0.99445915,0.00052234955,0.0005847254,0.00027003954,0.003673258],"study_design_scores_gemma":[0.00000875966,0.000021024638,0.0002911127,0.0000014589523,0.0000034143363,0.0000028780921,0.00000960437,0.99833715,0.000502458,0.00024330242,0.0005767675,0.0000021639848],"about_ca_topic_score_codex":0.020114249,"about_ca_topic_score_gemma":0.028622229,"teacher_disagreement_score":0.020114249,"about_ca_system_score_codex":0.0005695807,"about_ca_system_score_gemma":0.00087493483,"threshold_uncertainty_score":0.03999436},"labels":[],"label_agreement":null},{"id":"W4398144376","doi":"10.54254/2755-2721/62/20240398","title":"Typical artificial intelligence algorithms and real-world applications related to handwritten number classifier","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Vehicle License Plate Recognition","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; Artificial intelligence; Classifier (UML); Artificial neural network; Convolutional neural network; Machine learning; Human intelligence; Field (mathematics); Mathematics","score_opus":0.012400761658720592,"score_gpt":0.2439807393836271,"score_spread":0.23157997772490652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398144376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019760959,0.0376326,0.8399149,0.0047631655,0.0010788377,0.0003264119,0.00065170065,0.0012359077,0.0946355],"genre_scores_gemma":[0.17208268,0.037443068,0.7617358,0.0011962887,0.0010843347,0.0003699854,0.0011120939,0.00018518307,0.024790576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989569,0.00022729524,0.00010169742,0.0002556605,0.00040846868,0.000049964863],"domain_scores_gemma":[0.99822396,0.0009598144,0.00014461609,0.00020679555,0.00043202814,0.000032777807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011042672,0.00075259904,0.00050368643,0.001968342,0.00056452135,0.0026353388,0.00095454894,0.0017518965,0.0034536677],"category_scores_gemma":[0.007340717,0.00026439433,0.0004349834,0.0043596574,0.0014511794,0.002863946,0.00060021965,0.0013448788,0.0022178094],"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":[0.00007365832,0.00011008696,0.0035068025,0.001012718,0.000054160482,0.0005293972,0.00033148378,0.029196028,0.005247319,0.30279833,0.018408498,0.6387316],"study_design_scores_gemma":[0.000018171273,0.00010445692,0.0041216174,0.00066062325,0.000042711476,0.003141504,0.0003209977,0.24067113,0.015508215,0.44022432,0.29508305,0.000103224214],"about_ca_topic_score_codex":0.0011662962,"about_ca_topic_score_gemma":0.0011221451,"teacher_disagreement_score":0.0034536677,"about_ca_system_score_codex":0.00086252316,"about_ca_system_score_gemma":0.0007601547,"threshold_uncertainty_score":0.011553705},"labels":[],"label_agreement":null},{"id":"W4398144486","doi":"10.54254/2755-2721/62/20240450","title":"Comprehensive analysis on three-phase imbalance management technology of low-voltage distribution network","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Power Systems and Technologies","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":"La Cité Collégiale","funders":"","keywords":"Renewable energy; Wind power; Power electronics; Environmental economics; Automotive engineering; Reliability (semiconductor); Electronics; Power (physics); Three-phase; Greenhouse gas; Electrical engineering; Engineering; Reliability engineering; Environmental science; Voltage; Economics","score_opus":0.003674262948280095,"score_gpt":0.1992912768861542,"score_spread":0.1956170139378741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398144486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1606125,0.008592186,0.7600859,0.00058487814,0.00011671427,0.00013071303,0.00027320805,0.000643062,0.06896092],"genre_scores_gemma":[0.96695656,0.006569076,0.017173212,0.000043661224,0.000071434944,0.00004305628,0.00031922906,0.00003578022,0.008788002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997882,0.000024069672,0.000008235392,0.00003499239,0.000119255215,0.00002523067],"domain_scores_gemma":[0.99981743,0.000038219874,0.000029099643,0.000010779875,0.00009518984,0.000009332034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021085049,0.0004461463,0.00027608895,0.0013414345,0.0004160889,0.00094653125,0.00031530941,0.00022562362,0.002564459],"category_scores_gemma":[0.00046422236,0.00013886065,0.00037367875,0.0012741436,0.00020360123,0.0016028685,0.00019873674,0.0002287421,0.00031216937],"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":[0.00019744736,0.00011512675,0.0143377455,0.0007257661,0.00010532926,0.0005688352,0.00028215136,0.4665804,0.03203692,0.09676678,0.0057807392,0.38250276],"study_design_scores_gemma":[0.000008503205,0.00010314919,0.008423515,0.00006434543,0.000084749874,0.00036322855,0.0001433745,0.93645644,0.0073538776,0.033104002,0.013865563,0.00002932396],"about_ca_topic_score_codex":0.0031874445,"about_ca_topic_score_gemma":0.0018735995,"teacher_disagreement_score":0.0031874445,"about_ca_system_score_codex":0.00083569484,"about_ca_system_score_gemma":0.00047727893,"threshold_uncertainty_score":0.008578956},"labels":[],"label_agreement":null},{"id":"W4398220246","doi":"10.54254/2755-2721/65/20240472","title":"Machinery and logistics: Development trends and prospects of automated warehouse technology","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Manufacturing and Logistics Optimization","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":"McMaster University","funders":"","keywords":"Automation; Leverage (statistics); Computer science; Analytics; Robotics; Big data; Manufacturing engineering; Warehouse; Data warehouse; Data science; Engineering management; Process management; Systems engineering; Artificial intelligence; Engineering; Robot; Business; Marketing; Database; Data mining","score_opus":0.004341330753835601,"score_gpt":0.19440406131676005,"score_spread":0.19006273056292444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398220246","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05832545,0.5892182,0.100257404,0.04651158,0.0015455531,0.000103907056,0.00057073287,0.00048304154,0.20298414],"genre_scores_gemma":[0.351158,0.5663587,0.054135993,0.003937752,0.0023299525,0.00008846973,0.0007496888,0.00010905796,0.021132438],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994671,0.00010270311,0.000037053138,0.00012004793,0.00021998161,0.000053220225],"domain_scores_gemma":[0.99906033,0.00027681465,0.0001632593,0.00007654349,0.00033382516,0.00008926586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090487214,0.00039634822,0.00024511764,0.0020917035,0.0005337895,0.0038180673,0.00069228304,0.0011525743,0.0047159134],"category_scores_gemma":[0.0011482998,0.00025944653,0.0003599754,0.004202304,0.0012702927,0.007040046,0.001189957,0.0011842726,0.0013156596],"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":[0.00007415751,0.000104140534,0.009726645,0.0013115343,0.00003182204,0.00026747517,0.0006479876,0.0040454986,0.0032365457,0.44032598,0.017761262,0.5224669],"study_design_scores_gemma":[0.000008561023,0.00017371372,0.010839336,0.0013224389,0.000032957512,0.0010240839,0.002191765,0.012986347,0.0025745456,0.11703909,0.8517395,0.000067674686],"about_ca_topic_score_codex":0.0014310995,"about_ca_topic_score_gemma":0.0013684031,"teacher_disagreement_score":0.0047159134,"about_ca_system_score_codex":0.0014664953,"about_ca_system_score_gemma":0.0018200489,"threshold_uncertainty_score":0.015776336},"labels":[],"label_agreement":null},{"id":"W4399979526","doi":"10.54254/2755-2721/56/20240660","title":"The change of renewable energy and zero-carbon economy in an anthropogenically warming climate","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Global Energy and Sustainability Research","field":"Energy","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; Global warming; Fossil fuel; Climate change; Natural resource economics; Greenhouse gas; Economics; Environmental science; Renewable fuels; Economy; Ecology","score_opus":0.01079342805596553,"score_gpt":0.24006795024062302,"score_spread":0.2292745221846575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399979526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95855004,0.003107949,0.0035954535,0.0030675335,0.00006826949,0.00001124649,0.00063514686,0.000029517429,0.030934863],"genre_scores_gemma":[0.99634093,0.001437648,0.0009031538,0.000108253604,0.000024754047,0.0000054156853,0.00012478502,0.0000034011719,0.0010516503],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9998373,0.00004034817,0.000008002621,0.000028574977,0.000048387617,0.000037443864],"domain_scores_gemma":[0.99974316,0.000043997647,0.000120504876,0.000016249536,0.000048367834,0.000027689013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021632326,0.00012377297,0.00013573263,0.0005195789,0.0004358505,0.0012034808,0.00015702922,0.00025026716,0.000978418],"category_scores_gemma":[0.00055951014,0.000050044695,0.0001547548,0.001467321,0.00069665076,0.0009379149,0.00036222313,0.0003131062,0.00011948254],"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":[0.00013016521,0.000113758404,0.66107047,0.00036618082,0.00016257669,0.0022129666,0.0025199554,0.013143794,0.0059218197,0.20240898,0.0069635683,0.10498576],"study_design_scores_gemma":[0.0000048559587,0.00006692891,0.8936286,0.00012657611,0.000056572448,0.00051048177,0.005587437,0.0065057203,0.0017244049,0.0470024,0.044748917,0.00003707063],"about_ca_topic_score_codex":0.012889604,"about_ca_topic_score_gemma":0.022087026,"teacher_disagreement_score":0.012889604,"about_ca_system_score_codex":0.0010683164,"about_ca_system_score_gemma":0.0008839651,"threshold_uncertainty_score":0.025629163},"labels":[],"label_agreement":null},{"id":"W4400216602","doi":"10.54254/2755-2721/70/20240977","title":"Aligning building information modeling and prefabricated construction with sustainable development goals: A framework for sustainable urbanization","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"BIM and Construction Integration","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":"Sustainable development; Urbanization; Construction engineering; Building information modeling; Architectural engineering; Environmental planning; Computer science; Business; Civil engineering; Engineering; Environmental science; Political science; Operations management; Economic growth; Economics","score_opus":0.003513634372817561,"score_gpt":0.17994127619075745,"score_spread":0.17642764181793988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400216602","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019547949,0.0055576144,0.7877749,0.03114862,0.00033538157,0.0002635389,0.00016269548,0.0002734921,0.15493587],"genre_scores_gemma":[0.56874025,0.005911633,0.4143539,0.0011584982,0.00014960745,0.0005541955,0.00032307522,0.00012512779,0.0086837225],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9951079,0.003004733,0.00022109688,0.00033706432,0.0009414139,0.00038776908],"domain_scores_gemma":[0.9977399,0.00088737806,0.00028282084,0.0003973315,0.00046228472,0.00023022026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007845055,0.0011039785,0.0006403527,0.0040253485,0.0026434236,0.00867503,0.0027505844,0.0029240672,0.0021958402],"category_scores_gemma":[0.0042818035,0.0005773456,0.0009950048,0.004282759,0.014260542,0.009924155,0.009746417,0.0037385395,0.00047111802],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024160122,0.000020035875,0.0002762198,0.00006522036,0.000006698287,0.000061705316,0.0010831546,0.007141485,0.00013562222,0.9803791,0.00052085385,0.010307495],"study_design_scores_gemma":[0.000005151545,0.000021009575,0.0005000315,0.00047994402,0.000013274211,0.00009613267,0.004592626,0.018590845,0.0005630537,0.87944746,0.0956597,0.000030791878],"about_ca_topic_score_codex":0.011390276,"about_ca_topic_score_gemma":0.011838642,"teacher_disagreement_score":0.011390276,"about_ca_system_score_codex":0.0065404302,"about_ca_system_score_gemma":0.010602459,"threshold_uncertainty_score":0.047454357},"labels":[],"label_agreement":null},{"id":"W4400563339","doi":"10.54254/2755-2721/74/20240458","title":"Review of the data science in the field of healthcare","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","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":"Multidisciplinary approach; Data science; Health care; Big data; Computer science; Field (mathematics); Domain (mathematical analysis); Demographics; Data quality; Quality (philosophy); Data analysis; Data mining; Knowledge management; Engineering","score_opus":0.1159029363055489,"score_gpt":0.47530386024268534,"score_spread":0.3594009239371364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400563339","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019001722,0.9878868,0.0017212108,0.0064688157,0.0015942982,0.000019561692,0.00021578948,0.000016245383,0.0018872999],"genre_scores_gemma":[0.001807055,0.99213505,0.0018466708,0.0019019672,0.0016354597,0.000027520495,0.00023502819,0.000010448491,0.00040076795],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99613994,0.0012505056,0.0008408209,0.00036265847,0.0012866263,0.00011941564],"domain_scores_gemma":[0.95533276,0.033407934,0.0023175376,0.00095237215,0.0072931335,0.0006961745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061245626,0.00069948816,0.0016536887,0.011928553,0.00094985217,0.0031655529,0.0014668725,0.001760034,0.0061957664],"category_scores_gemma":[0.022876076,0.000671068,0.0013559143,0.017619684,0.0018322347,0.0042423,0.0013944482,0.002762857,0.001886285],"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":[0.00007529562,0.000058531226,0.0009999132,0.049657702,0.00026716542,0.00020506978,0.00035763782,0.0009082303,0.0005890487,0.02845748,0.13959888,0.77882504],"study_design_scores_gemma":[0.000006199905,0.000035345285,0.0018467243,0.028857112,0.00017830626,0.0004784418,0.0002086294,0.00025728825,0.00019176851,0.010699166,0.95720506,0.000036020792],"about_ca_topic_score_codex":0.004750914,"about_ca_topic_score_gemma":0.007541978,"teacher_disagreement_score":0.011928553,"about_ca_system_score_codex":0.0029649932,"about_ca_system_score_gemma":0.008002052,"threshold_uncertainty_score":0.032390118},"labels":[],"label_agreement":null},{"id":"W4400566749","doi":"10.54254/2755-2721/55/20241512","title":"Research on the applicability of suicide tweet detection algorithms","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Mental Health via Writing","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Machine learning; Random forest; Artificial intelligence; Field (mathematics); Social media; Process (computing); Data science; Natural language processing; World Wide Web","score_opus":0.06353663811093299,"score_gpt":0.3885069942259575,"score_spread":0.32497035611502456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400566749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22721452,0.013301109,0.7089851,0.00647573,0.00075992005,0.0005316292,0.0010328422,0.002553388,0.039145872],"genre_scores_gemma":[0.7408605,0.005079167,0.24670486,0.0005901055,0.00037907585,0.00016847783,0.0009947143,0.00021352667,0.0050096437],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99521554,0.0023934238,0.00033255917,0.0009279112,0.000974183,0.00015643174],"domain_scores_gemma":[0.9321635,0.05660788,0.0016498677,0.0033674573,0.0059262137,0.0002852161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00914793,0.0010959948,0.0008150429,0.0038655754,0.0008515542,0.0033665104,0.0016624208,0.0015094739,0.0029944272],"category_scores_gemma":[0.066438995,0.00056096923,0.00087122706,0.0028101518,0.0008866911,0.004512343,0.0009061978,0.0017191457,0.0021026435],"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":[0.00026701236,0.0003954897,0.046358395,0.0006297184,0.00040854505,0.00007660172,0.00062201585,0.08644809,0.0034310743,0.019280078,0.0051229494,0.83696014],"study_design_scores_gemma":[0.00003263123,0.0002852528,0.011998757,0.0003133194,0.00013666129,0.0004196121,0.0008836425,0.93812525,0.007977619,0.025235731,0.014518788,0.00007270413],"about_ca_topic_score_codex":0.005968015,"about_ca_topic_score_gemma":0.0032645466,"teacher_disagreement_score":0.00914793,"about_ca_system_score_codex":0.0012555149,"about_ca_system_score_gemma":0.0012844892,"threshold_uncertainty_score":0.04837942},"labels":[],"label_agreement":null},{"id":"W4400663124","doi":"10.54254/2755-2721/76/20240608","title":"A review of methods for alleviating hallucination issues in large language models","year":2024,"lang":"en","type":"review","venue":"Applied and Computational Engineering","topic":"Text Readability and Simplification","field":"Computer Science","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":"Milton District Hospital","funders":"","keywords":"Psychology; Computer science; Linguistics; Philosophy","score_opus":0.042605813696583734,"score_gpt":0.3892562671185863,"score_spread":0.34665045342200257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400663124","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022252435,0.36192355,0.62239945,0.0027238966,0.0011963117,0.00013982259,0.00033274485,0.0010933396,0.007965564],"genre_scores_gemma":[0.054959,0.5068038,0.4206019,0.0022999556,0.0046544992,0.0004676383,0.0014884919,0.0006095345,0.008115122],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981414,0.0005835333,0.00029974707,0.00031522248,0.0005919448,0.00006814089],"domain_scores_gemma":[0.9936248,0.0045906478,0.00028186236,0.0004940813,0.00092071446,0.000087820124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002655141,0.0015754933,0.0013737895,0.0024724326,0.00064649584,0.0023907733,0.0023217637,0.0014711188,0.0038522733],"category_scores_gemma":[0.010976394,0.0007856787,0.002017578,0.0031077748,0.0010329408,0.003808711,0.0015020008,0.0026504959,0.0024248231],"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":[0.00007793928,0.000079572885,0.00089598336,0.006369736,0.00024977865,0.0002863377,0.00044384348,0.012094006,0.0022822248,0.039467413,0.024250882,0.9135023],"study_design_scores_gemma":[0.00006574962,0.00032849517,0.0033907401,0.004920884,0.000745619,0.0028132752,0.0007432882,0.18395437,0.008365861,0.14761822,0.64671594,0.00033749064],"about_ca_topic_score_codex":0.0029421886,"about_ca_topic_score_gemma":0.002665148,"teacher_disagreement_score":0.0038522733,"about_ca_system_score_codex":0.00082122505,"about_ca_system_score_gemma":0.0016040793,"threshold_uncertainty_score":0.014041841},"labels":[],"label_agreement":null},{"id":"W4400663409","doi":"10.54254/2755-2721/76/20240568","title":"Automating pouring process in precision casting","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Automation; Computer science; Process (computing); Flowchart; Robot; Intersection (aeronautics); Flexibility (engineering); Adaptability; Object (grammar); Manufacturing engineering; Software engineering; Artificial intelligence; Engineering","score_opus":0.0044483127732745514,"score_gpt":0.2060582041822376,"score_spread":0.20160989140896307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400663409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.082499176,0.00039806488,0.905122,0.00009038383,0.00005310519,0.00017580162,0.000067577996,0.0035538836,0.00804012],"genre_scores_gemma":[0.6292747,0.000439588,0.36665365,0.00002946837,0.000012351675,0.000068848494,0.000118170734,0.00019118,0.00321192],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993888,0.000092872244,0.00003552355,0.00012119596,0.00023726776,0.00012430608],"domain_scores_gemma":[0.99964714,0.00012960954,0.00004785393,0.00007937408,0.00007511519,0.000020912854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056372746,0.00069456454,0.00082022784,0.00065500307,0.00089222146,0.0013260847,0.0010333365,0.00081129593,0.0031718204],"category_scores_gemma":[0.0009106571,0.0004887676,0.000615322,0.0006165345,0.0008407836,0.00081526535,0.0009252359,0.0006007911,0.0008142219],"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":[0.00050708494,0.0001888406,0.0034241118,0.0006701231,0.000029057186,0.00049558,0.0008392887,0.34592307,0.17604364,0.02538419,0.0016575482,0.44483736],"study_design_scores_gemma":[0.000042797696,0.0003928148,0.0025783582,0.00006332072,0.000057422498,0.00036387527,0.00021448065,0.8234028,0.14924458,0.008550128,0.014999174,0.00009025302],"about_ca_topic_score_codex":0.0057371184,"about_ca_topic_score_gemma":0.0051153474,"teacher_disagreement_score":0.0057371184,"about_ca_system_score_codex":0.00048436338,"about_ca_system_score_gemma":0.0015536419,"threshold_uncertainty_score":0.011407435},"labels":[],"label_agreement":null},{"id":"W4400780666","doi":"10.54254/2755-2721/45/20241041","title":"Analysis of the current development and future prospect of autonomous driving","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Simulation and Modeling Applications","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":"Queen's University","funders":"","keywords":"Current (fluid); Engineering; Systems engineering; Electrical engineering","score_opus":0.005403117959854049,"score_gpt":0.2069454775466015,"score_spread":0.20154235958674746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400780666","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24746065,0.14371558,0.0777756,0.04566083,0.0019865893,0.00019786604,0.002079658,0.00061231595,0.48051092],"genre_scores_gemma":[0.885701,0.07695884,0.010282774,0.001621405,0.00054256734,0.000084884916,0.0015836201,0.0000870918,0.02313781],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99908173,0.0001084318,0.000033722037,0.0001187283,0.0005088219,0.00014865077],"domain_scores_gemma":[0.99784005,0.0003936632,0.00018559616,0.000057828907,0.0013521021,0.00017070156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009841495,0.00032935798,0.00021546402,0.001143112,0.0006964998,0.0032434775,0.0008310715,0.0010625832,0.0054129963],"category_scores_gemma":[0.0030496742,0.00020400231,0.00034344714,0.0011503728,0.00059548364,0.0041610165,0.00064512517,0.0009708882,0.0019147788],"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":[0.00024026896,0.00018194853,0.039379857,0.0017617447,0.000068502246,0.00063601794,0.00090859766,0.029164603,0.0057347263,0.23753403,0.040412564,0.6439772],"study_design_scores_gemma":[0.00001696048,0.00052101293,0.048999418,0.0009365905,0.00011035442,0.0012788995,0.0034302943,0.07851008,0.0062176627,0.07786576,0.78197443,0.00013850855],"about_ca_topic_score_codex":0.0066454806,"about_ca_topic_score_gemma":0.006602686,"teacher_disagreement_score":0.0066454806,"about_ca_system_score_codex":0.0015927713,"about_ca_system_score_gemma":0.002248013,"threshold_uncertainty_score":0.018108249},"labels":[],"label_agreement":null},{"id":"W4400780667","doi":"10.54254/2755-2721/45/20241691","title":"A pneumonia detection system based on MobileNetv2 network and model callback","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Network Security and Intrusion Detection","field":"Computer Science","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":"Callback; Pneumonia; Computer science; Medicine; Computer network; Internal medicine","score_opus":0.004512161076508663,"score_gpt":0.17694053247199265,"score_spread":0.17242837139548398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400780667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3971163,0.00214384,0.5118212,0.0010051413,0.00085753034,0.0007894411,0.00366736,0.06289397,0.019705325],"genre_scores_gemma":[0.90561134,0.00034114753,0.08196986,0.00043297675,0.000072070216,0.00030780368,0.0039912025,0.00022993381,0.007043537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981457,0.000023393259,0.000010646191,0.0000621018,0.000049367776,0.000039982755],"domain_scores_gemma":[0.99984586,0.000026693628,0.000014137158,0.000028067785,0.00006658978,0.00001858819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038422688,0.001048311,0.00057914783,0.00095762004,0.0003517792,0.0006098775,0.0014162536,0.0006349391,0.0022505298],"category_scores_gemma":[0.0007698059,0.00028617308,0.00030680856,0.0004532613,0.000171367,0.0011976109,0.0007460329,0.00044240843,0.0010197491],"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":[0.0017801574,0.0011179708,0.018577049,0.00042484485,0.00038914933,0.0012693544,0.000225895,0.15949452,0.045289196,0.005094843,0.051175807,0.7151613],"study_design_scores_gemma":[0.00003513374,0.00020514849,0.0020631284,0.000018377372,0.000041860927,0.0001917565,0.000030849387,0.97939,0.012467425,0.0012204011,0.0043002404,0.000035733334],"about_ca_topic_score_codex":0.01191226,"about_ca_topic_score_gemma":0.011553384,"teacher_disagreement_score":0.01191226,"about_ca_system_score_codex":0.00089012284,"about_ca_system_score_gemma":0.0007591202,"threshold_uncertainty_score":0.023685813},"labels":[],"label_agreement":null},{"id":"W4400780670","doi":"10.54254/2755-2721/46/20241314","title":"Angle calculation method based on Cognex binary image processing and edge tool positioning","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Measurement and Metrology Techniques","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":"Image processing; Binary image; Enhanced Data Rates for GSM Evolution; Computer vision; Computer science; Artificial intelligence; Binary number; Image (mathematics); Computer graphics (images); Mathematics; Arithmetic","score_opus":0.007106024994393227,"score_gpt":0.24065083273167295,"score_spread":0.23354480773727973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400780670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009859652,0.00012730174,0.98686326,0.00003862312,0.000093752154,0.00006952042,0.0000268638,0.0006173347,0.0023037079],"genre_scores_gemma":[0.2744933,0.0003066052,0.71998525,0.000112635404,0.00006909464,0.00015682758,0.0001962653,0.00010278042,0.0045771957],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985567,0.00013372628,0.00010293544,0.00030471612,0.00082582165,0.000076079414],"domain_scores_gemma":[0.9992508,0.00009102593,0.00008504,0.00010157884,0.00044613305,0.000025411668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005763018,0.00066489546,0.00060826546,0.0015775446,0.0004508555,0.001254521,0.0010753844,0.00059236574,0.0019039206],"category_scores_gemma":[0.0013899078,0.0004293388,0.00041713918,0.0011029809,0.0005610607,0.0014718167,0.00073093857,0.0006140785,0.00064178393],"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":[0.00050209096,0.000112714406,0.0038805676,0.00041899932,0.00006820695,0.00031830874,0.00031036724,0.023015141,0.15136808,0.028057002,0.0047875238,0.787161],"study_design_scores_gemma":[0.00013343405,0.0006023284,0.0070863897,0.000053061165,0.000106242675,0.0023784048,0.00017047329,0.6970803,0.25808173,0.004251825,0.029838618,0.0002171685],"about_ca_topic_score_codex":0.0026612608,"about_ca_topic_score_gemma":0.0018758908,"teacher_disagreement_score":0.0026612608,"about_ca_system_score_codex":0.00055021397,"about_ca_system_score_gemma":0.0009926656,"threshold_uncertainty_score":0.006369233},"labels":[],"label_agreement":null},{"id":"W4400781246","doi":"10.54254/2755-2721/46/20241064","title":"The investigation of application related to deep learning on brain tumor diagnosis","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Brain tumor; Deep learning; Artificial intelligence; Computer science; Neuroscience; Psychology; Medicine; Pathology","score_opus":0.009654340917962112,"score_gpt":0.21895675066590453,"score_spread":0.20930240974794243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400781246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24982601,0.24880475,0.4225072,0.013055845,0.0020917894,0.00023488997,0.00052468677,0.00079884415,0.062156014],"genre_scores_gemma":[0.8128889,0.11680893,0.05814396,0.0012031643,0.0006306876,0.00006924333,0.000452249,0.000046763907,0.009756074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944156,0.0001322017,0.00003394861,0.0000938437,0.00023595906,0.000062501764],"domain_scores_gemma":[0.9985886,0.0007233909,0.00006527901,0.000051967694,0.0005202058,0.000050545033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014038386,0.00045665292,0.00035931435,0.0010163714,0.00021501523,0.0009375231,0.000531723,0.0008860113,0.0015823036],"category_scores_gemma":[0.0040298644,0.00014737359,0.0005086792,0.0012554078,0.00041978253,0.0013350078,0.000505988,0.0007574993,0.00030507075],"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":[0.00018840168,0.0002166576,0.016809031,0.001142137,0.00020095076,0.00030078797,0.00019385069,0.06294495,0.008585002,0.01861516,0.008800238,0.8820027],"study_design_scores_gemma":[0.000029096058,0.0006631522,0.017507855,0.0005579356,0.0003271409,0.00077131594,0.00043458157,0.87872976,0.025817167,0.02669506,0.048389338,0.000077620716],"about_ca_topic_score_codex":0.0042285384,"about_ca_topic_score_gemma":0.0033437985,"teacher_disagreement_score":0.0042285384,"about_ca_system_score_codex":0.00067566935,"about_ca_system_score_gemma":0.0008488973,"threshold_uncertainty_score":0.008407831},"labels":[],"label_agreement":null},{"id":"W4400781604","doi":"10.54254/2755-2721/46/20241053","title":"An investigation on strategies for optimizing consumer trust in chatbots","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Business; Computer science","score_opus":0.017115351327167527,"score_gpt":0.25059554412110285,"score_spread":0.23348019279393534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400781604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9056553,0.00047137658,0.05329715,0.002677925,0.000046263707,0.00049624505,0.000036412566,0.00015237313,0.03716691],"genre_scores_gemma":[0.9911991,0.00008644376,0.0069699897,0.00010427934,0.0000065242116,0.00006804949,0.000012603098,0.000012113647,0.0015409716],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99506444,0.0035260064,0.00016178603,0.0003528906,0.00051321875,0.00038160355],"domain_scores_gemma":[0.97360367,0.017447578,0.002998351,0.0016412553,0.0030091745,0.0012999567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006969377,0.0005449257,0.00037680988,0.0006884516,0.0019049756,0.0036683818,0.0010225854,0.0013365727,0.0045967666],"category_scores_gemma":[0.032404434,0.00032215533,0.00043558635,0.00051571603,0.0016384098,0.0041291397,0.0018248578,0.001146362,0.00046612878],"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":[0.0033026463,0.004239046,0.24301331,0.0020707452,0.00035784507,0.002425158,0.09912009,0.0120969545,0.02977135,0.23705986,0.007282283,0.35926077],"study_design_scores_gemma":[0.00088962127,0.0073965606,0.174567,0.0010638034,0.0014592041,0.0021919876,0.17707464,0.36558837,0.03285515,0.16044475,0.07599529,0.0004735576],"about_ca_topic_score_codex":0.0037967744,"about_ca_topic_score_gemma":0.0045657237,"teacher_disagreement_score":0.006969377,"about_ca_system_score_codex":0.0029251974,"about_ca_system_score_gemma":0.0024439818,"threshold_uncertainty_score":0.036858022},"labels":[],"label_agreement":null},{"id":"W4400985398","doi":"10.54254/2755-2721/79/20241631","title":"Analysis of clustering algorithms in Iris and breast cancer datasets","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cluster analysis; DBSCAN; Expansive; Computer science; Noise (video); Data mining; Domain (mathematical analysis); Data science; Artificial intelligence; CURE data clustering algorithm; Correlation clustering; Mathematics; Image (mathematics)","score_opus":0.009490061202234312,"score_gpt":0.2762931799248556,"score_spread":0.26680311872262125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400985398","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8620221,0.008205457,0.08428194,0.0027894361,0.0004422343,0.0005275964,0.026137264,0.0037651751,0.011828756],"genre_scores_gemma":[0.74690175,0.0021487577,0.18705429,0.00029105355,0.00012959396,0.00036011578,0.059811495,0.00034875702,0.00295417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99610823,0.0009865684,0.00033913855,0.0006911473,0.0015493925,0.00032555548],"domain_scores_gemma":[0.9938876,0.0025446818,0.00055990665,0.00088011107,0.0019947633,0.00013291517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005729587,0.00083236344,0.00082362466,0.005378052,0.0012034206,0.0015196638,0.0012278518,0.0011110451,0.00087981165],"category_scores_gemma":[0.015226041,0.0001760186,0.0012378059,0.006120843,0.000510924,0.00096970017,0.0008300506,0.0009848638,0.0007100225],"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":[0.0019172962,0.0008751011,0.09877487,0.001626293,0.0011894488,0.00043584887,0.00094050023,0.28262106,0.008395379,0.019213147,0.08289592,0.50111514],"study_design_scores_gemma":[0.00009664997,0.00043855957,0.1360286,0.00016760085,0.00022799411,0.000618479,0.0013364938,0.79983807,0.01517404,0.011857635,0.034093514,0.0001223945],"about_ca_topic_score_codex":0.015609762,"about_ca_topic_score_gemma":0.02139819,"teacher_disagreement_score":0.015609762,"about_ca_system_score_codex":0.0022006596,"about_ca_system_score_gemma":0.0013788536,"threshold_uncertainty_score":0.031037807},"labels":[],"label_agreement":null},{"id":"W4400985439","doi":"10.54254/2755-2721/79/20241329","title":"Predicting drug-drug interactions using heterogeneous graph neural networks: HGNN-DDI","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Graph; Drug; Artificial neural network; Machine learning; Similarity (geometry); Artificial intelligence; Data mining; Theoretical computer science; Medicine; Pharmacology","score_opus":0.011309459401854783,"score_gpt":0.25157576074684856,"score_spread":0.24026630134499377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400985439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25492722,0.003048639,0.7320564,0.0011110797,0.00018126135,0.0001973207,0.0013479062,0.002615355,0.0045148553],"genre_scores_gemma":[0.9159002,0.00066292554,0.07999461,0.00031190395,0.00006411145,0.00008329969,0.0010986829,0.000053827887,0.0018304589],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971384,0.000082177045,0.000018729756,0.00009678142,0.000048844227,0.000039654675],"domain_scores_gemma":[0.9995136,0.0002897878,0.000065060056,0.00004170937,0.00005883339,0.000030974705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059054437,0.0008807883,0.0007741533,0.0012915325,0.00023794454,0.00060457195,0.00083835516,0.00081815186,0.0012000998],"category_scores_gemma":[0.0018774271,0.00029029974,0.0007072156,0.00093624846,0.00027590184,0.0008114049,0.00060469296,0.00071594235,0.00019816341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012765988,0.00011530613,0.004322483,0.00006632394,0.00015201811,0.0000956838,0.000012279208,0.9136764,0.0012266954,0.0014439406,0.0011105905,0.077650614],"study_design_scores_gemma":[0.000003232545,0.000019876736,0.00025306203,0.0000018813648,0.000010639139,0.000010641006,0.0000020638133,0.99833125,0.00022510292,0.0010359875,0.00010401167,0.0000022897902],"about_ca_topic_score_codex":0.015784902,"about_ca_topic_score_gemma":0.015902556,"teacher_disagreement_score":0.015784902,"about_ca_system_score_codex":0.0010368071,"about_ca_system_score_gemma":0.0007564299,"threshold_uncertainty_score":0.031386018},"labels":[],"label_agreement":null},{"id":"W4400985571","doi":"10.54254/2755-2721/79/20241399","title":"Label noise learning with the combination of CausalNL and CGAN models","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Overfitting; Artificial intelligence; Computer science; Noise (video); Artificial neural network; Machine learning; Transfer of learning; Set (abstract data type); Training set; Deep learning; Algorithm; Pattern recognition (psychology); Image (mathematics)","score_opus":0.007219495042634706,"score_gpt":0.19885643849389767,"score_spread":0.19163694345126298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400985571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01330366,0.0005869925,0.9788558,0.0010960039,0.00013573148,0.00008479711,0.00025636322,0.0023920669,0.0032886264],"genre_scores_gemma":[0.5631014,0.0006151875,0.42175862,0.0023561025,0.00044442865,0.00039523627,0.0018469113,0.000619647,0.008862436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966654,0.001424493,0.00013310958,0.0007850792,0.0007589427,0.00023296627],"domain_scores_gemma":[0.99312145,0.0031902802,0.0005295452,0.0018929818,0.0010677415,0.00019794774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065992153,0.0013905255,0.0013393774,0.0016514953,0.0008904252,0.0018205062,0.0031595957,0.0024323529,0.0025447756],"category_scores_gemma":[0.014585101,0.0006297328,0.0013443903,0.0018684043,0.0018047387,0.0046088286,0.0031673172,0.0044991164,0.00079251983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037027925,0.00038602256,0.0044790525,0.00031667796,0.00020267349,0.00015580918,0.00023056856,0.4350884,0.0022270083,0.16169336,0.018711617,0.3761385],"study_design_scores_gemma":[0.00001838066,0.000041641357,0.00018444694,0.00002067161,0.00001622377,0.000039693823,0.000012452441,0.93046623,0.0010089548,0.066181086,0.0019965763,0.000013765508],"about_ca_topic_score_codex":0.0051857876,"about_ca_topic_score_gemma":0.01003758,"teacher_disagreement_score":0.0065992153,"about_ca_system_score_codex":0.0024258324,"about_ca_system_score_gemma":0.0021379092,"threshold_uncertainty_score":0.034900427},"labels":[],"label_agreement":null},{"id":"W4400986356","doi":"10.54254/2755-2721/55/20241422","title":"Improvement research of Invariant Collaborative Filtering","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Wireless Sensor Networks and IoT","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":"Invariant (physics); Collaborative filtering; Computer science; Mathematics; Information retrieval; Recommender system","score_opus":0.013502863587225807,"score_gpt":0.24219876661499717,"score_spread":0.22869590302777137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400986356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0120607205,0.00080873835,0.9850117,0.00023984691,0.000069775866,0.000033687647,0.00005632405,0.00039272013,0.0013264338],"genre_scores_gemma":[0.67685753,0.0022938543,0.31423655,0.0005569613,0.00044638244,0.00013441071,0.0005194654,0.0002006348,0.0047542118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99379647,0.0018766158,0.0004224329,0.0013310942,0.002167489,0.00040585303],"domain_scores_gemma":[0.9876232,0.006614587,0.0007607775,0.0022267755,0.002554221,0.00022035913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066266805,0.0013655612,0.0021226984,0.0016670744,0.0008965104,0.0017181565,0.0024094586,0.0017498495,0.00174308],"category_scores_gemma":[0.023512375,0.0005171391,0.0019210111,0.002289149,0.0013186239,0.004125904,0.0014272239,0.0021532807,0.0007011393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025841757,0.00023588097,0.005295522,0.00032048512,0.00030011203,0.00014758034,0.0002672562,0.5467285,0.0076273875,0.076301344,0.004065894,0.35845166],"study_design_scores_gemma":[0.00001167422,0.000084008876,0.00036595788,0.000008189483,0.000032214502,0.00005229763,0.00000998707,0.9899233,0.00092791533,0.0074542034,0.0011155132,0.000014733299],"about_ca_topic_score_codex":0.0120631745,"about_ca_topic_score_gemma":0.0062032198,"teacher_disagreement_score":0.0120631745,"about_ca_system_score_codex":0.0019671668,"about_ca_system_score_gemma":0.001962741,"threshold_uncertainty_score":0.035045683},"labels":[],"label_agreement":null},{"id":"W4401032683","doi":"10.54254/2755-2721/72/20240986","title":"Maximizing project efficiency and collaboration in construction management through Building Information Modeling (BIM)","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"BIM and Construction Integration","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":"Toronto Metropolitan University","funders":"","keywords":"Building information modeling; Stakeholder; Workflow; Process management; Profitability index; Project management; Business; Return on investment; Knowledge management; Computer science; Systems engineering; Engineering; Operations management","score_opus":0.005383347659076168,"score_gpt":0.2061643089723207,"score_spread":0.20078096131324452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401032683","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30750865,0.0015106138,0.62979364,0.0031299724,0.00003589037,0.00023331103,0.00011272957,0.00041642712,0.057258736],"genre_scores_gemma":[0.8710425,0.00055563456,0.12697558,0.000054131,0.0000108159,0.00013441678,0.000057968617,0.000049578553,0.0011193571],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98941493,0.006993299,0.00030682707,0.00045363966,0.0022622596,0.0005691816],"domain_scores_gemma":[0.99482006,0.002763224,0.0010019165,0.00062495185,0.0005234197,0.00026642927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007678846,0.0006186444,0.00052951026,0.002402326,0.0014583603,0.0049467203,0.0011042621,0.0011296221,0.0015959145],"category_scores_gemma":[0.012029902,0.0005103185,0.00040285548,0.0041860207,0.0017558886,0.0050339014,0.0050377836,0.0008780308,0.0004018419],"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":[0.00017586001,0.0005393447,0.02869167,0.00052678393,0.00012985965,0.0002546542,0.005343897,0.34811932,0.006708004,0.20196341,0.003822811,0.4037243],"study_design_scores_gemma":[0.0000839148,0.000527574,0.02735458,0.00047025026,0.00017762899,0.00047834945,0.009189763,0.64801687,0.017169861,0.2462399,0.05013848,0.00015284316],"about_ca_topic_score_codex":0.00207254,"about_ca_topic_score_gemma":0.0033256032,"teacher_disagreement_score":0.007678846,"about_ca_system_score_codex":0.0021954286,"about_ca_system_score_gemma":0.003934962,"threshold_uncertainty_score":0.040610135},"labels":[],"label_agreement":null},{"id":"W4401184811","doi":"10.54254/2755-2721/84/20240911","title":"Solar photovoltaic buildings: The combination of sustainable energy and green buildings","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Photovoltaic Systems and Sustainability","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thornhill Medical (Canada)","funders":"","keywords":"Photovoltaic system; Architectural engineering; Sustainable energy; Environmental science; Solar energy; Zero-energy building; Renewable energy; Photovoltaic mounting system; Engineering physics; Engineering; Electrical engineering","score_opus":0.002682336951205877,"score_gpt":0.17441117960078908,"score_spread":0.1717288426495832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401184811","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073713744,0.8855915,0.015916348,0.009931395,0.0018003106,0.000040903953,0.00016027167,0.0001837725,0.0790042],"genre_scores_gemma":[0.113736026,0.86276937,0.008171639,0.0021956335,0.0018980961,0.000041909734,0.00018272926,0.00005712381,0.010947623],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99939275,0.00016789717,0.000025049492,0.00007153707,0.00028457667,0.00005818663],"domain_scores_gemma":[0.99978477,0.00007844017,0.000024695008,0.00001625846,0.00006762049,0.000028245138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055731274,0.00052288006,0.0006670178,0.0007758005,0.00044268687,0.0022786213,0.00053797587,0.0011211177,0.0047586244],"category_scores_gemma":[0.00045568432,0.00023986975,0.00047232804,0.0017358572,0.0007545095,0.0023652618,0.0014071822,0.0014319031,0.0012868863],"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":[0.00005729198,0.000064110456,0.0010098749,0.008274488,0.00015343861,0.00029916878,0.0002485972,0.0032475907,0.0079854205,0.11682403,0.031353526,0.8304823],"study_design_scores_gemma":[0.000007742674,0.00011624388,0.0011917072,0.0015786553,0.00006231232,0.0007337205,0.00022288908,0.0007862458,0.002785416,0.041763287,0.9507224,0.000029296802],"about_ca_topic_score_codex":0.000752075,"about_ca_topic_score_gemma":0.0017020191,"teacher_disagreement_score":0.0047586244,"about_ca_system_score_codex":0.0006166853,"about_ca_system_score_gemma":0.001032989,"threshold_uncertainty_score":0.015919209},"labels":[],"label_agreement":null},{"id":"W4401919341","doi":"10.54254/2755-2721/71/20241635","title":"Evaluating the efficacy of machine learning in calibrating low-cost sensors","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Random forest; Computer science; Machine learning; Calibration; Reliability (semiconductor); Support vector machine; Artificial neural network; Mean squared error; Sensitivity (control systems); Artificial intelligence; Data mining; Engineering; Statistics","score_opus":0.020999994695238826,"score_gpt":0.2721360570079214,"score_spread":0.25113606231268254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401919341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2882003,0.008309468,0.6892071,0.000952582,0.00050096255,0.000423153,0.00076673843,0.0016785768,0.009961238],"genre_scores_gemma":[0.7942973,0.002428626,0.20075588,0.00020638603,0.00009832422,0.00028188893,0.00074146263,0.00019938826,0.0009908188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99375796,0.0025314193,0.0003408376,0.0007870952,0.0023736593,0.00020904932],"domain_scores_gemma":[0.98326194,0.011556699,0.0009251634,0.0017963226,0.0023732095,0.00008660175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009990132,0.0013222061,0.00072162814,0.0011765889,0.0005101631,0.0013634274,0.0012840084,0.0015998207,0.0010123536],"category_scores_gemma":[0.036527984,0.00038748686,0.0006595609,0.0013549603,0.0007383185,0.0021555708,0.0008485884,0.0010156091,0.0005503847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007680456,0.000497892,0.032258403,0.0016663296,0.0005670368,0.0001182578,0.0001541649,0.5856063,0.025127536,0.006551099,0.0023668585,0.3443181],"study_design_scores_gemma":[0.00005288818,0.0009162356,0.015373479,0.00029985662,0.00015568052,0.00014917036,0.00012336619,0.91668546,0.05331068,0.006217314,0.0066339425,0.0000819545],"about_ca_topic_score_codex":0.0030099927,"about_ca_topic_score_gemma":0.0021650903,"teacher_disagreement_score":0.009990132,"about_ca_system_score_codex":0.0007401698,"about_ca_system_score_gemma":0.0006945189,"threshold_uncertainty_score":0.052833438},"labels":[],"label_agreement":null},{"id":"W4402420751","doi":"10.54254/2755-2721/71/20241645","title":"Review of machine learning with sentimental analysis method for cross-model stock price prediction","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Stock price; Stock (firearms); Computer science; Artificial intelligence; Econometrics; Machine learning; Economics; Engineering; Series (stratigraphy)","score_opus":0.03811610425321107,"score_gpt":0.37991161495098913,"score_spread":0.3417955106977781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402420751","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008303097,0.68001723,0.28623995,0.0033834483,0.00312245,0.00015707112,0.0010378931,0.0009746115,0.01676431],"genre_scores_gemma":[0.097048275,0.7147402,0.16153276,0.0021314493,0.0055112396,0.00030651403,0.0029656882,0.0003133842,0.015450531],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993523,0.00011280478,0.0001056636,0.00017938399,0.00021939886,0.000030368261],"domain_scores_gemma":[0.99906975,0.00042810477,0.00006419376,0.000050292456,0.00036352017,0.000024170291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013133204,0.00088252476,0.00076723244,0.0016725687,0.0002634216,0.0011043277,0.0011227395,0.0008948126,0.0032096563],"category_scores_gemma":[0.003159824,0.00041433368,0.0010854256,0.002588399,0.00023434042,0.0014475927,0.00040513385,0.000981328,0.002098666],"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":[0.00007874747,0.00008335202,0.0026018037,0.0031820226,0.0002608008,0.00013442311,0.00007457474,0.010288144,0.001978842,0.008466696,0.032393046,0.9404576],"study_design_scores_gemma":[0.000065328364,0.0004610148,0.01300897,0.0029312563,0.00081532024,0.0011477624,0.00020394656,0.25582173,0.008895387,0.029103864,0.687285,0.00026050382],"about_ca_topic_score_codex":0.0036132878,"about_ca_topic_score_gemma":0.0024380127,"teacher_disagreement_score":0.0036132878,"about_ca_system_score_codex":0.00042594122,"about_ca_system_score_gemma":0.00074258714,"threshold_uncertainty_score":0.0107373595},"labels":[],"label_agreement":null},{"id":"W4402952184","doi":"10.54254/2755-2721/83/2024glg0059","title":"A comparative study between WGAN-GP and WGAN-CP for image generation","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Image Processing Techniques and Applications","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":"Image (mathematics); Computer science; Artificial intelligence","score_opus":0.020993926160130218,"score_gpt":0.27467812016717635,"score_spread":0.2536841940070461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402952184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28451246,0.012347466,0.6524206,0.0019880687,0.00085942075,0.0005503247,0.0015111151,0.0065285456,0.039282087],"genre_scores_gemma":[0.8061364,0.0031921691,0.17711489,0.00067423907,0.00013064186,0.00023759872,0.0039457777,0.00090015907,0.007668182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988625,0.0003409757,0.000051021663,0.00024540667,0.00038126262,0.00011894305],"domain_scores_gemma":[0.9974692,0.0015460997,0.000107695065,0.0004118099,0.00036127516,0.00010389401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023607598,0.0011902653,0.000679804,0.0009381473,0.00040157425,0.0011787332,0.0012328887,0.0012860451,0.002492077],"category_scores_gemma":[0.0073773745,0.00034306166,0.00064674794,0.0008226928,0.00066453946,0.0016940323,0.0010788795,0.0016740185,0.00068377896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086126995,0.0003131722,0.0027355433,0.0006276808,0.00023752831,0.00028862883,0.00012683227,0.61323196,0.009503738,0.008986578,0.013531835,0.34955516],"study_design_scores_gemma":[0.000034093715,0.00020104735,0.0013304587,0.00003850902,0.00003456416,0.00016721876,0.00003704885,0.98684317,0.0051016333,0.003067653,0.0031260832,0.000018584222],"about_ca_topic_score_codex":0.0058293645,"about_ca_topic_score_gemma":0.0069535905,"teacher_disagreement_score":0.0058293645,"about_ca_system_score_codex":0.00080900005,"about_ca_system_score_gemma":0.0008239426,"threshold_uncertainty_score":0.012485027},"labels":[],"label_agreement":null},{"id":"W4403371266","doi":"10.54254/2755-2721/90/20241764","title":"Predictive modeling in high-frequency trading using machine learning","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"High-frequency trading; Computer science; High frequency ultrasound; Artificial intelligence; Machine learning; Algorithmic trading; Economics; Financial economics; Physics; Acoustics","score_opus":0.06867051075802319,"score_gpt":0.32284928062934604,"score_spread":0.2541787698713228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403371266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2296384,0.001016069,0.7654796,0.0005665748,0.000054243777,0.000055597535,0.0001330712,0.00070089806,0.0023555672],"genre_scores_gemma":[0.952707,0.00031903086,0.04608403,0.00006368258,0.000052301948,0.000032375843,0.00013371522,0.000022748165,0.00058510416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923956,0.0003819232,0.00003677788,0.00010673002,0.00017936548,0.00005562501],"domain_scores_gemma":[0.99558026,0.0035007843,0.00031542216,0.00022959907,0.0003306211,0.000043234744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00394086,0.0006865793,0.00085816317,0.001234562,0.00034760585,0.0013110171,0.0007665696,0.0005774576,0.000594972],"category_scores_gemma":[0.0074118106,0.0003257596,0.00056575245,0.0009900093,0.00040439956,0.0013498283,0.0004883133,0.0011199686,0.00024153589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041253916,0.00005309661,0.0058290004,0.000029126326,0.00006575536,0.000042384014,0.000034838944,0.9394307,0.0008038495,0.0027393221,0.0002920712,0.050638705],"study_design_scores_gemma":[0.0000011387293,0.000007208731,0.00041698123,0.000002607587,0.0000028128427,0.0000053701015,0.0000030111364,0.99781656,0.0001672139,0.0014999941,0.00007513325,0.000002062061],"about_ca_topic_score_codex":0.005004938,"about_ca_topic_score_gemma":0.0048084925,"teacher_disagreement_score":0.005004938,"about_ca_system_score_codex":0.000601889,"about_ca_system_score_gemma":0.00060807157,"threshold_uncertainty_score":0.020841539},"labels":[],"label_agreement":null},{"id":"W4403844223","doi":"10.54254/2755-2721/95/2024ch0053","title":"Enhanced robustness in machine learning: Application of an adaptive robust loss function","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Fault Detection and Control Systems","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":"Robustness (evolution); Computer science; Artificial intelligence; Machine learning; Chemistry","score_opus":0.004858550943950819,"score_gpt":0.18246403170081535,"score_spread":0.17760548075686453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403844223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008943578,0.00049171265,0.988818,0.0003314787,0.00006020038,0.000027453032,0.000025650706,0.0003005702,0.0010012984],"genre_scores_gemma":[0.664443,0.0014718989,0.3298338,0.00051757105,0.00038260853,0.00019893755,0.00020640162,0.00038191365,0.0025639397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970107,0.001227442,0.00019473344,0.00051760935,0.0008809775,0.00016859254],"domain_scores_gemma":[0.9921633,0.004770115,0.0007760476,0.0011255356,0.0010170878,0.00014795316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006585868,0.0015727894,0.0011209939,0.0012632747,0.00036344974,0.001532795,0.0019267531,0.0019468664,0.0009372217],"category_scores_gemma":[0.021364257,0.00047476942,0.0011121046,0.00095362664,0.0015838419,0.0032151986,0.0026568058,0.0028134477,0.00043448427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016986975,0.00010438986,0.0016473113,0.0001518597,0.00015938484,0.00019168838,0.00010291926,0.86532515,0.012731359,0.029705407,0.001558996,0.08815171],"study_design_scores_gemma":[0.0000052411083,0.00006618059,0.00025602506,0.000013202143,0.000013462378,0.000052706335,0.0000057306843,0.9896625,0.0024158685,0.006815814,0.00067840214,0.000014900087],"about_ca_topic_score_codex":0.0011715362,"about_ca_topic_score_gemma":0.0005804072,"teacher_disagreement_score":0.006585868,"about_ca_system_score_codex":0.0008664436,"about_ca_system_score_gemma":0.0008063706,"threshold_uncertainty_score":0.034829795},"labels":[],"label_agreement":null},{"id":"W4404222786","doi":"10.54254/2755-2721/93/20240986","title":"Research on image recognition and processing application technology of unmanned vehicle based on deep learning","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Algorithms and Applications","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":"Artificial intelligence; Computer science; Deep learning; Computer vision; Image processing; Image (mathematics)","score_opus":0.011926809053736467,"score_gpt":0.2623430311204338,"score_spread":0.25041622206669734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404222786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031759977,0.057348784,0.8721548,0.003384235,0.00057072734,0.000092131064,0.00020833359,0.0006405173,0.033840463],"genre_scores_gemma":[0.5164677,0.07359981,0.38565075,0.001297145,0.0007442657,0.00014123418,0.0005668985,0.00012310571,0.021409085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950445,0.00006791279,0.000026346352,0.00013585338,0.00021787814,0.000047576454],"domain_scores_gemma":[0.9992186,0.00028197168,0.00007651883,0.000119538556,0.00027454394,0.000028843266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007393601,0.0005495769,0.00046210136,0.0009002991,0.00018442866,0.001145994,0.00081237283,0.0008663632,0.0017317185],"category_scores_gemma":[0.0014598124,0.00027885658,0.0005388059,0.001307865,0.0009253514,0.0024865924,0.00066268956,0.0014494343,0.0007950542],"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":[0.00006408876,0.000119408236,0.0027095745,0.0009869235,0.00011568995,0.0001027988,0.00018667612,0.053399567,0.03038473,0.2002837,0.0055978405,0.706049],"study_design_scores_gemma":[0.000016557115,0.00030235475,0.006163777,0.0003625151,0.000099121244,0.00046019012,0.00014998953,0.6509483,0.06336333,0.1352755,0.1427654,0.00009301444],"about_ca_topic_score_codex":0.0021452103,"about_ca_topic_score_gemma":0.001234002,"teacher_disagreement_score":0.0021452103,"about_ca_system_score_codex":0.0011128203,"about_ca_system_score_gemma":0.00089614274,"threshold_uncertainty_score":0.008074105},"labels":[],"label_agreement":null},{"id":"W4404223012","doi":"10.54254/2755-2721/82/20241022","title":"AI-driven automatic generation and rendering of game characters","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Rendering (computer graphics); Computer science; Video game; Computer graphics (images); Human–computer interaction; Multimedia","score_opus":0.010396210590959786,"score_gpt":0.23584067458210156,"score_spread":0.22544446399114176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404223012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068293354,0.000699503,0.97956985,0.0002317347,0.0001556314,0.00013568239,0.00014952246,0.0019377233,0.010291131],"genre_scores_gemma":[0.42920896,0.0017830015,0.54852694,0.000413733,0.00011338361,0.0003232354,0.00079755974,0.001677176,0.017155968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995735,0.00012023076,0.000020330817,0.000085587206,0.00016416132,0.000036113906],"domain_scores_gemma":[0.9994935,0.00027894625,0.00003069279,0.000090550304,0.0000702931,0.00003600455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006625607,0.00082727586,0.000451791,0.00038419868,0.00024441848,0.0013908413,0.0010349842,0.0007271708,0.006644697],"category_scores_gemma":[0.0025913268,0.00039075714,0.0006237101,0.0002264389,0.0007228801,0.0009241989,0.0011364223,0.0012888188,0.0018725645],"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":[0.00017541458,0.00009891039,0.0010499175,0.000615784,0.00011602558,0.00039353743,0.00040240525,0.54729,0.0517657,0.07160469,0.014986502,0.31150106],"study_design_scores_gemma":[0.000016056554,0.000047957285,0.00022934847,0.00006526446,0.000012533321,0.0002127423,0.000038950457,0.9466791,0.013543252,0.019771835,0.019356439,0.000026454765],"about_ca_topic_score_codex":0.000768152,"about_ca_topic_score_gemma":0.0012281238,"teacher_disagreement_score":0.006644697,"about_ca_system_score_codex":0.000424136,"about_ca_system_score_gemma":0.0003374876,"threshold_uncertainty_score":0.022228718},"labels":[],"label_agreement":null},{"id":"W4404223187","doi":"10.54254/2755-2721/98/20241127","title":"Innovative Applications of Parametric Design and Digital Tools in Architecture: Exploring the Integration of Generative Design and BIM Technology","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Architecture and Computational Design","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":"Generative Design; Architecture; Systems engineering; Computer architecture; Generative grammar; Computer science; Design technology; Parametric design; Parametric statistics; Engineering; Human–computer interaction; Software engineering; Artificial intelligence; Geography","score_opus":0.02434388050290527,"score_gpt":0.21771192955516677,"score_spread":0.19336804905226151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404223187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032344006,0.006341315,0.7637636,0.005405182,0.00020726661,0.00008038098,0.000041870906,0.00018285512,0.19163352],"genre_scores_gemma":[0.65073335,0.008495867,0.32913536,0.0006813823,0.00018429679,0.00018302538,0.0000542228,0.00013560338,0.010396913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970149,0.0018507122,0.00007049863,0.00016466109,0.0007628526,0.00013652381],"domain_scores_gemma":[0.996838,0.0022295553,0.00012062221,0.00054761855,0.00018058906,0.00008361769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036189624,0.0006725941,0.0003874512,0.002466217,0.0012708756,0.005529894,0.0013362112,0.0016411876,0.003560832],"category_scores_gemma":[0.004175216,0.0005338027,0.00072520407,0.0018870843,0.012073181,0.00512229,0.0046868715,0.0016459753,0.00057638565],"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":[0.000010725771,0.000028162032,0.0007060936,0.00015454784,0.00001548183,0.00014480896,0.0022674573,0.015110198,0.0012842793,0.93123925,0.00061039365,0.048428632],"study_design_scores_gemma":[0.000015502215,0.00008162306,0.0010391816,0.0003880669,0.000022877775,0.000659005,0.0029126022,0.03929574,0.002568756,0.80274606,0.1502145,0.00005605218],"about_ca_topic_score_codex":0.0011904088,"about_ca_topic_score_gemma":0.0021618663,"teacher_disagreement_score":0.005529894,"about_ca_system_score_codex":0.0020140086,"about_ca_system_score_gemma":0.0014666155,"threshold_uncertainty_score":0.019139111},"labels":[],"label_agreement":null},{"id":"W4404250208","doi":"10.54254/2755-2721/89/20241075","title":"Research on the design and application of Analog-to-Digital converters","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Sensor Technology and Measurement Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Converters; Computer science; Electronic engineering; Electrical engineering; Engineering; Voltage","score_opus":0.03429933527692071,"score_gpt":0.2606574793753703,"score_spread":0.22635814409844962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404250208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01988228,0.18005164,0.6739109,0.002373835,0.0011247315,0.00019181242,0.00015659393,0.00078943465,0.12151879],"genre_scores_gemma":[0.34890056,0.26619124,0.33263588,0.0012667142,0.0013303789,0.00021911367,0.00031820132,0.00019287097,0.048944917],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987748,0.00016298244,0.000096403244,0.00023222758,0.00067985756,0.000053618634],"domain_scores_gemma":[0.99835455,0.00059620943,0.00009497118,0.00016569572,0.00074568874,0.000042833002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096798374,0.00066333276,0.00051365653,0.0012615277,0.00044848764,0.0025399716,0.001084449,0.0010166982,0.0036233964],"category_scores_gemma":[0.003381515,0.00046086148,0.00038292928,0.00229433,0.0008760982,0.0031319803,0.0004735193,0.0013145587,0.0020926322],"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":[0.0000876151,0.00009864237,0.0012794717,0.0023099415,0.00005945501,0.0002731672,0.0003317523,0.021544894,0.0427779,0.29325753,0.004036805,0.6339429],"study_design_scores_gemma":[0.000036542537,0.00059086987,0.0024606222,0.0014748112,0.0001417019,0.002789395,0.00038001832,0.101467825,0.13413598,0.1414612,0.61494976,0.00011120146],"about_ca_topic_score_codex":0.0006436807,"about_ca_topic_score_gemma":0.00046399428,"teacher_disagreement_score":0.0036233964,"about_ca_system_score_codex":0.0010126401,"about_ca_system_score_gemma":0.0011430238,"threshold_uncertainty_score":0.012121439},"labels":[],"label_agreement":null},{"id":"W4404681470","doi":"10.54254/2755-2721/106/20241242","title":"A Review of Research on Coding Methods for Open-ended Text Responses in Survey Questionnaires","year":2024,"lang":"en","type":"review","venue":"Applied and Computational Engineering","topic":"Survey Methodology and Nonresponse","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Earl Haig Secondary School","funders":"","keywords":"Survey research; Coding (social sciences); Open research; Psychology; Information retrieval; Data science; Computer science; Statistics; Applied psychology; World Wide Web; Mathematics","score_opus":0.568712899526554,"score_gpt":0.6218411303159246,"score_spread":0.05312823078937057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404681470","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032379986,0.77308017,0.18553819,0.008101492,0.002479727,0.009132115,0.0017846237,0.00044608975,0.01619952],"genre_scores_gemma":[0.029871194,0.7353804,0.19419375,0.004808367,0.00074849225,0.029935146,0.0018037795,0.00039662793,0.0028623054],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.7983179,0.1482932,0.020930292,0.006272253,0.02514661,0.0010397241],"domain_scores_gemma":[0.5866167,0.34309143,0.016639665,0.012339244,0.04056357,0.0007494262],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.15120168,0.0023775152,0.0035199414,0.015688665,0.0020022497,0.004551687,0.0038475245,0.0023278212,0.00830516],"category_scores_gemma":[0.2558393,0.0013706138,0.004310347,0.024589699,0.0048270547,0.0064364616,0.0037734949,0.002935616,0.004194762],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087473374,0.000114021735,0.0019785636,0.1001035,0.00035410945,0.00005834523,0.0052305968,0.00041974845,0.0004587368,0.025396608,0.016497778,0.8493005],"study_design_scores_gemma":[0.00018251424,0.00031721257,0.011751694,0.30764806,0.0011049735,0.0008135602,0.00809579,0.0015932671,0.0019365941,0.037918825,0.62828624,0.0003512238],"about_ca_topic_score_codex":0.005528924,"about_ca_topic_score_gemma":0.007614462,"teacher_disagreement_score":0.84879833,"about_ca_system_score_codex":0.0066011283,"about_ca_system_score_gemma":0.015333112,"threshold_uncertainty_score":0.7996405},"labels":[],"label_agreement":null},{"id":"W4404681768","doi":"10.54254/2755-2721/97/20241271","title":"Research on the Algorithmic Structures in Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"AI and Big Data Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Deep learning; Computer science; Machine learning; Adaptability; Scalability; Key (lock); Data science","score_opus":0.0650104812436966,"score_gpt":0.3227467215116907,"score_spread":0.25773624026799413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404681768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01579905,0.20089759,0.56462044,0.058112357,0.0014750016,0.00016861803,0.00044461,0.0004360737,0.15804626],"genre_scores_gemma":[0.37340057,0.22208437,0.37260598,0.0090959985,0.0043527605,0.00079643313,0.0008118792,0.00039128345,0.016460791],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99492055,0.0019707025,0.0003570389,0.0008987113,0.0016654021,0.00018761415],"domain_scores_gemma":[0.9754606,0.019885596,0.0010201414,0.001889867,0.0014355216,0.00030828008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005849137,0.0012183783,0.001065635,0.0031888415,0.0016359977,0.0072841896,0.0018821732,0.0027883733,0.0070638456],"category_scores_gemma":[0.0236879,0.0007448356,0.0010221744,0.00494155,0.015716186,0.017417006,0.0027663563,0.0061879614,0.0017359812],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000097483135,0.000016488202,0.0004013745,0.0003963455,0.000021767604,0.000019497511,0.00021013079,0.0039020302,0.000106975844,0.95381975,0.0020240222,0.039071977],"study_design_scores_gemma":[0.000005407362,0.000016099553,0.00021792679,0.00030621458,0.0000081086455,0.00004328681,0.0000872535,0.004378982,0.00016360813,0.9628959,0.031865273,0.000012041208],"about_ca_topic_score_codex":0.0016797995,"about_ca_topic_score_gemma":0.0014217691,"teacher_disagreement_score":0.0072841896,"about_ca_system_score_codex":0.003315582,"about_ca_system_score_gemma":0.0038435715,"threshold_uncertainty_score":0.030933559},"labels":[],"label_agreement":null},{"id":"W4404682082","doi":"10.54254/2755-2721/97/20241397","title":"Review on Application of Chi-square Statistic in Text Classification in Recent Five Years","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Feature selection; Statistic; Chi-square test; Computer science; Feature (linguistics); Selection (genetic algorithm); Test (biology); Natural language processing; Artificial intelligence; Information retrieval; Statistics; Mathematics; Linguistics","score_opus":0.012529627397966573,"score_gpt":0.2512400210169778,"score_spread":0.23871039361901122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404682082","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017099503,0.9738722,0.017301042,0.002777231,0.0009681492,0.00006604181,0.00021204642,0.000119008764,0.0029742946],"genre_scores_gemma":[0.025930457,0.94547564,0.023029128,0.0013037791,0.0025439411,0.00021041988,0.00055669673,0.00006608331,0.0008838522],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9905318,0.003863971,0.0014768713,0.0010811473,0.0028900972,0.00015608044],"domain_scores_gemma":[0.92575496,0.060708504,0.0026300228,0.0008561371,0.009691757,0.00035850695],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.010837954,0.0010201012,0.0021228036,0.009132147,0.00060525804,0.0022279464,0.0022057563,0.0013755929,0.0024878487],"category_scores_gemma":[0.037998486,0.00054828345,0.0017315033,0.012087612,0.0018717946,0.0028037333,0.00079265353,0.001878253,0.0013978113],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013909835,0.000054461365,0.006171899,0.018744165,0.00037501153,0.00014937058,0.00039760306,0.0010011777,0.0007623368,0.0060349014,0.01707758,0.9490925],"study_design_scores_gemma":[0.00007570523,0.0007514357,0.039931998,0.027733985,0.0016015209,0.0033413216,0.0018299259,0.009314071,0.0058336626,0.023949929,0.8852174,0.00041890878],"about_ca_topic_score_codex":0.005369814,"about_ca_topic_score_gemma":0.004951788,"teacher_disagreement_score":0.989162,"about_ca_system_score_codex":0.001743246,"about_ca_system_score_gemma":0.004060589,"threshold_uncertainty_score":0.057317257},"labels":[],"label_agreement":null},{"id":"W4404727662","doi":"10.54254/2755-2721/110/2024melb0098","title":"The Application and Practice of Artificial Intelligence in the Entertainment Field","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Medical Research and Treatments","field":"Medicine","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":"Sheridan College","funders":"","keywords":"Field (mathematics); Entertainment; Computer science; Artificial intelligence; Art; Mathematics; Visual arts","score_opus":0.014965314799500787,"score_gpt":0.321007478220348,"score_spread":0.30604216342084717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404727662","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026989236,0.12741232,0.23666053,0.096265204,0.0017902183,0.00032138283,0.00017046041,0.0002348262,0.5101558],"genre_scores_gemma":[0.7106476,0.09592279,0.16554427,0.01048405,0.0018530835,0.00070731516,0.00013147543,0.00017574476,0.01453367],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.98285484,0.012033027,0.0007412721,0.0013714123,0.0026163645,0.00038297995],"domain_scores_gemma":[0.9749947,0.020148074,0.0009603378,0.002429686,0.0010773069,0.0003897725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014272451,0.0007321795,0.00077518006,0.0035894897,0.002152498,0.011823461,0.0018765667,0.004132396,0.0035296553],"category_scores_gemma":[0.018076157,0.00041858896,0.00064170925,0.0030663847,0.03137008,0.0080283675,0.0044903727,0.004761132,0.0010588287],"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":[0.000012979408,0.000038074966,0.0013089782,0.0005831712,0.00003059573,0.00012489183,0.0034115552,0.0013962459,0.0003332093,0.9372702,0.0024285144,0.05306142],"study_design_scores_gemma":[0.000010746305,0.00005329961,0.0016742643,0.001922969,0.000019046909,0.00033040222,0.0028314067,0.002695653,0.0006243612,0.8256419,0.16415189,0.000043995857],"about_ca_topic_score_codex":0.002648451,"about_ca_topic_score_gemma":0.0018650496,"teacher_disagreement_score":0.014272451,"about_ca_system_score_codex":0.0040890775,"about_ca_system_score_gemma":0.005793975,"threshold_uncertainty_score":0.07548082},"labels":[],"label_agreement":null},{"id":"W4404727772","doi":"10.54254/2755-2721/110/2024melb0096","title":"Blockchain Technology in Supply Chain Management: Current Applications and Future Prospects","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Blockchain; Current (fluid); Supply chain management; Supply chain; Business; Risk analysis (engineering); Computer science; Engineering; Computer security; Electrical engineering; Marketing","score_opus":0.002921612271421459,"score_gpt":0.19813233857116527,"score_spread":0.19521072629974381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404727772","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03294338,0.82846636,0.039434552,0.038138796,0.00087985,0.00008030199,0.000118841956,0.00026632275,0.0596716],"genre_scores_gemma":[0.2892398,0.68215615,0.014909323,0.0016684048,0.0010200193,0.00006861398,0.00017819057,0.000029211378,0.010730188],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99857235,0.00057065906,0.000057635287,0.00018384568,0.00043932337,0.00017610061],"domain_scores_gemma":[0.99516124,0.003012841,0.00032934235,0.00030840604,0.00086892827,0.00031920132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003134955,0.000393063,0.00043546595,0.00103637,0.0005300112,0.0028193572,0.00095517345,0.002415727,0.0072243656],"category_scores_gemma":[0.0030101603,0.00025303545,0.00036121503,0.0030820651,0.0020833216,0.0059816684,0.0015725018,0.001302426,0.0021062684],"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":[0.00023117008,0.00021099324,0.0033877506,0.0025257533,0.000038973438,0.000256026,0.0005470771,0.008792798,0.0021754967,0.18306936,0.012061698,0.7867028],"study_design_scores_gemma":[0.00006318108,0.000554325,0.0026454297,0.0041848454,0.00005497634,0.00071084005,0.0022468262,0.025291493,0.00355498,0.31692696,0.6436729,0.00009329226],"about_ca_topic_score_codex":0.0015448461,"about_ca_topic_score_gemma":0.0013796662,"teacher_disagreement_score":0.0072243656,"about_ca_system_score_codex":0.0013781328,"about_ca_system_score_gemma":0.0019665689,"threshold_uncertainty_score":0.024167836},"labels":[],"label_agreement":null},{"id":"W4404727836","doi":"10.54254/2755-2721/109/20241456","title":"Analysis of the Applications of Algorithm and Automatic Pathfinding","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Computer science; Algorithm; Computer graphics (images); Artificial intelligence; Theoretical computer science; Shortest path problem; Graph","score_opus":0.0050638141270246675,"score_gpt":0.20821624536107827,"score_spread":0.2031524312340536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404727836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04219024,0.3080073,0.5346471,0.0028029066,0.0008979508,0.0002520788,0.0005980689,0.0005516219,0.11005273],"genre_scores_gemma":[0.411947,0.31615585,0.2550582,0.0007421355,0.0006345685,0.00036632715,0.00152216,0.0003247762,0.013248961],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982956,0.0004189103,0.00012120779,0.00032076874,0.0007441437,0.00009926107],"domain_scores_gemma":[0.99598664,0.0026719435,0.0002098409,0.00021307897,0.0008711386,0.000047341782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014289634,0.0007727812,0.0005658171,0.002508498,0.00040520378,0.0025073243,0.0012650591,0.0010869312,0.004031779],"category_scores_gemma":[0.008522006,0.0003809633,0.0009622057,0.0036616463,0.00084843603,0.003331081,0.0007577712,0.0010065689,0.0010852112],"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":[0.0000635628,0.00006598061,0.003240514,0.0046468456,0.00017498627,0.00020943832,0.00031880423,0.063942194,0.0037547436,0.16348186,0.006544081,0.7535569],"study_design_scores_gemma":[0.000027993232,0.0005007016,0.0105774095,0.0027909665,0.00039093202,0.002235027,0.0008328223,0.22778153,0.017840546,0.21487229,0.5219755,0.00017437918],"about_ca_topic_score_codex":0.0020618516,"about_ca_topic_score_gemma":0.0014929262,"teacher_disagreement_score":0.004031779,"about_ca_system_score_codex":0.0013776965,"about_ca_system_score_gemma":0.002021623,"threshold_uncertainty_score":0.013487637},"labels":[],"label_agreement":null},{"id":"W4404727858","doi":"10.54254/2755-2721/2024.17892","title":"Using LLM Model to Process Sensor-detected Images","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Process (computing); Computer science; Operating system","score_opus":0.018830862268492156,"score_gpt":0.2591383070586208,"score_spread":0.24030744479012864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404727858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041498896,0.0002430848,0.95051235,0.00042916034,0.00010509618,0.00010386563,0.00016824876,0.003047802,0.0038915384],"genre_scores_gemma":[0.7097362,0.00030951464,0.27898183,0.0003480406,0.000056347773,0.00025013438,0.00050620374,0.00024397866,0.009567741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967325,0.00005359894,0.000022595057,0.00010068931,0.00011154861,0.000038252572],"domain_scores_gemma":[0.999559,0.00017677728,0.000046698588,0.00004995866,0.00014749603,0.000020064577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053656293,0.0004684363,0.00041831427,0.00048450488,0.00032734952,0.0009372637,0.0010089701,0.0007797003,0.0026683055],"category_scores_gemma":[0.0015950296,0.00020462881,0.0007207196,0.0003860003,0.0003357529,0.0013194084,0.0005220208,0.00084006565,0.0011571727],"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":[0.00061680033,0.00030980379,0.0047507845,0.00039905857,0.00011696612,0.0007093498,0.0005437099,0.4919673,0.09461392,0.021317504,0.004898899,0.3797559],"study_design_scores_gemma":[0.000007128046,0.000049847873,0.0002825575,0.000005098929,0.000010021364,0.000053053172,0.000025075678,0.988567,0.0077520795,0.0022047204,0.0010330325,0.000010397122],"about_ca_topic_score_codex":0.0055342643,"about_ca_topic_score_gemma":0.004048314,"teacher_disagreement_score":0.0055342643,"about_ca_system_score_codex":0.0006718734,"about_ca_system_score_gemma":0.000808315,"threshold_uncertainty_score":0.01100409},"labels":[],"label_agreement":null},{"id":"W4404727903","doi":"10.54254/2755-2721/109/20241349","title":"Applications of Artificial Intelligence on Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Traffic Prediction and Management Techniques","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; Artificial intelligence","score_opus":0.006006744877167509,"score_gpt":0.2070655140749837,"score_spread":0.20105876919781618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404727903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031416185,0.069423385,0.4840113,0.02055179,0.0021996826,0.00016208302,0.00020641554,0.00042105975,0.3916081],"genre_scores_gemma":[0.76972157,0.06982619,0.12573105,0.002724493,0.002238992,0.00018017463,0.00020379128,0.00009090655,0.029282779],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993401,0.0002466286,0.00003653278,0.0000880846,0.00023842206,0.000050181723],"domain_scores_gemma":[0.9992767,0.0004604183,0.000046327394,0.000066899134,0.00012283506,0.000026785452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047959108,0.00055003806,0.00032974192,0.0009874128,0.00069791335,0.0018662163,0.00061495,0.0011822484,0.0024069657],"category_scores_gemma":[0.001977505,0.00022363717,0.0004727627,0.0011305738,0.0019939921,0.0017529441,0.0014385825,0.0013879766,0.0005913267],"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":[0.000028380755,0.000063032865,0.0016011923,0.00035892738,0.000048168844,0.0004987965,0.0005923002,0.04624247,0.0021476145,0.804553,0.007660937,0.13620512],"study_design_scores_gemma":[0.000009454578,0.00006051013,0.0016461973,0.0003357565,0.000026955195,0.0006182484,0.00038138233,0.1169771,0.0014976294,0.71139985,0.16699094,0.00005592735],"about_ca_topic_score_codex":0.0019472528,"about_ca_topic_score_gemma":0.001776187,"teacher_disagreement_score":0.0024069657,"about_ca_system_score_codex":0.0006707528,"about_ca_system_score_gemma":0.00058204593,"threshold_uncertainty_score":0.008052051},"labels":[],"label_agreement":null},{"id":"W4404867079","doi":"10.54254/2755-2721/2024.17919","title":"A Review of the State of the Art 3D Generative Models and Their Applications","year":2024,"lang":"en","type":"review","venue":"Applied and Computational Engineering","topic":"3D Modeling in Geospatial Applications","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":"Generative grammar; State (computer science); Computer science; Artificial intelligence; Programming language","score_opus":0.018208369643955325,"score_gpt":0.23942161412004692,"score_spread":0.2212132444760916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404867079","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00058956223,0.9811638,0.009911457,0.00043383564,0.00036823208,0.00001813546,0.0001279686,0.00011016296,0.007276839],"genre_scores_gemma":[0.0042700125,0.98819435,0.004629544,0.00019998923,0.00031155744,0.000019957035,0.00021255654,0.000032960495,0.0021291678],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996891,0.000049383525,0.00004168468,0.000069277274,0.00013083317,0.000019652254],"domain_scores_gemma":[0.99906796,0.0006283972,0.000066412525,0.000041193158,0.00016938632,0.000026653966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073755364,0.0011985119,0.0010264947,0.0022601762,0.00034096238,0.001326525,0.0010992534,0.0010834319,0.007889617],"category_scores_gemma":[0.0019039124,0.000695297,0.0010462068,0.0030170856,0.00041112438,0.0017898654,0.0006361812,0.0011709795,0.0032606914],"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":[0.000033956367,0.00005795496,0.00036442216,0.013628616,0.00009207114,0.00013578493,0.00010121974,0.0069528813,0.0015113101,0.016954852,0.027794488,0.93237257],"study_design_scores_gemma":[0.000007346488,0.000082802944,0.0006782951,0.0038818975,0.00016857666,0.00093349145,0.00007707425,0.004804693,0.0015770413,0.008448615,0.9792835,0.00005677676],"about_ca_topic_score_codex":0.0022443389,"about_ca_topic_score_gemma":0.002272348,"teacher_disagreement_score":0.007889617,"about_ca_system_score_codex":0.0004671786,"about_ca_system_score_gemma":0.0011219376,"threshold_uncertainty_score":0.026393414},"labels":[],"label_agreement":null},{"id":"W4405121213","doi":"10.54254/2755-2721/2024.melb17890","title":"The Application of VR in the Film Industry","year":2024,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Aurora College","funders":"","keywords":"Narrative; Virtual reality; Transformative learning; Storytelling; Computer science; Human–computer interaction; Multimedia; Psychology; Art","score_opus":0.008249428574621818,"score_gpt":0.23545235615607757,"score_spread":0.22720292758145574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405121213","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16009624,0.09006151,0.070285425,0.03789476,0.0017559554,0.00015437898,0.00016714809,0.0005578507,0.6390267],"genre_scores_gemma":[0.8885597,0.05266436,0.036949977,0.0018002102,0.001096861,0.000054096075,0.000065852655,0.00013916644,0.018669728],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983612,0.0010814578,0.000054515054,0.00013010952,0.00026301751,0.00010966984],"domain_scores_gemma":[0.99802446,0.0014292569,0.000102834485,0.00019131653,0.00015640956,0.00009572877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017392201,0.00024638503,0.00015829278,0.00093105953,0.0012698851,0.0051490483,0.0006556179,0.0012193217,0.007439765],"category_scores_gemma":[0.0038613637,0.00023451027,0.00028163774,0.00070245215,0.0020311852,0.0032071765,0.0023411459,0.0011824742,0.0006246045],"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":[0.0001083354,0.000098214354,0.0033351043,0.0014206556,0.00006556427,0.0021877792,0.024879046,0.003455759,0.017332902,0.3883804,0.0203908,0.5383454],"study_design_scores_gemma":[0.000029482391,0.00030139484,0.0071589802,0.0017573986,0.0000623545,0.0041757068,0.022962501,0.006401593,0.008158684,0.07135659,0.8775459,0.00008945661],"about_ca_topic_score_codex":0.0017437148,"about_ca_topic_score_gemma":0.0020810605,"teacher_disagreement_score":0.007439765,"about_ca_system_score_codex":0.000888242,"about_ca_system_score_gemma":0.00057328294,"threshold_uncertainty_score":0.024888456},"labels":[],"label_agreement":null},{"id":"W4406247465","doi":"10.54254/2755-2721/2025.20018","title":"Analysis on the Current Status of Ecological Protection and Water Use in the Yellow River Basin","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Regional Development and Environment","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kraft Heinz (Canada)","funders":"","keywords":"Current (fluid); Drainage basin; Ecology; Structural basin; Environmental science; Water resource management; Geography; Hydrology (agriculture); Biology; Geology; Oceanography; Cartography","score_opus":0.016587814338710958,"score_gpt":0.22178311628096503,"score_spread":0.20519530194225408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406247465","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9903464,0.0014762386,0.0004276478,0.0012449292,0.000010829084,0.000013210359,0.0008774257,0.000012544479,0.0055909543],"genre_scores_gemma":[0.99768925,0.0010500924,0.00019915368,0.00004778025,0.000005376149,0.000010169684,0.0003193747,0.0000021165658,0.000676729],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99954337,0.00008696215,0.000063932464,0.00006649255,0.00012817874,0.00011093719],"domain_scores_gemma":[0.9986908,0.0001861992,0.00029010177,0.000084538595,0.0005573753,0.00019095904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012374253,0.000111940055,0.00022012184,0.001339977,0.0005699106,0.0011107996,0.0004077684,0.00021377746,0.0011273549],"category_scores_gemma":[0.0016416775,0.00011513246,0.00021621726,0.003160063,0.00070372294,0.0010803527,0.00087731564,0.0002607631,0.000063128646],"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":[0.000057105448,0.000035937726,0.9378017,0.00020017539,0.0000835488,0.00019113679,0.0027740337,0.0017113526,0.0013629878,0.0037939134,0.0014528674,0.050535247],"study_design_scores_gemma":[0.0000022784225,0.000015213312,0.9865324,0.000056641187,0.000019588939,0.000030302233,0.0030746115,0.0009866466,0.00017395112,0.0005432568,0.008554593,0.000010555696],"about_ca_topic_score_codex":0.13587816,"about_ca_topic_score_gemma":0.19009571,"teacher_disagreement_score":0.13587816,"about_ca_system_score_codex":0.0025383665,"about_ca_system_score_gemma":0.003502703,"threshold_uncertainty_score":0.2701745},"labels":[],"label_agreement":null},{"id":"W4406247473","doi":"10.54254/2755-2721/2025.19720","title":"Harnessing More Power of Wind with a Novel Wind Turbine Design","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Wind Energy Research and Development","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":"Surrey Memorial Hospital","funders":"","keywords":"Wind power; Turbine; Marine engineering; Environmental science; Computer science; Engineering; Aerospace engineering; Electrical engineering","score_opus":0.006908615629404465,"score_gpt":0.19697553912814847,"score_spread":0.19006692349874402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406247473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14322646,0.0005954152,0.8417247,0.0003706761,0.00061626645,0.00015708127,0.00013563054,0.0006756945,0.01249812],"genre_scores_gemma":[0.6676853,0.0004170458,0.32699475,0.00016495811,0.00014093758,0.0001686556,0.000113077745,0.00009137033,0.0042239325],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998579,0.000020238742,0.000014398135,0.000039603467,0.00005474934,0.000013158539],"domain_scores_gemma":[0.9998498,0.000016365051,0.00003106403,0.000032723256,0.000053453365,0.00001655221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015082218,0.0004025309,0.00040724105,0.00020942293,0.00018642652,0.0004312511,0.00061164977,0.0006180407,0.0012048853],"category_scores_gemma":[0.00023199874,0.00022818705,0.00031488526,0.00016392485,0.0002522632,0.00078157714,0.00024315868,0.00044426092,0.00062432536],"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":[0.00015127118,0.0001525824,0.0010300811,0.0003337262,0.00007934737,0.0004743127,0.00006200606,0.03312041,0.88942003,0.013971192,0.001765395,0.05943966],"study_design_scores_gemma":[0.00029878912,0.0025106075,0.0051400005,0.00009591509,0.00032977998,0.0024760524,0.00005497177,0.60695386,0.29467595,0.009288204,0.07802274,0.00015309117],"about_ca_topic_score_codex":0.00008355012,"about_ca_topic_score_gemma":0.00024251499,"teacher_disagreement_score":0.0012048853,"about_ca_system_score_codex":0.000093187686,"about_ca_system_score_gemma":0.00015068604,"threshold_uncertainty_score":0.0040307045},"labels":[],"label_agreement":null},{"id":"W4406298155","doi":"10.54254/2755-2721/2025.20142","title":"Colloidal Crystal Arrays for New Data Storage of the Future","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nanotechnology; Materials science; Colloid; Colloidal crystal; Computer science; Chemical engineering; Engineering","score_opus":0.009257421947844536,"score_gpt":0.21720852450436104,"score_spread":0.2079511025565165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406298155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4165968,0.061011035,0.38120085,0.009042656,0.0023913067,0.00047541753,0.0015159514,0.0028118107,0.12495418],"genre_scores_gemma":[0.6735213,0.01168859,0.2862243,0.001048816,0.00017809546,0.0003961895,0.000621951,0.00019745732,0.026123272],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998636,0.000016616179,0.000007864017,0.000026604539,0.000075672564,0.00000954155],"domain_scores_gemma":[0.9998729,0.00003601386,0.000016046048,0.000013239158,0.00004914114,0.0000126395935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016686076,0.0001361795,0.00020655949,0.0002344499,0.00039601457,0.0007518596,0.00027985004,0.0004860712,0.002257552],"category_scores_gemma":[0.0003302925,0.00017415517,0.00015009065,0.00021200863,0.00032007118,0.00052476063,0.0004222391,0.0004896095,0.0007869164],"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":[0.00004657647,0.00004113653,0.00017578999,0.0002650222,0.000012805,0.00011850851,0.000093019735,0.0019298083,0.9141279,0.041245632,0.004528086,0.03741578],"study_design_scores_gemma":[0.0000539402,0.00024199669,0.0005191095,0.00007055325,0.000030002693,0.00032709975,0.00009759255,0.044253998,0.77883226,0.009991852,0.1655336,0.000048035843],"about_ca_topic_score_codex":0.0012966137,"about_ca_topic_score_gemma":0.0033057828,"teacher_disagreement_score":0.002257552,"about_ca_system_score_codex":0.0005983003,"about_ca_system_score_gemma":0.00055639294,"threshold_uncertainty_score":0.007552266},"labels":[],"label_agreement":null},{"id":"W4406314160","doi":"10.54254/2755-2721/2025.20130","title":"Advances in Blade-Coated Organic Photovoltaics","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Molecular Junctions and Nanostructures","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":"Blade (archaeology); Photovoltaics; Organic solar cell; Materials science; Nanotechnology; Environmental science; Engineering physics; Engineering; Mechanical engineering; Composite material; Photovoltaic system; Electrical engineering; Polymer","score_opus":0.0015618020533385677,"score_gpt":0.17024349317019277,"score_spread":0.16868169111685422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406314160","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03711998,0.7774849,0.06459714,0.008298087,0.00601395,0.00009683025,0.000481068,0.00075096526,0.105157],"genre_scores_gemma":[0.17807785,0.7045702,0.07998524,0.0027474998,0.00223418,0.0000787307,0.0007812781,0.00021696366,0.031308156],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953747,0.000050121846,0.000024266483,0.000101025915,0.00023279776,0.000054266005],"domain_scores_gemma":[0.99951077,0.00010954855,0.000043425807,0.00004002828,0.00023346675,0.00006288427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085863756,0.0005713032,0.00042464086,0.00074026996,0.0003424351,0.0014085256,0.000767465,0.0011441823,0.0050295424],"category_scores_gemma":[0.0009327977,0.0003675074,0.00043120628,0.0007470844,0.0004758248,0.0021523118,0.0010679206,0.001702635,0.0033159114],"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":[0.00017707492,0.00020878897,0.00093145535,0.0045012413,0.00007090664,0.00043505148,0.00026038135,0.0036391031,0.21715231,0.11547468,0.031000093,0.626149],"study_design_scores_gemma":[0.000011701234,0.00022580795,0.00058171473,0.00034655235,0.000031620642,0.0005091221,0.000061515835,0.004888029,0.074457884,0.007760391,0.9110852,0.00004050711],"about_ca_topic_score_codex":0.00088323356,"about_ca_topic_score_gemma":0.0012741837,"teacher_disagreement_score":0.0050295424,"about_ca_system_score_codex":0.0009014674,"about_ca_system_score_gemma":0.0009804958,"threshold_uncertainty_score":0.016825438},"labels":[],"label_agreement":null},{"id":"W4406314528","doi":"10.54254/2755-2721/2025.20005","title":"Application of Photolithography in Integrated Circuits","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advancements in Photolithography Techniques","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 Ottawa","funders":"","keywords":"Photolithography; Integrated circuit; Electronic circuit; Computer science; Electronic engineering; Materials science; Engineering; Nanotechnology; Electrical engineering","score_opus":0.002703926207728099,"score_gpt":0.20496745660269303,"score_spread":0.20226353039496492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406314528","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12162901,0.33460432,0.34984037,0.002946007,0.0024824103,0.0003090046,0.00044789768,0.0020437127,0.18569735],"genre_scores_gemma":[0.70311135,0.11683059,0.15577178,0.0010587659,0.00057175267,0.00013899966,0.0002278843,0.00010360457,0.022185327],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997209,0.000030710697,0.000016969458,0.000053942404,0.00015441123,0.000023023262],"domain_scores_gemma":[0.9998437,0.000056174245,0.000026627615,0.00003660358,0.000030721356,0.000006106354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017976109,0.00032065483,0.00019667916,0.00056556077,0.00025218335,0.0007067847,0.00041382926,0.0005354022,0.0022442727],"category_scores_gemma":[0.00038331462,0.00024083821,0.00028854483,0.00063117425,0.00043936435,0.0006993665,0.0004754961,0.0005732114,0.0009935967],"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":[0.000065825225,0.000085818545,0.001285969,0.0023646953,0.00003832692,0.0005822598,0.0002171055,0.0033901555,0.55394685,0.057105206,0.0040394412,0.3768783],"study_design_scores_gemma":[0.000024736373,0.00044762366,0.0046439483,0.00033828334,0.00006593586,0.002293674,0.0001468155,0.015927313,0.64807737,0.017166365,0.3107993,0.00006863205],"about_ca_topic_score_codex":0.00033831148,"about_ca_topic_score_gemma":0.0004069077,"teacher_disagreement_score":0.0022442727,"about_ca_system_score_codex":0.00045750666,"about_ca_system_score_gemma":0.00036550028,"threshold_uncertainty_score":0.007507801},"labels":[],"label_agreement":null},{"id":"W4406314591","doi":"10.54254/2755-2721/2025.20020","title":"Report on the Elements and Composition of Emerging Contaminants in Water Bodies of China","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"China; Composition (language); Contamination; Environmental science; Water contamination; Environmental protection; Environmental planning; Business; Geography; Ecology; Biology; Archaeology; Art","score_opus":0.006338511523419138,"score_gpt":0.2278202268657097,"score_spread":0.22148171534229058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406314591","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9296269,0.014584697,0.0067533525,0.0011937952,0.00031642322,0.00019993355,0.023843452,0.00027067369,0.023210838],"genre_scores_gemma":[0.9554487,0.009394657,0.005810273,0.00091704866,0.00016848007,0.0001465533,0.011202992,0.00005926215,0.01685212],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99925214,0.000041321753,0.00007550335,0.00012770563,0.00041487537,0.00008834631],"domain_scores_gemma":[0.99933064,0.00005593069,0.00011259801,0.000029604056,0.00042881607,0.00004244591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008310937,0.0005772587,0.0005121844,0.004551006,0.0012426884,0.0009332436,0.00039945447,0.0006334556,0.0026116949],"category_scores_gemma":[0.00061654364,0.00020973326,0.00072018744,0.004845343,0.00035168527,0.00069083145,0.0011090952,0.00035116583,0.0005035803],"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":[0.00037084683,0.0001364004,0.75325096,0.0029858418,0.00066149194,0.001994099,0.0029306912,0.0015130673,0.07745067,0.0013780985,0.015038709,0.14228907],"study_design_scores_gemma":[0.000016302633,0.0002367584,0.8718824,0.00023576159,0.00063897524,0.0008208819,0.0035293726,0.0012108078,0.031769797,0.0011696835,0.08841034,0.00007891762],"about_ca_topic_score_codex":0.0415475,"about_ca_topic_score_gemma":0.07004164,"teacher_disagreement_score":0.0415475,"about_ca_system_score_codex":0.0009367615,"about_ca_system_score_gemma":0.00212285,"threshold_uncertainty_score":0.08261132},"labels":[],"label_agreement":null},{"id":"W4406314625","doi":"10.54254/2755-2721/2025.20083","title":"Performance Enhancement of Carbon Nanotube in Composites: An Analysis of Key Factors in Mechanical, Electrical, and Thermal Properties","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Conducting polymers and applications","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Materials science; Carbon nanotube; Composite material; Carbon nanotube metal matrix composites; Thermal; Nanotube; Carbon nanotube actuators; Mechanical properties of carbon nanotubes","score_opus":0.009754436137864685,"score_gpt":0.21312945045395607,"score_spread":0.20337501431609137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406314625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87776655,0.05913676,0.033662938,0.0007024552,0.00043652934,0.00007521285,0.00024618927,0.00024900577,0.02772423],"genre_scores_gemma":[0.9380764,0.030265562,0.021570887,0.000117814496,0.00015806811,0.000038973703,0.00015099718,0.00007295941,0.009548309],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99989784,0.0000092728815,0.0000040177183,0.000020683447,0.000055595712,0.000012554133],"domain_scores_gemma":[0.9999504,0.000013553119,0.000011020698,0.000002329897,0.000018470988,0.0000042393463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000112904025,0.00041294235,0.00024350063,0.00030917936,0.0001787046,0.00028221507,0.00012348568,0.0003190761,0.00057902833],"category_scores_gemma":[0.00015034461,0.00012768501,0.00020301231,0.00031076453,0.00012364687,0.00030343648,0.00009462814,0.00022708405,0.0001620971],"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":[0.000024802213,0.00002461413,0.00025076693,0.00023565462,0.000005498192,0.00005406556,0.000028012271,0.0010533817,0.9847185,0.00073366374,0.0003290719,0.012542025],"study_design_scores_gemma":[0.000002236397,0.0002711049,0.004872353,0.00004115865,0.000031501262,0.00019532225,0.000057157078,0.016143613,0.96123165,0.00048856763,0.01664487,0.000020409216],"about_ca_topic_score_codex":0.0002668992,"about_ca_topic_score_gemma":0.000665355,"teacher_disagreement_score":0.00057902833,"about_ca_system_score_codex":0.00022945288,"about_ca_system_score_gemma":0.00011726055,"threshold_uncertainty_score":0.0019370317},"labels":[],"label_agreement":null},{"id":"W4406346804","doi":"10.54254/2755-2721/2025.20298","title":"Global Insights, Local Applications: Irrigation Technologies and Agricultural Drought Mitigation in the Canadian Prairies","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Water scarcity; Agriculture; Irrigation; Water conservation; Food security; Water security; Water resources; Environmental science; Farm water; Water resource management; Irrigation statistics; Business; Agroforestry; Geography; Agronomy; Ecology","score_opus":0.004310542001014286,"score_gpt":0.18440258928951145,"score_spread":0.18009204728849715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406346804","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11556374,0.56281483,0.007236329,0.04988022,0.0009590346,0.000169182,0.0043387925,0.00016020505,0.2588776],"genre_scores_gemma":[0.4805251,0.4956939,0.005124934,0.0026993696,0.00012553169,0.000044706503,0.00077598885,0.000037575308,0.014973019],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952054,0.00006768887,0.000019319643,0.000060507875,0.00020442062,0.00012752935],"domain_scores_gemma":[0.9990695,0.00016501348,0.0000653959,0.000026256972,0.00059970084,0.00007422165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009010058,0.00048211342,0.00038969357,0.0016773831,0.001854067,0.0027021116,0.0005692437,0.0004031255,0.0036157507],"category_scores_gemma":[0.0018506717,0.00012862377,0.0003955616,0.0066463277,0.001359882,0.0009905456,0.0009999074,0.0009587526,0.00015467544],"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":[0.00011376788,0.000066419605,0.034745853,0.009755274,0.00036079698,0.0012242671,0.012571689,0.014510819,0.0046383264,0.07776413,0.065150715,0.779098],"study_design_scores_gemma":[0.000015104417,0.00007038043,0.12205695,0.005127364,0.00042913915,0.00031766662,0.01853637,0.0020058614,0.0015067019,0.010388116,0.8394262,0.00012016175],"about_ca_topic_score_codex":0.9698714,"about_ca_topic_score_gemma":0.9890485,"teacher_disagreement_score":0.030128598,"about_ca_system_score_codex":0.022012135,"about_ca_system_score_gemma":0.052790556,"threshold_uncertainty_score":0.15970999},"labels":[],"label_agreement":null},{"id":"W4406405345","doi":"10.54254/2755-2721/2025.20468","title":"Polymer-based Solid Electrolyte and Electrode/Electrolyte Interfacial Contact Characteristics Affecting Lithium-ion Battery Performance","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Advanced Battery Materials and Technologies","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":"Electrolyte; Materials science; Electrode; Lithium (medication); Battery (electricity); Polymer electrolytes; Polymer; Chemical engineering; Lithium-ion battery; Composite material; Chemistry; Ionic conductivity; Engineering; Medicine","score_opus":0.00278707023726146,"score_gpt":0.18432348053629335,"score_spread":0.1815364102990319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406405345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96635425,0.013084013,0.011542462,0.00023012066,0.00010751997,0.00006342263,0.0006292442,0.00018850772,0.0078004035],"genre_scores_gemma":[0.9886812,0.0059543923,0.0030953882,0.000051986615,0.000024380442,0.000033705997,0.00029343,0.00003279105,0.001832726],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997321,0.000027244541,0.000028491459,0.000062508545,0.000115387,0.000034288],"domain_scores_gemma":[0.9993399,0.00031924172,0.00013032951,0.000026363225,0.00015778703,0.000026385787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027329402,0.00033096262,0.00017163814,0.00042708614,0.00015085496,0.0005155592,0.00025685653,0.00043032222,0.0017801836],"category_scores_gemma":[0.0013649351,0.00013868703,0.00016193368,0.0006790228,0.00019196888,0.0009305865,0.00024047268,0.00042079377,0.00041414276],"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":[0.00018480592,0.00006301554,0.0042547057,0.00068113214,0.000027342006,0.00040173574,0.0001419438,0.0016321584,0.95848006,0.0010622013,0.00053160696,0.03253933],"study_design_scores_gemma":[0.0000033981837,0.00018917276,0.005155874,0.000030999046,0.000039899754,0.00037699472,0.00008486414,0.0036323408,0.9860765,0.00022051712,0.0041717235,0.000017802036],"about_ca_topic_score_codex":0.0003297215,"about_ca_topic_score_gemma":0.00053868524,"teacher_disagreement_score":0.0017801836,"about_ca_system_score_codex":0.00019911991,"about_ca_system_score_gemma":0.00018255589,"threshold_uncertainty_score":0.005955279},"labels":[],"label_agreement":null},{"id":"W4406782139","doi":"10.54254/2755-2721/2025.20609","title":"Application of Supervised Learning Algorithms in Data Prediction","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","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":"Ste. Anne's Hospital","funders":"","keywords":"Machine learning; Computer science; Artificial intelligence; Semi-supervised learning; Online machine learning; Supervised learning; Instance-based learning; Context (archaeology); Unsupervised learning; Algorithm; Quality (philosophy); Artificial neural network","score_opus":0.0576021101109556,"score_gpt":0.38887511029541705,"score_spread":0.33127300018446143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406782139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011496304,0.0014174475,0.98371845,0.00054877606,0.0001075639,0.00011029527,0.00012220432,0.0005920067,0.0018869243],"genre_scores_gemma":[0.40085587,0.0022206593,0.5933486,0.00035514793,0.00042205118,0.00034449293,0.0007497553,0.00012904646,0.0015743627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9908771,0.004837646,0.00068922713,0.0011998819,0.0022272386,0.00016890236],"domain_scores_gemma":[0.9664411,0.02535841,0.001787337,0.0021072293,0.004082554,0.00022345305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009860391,0.0010053867,0.0015059393,0.003101414,0.0007048317,0.0021110575,0.0014316399,0.0012914767,0.0009778356],"category_scores_gemma":[0.032458905,0.00047871988,0.0010357759,0.0028471234,0.0011814096,0.0022858437,0.0011943078,0.0020152577,0.0005939722],"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":[0.00018275535,0.0003905157,0.014976824,0.00051622954,0.00051445386,0.00017963535,0.00036245916,0.44754988,0.0016916903,0.031795096,0.004725475,0.49711502],"study_design_scores_gemma":[0.000011925601,0.000049625505,0.0008110317,0.000053354353,0.000025479936,0.00005858941,0.000041678497,0.97066003,0.0014578801,0.024735315,0.002077288,0.000017810915],"about_ca_topic_score_codex":0.0033002633,"about_ca_topic_score_gemma":0.002530849,"teacher_disagreement_score":0.009860391,"about_ca_system_score_codex":0.0010012711,"about_ca_system_score_gemma":0.002116645,"threshold_uncertainty_score":0.05214739},"labels":[],"label_agreement":null},{"id":"W4406801674","doi":"10.54254/2755-2721/2024.20577","title":"Bridging Educational Achievement Gaps with Generative AI: Personalized Curriculum for Targeted Learning Support","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Bridging (networking); Generative grammar; Curriculum; Computer science; Personalized learning; Mathematics education; Psychology; Artificial intelligence; Pedagogy; Teaching method; Cooperative learning; Open learning","score_opus":0.0035991255807860142,"score_gpt":0.23258269955991873,"score_spread":0.22898357397913272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406801674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2317008,0.00035879237,0.702707,0.0024423257,0.00032910952,0.0022585213,0.0003630806,0.014678488,0.045161963],"genre_scores_gemma":[0.4941222,0.0002725648,0.48744988,0.00083250937,0.00005848208,0.002141187,0.00055141945,0.00073496404,0.013836809],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99734503,0.0014099794,0.00013821115,0.00030992998,0.0005913134,0.00020550023],"domain_scores_gemma":[0.99324185,0.0041583492,0.0003308473,0.0008815076,0.0006813098,0.00070620654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028599247,0.00067703356,0.0003051109,0.0008394903,0.0004898931,0.0025623974,0.0021793884,0.001015808,0.009090328],"category_scores_gemma":[0.015551326,0.000332859,0.00053999096,0.00042433254,0.00082896295,0.002032709,0.0036590623,0.0014513361,0.0027140393],"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":[0.00045015017,0.004431172,0.008075499,0.0014066194,0.00007535596,0.0006136129,0.011999111,0.008109657,0.034872483,0.025437914,0.015214767,0.8893137],"study_design_scores_gemma":[0.0010019864,0.008931295,0.04170534,0.0022131607,0.00048182392,0.002940045,0.010434424,0.14528629,0.11024166,0.1494342,0.52677613,0.00055358734],"about_ca_topic_score_codex":0.00042423807,"about_ca_topic_score_gemma":0.00076194096,"teacher_disagreement_score":0.009090328,"about_ca_system_score_codex":0.0006615042,"about_ca_system_score_gemma":0.0013686717,"threshold_uncertainty_score":0.03041017},"labels":[],"label_agreement":null},{"id":"W4406801707","doi":"10.54254/2755-2721/2024.20541","title":"A Machine Learning-Enhanced Chat Application for the Identification of Mental Disorders","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Mental Health via Writing","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Earl Haig Secondary School","funders":"","keywords":"Identification (biology); Psychology; Computer science; Biology","score_opus":0.0063404861104199035,"score_gpt":0.2833104191664043,"score_spread":0.2769699330559844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406801707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39404622,0.00093973347,0.3212647,0.00083645614,0.0005278955,0.0018750449,0.017309895,0.25335512,0.009844881],"genre_scores_gemma":[0.6305895,0.00035649646,0.33275598,0.000441197,0.00020920951,0.0015359716,0.014803946,0.0018884703,0.01741931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995396,0.00017491211,0.000038092607,0.00012614713,0.00009198297,0.000029241786],"domain_scores_gemma":[0.9964754,0.0026077768,0.00014628322,0.0002065806,0.00033258184,0.00023134542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010671355,0.0010594578,0.0006252177,0.0012101093,0.00031773164,0.00046073608,0.0008129447,0.0006449626,0.0077336496],"category_scores_gemma":[0.0047852034,0.00019219342,0.0004174827,0.0005625397,0.00012317768,0.000717002,0.0008132992,0.0006369065,0.0030066103],"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":[0.0035741965,0.0018602004,0.031554084,0.0014259137,0.0004041736,0.0028190555,0.0017167681,0.012219317,0.04851632,0.0010315257,0.07694014,0.81793827],"study_design_scores_gemma":[0.00050578435,0.0020396693,0.08560134,0.00027441882,0.00020728073,0.0034184314,0.0008363194,0.80296206,0.05535262,0.0060712104,0.04250809,0.00022277712],"about_ca_topic_score_codex":0.0011404076,"about_ca_topic_score_gemma":0.0022270277,"teacher_disagreement_score":0.0077336496,"about_ca_system_score_codex":0.0002395733,"about_ca_system_score_gemma":0.00035805602,"threshold_uncertainty_score":0.025871634},"labels":[],"label_agreement":null},{"id":"W4406806192","doi":"10.54254/2755-2721/2024.20579","title":"AI-HR: An Approach to Improve Performance of Large Language Model in the Pre-screening Process","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Process (computing); Computer science; Natural language processing; Artificial intelligence; Programming language","score_opus":0.005501971452482528,"score_gpt":0.21739770696114255,"score_spread":0.21189573550866003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406806192","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03711737,0.00017887296,0.905046,0.0008699214,0.00015245705,0.0013555209,0.0005005765,0.046536226,0.008243004],"genre_scores_gemma":[0.1968611,0.000096710064,0.79537743,0.00046770507,0.00006468135,0.0009094246,0.0009303209,0.001609836,0.003682706],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9799928,0.013270919,0.00094047486,0.0019893341,0.0031460172,0.00066054345],"domain_scores_gemma":[0.9503221,0.03227077,0.0019647207,0.00730915,0.0067941905,0.0013391331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021033935,0.0020182387,0.0009863329,0.0033907099,0.0013629048,0.0040817033,0.0029847084,0.0014074892,0.011256937],"category_scores_gemma":[0.07406442,0.00075576635,0.0010504493,0.0017925986,0.0010041695,0.006271935,0.006050664,0.0031371643,0.0054773963],"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":[0.0014561313,0.0022196588,0.009456776,0.0008133341,0.00016024013,0.0003270985,0.004830799,0.016651673,0.026070362,0.011656847,0.022251142,0.90410596],"study_design_scores_gemma":[0.00042540522,0.0015291704,0.00873043,0.00023403314,0.0002163126,0.0005180288,0.002486569,0.8315698,0.07196117,0.030701978,0.051239815,0.0003873039],"about_ca_topic_score_codex":0.0057078227,"about_ca_topic_score_gemma":0.006320842,"teacher_disagreement_score":0.021033935,"about_ca_system_score_codex":0.0015229008,"about_ca_system_score_gemma":0.0051280307,"threshold_uncertainty_score":0.11123937},"labels":[],"label_agreement":null},{"id":"W4406806199","doi":"10.54254/2755-2721/2024.20585","title":"Experimental Research on Recidivism Prediction","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Recidivism; Psychology; Environmental science; Econometrics; Criminology; Computer science; Mathematics","score_opus":0.019854927905639228,"score_gpt":0.2689683001082034,"score_spread":0.24911337220256416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406806199","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8806026,0.003817118,0.08798942,0.0020959212,0.0009851789,0.00075520616,0.005121887,0.0016464713,0.016986115],"genre_scores_gemma":[0.94513553,0.0013617576,0.04517145,0.00029759735,0.00017902842,0.0004810835,0.003731472,0.00013336452,0.0035087634],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9917379,0.004537798,0.000645662,0.00118692,0.0014716671,0.00042013748],"domain_scores_gemma":[0.92312574,0.057508074,0.0033520055,0.0072853225,0.008059642,0.0006693023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012211916,0.001064491,0.0009989372,0.0015915685,0.001051709,0.0010843292,0.0014430905,0.0011447857,0.0064543495],"category_scores_gemma":[0.061423942,0.000385967,0.00097297487,0.0017179217,0.0010789123,0.0023427901,0.0006345139,0.0016930004,0.0012796317],"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":[0.0070627984,0.012972266,0.1609627,0.0039667706,0.0011670145,0.00067677634,0.0023670862,0.18598081,0.013549204,0.027475668,0.047651984,0.53616697],"study_design_scores_gemma":[0.00038434134,0.004438911,0.066094495,0.00046633653,0.0005422912,0.000706001,0.0014915456,0.8609545,0.027493251,0.021492558,0.015701786,0.00023390578],"about_ca_topic_score_codex":0.006485255,"about_ca_topic_score_gemma":0.0037718639,"teacher_disagreement_score":0.012211916,"about_ca_system_score_codex":0.0010255734,"about_ca_system_score_gemma":0.001070921,"threshold_uncertainty_score":0.0645836},"labels":[],"label_agreement":null},{"id":"W4407588263","doi":"10.54254/2755-2721/2024.20851","title":"Comparative Analysis of Improved Versions of BERT Models on Chinese NLP Tasks","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Earl Haig Secondary School","funders":"","keywords":"Natural language processing; Artificial intelligence; Computer science; Linguistics; Philosophy","score_opus":0.009348161555759269,"score_gpt":0.23727091577846823,"score_spread":0.22792275422270897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407588263","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77287763,0.025795763,0.12450753,0.006926127,0.0022165717,0.0005886949,0.010608465,0.016939607,0.039539624],"genre_scores_gemma":[0.9098757,0.003496165,0.049287967,0.00076466677,0.00039157172,0.00038908026,0.022373414,0.00078338577,0.0126381125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976708,0.0010215678,0.00016960497,0.00049062737,0.0003674724,0.0002798773],"domain_scores_gemma":[0.9888343,0.007693953,0.0002470293,0.0010738858,0.0017716617,0.0003790802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073080757,0.0023784346,0.0015977748,0.00198963,0.0010002828,0.0014579293,0.002562924,0.0017275327,0.0035736896],"category_scores_gemma":[0.014274572,0.0005933905,0.0012992251,0.0023866042,0.00064171234,0.005222469,0.0013206814,0.0027200875,0.0017873512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023757638,0.00092635176,0.008417529,0.00091098127,0.00066217966,0.00022636654,0.0003950503,0.6209872,0.002264488,0.005856541,0.046170678,0.31080687],"study_design_scores_gemma":[0.00008223506,0.00029388012,0.002661954,0.000037720252,0.00013223596,0.000047052934,0.00009667267,0.99019027,0.0012431035,0.0022118478,0.0029542465,0.00004882467],"about_ca_topic_score_codex":0.056053285,"about_ca_topic_score_gemma":0.06370008,"teacher_disagreement_score":0.056053285,"about_ca_system_score_codex":0.003944762,"about_ca_system_score_gemma":0.002714845,"threshold_uncertainty_score":0.11145401},"labels":[],"label_agreement":null},{"id":"W4407594231","doi":"10.54254/2755-2721/2024.20849","title":"The Impact of the US Election Cycle and Partisan Factors on Dollar Exchange Rate Volatility","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","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":"Liberian dollar; Volatility (finance); Us dollar; Economics; Exchange rate; Monetary economics; Financial economics; Finance","score_opus":0.01597042585446429,"score_gpt":0.20531849920324466,"score_spread":0.18934807334878037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407594231","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99410146,0.00058531645,0.0006720703,0.00029363125,0.000038377224,0.0000054106595,0.00025505046,0.000010505676,0.004038231],"genre_scores_gemma":[0.99913293,0.00015885234,0.000047429414,0.00003076617,0.000021006108,0.0000017432562,0.0002255053,0.0000037938191,0.00037797602],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99963355,0.00013090839,0.00002288767,0.000057546513,0.000054658292,0.000100375255],"domain_scores_gemma":[0.9977356,0.00093754,0.0007333741,0.00011850221,0.00025390432,0.00022095063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086305663,0.00018415716,0.0003040536,0.0007735209,0.00028734186,0.0012005531,0.00012238038,0.00024098843,0.0015083173],"category_scores_gemma":[0.004680854,0.0000948398,0.0002828648,0.0010587147,0.00025862065,0.00041249054,0.00051179057,0.0005330284,0.00029833967],"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":[0.00041545948,0.00008090346,0.9671472,0.000037220078,0.00023874173,0.00022120909,0.0003314392,0.008092685,0.0008652323,0.003533364,0.0018205042,0.017216064],"study_design_scores_gemma":[0.000009434489,0.00008365068,0.97723174,0.000017701546,0.00009364832,0.00008192531,0.00054426125,0.016449967,0.00053455797,0.0012255477,0.0037080015,0.000019500952],"about_ca_topic_score_codex":0.008340608,"about_ca_topic_score_gemma":0.009977308,"teacher_disagreement_score":0.008340608,"about_ca_system_score_codex":0.0003814357,"about_ca_system_score_gemma":0.00029062043,"threshold_uncertainty_score":0.016584098},"labels":[],"label_agreement":null},{"id":"W4407827561","doi":"10.54254/2755-2721/2025.20933","title":"Investigating the Potential of Brain-Computer Interfaces in Controlling Smart Home Devices for Individuals with Mobility Impairments","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Human–computer interaction; Computer science; Brain–computer interface; Psychology; Neuroscience; Electroencephalography","score_opus":0.008064633509173896,"score_gpt":0.23000458306739768,"score_spread":0.2219399495582238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407827561","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9888108,0.0014407409,0.0036709313,0.00036772387,0.000016002683,0.00015603809,0.00005758277,0.0000151508475,0.0054651047],"genre_scores_gemma":[0.99321413,0.0011161846,0.004790125,0.00010761225,0.000011089568,0.00008474782,0.000036175483,0.0000016864374,0.00063817797],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997137,0.00015373717,0.000017634333,0.000032832264,0.000051498053,0.000030570685],"domain_scores_gemma":[0.9989538,0.00075696677,0.000072194285,0.000034889308,0.0001298947,0.00005219396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083279173,0.00026266548,0.00014975885,0.00039148904,0.0001490714,0.0005264538,0.00017277455,0.0003904661,0.0013661602],"category_scores_gemma":[0.0040776404,0.000058229427,0.00015631647,0.00023656241,0.00035171933,0.00054294645,0.00031720867,0.00018947896,0.00020889723],"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":[0.0029459188,0.002239101,0.21487162,0.0020233318,0.00027807802,0.00088697165,0.00586607,0.0031058746,0.054118056,0.0044494565,0.0012816866,0.70793384],"study_design_scores_gemma":[0.00041698298,0.030276064,0.8469796,0.0008115102,0.00087388046,0.004236903,0.016464127,0.019513398,0.056776192,0.008917921,0.014652235,0.00008115369],"about_ca_topic_score_codex":0.00057799614,"about_ca_topic_score_gemma":0.001100245,"teacher_disagreement_score":0.0013661602,"about_ca_system_score_codex":0.00014321739,"about_ca_system_score_gemma":0.00039876183,"threshold_uncertainty_score":0.0045703053},"labels":[],"label_agreement":null},{"id":"W4407828245","doi":"10.54254/2755-2721/2025.21095","title":"Research on the Dynamics of Sea Lamprey Sex Ratio and Its Ecological Impacts in the Lake Ontario Ecosystem Based on the OLED Model","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Marine animal studies overview","field":"Environmental Science","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":"Lamprey; Environmental science; Ecosystem; Ecology; Aquatic ecosystem; Fishery; Ecosystem model; Geography; Oceanography; Environmental resource management; Geology; Biology","score_opus":0.02415077112834313,"score_gpt":0.24542344933978377,"score_spread":0.22127267821144064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407828245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9486258,0.001157381,0.039422676,0.00069303624,0.000034312277,0.00002228943,0.0011927799,0.00012197495,0.00872974],"genre_scores_gemma":[0.9926864,0.0004568064,0.0031267898,0.00003614634,0.00000897455,0.00001538235,0.00063743023,0.0000120947425,0.0030200058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998902,0.000022226166,0.0000047631256,0.000030584742,0.000024700581,0.000027571525],"domain_scores_gemma":[0.99979466,0.000066825254,0.000044455355,0.0000120630375,0.00005519852,0.000026875148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003216297,0.00038226403,0.0002946547,0.00040439088,0.00030202945,0.0006417845,0.0005653754,0.00025139356,0.0009468911],"category_scores_gemma":[0.00085281854,0.0001407213,0.0004212405,0.00042753894,0.00033432705,0.00044364328,0.000440275,0.00028938177,0.00009659929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014809264,0.000051361727,0.15421195,0.00016221915,0.00020396031,0.00042839706,0.0002608921,0.79394186,0.0048680278,0.012616718,0.0021784646,0.030928096],"study_design_scores_gemma":[0.000009382595,0.00002671813,0.026380595,0.00001202669,0.00005342803,0.00004388378,0.00013513572,0.9693205,0.00035686907,0.0018363146,0.0018088048,0.00001634016],"about_ca_topic_score_codex":0.27340567,"about_ca_topic_score_gemma":0.25933936,"teacher_disagreement_score":0.27340567,"about_ca_system_score_codex":0.0021027024,"about_ca_system_score_gemma":0.0017836891,"threshold_uncertainty_score":0.5436286},"labels":[],"label_agreement":null},{"id":"W4409619022","doi":"10.54254/2755-2721/2025.22240","title":"Enhancing Human-Computer Interaction Through Brain-Computer Interface: Technological Advances","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Human–computer interaction; Brain–computer interface; Interface (matter); Computer science; Neuroscience; Psychology; Operating system","score_opus":0.011263597924797742,"score_gpt":0.2731370957479595,"score_spread":0.26187349782316177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409619022","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03887974,0.51088727,0.35193622,0.012832016,0.0019317205,0.00014328143,0.00017125433,0.0013184715,0.08190004],"genre_scores_gemma":[0.33455878,0.4521558,0.18262045,0.0025586286,0.002389814,0.00017193767,0.00023353046,0.00018845328,0.025122568],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963045,0.00008295354,0.000020449059,0.000062781655,0.00018155736,0.000021725167],"domain_scores_gemma":[0.9996426,0.00017890744,0.000025118918,0.000024346184,0.00010877104,0.000020228072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043283176,0.00055670406,0.0002856666,0.000696849,0.00019812185,0.0013420583,0.0004884099,0.0009987986,0.0035061897],"category_scores_gemma":[0.0013193041,0.00014893444,0.00024472884,0.0010238966,0.0005070311,0.0022472527,0.0006947063,0.00085072574,0.0013861676],"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":[0.000082136605,0.0001028556,0.0005918039,0.002135608,0.000044066648,0.00015615148,0.00027807878,0.0024591063,0.043604802,0.033735275,0.00950459,0.9073056],"study_design_scores_gemma":[0.000053328313,0.001086544,0.009221722,0.0014481541,0.00017235108,0.0037577413,0.0005389473,0.06617067,0.079503916,0.0799275,0.75797653,0.00014253212],"about_ca_topic_score_codex":0.00045922582,"about_ca_topic_score_gemma":0.0005278795,"teacher_disagreement_score":0.0035061897,"about_ca_system_score_codex":0.0002857862,"about_ca_system_score_gemma":0.00039959766,"threshold_uncertainty_score":0.01172936},"labels":[],"label_agreement":null},{"id":"W4409778238","doi":"10.54254/2755-2721/2025.22279","title":"AI and Machine Learning Approaches to Adaptive Signal Processing in Future Wireless Networks","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Signal processing; Artificial intelligence; Wireless; Wireless network; Machine learning; Telecommunications","score_opus":0.02536174389969985,"score_gpt":0.22492649963087408,"score_spread":0.19956475573117421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409778238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003806526,0.0029487214,0.98740476,0.0012465274,0.00009895426,0.000015972826,0.000018084022,0.00007594672,0.0043844883],"genre_scores_gemma":[0.58314353,0.011126835,0.3902192,0.0009740552,0.0015195473,0.00023998403,0.000089742556,0.0000906025,0.012596468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992048,0.0003723317,0.000040441802,0.00013502693,0.00020078932,0.00004650581],"domain_scores_gemma":[0.99769336,0.0017701196,0.00014727004,0.00012414562,0.00022636417,0.0000388176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017161341,0.0007369814,0.0006554436,0.0008642763,0.0004043602,0.0015759794,0.001053767,0.0012436522,0.0013708097],"category_scores_gemma":[0.0033372622,0.00031542158,0.000554324,0.0009851364,0.0021433514,0.0017469374,0.00097534806,0.002339141,0.00029125175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031431577,0.000065640605,0.000640559,0.00018891544,0.000094125324,0.000098503086,0.00017474806,0.5925082,0.0016547685,0.32209694,0.0015343697,0.080911815],"study_design_scores_gemma":[0.000004756533,0.000023780103,0.00013445354,0.000017803188,0.0000071736627,0.00002952344,0.000020488862,0.8638583,0.00036789602,0.13326631,0.0022588253,0.00001072749],"about_ca_topic_score_codex":0.001995497,"about_ca_topic_score_gemma":0.0015965111,"teacher_disagreement_score":0.001995497,"about_ca_system_score_codex":0.00096913404,"about_ca_system_score_gemma":0.00063626,"threshold_uncertainty_score":0.00907588},"labels":[],"label_agreement":null},{"id":"W4410350340","doi":"10.54254/2755-2721/2025.22852","title":"Application of Machine Learning on Predicting the Risk of Death of Respiratory Infectious Diseases Patients — Using COVID-19 as Example","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"COVID-19 diagnosis using AI","field":"Medicine","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":"Coronavirus disease 2019 (COVID-19); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Respiratory system; Infectious disease (medical specialty); Medicine; Virology; Intensive care medicine; Computer science; Artificial intelligence; Internal medicine; Disease; Outbreak","score_opus":0.012682243253817406,"score_gpt":0.2729801405664308,"score_spread":0.2602978973126134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410350340","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86688155,0.0018671491,0.12316367,0.0022625406,0.0002972497,0.00019767164,0.0010657308,0.00036263157,0.0039019224],"genre_scores_gemma":[0.98416394,0.00039677403,0.014221437,0.00007795896,0.00005326093,0.000059461076,0.00045990726,0.000007850999,0.0005594213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993272,0.00032933836,0.000058718724,0.00011150658,0.00009928332,0.00007402115],"domain_scores_gemma":[0.9962858,0.0028240248,0.00025635495,0.00013175474,0.0003905574,0.000111657464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021404629,0.0010345057,0.0007024549,0.0015457557,0.00037270325,0.0007395158,0.00067251787,0.0011663924,0.00077156303],"category_scores_gemma":[0.006512665,0.00019410004,0.00080551347,0.00082983903,0.00032377525,0.00064967346,0.0006234982,0.0012037165,0.00016118267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027402065,0.00036499382,0.068746656,0.00008941125,0.00018465429,0.00037947833,0.00008129536,0.8808823,0.00045361422,0.0011094472,0.0012219747,0.046212163],"study_design_scores_gemma":[0.0000056413805,0.000059597845,0.0038242638,0.000009515378,0.00001244982,0.000036178546,0.000026924023,0.99515367,0.00019891562,0.0005456885,0.000119566335,0.000007518316],"about_ca_topic_score_codex":0.016198024,"about_ca_topic_score_gemma":0.0075260904,"teacher_disagreement_score":0.016198024,"about_ca_system_score_codex":0.0006642447,"about_ca_system_score_gemma":0.00081505754,"threshold_uncertainty_score":0.03220749},"labels":[],"label_agreement":null},{"id":"W4410474725","doi":"10.54254/2755-2721/2025.gl23044","title":"Comparative Analysis of Personalized Federated Learning Optimization Algorithms for Image Classification","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","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; Artificial intelligence; Machine learning; Image (mathematics); Optimization algorithm; Pattern recognition (psychology); Algorithm; Mathematics; Mathematical optimization","score_opus":0.024172253978925008,"score_gpt":0.28289901480267626,"score_spread":0.25872676082375123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410474725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25005317,0.0036932102,0.7343937,0.0010893812,0.00022665247,0.00021479581,0.00021366397,0.0025746939,0.0075406255],"genre_scores_gemma":[0.85325086,0.00071053964,0.14351134,0.0002242077,0.00005921375,0.00011099,0.0003957499,0.00011149435,0.001625566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965773,0.0012440184,0.00025623242,0.00060832483,0.00093644357,0.00037773314],"domain_scores_gemma":[0.99213904,0.004303847,0.00044900159,0.0016506767,0.0012585683,0.00019894932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007103108,0.0009373901,0.001454921,0.0012214151,0.00070913427,0.0017878234,0.0016974136,0.0014543391,0.0012665493],"category_scores_gemma":[0.015949652,0.00026677302,0.0009465967,0.0015426663,0.00077699276,0.0025416997,0.0013430351,0.0013585143,0.000345787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077913963,0.00045149378,0.00463693,0.0001486023,0.000168163,0.00006457738,0.00008792622,0.7544522,0.0013714394,0.009842764,0.0024335056,0.2255632],"study_design_scores_gemma":[0.000012910733,0.00008375132,0.00056890637,0.000008644073,0.000016831063,0.000041067182,0.00003252314,0.9944646,0.0011048419,0.0033067164,0.00035265984,0.0000065094628],"about_ca_topic_score_codex":0.0032942174,"about_ca_topic_score_gemma":0.002936652,"teacher_disagreement_score":0.007103108,"about_ca_system_score_codex":0.0017220057,"about_ca_system_score_gemma":0.0019445571,"threshold_uncertainty_score":0.03756523},"labels":[],"label_agreement":null},{"id":"W4410617847","doi":"10.54254/2755-2721/2025.tj23214","title":"Study for Automatic Speech Recognition for Wav2Vec2.0","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Speech recognition; Computer science; Natural language processing","score_opus":0.019156557871145046,"score_gpt":0.24763702891965278,"score_spread":0.22848047104850774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410617847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23502444,0.0042861337,0.7125656,0.0024295885,0.001874535,0.00085888343,0.007411258,0.02148932,0.014060288],"genre_scores_gemma":[0.61412233,0.0012713998,0.31197765,0.0010136038,0.00036815563,0.001081036,0.045388885,0.001808687,0.022968147],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99844956,0.00048393852,0.000107843276,0.00034484908,0.00047374945,0.00014012677],"domain_scores_gemma":[0.99766564,0.00071410416,0.000055841072,0.00034536264,0.0011556055,0.00006335068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016716875,0.0010733457,0.00050516706,0.0006956874,0.000554759,0.0010762454,0.00088019355,0.0007411802,0.006066736],"category_scores_gemma":[0.0048794863,0.00032397997,0.0008971192,0.0006884493,0.0003099473,0.001800782,0.0005798554,0.0013113106,0.004844561],"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":[0.00076308387,0.0007257844,0.009111036,0.0006457707,0.0003307538,0.0005815821,0.00033716124,0.08944024,0.07140314,0.011148117,0.082508616,0.7330048],"study_design_scores_gemma":[0.000049824495,0.00039667543,0.004292229,0.000040408333,0.00004979419,0.0002997086,0.0002025452,0.9225529,0.04666231,0.001985528,0.023432178,0.000035891826],"about_ca_topic_score_codex":0.015615127,"about_ca_topic_score_gemma":0.013448612,"teacher_disagreement_score":0.015615127,"about_ca_system_score_codex":0.0007314947,"about_ca_system_score_gemma":0.0013008298,"threshold_uncertainty_score":0.031048477},"labels":[],"label_agreement":null},{"id":"W4410900620","doi":"10.54254/2755-2721/2025.tj23539","title":"Analysis on Optimizing Federated Proximal Algorithm for Heterogeneous and Secure Collaborative Learning","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Educational Technology and Assessment","field":"Computer Science","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; Algorithm","score_opus":0.003900710026242722,"score_gpt":0.23646749604819622,"score_spread":0.2325667860219535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410900620","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029148452,0.00057310855,0.9667991,0.00043576563,0.00006270847,0.000068593385,0.000044835808,0.0003024125,0.0025650773],"genre_scores_gemma":[0.7974122,0.00069608615,0.19618025,0.0003902017,0.000099024546,0.00022877123,0.0002543531,0.00013909646,0.004600118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997928,0.0008323218,0.00009563575,0.0003915309,0.00046279584,0.00028973538],"domain_scores_gemma":[0.99364203,0.0043857004,0.0003419762,0.000490091,0.0008317329,0.00030849696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044309814,0.0011225999,0.0016403383,0.0007181694,0.000802929,0.0015298707,0.0018553892,0.001780843,0.0031572385],"category_scores_gemma":[0.014006103,0.00044524073,0.00092547433,0.00083954434,0.0013154993,0.0018732251,0.0022940908,0.0019176214,0.00056103716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002157224,0.000119081764,0.0016004029,0.00012885034,0.00008598358,0.00009935403,0.00009287895,0.9277682,0.0013013106,0.020097695,0.0018823836,0.04660807],"study_design_scores_gemma":[0.0000101854375,0.000044107954,0.000097715834,0.0000090701815,0.000008336716,0.000020772428,0.00001256415,0.9939314,0.0003031092,0.0053532734,0.0002057414,0.0000037602367],"about_ca_topic_score_codex":0.0037757417,"about_ca_topic_score_gemma":0.002472828,"teacher_disagreement_score":0.0044309814,"about_ca_system_score_codex":0.0012259663,"about_ca_system_score_gemma":0.0028043333,"threshold_uncertainty_score":0.023433566},"labels":[],"label_agreement":null},{"id":"W4411132236","doi":"10.54254/2755-2721/2025.tj23594","title":"Communication-Efficient Distributed Machine Learning: Techniques and Innovations","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Distributed learning; Artificial intelligence; Psychology; Pedagogy","score_opus":0.004919266137499274,"score_gpt":0.21226038561700714,"score_spread":0.20734111947950787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411132236","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010979628,0.002383267,0.98265356,0.0006112199,0.000109992776,0.00003639311,0.000029086486,0.0008131219,0.0023837604],"genre_scores_gemma":[0.58902276,0.0037142215,0.40175062,0.00030266537,0.00042828222,0.00021629383,0.00023527518,0.00022248909,0.0041073593],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915445,0.00022546586,0.00004943088,0.00018818051,0.0003144061,0.000067973495],"domain_scores_gemma":[0.99868566,0.00051863474,0.00009255226,0.00039351126,0.00026344598,0.00004623601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011833913,0.0006975756,0.00072761934,0.0006713725,0.00044171503,0.0009426806,0.0016675172,0.0006337669,0.0009879594],"category_scores_gemma":[0.0031634066,0.00032013067,0.00037315706,0.00133381,0.0007034111,0.0020622998,0.0011396001,0.0015434904,0.00044073962],"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":[0.00016843235,0.00013779652,0.0010920352,0.00020147066,0.00006480493,0.00013543887,0.00018525722,0.3316344,0.008067724,0.044290453,0.005677165,0.60834503],"study_design_scores_gemma":[0.00002195006,0.000051239367,0.00019384026,0.000013921976,0.000010419482,0.000059096743,0.000025640544,0.9697151,0.0036458902,0.02177305,0.0044797473,0.000010085041],"about_ca_topic_score_codex":0.0021834725,"about_ca_topic_score_gemma":0.0015915055,"teacher_disagreement_score":0.0021834725,"about_ca_system_score_codex":0.0007632119,"about_ca_system_score_gemma":0.00090360094,"threshold_uncertainty_score":0.006258428},"labels":[],"label_agreement":null},{"id":"W4411494926","doi":"10.54254/2755-2721/2025.24245","title":"Bio-Inspired Surfaces for Fouling Resistance, Their Applications, Challenges, and Opportunities","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Marine Biology and Environmental Chemistry","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":"Fouling; Biochemical engineering; Durability; Nanotechnology; Wetting; Biofouling; Construction engineering; Environmental science; Computer science; Process engineering; Materials science; Engineering; Chemistry; Composite material","score_opus":0.013690818098360332,"score_gpt":0.18412922628377207,"score_spread":0.17043840818541173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411494926","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23485734,0.47555998,0.23427479,0.009947523,0.0012570339,0.00011000733,0.00023336429,0.00070444884,0.043055527],"genre_scores_gemma":[0.77731675,0.12623131,0.08350841,0.0012348545,0.0002786363,0.00017738313,0.00020466423,0.00012916386,0.010918779],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99979967,0.00005086863,0.0000070890947,0.000024412322,0.00009117692,0.000026854708],"domain_scores_gemma":[0.9998779,0.000048216287,0.00001744657,0.000012355776,0.000029487997,0.000014627342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004713669,0.00031500863,0.0003209809,0.00027963132,0.0002836123,0.00091236504,0.00050829916,0.0009114905,0.0010171342],"category_scores_gemma":[0.00045087718,0.00022404607,0.0003105439,0.00031534611,0.0005743571,0.0009074922,0.0005098986,0.0009438405,0.00046891143],"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":[0.00012803926,0.00022042624,0.00097597763,0.0028482017,0.000094822055,0.0005140816,0.00043431565,0.04700927,0.54644215,0.14951298,0.00824789,0.24357186],"study_design_scores_gemma":[0.00005221119,0.00081314,0.0025441432,0.00064215873,0.000080137936,0.0010818575,0.00061563513,0.15923324,0.37677827,0.10105105,0.35695958,0.00014854604],"about_ca_topic_score_codex":0.00025290716,"about_ca_topic_score_gemma":0.0003894891,"teacher_disagreement_score":0.0010171342,"about_ca_system_score_codex":0.00040379917,"about_ca_system_score_gemma":0.0001944926,"threshold_uncertainty_score":0.0034025908},"labels":[],"label_agreement":null},{"id":"W4412185637","doi":"10.54254/2755-2721/2025.ast24879","title":"Comparative Study of Reinforcement Learning Performance Based on PPO and DQN Algorithms","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Elevator Systems and Control","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":"Reinforcement learning; Computer science; Reinforcement; Artificial intelligence; Algorithm; Machine learning; Psychology; Social psychology","score_opus":0.0053021198439268105,"score_gpt":0.19820416305760571,"score_spread":0.1929020432136789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412185637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6184137,0.0043707,0.35545808,0.00094921445,0.0004227158,0.00036988733,0.00028650195,0.0016435237,0.01808571],"genre_scores_gemma":[0.9588125,0.00040682635,0.039271303,0.00010322551,0.000024439605,0.00013689604,0.00021292843,0.00006323598,0.0009687259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986645,0.0004236302,0.00012825562,0.00027171272,0.0003193846,0.00019256551],"domain_scores_gemma":[0.9909142,0.006263346,0.0005988779,0.0004883836,0.0013708151,0.00036426965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003908594,0.0009211427,0.0009975961,0.0009796951,0.00042427648,0.00083232217,0.00084765133,0.0010294189,0.0012964545],"category_scores_gemma":[0.016729927,0.00022744465,0.00036487536,0.0005191324,0.00075132534,0.0011219484,0.0010510209,0.0011586105,0.00025165302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067240855,0.00034714903,0.005585148,0.0003595017,0.000116583615,0.000063868545,0.00010168846,0.88654524,0.001968978,0.004952437,0.0011107402,0.09817628],"study_design_scores_gemma":[0.00004320031,0.00029550027,0.00096493226,0.00001918584,0.000018606874,0.000027402002,0.0000337642,0.99575543,0.0013035658,0.0011746708,0.00035275664,0.000011003169],"about_ca_topic_score_codex":0.006866038,"about_ca_topic_score_gemma":0.0034233222,"teacher_disagreement_score":0.006866038,"about_ca_system_score_codex":0.00096224924,"about_ca_system_score_gemma":0.0014236931,"threshold_uncertainty_score":0.020670831},"labels":[],"label_agreement":null},{"id":"W4412495092","doi":"10.54254/2755-2721/2025.po25263","title":"DSA-Net: A Dual-Path Spatial-Temporal Attention Network for WiFi-Based Human Activity Recognition","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Indoor and Outdoor Localization Technologies","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":"Bishop's University","funders":"","keywords":"Dual (grammatical number); Computer science; Path (computing); Net (polyhedron); Computer network; Real-time computing; Artificial intelligence; Mathematics; Art","score_opus":0.008029380998463242,"score_gpt":0.20688873240996278,"score_spread":0.19885935141149955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412495092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12006063,0.0024832052,0.82575864,0.0006378062,0.00065114413,0.00041522397,0.0087440265,0.03116259,0.010086625],"genre_scores_gemma":[0.7777894,0.0007812498,0.1934723,0.0006795774,0.00019674437,0.0004097157,0.015359251,0.00028293737,0.011028826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996824,0.000044572964,0.000014443918,0.00013156474,0.00007505246,0.00005200428],"domain_scores_gemma":[0.9997385,0.000078063415,0.000025679834,0.000043714084,0.00008042444,0.000033574783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044394174,0.0011053813,0.0007926014,0.0013059338,0.0003101143,0.00057378534,0.0012393509,0.0004919129,0.002923657],"category_scores_gemma":[0.0013229866,0.00027863545,0.0004925196,0.0009073456,0.00021466556,0.0009538816,0.0016979956,0.00061590143,0.0013921586],"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":[0.0006593073,0.0005084127,0.012022095,0.00029051065,0.0002902916,0.00022012726,0.00011098027,0.035829514,0.024871841,0.00282977,0.040046096,0.8823211],"study_design_scores_gemma":[0.00007328252,0.000310363,0.010000356,0.000028318424,0.00011729504,0.00047316312,0.000112136535,0.94436103,0.020780655,0.006436718,0.017254183,0.000052500578],"about_ca_topic_score_codex":0.010735365,"about_ca_topic_score_gemma":0.023552932,"teacher_disagreement_score":0.010735365,"about_ca_system_score_codex":0.000694885,"about_ca_system_score_gemma":0.00070034456,"threshold_uncertainty_score":0.021345735},"labels":[],"label_agreement":null},{"id":"W4412762215","doi":"10.54254/2755-2721/2025.po25557","title":"Literature Review on Attention Based Image Enhancement Techniques","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Image enhancement; Computer science; Image (mathematics); Artificial intelligence","score_opus":0.0038590473140243673,"score_gpt":0.21067523921039005,"score_spread":0.2068161918963657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412762215","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030340268,0.88562226,0.09074823,0.0012321199,0.0011533666,0.000066279186,0.00016390732,0.0005113472,0.017468441],"genre_scores_gemma":[0.03420686,0.8961242,0.05207439,0.0014778957,0.0021919475,0.000114097755,0.00058755436,0.00021502351,0.013008042],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99967825,0.000041048737,0.00003841578,0.000084378575,0.00013196883,0.000026038597],"domain_scores_gemma":[0.9988135,0.0008408185,0.000066957975,0.000053690714,0.00019897826,0.000026097106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049612485,0.0010586199,0.0008229186,0.0017798744,0.0002796241,0.0010654975,0.0012075485,0.0013015077,0.009693813],"category_scores_gemma":[0.0021372195,0.00047960412,0.00088837865,0.0023728248,0.00042173287,0.001738275,0.00059909973,0.001032772,0.0026546977],"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":[0.000081417114,0.000071773924,0.00022713782,0.00595913,0.000093593524,0.00019148238,0.000077887504,0.0054790615,0.0046951324,0.007263352,0.020757398,0.9551026],"study_design_scores_gemma":[0.000058143483,0.00041323376,0.0023614692,0.0064854934,0.0006155151,0.003826135,0.00023609861,0.045862492,0.018440152,0.021606162,0.89993286,0.00016222736],"about_ca_topic_score_codex":0.0018363739,"about_ca_topic_score_gemma":0.001394415,"teacher_disagreement_score":0.009693813,"about_ca_system_score_codex":0.0003779374,"about_ca_system_score_gemma":0.000589026,"threshold_uncertainty_score":0.03242898},"labels":[],"label_agreement":null},{"id":"W4412762357","doi":"10.54254/2755-2721/2025.ast25502","title":"A Comparative Study on the Integration of Attention Mechanisms in GAN Architectures","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Earl Haig Secondary School","funders":"","keywords":"Materials science; Computer science; Psychology","score_opus":0.013437579954688634,"score_gpt":0.23527173698444248,"score_spread":0.22183415702975384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412762357","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57612944,0.007191785,0.39744598,0.0005742049,0.00018100806,0.00017117211,0.00012766394,0.0017080872,0.01647061],"genre_scores_gemma":[0.95742387,0.0009881401,0.039819162,0.000096397074,0.000031066458,0.0000512426,0.00011026964,0.00009145651,0.0013884521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993612,0.00022497206,0.000030601637,0.00013348757,0.00015334667,0.00009643169],"domain_scores_gemma":[0.99874544,0.0007173821,0.00006691307,0.00018894093,0.00022468447,0.000056597204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021842914,0.0008900613,0.00047873374,0.00056164037,0.0001874561,0.0006763283,0.00082008756,0.0006128153,0.0012081284],"category_scores_gemma":[0.0041121724,0.00024471717,0.00049882586,0.00031700367,0.0004058172,0.0013569122,0.000768547,0.000748464,0.00021245179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008311777,0.0003226213,0.0069552553,0.0005452473,0.00043703144,0.00031161748,0.00024581928,0.5155413,0.06933341,0.008312221,0.0017324057,0.39543182],"study_design_scores_gemma":[0.00003047257,0.0009339483,0.004526107,0.00008378339,0.00018517968,0.0002462592,0.000067943496,0.9550862,0.033464715,0.0023868568,0.0029573236,0.000031197113],"about_ca_topic_score_codex":0.0014633783,"about_ca_topic_score_gemma":0.0020602793,"teacher_disagreement_score":0.0021842914,"about_ca_system_score_codex":0.0005793102,"about_ca_system_score_gemma":0.00036432306,"threshold_uncertainty_score":0.011551738},"labels":[],"label_agreement":null},{"id":"W4413738028","doi":"10.54254/2755-2721/2025.26390","title":"The Interplay of Structural Stiffness and MechanicalVibrations in Multi-Level Constructions","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Civil and Structural Engineering Research","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":"Earl Haig Secondary School","funders":"","keywords":"Computer science","score_opus":0.009008360993134054,"score_gpt":0.24647088979156495,"score_spread":0.23746252879843088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413738028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44494227,0.0010028045,0.53112197,0.00074381835,0.00012134426,0.000023016348,0.00006512954,0.00025870054,0.021720996],"genre_scores_gemma":[0.9912578,0.0001265207,0.0060419603,0.000017039833,0.00001650833,0.000008664238,0.000009117172,0.00003653243,0.0024859388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994405,0.00020738035,0.00002035597,0.000115511095,0.0001278795,0.00008841952],"domain_scores_gemma":[0.9987104,0.00065720984,0.00028476582,0.00014240932,0.000095965726,0.00010930863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053999433,0.000540491,0.00032324228,0.0007939738,0.00065642135,0.001352396,0.0005984414,0.0009526432,0.0030451873],"category_scores_gemma":[0.0030697086,0.0005813595,0.00039569853,0.000326503,0.0018573852,0.0024674628,0.0012562692,0.0006024247,0.00034603963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011332722,0.000078799196,0.0060117156,0.00014406382,0.000043079926,0.00063586823,0.00065271376,0.72204375,0.032450035,0.21483663,0.0004681206,0.022521948],"study_design_scores_gemma":[0.0000098602895,0.00010793739,0.0056262105,0.00003068021,0.000022319751,0.00028632564,0.00025666773,0.9265339,0.003020482,0.06248134,0.0015779751,0.0000463203],"about_ca_topic_score_codex":0.0010133861,"about_ca_topic_score_gemma":0.001253113,"teacher_disagreement_score":0.0030451873,"about_ca_system_score_codex":0.00051180157,"about_ca_system_score_gemma":0.00029519905,"threshold_uncertainty_score":0.010187209},"labels":[],"label_agreement":null},{"id":"W4414148343","doi":"10.54254/2755-2721/2025.gl26667","title":"Fundamental Characteristics of Photodetectors and Applications of Two-Dimensional Materials in Photodetection","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"2D Materials and Applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Photodetection; Photodetector; Heterojunction; Ultrashort pulse; Dark current; Interface (matter); Doping; Quantum dot","score_opus":0.0037957397917611525,"score_gpt":0.21233400029474805,"score_spread":0.2085382605029869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414148343","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7443232,0.063905895,0.10617631,0.002043109,0.0003385428,0.00027360098,0.0021146229,0.0009036456,0.07992108],"genre_scores_gemma":[0.9644525,0.008215121,0.017398668,0.00021283948,0.00007825597,0.0001284171,0.00043378983,0.00007004485,0.009010283],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996433,0.000046318673,0.000020281383,0.00010884479,0.00013666251,0.000044543383],"domain_scores_gemma":[0.9992974,0.00037599923,0.000086568354,0.00008298816,0.0001268266,0.000030152874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005796664,0.0004832669,0.00037724627,0.00073316507,0.00028577558,0.0014020136,0.00057652546,0.0012215967,0.0019182458],"category_scores_gemma":[0.0013584554,0.00037842375,0.00026601463,0.00092120847,0.00051295565,0.0012260999,0.0003940702,0.0006690217,0.0006298363],"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":[0.0003369777,0.00014309408,0.0055293627,0.000698796,0.000038904738,0.00022462258,0.00029554532,0.0060534663,0.8674521,0.041398957,0.0015211579,0.07630706],"study_design_scores_gemma":[0.00002750552,0.0004222004,0.016906537,0.00015631669,0.000038901064,0.0014117371,0.00023125629,0.031246739,0.8991199,0.011020496,0.03932719,0.00009116585],"about_ca_topic_score_codex":0.0002810763,"about_ca_topic_score_gemma":0.00030486548,"teacher_disagreement_score":0.0019182458,"about_ca_system_score_codex":0.0007705847,"about_ca_system_score_gemma":0.00027581924,"threshold_uncertainty_score":0.006417215},"labels":[],"label_agreement":null},{"id":"W4414256966","doi":"10.54254/2755-2721/2025.gl26848","title":"Research on Key Process Technologies for Integrated Circuit Chip Manufacturing","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Manufacturing Process and Optimization","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":"Integrated circuit; Key (lock); Chip; Physical design; Process (computing); Circuit design; Semiconductor device fabrication; Integrated circuit design; Manufacturing process","score_opus":0.016111968787664823,"score_gpt":0.25718587539645915,"score_spread":0.24107390660879432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414256966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018681,0.19465767,0.6061009,0.006802841,0.0020786233,0.00034324886,0.00042811036,0.00046709046,0.17044042],"genre_scores_gemma":[0.26753658,0.30718634,0.39318222,0.0017399199,0.0013751114,0.00070100353,0.00077347405,0.00039264798,0.027112693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986853,0.00019466526,0.00007027947,0.0002986651,0.00065062166,0.0001004713],"domain_scores_gemma":[0.99890053,0.0005429371,0.00010463166,0.00014873526,0.00026976893,0.000033350465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013459981,0.0010527687,0.00077008764,0.0011694138,0.0007755595,0.0030390874,0.0016610443,0.0016084365,0.0054097315],"category_scores_gemma":[0.004021489,0.00079178874,0.00079801935,0.002541939,0.0014705845,0.0050382502,0.0011285105,0.0028720677,0.002498273],"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":[0.00004074888,0.00008722433,0.00039573835,0.0012882398,0.000037110094,0.00010890416,0.000112138056,0.019430853,0.018944344,0.8276969,0.0039867884,0.1278709],"study_design_scores_gemma":[0.00005235064,0.00030820115,0.0012086723,0.0010034916,0.000080300495,0.0005169102,0.0001471685,0.07105397,0.06838656,0.5007406,0.3564117,0.00009007288],"about_ca_topic_score_codex":0.0011993507,"about_ca_topic_score_gemma":0.0010829922,"teacher_disagreement_score":0.0054097315,"about_ca_system_score_codex":0.0026042922,"about_ca_system_score_gemma":0.0020819758,"threshold_uncertainty_score":0.018895566},"labels":[],"label_agreement":null},{"id":"W4414257598","doi":"10.54254/2755-2721/2025.ast26989","title":"ZIP-Code–Level Drivers of EV Adoption in Washington: Socioeconomics, Urbanization, and Charger Proximity (2020–2025)","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Electric Vehicles and Infrastructure","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":"Rurality; Socioeconomic status; Work (physics); State (computer science); Noise (video)","score_opus":0.0036643486700529107,"score_gpt":0.1715421387050375,"score_spread":0.1678777900349846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414257598","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99701995,0.00003857982,0.00009388392,0.00003606513,0.000002975935,0.000004482182,0.0019270834,0.0000047341186,0.0008722028],"genre_scores_gemma":[0.9968125,0.00005096101,0.00008701095,0.000010402139,0.0000026293806,0.000006163534,0.0020476573,0.000001873799,0.0009808566],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99992085,0.000011937908,0.000006109755,0.000023487337,0.000014243073,0.000023420414],"domain_scores_gemma":[0.99946326,0.00008126678,0.0002620413,0.00003048446,0.0000939964,0.000069024434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017933789,0.00011659757,0.00006763996,0.00044883342,0.00017553523,0.00039659828,0.00015164906,0.00014043042,0.0021334207],"category_scores_gemma":[0.0007108959,0.000082178696,0.00023394592,0.0008216242,0.0001274506,0.00030032528,0.0003446077,0.0002337165,0.00028444824],"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":[0.00002494935,0.000022128344,0.996617,0.0000054981406,0.00002643551,0.000029272827,0.00009120724,0.00031225066,0.00012245767,0.00008090268,0.00050255255,0.002165348],"study_design_scores_gemma":[8.038648e-7,0.000012911992,0.9984865,0.0000066668067,0.000008660323,0.000024952327,0.0003493955,0.00040793075,0.00006603804,0.000017015782,0.000617234,0.000001880739],"about_ca_topic_score_codex":0.08356465,"about_ca_topic_score_gemma":0.16152382,"teacher_disagreement_score":0.08356465,"about_ca_system_score_codex":0.00042550414,"about_ca_system_score_gemma":0.00022985622,"threshold_uncertainty_score":0.16615647},"labels":[],"label_agreement":null},{"id":"W4414723343","doi":"10.54254/2755-2721/2025.ld27477","title":"XGBoost-Based Synergistic Partner Recommendation in Strategy Games","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Educational Games and Gamification","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Earl Haig Secondary School","funders":"","keywords":"Baseline (sea); Order (exchange); Binary number; Work (physics); Binary classification; Battle; Base (topology)","score_opus":0.014014362414516169,"score_gpt":0.29164424552048557,"score_spread":0.2776298831059694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414723343","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61637074,0.00211187,0.3660864,0.0010134182,0.00025514993,0.00027203263,0.0013021099,0.002335001,0.01025325],"genre_scores_gemma":[0.9417587,0.00020242513,0.05218261,0.00020446388,0.00004832354,0.00009520676,0.0010812029,0.000050316772,0.004376855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994498,0.00017498464,0.00002713594,0.00015931834,0.00007680532,0.00011187765],"domain_scores_gemma":[0.9992874,0.00038716395,0.00007897763,0.00005444763,0.00009807651,0.00009394851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010780257,0.0010760793,0.0011498777,0.0014056722,0.0004930344,0.00096672017,0.0014792172,0.0010822265,0.0023823404],"category_scores_gemma":[0.0022572274,0.00036609103,0.0006638467,0.0011600992,0.00047335544,0.0009976889,0.0006050968,0.0011130011,0.00082871894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008657596,0.0007920925,0.030275889,0.00016356887,0.00028255428,0.00018145889,0.00015148234,0.686947,0.001649952,0.0044797957,0.007246688,0.2669638],"study_design_scores_gemma":[0.000015790309,0.00006829084,0.0014091638,0.000012397359,0.000011277261,0.00001954678,0.000027259799,0.99560094,0.0002353291,0.0021789744,0.00041455583,0.00000642271],"about_ca_topic_score_codex":0.012531471,"about_ca_topic_score_gemma":0.02247354,"teacher_disagreement_score":0.012531471,"about_ca_system_score_codex":0.0008400456,"about_ca_system_score_gemma":0.00084407115,"threshold_uncertainty_score":0.024917006},"labels":[],"label_agreement":null},{"id":"W4414723576","doi":"10.54254/2755-2721/2025.gl27355","title":"Re-Engaging Spinal Reflexes: Toward Multisensory Feedback Integration in Neuroprosthetic Control","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Proprioception; Neural Prosthesis; Sensory system; Neuroprosthetics; Reflex; Electromyography; Stretch reflex; Prosthetic hand; Motor control; Embodied cognition","score_opus":0.014253579688304586,"score_gpt":0.2560691859394168,"score_spread":0.2418156062511122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414723576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038569257,0.009334637,0.9307934,0.0013922555,0.0002447785,0.00009271803,0.00003104353,0.000981063,0.01856091],"genre_scores_gemma":[0.590401,0.0068071806,0.39187723,0.00049445254,0.00026810207,0.00016750558,0.00005579769,0.00015682568,0.009771934],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996649,0.000078555626,0.000022390102,0.00007723366,0.00012820058,0.000028671535],"domain_scores_gemma":[0.9997311,0.00009868406,0.00003096806,0.00004707852,0.000064604385,0.000027545037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006287849,0.0005593526,0.00032832657,0.00036396374,0.00019603847,0.0014046584,0.0008960688,0.00072550814,0.002829614],"category_scores_gemma":[0.0009677223,0.00019472362,0.00034895373,0.00021627167,0.0010267989,0.0015175006,0.0009271041,0.0008563387,0.0005998988],"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":[0.00026906232,0.0002714088,0.00080084126,0.0008492303,0.000091645255,0.00033474553,0.00091330946,0.019474998,0.35317972,0.10081919,0.0015984674,0.5213973],"study_design_scores_gemma":[0.00018148267,0.0023716656,0.0065692156,0.0009190265,0.00024456612,0.0019374939,0.0009008577,0.34554166,0.24454434,0.29784128,0.09875515,0.0001933685],"about_ca_topic_score_codex":0.0002587817,"about_ca_topic_score_gemma":0.00027676995,"teacher_disagreement_score":0.002829614,"about_ca_system_score_codex":0.0002863371,"about_ca_system_score_gemma":0.00034667822,"threshold_uncertainty_score":0.009465992},"labels":[],"label_agreement":null},{"id":"W4415166449","doi":"10.54254/2755-2721/2025.ld27822","title":"A Comparative Study on Deep Learning-Based: Temperature Prediction Models: Performance Evaluation of CNN, Transformer and Random Forest","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Random forest; Artificial neural network; Preprocessor; Data pre-processing; Deep learning; Predictive modelling; Backpropagation; Transformer","score_opus":0.014893991779659247,"score_gpt":0.24203915184090366,"score_spread":0.2271451600612444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415166449","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7654683,0.008702057,0.2108245,0.0009707851,0.00053735636,0.00017072876,0.0015887484,0.004321638,0.0074158087],"genre_scores_gemma":[0.9623136,0.0015797418,0.032793097,0.00011481796,0.00003901737,0.00006500281,0.0017419073,0.00008445984,0.0012683556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994506,0.00013208255,0.00004987071,0.00015034695,0.00012888563,0.000088069646],"domain_scores_gemma":[0.99866366,0.0006236482,0.000099157995,0.00012822163,0.00042350442,0.00006183876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002081666,0.0015032444,0.00088867964,0.0008628342,0.00027417013,0.0006313987,0.0011560417,0.00077988574,0.00077542645],"category_scores_gemma":[0.003876993,0.0002786275,0.0007461431,0.00095290097,0.00028034727,0.0017239446,0.0005088259,0.0008573568,0.00029962204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091706135,0.00031099605,0.016165765,0.00036891393,0.0002745562,0.000101155114,0.00005505628,0.7512295,0.0038433976,0.0013037676,0.0031700728,0.22225973],"study_design_scores_gemma":[0.00001009154,0.00012992957,0.001342599,0.000016105043,0.00003399324,0.000019342351,0.000015916943,0.99578214,0.0019924643,0.00035666293,0.00029052637,0.000010252905],"about_ca_topic_score_codex":0.027090367,"about_ca_topic_score_gemma":0.020376705,"teacher_disagreement_score":0.027090367,"about_ca_system_score_codex":0.0010144777,"about_ca_system_score_gemma":0.0012279787,"threshold_uncertainty_score":0.053865373},"labels":[],"label_agreement":null},{"id":"W4415166551","doi":"10.54254/2755-2721/2026.ka27660","title":"Research on Intelligent Control and Optimization Strategies for Household Electricity Usage","year":2025,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Smart Grid Energy Management","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":"Earl Haig Secondary School","funders":"","keywords":"Electricity; Consumption (sociology); Energy consumption; Energy conservation; Electricity generation; Control (management); Mains electricity; Power consumption","score_opus":0.018318544722487253,"score_gpt":0.24571420295108534,"score_spread":0.22739565822859809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415166551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02973402,0.0032937191,0.9405197,0.0009771265,0.000118327,0.00009953097,0.00004287233,0.00021388565,0.025000831],"genre_scores_gemma":[0.9192209,0.0036344326,0.06951291,0.0002553235,0.00014785746,0.00017232627,0.00008212838,0.000050403258,0.0069236476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995154,0.00015079958,0.00003242847,0.000134089,0.00011295971,0.00005431985],"domain_scores_gemma":[0.9988681,0.00076780573,0.0001349526,0.000047926573,0.00015310154,0.000028100516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000937575,0.0010920719,0.0007920042,0.000561665,0.00036830988,0.0015915859,0.0008805295,0.0008245238,0.0032565116],"category_scores_gemma":[0.0031369806,0.0003791853,0.00053839985,0.0007632574,0.00079499546,0.0012470257,0.00055593596,0.00091855414,0.0003385304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007343381,0.00016962376,0.0011729684,0.0002720953,0.00012867943,0.000098236625,0.00015329229,0.84050626,0.0030100383,0.05180605,0.0015199061,0.10108934],"study_design_scores_gemma":[0.000011084585,0.000073813666,0.00034688076,0.000026115526,0.000018087265,0.00002421881,0.00004350038,0.98308253,0.0006293584,0.0139575945,0.001777702,0.000009181228],"about_ca_topic_score_codex":0.004414401,"about_ca_topic_score_gemma":0.0027194007,"teacher_disagreement_score":0.004414401,"about_ca_system_score_codex":0.00075301586,"about_ca_system_score_gemma":0.0007951068,"threshold_uncertainty_score":0.01089406},"labels":[],"label_agreement":null},{"id":"W4415445307","doi":"10.54254/2755-2721/2025.bj28430","title":"Multi‐Step Forecasting of U.S. Maritime Transportation Flows Using Hybrid ARIMA, PCR, CNN, and Recurrent Neural Network Models","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Offshore Engineering and Technologies","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":"Port (circuit theory); Recurrent neural network; State (computer science); Supply chain; Time series; Mode (computer interface); Competition (biology)","score_opus":0.016658089800893998,"score_gpt":0.20036369756853328,"score_spread":0.18370560776763928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415445307","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9245588,0.00043736474,0.06762592,0.00067097164,0.00013022969,0.0000248829,0.0014677846,0.0008699663,0.0042140316],"genre_scores_gemma":[0.9894282,0.00012453565,0.0086633125,0.00003140756,0.000013940715,0.000013200062,0.0007172556,0.000015301743,0.0009927844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989605,0.000021401216,0.000007880353,0.000035469926,0.000021999324,0.000017123126],"domain_scores_gemma":[0.99966884,0.0001543711,0.000049550657,0.000021325906,0.000087929824,0.000017966488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053603825,0.0005331495,0.00025804972,0.0004902079,0.00020893269,0.00049607403,0.0005148317,0.00043642923,0.00067669235],"category_scores_gemma":[0.0015312275,0.00023907404,0.00046833255,0.0005576385,0.00013324557,0.0006852797,0.00029902306,0.00067613344,0.00015363534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068119174,0.00006099645,0.0086763,0.00002053864,0.00004649739,0.000051924097,0.000027483004,0.9655889,0.0011515027,0.00077302626,0.0012810364,0.022253636],"study_design_scores_gemma":[7.873544e-7,0.0000037742477,0.000760528,7.3796565e-7,0.0000024657973,0.00000151281,0.0000028888144,0.9989735,0.00013148347,0.000082506645,0.000038156017,0.0000017133132],"about_ca_topic_score_codex":0.07170568,"about_ca_topic_score_gemma":0.05129026,"teacher_disagreement_score":0.07170568,"about_ca_system_score_codex":0.0006927668,"about_ca_system_score_gemma":0.0006314306,"threshold_uncertainty_score":0.14257658},"labels":[],"label_agreement":null},{"id":"W4415454568","doi":"10.54254/2755-2721/2025.28012","title":"Review of Yangtze River PFAS Treatments: A Comparison Between Activated Carbon, Reverse Osmosis, and Foam Fractionation in the Context of the Yangtze River","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Positive Living North","funders":"","keywords":"Yangtze river; Context (archaeology); Pollutant; Environmental remediation; Water pollutants; Aquatic ecosystem; Human health; Fractionation","score_opus":0.008715147107363984,"score_gpt":0.2317074751767519,"score_spread":0.2229923280693879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415454568","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006729056,0.9974727,0.00031305838,0.00019366092,0.00023715888,0.00001274801,0.00007133259,0.000010275552,0.0010162019],"genre_scores_gemma":[0.002017333,0.9967128,0.00034101907,0.00019276666,0.00011714513,0.00001628422,0.000081420716,0.0000032957498,0.00051797833],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997423,0.000040705538,0.00005271316,0.000055639994,0.00008697946,0.000021621145],"domain_scores_gemma":[0.9995832,0.00018415134,0.00007636323,0.000011340589,0.00011996563,0.000024947783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000510085,0.0010477927,0.00139682,0.0031505674,0.00036976073,0.00081429293,0.00063642894,0.0007827086,0.0021061993],"category_scores_gemma":[0.0006389566,0.00036443386,0.0010313902,0.004036649,0.00027921252,0.0011845699,0.0003734476,0.0005656664,0.00062799884],"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":[0.00015602601,0.00012466889,0.00048946094,0.17497961,0.0005318538,0.00030298473,0.00022722292,0.0012586748,0.01454111,0.0033230947,0.037897155,0.7661681],"study_design_scores_gemma":[0.000017231429,0.00027519825,0.002625011,0.009758826,0.0008060581,0.00074683706,0.0001442891,0.00027927457,0.005257871,0.0008936688,0.9791455,0.000050223967],"about_ca_topic_score_codex":0.0019156436,"about_ca_topic_score_gemma":0.0026455177,"teacher_disagreement_score":0.0031505674,"about_ca_system_score_codex":0.00049578067,"about_ca_system_score_gemma":0.0014962637,"threshold_uncertainty_score":0.0070459247},"labels":[],"label_agreement":null},{"id":"W4415454737","doi":"10.54254/2755-2721/2025.gl27988","title":"Analysis of the Principles and Potential Effects of BCI Applications in Mental Disorders","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Neurofeedback; Brain–computer interface; Electroencephalography; Functional electrical stimulation; Brain activity and meditation; Prefrontal cortex; Brain stimulation; Sensorimotor rhythm","score_opus":0.0035829430943399767,"score_gpt":0.20971735022677693,"score_spread":0.20613440713243694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415454737","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11087939,0.6167429,0.20775303,0.014160499,0.0006242226,0.0008428978,0.0018751277,0.00053764874,0.046584204],"genre_scores_gemma":[0.6220422,0.288846,0.079952106,0.0029316682,0.0007310978,0.0007487044,0.00057793054,0.00013841929,0.0040320205],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99763954,0.0009818263,0.00014918578,0.00030385962,0.0008657789,0.000059900474],"domain_scores_gemma":[0.99072194,0.007625632,0.00043619456,0.00030093503,0.00086051883,0.00005484251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003039177,0.00082432193,0.0005981922,0.0023856813,0.00026834058,0.0015708406,0.000628082,0.0007389509,0.0034521092],"category_scores_gemma":[0.016206041,0.00021654944,0.0007679957,0.0013737758,0.0011318162,0.0011649278,0.0005997565,0.0011147971,0.00059297297],"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":[0.00028239828,0.0001168674,0.0060036224,0.0071469974,0.0005238533,0.00012947101,0.00027903586,0.0039305487,0.014543413,0.016365048,0.0018499108,0.9488288],"study_design_scores_gemma":[0.00027409234,0.0066422992,0.30668464,0.01922366,0.0037176192,0.0057938616,0.0018580245,0.04755994,0.09746239,0.2670006,0.24340494,0.00037802834],"about_ca_topic_score_codex":0.0018156059,"about_ca_topic_score_gemma":0.0021511293,"teacher_disagreement_score":0.0034521092,"about_ca_system_score_codex":0.0011467732,"about_ca_system_score_gemma":0.0010709312,"threshold_uncertainty_score":0.016072929},"labels":[],"label_agreement":null},{"id":"W4415454760","doi":"10.54254/2755-2721/2025.28006","title":"Research on Segmented Pressure Prediction and Drilling Boundary of Encrypted Horizontal Wells in Water Drive Ultra-low Permeability Reservoirs—Taking Oil Fields as an Example","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Petro-Canada","funders":"","keywords":"Permeability (electromagnetism); Drilling; Directional drilling; Pressure control; Inversion (geology); Oil field; Engineering geology; Relative permeability; Pressure system; Reservoir simulation","score_opus":0.012213816816575249,"score_gpt":0.25086825986573186,"score_spread":0.23865444304915662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415454760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39236686,0.0006160571,0.6031373,0.00030244343,0.000064692555,0.0000420564,0.00014311215,0.0007549997,0.0025725632],"genre_scores_gemma":[0.9716517,0.00034902795,0.027181715,0.0000150302485,0.000012256514,0.000021657785,0.00008856104,0.000026566107,0.00065353926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99972457,0.00003632431,0.000021111357,0.00008118607,0.00010357963,0.00003319899],"domain_scores_gemma":[0.9996606,0.00010701833,0.000060580736,0.000031235068,0.000112444264,0.000028098792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039278096,0.00065901515,0.0005083779,0.0005701456,0.0004914342,0.00088595646,0.0007820413,0.00065539486,0.00068997045],"category_scores_gemma":[0.0009952504,0.00041383356,0.0006186108,0.0004989599,0.00043886667,0.0017031044,0.0005536642,0.0004982,0.00012125909],"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":[0.00017889526,0.00009124493,0.028719408,0.0003112648,0.00004246502,0.0004554105,0.00038654756,0.8311173,0.05263383,0.004332562,0.00062789343,0.081103176],"study_design_scores_gemma":[0.0000052572905,0.000028958066,0.0016681524,0.000005371841,0.00000936194,0.000030000836,0.000057626577,0.99037117,0.0069982787,0.00057987706,0.0002326381,0.000013334824],"about_ca_topic_score_codex":0.009141548,"about_ca_topic_score_gemma":0.0037549338,"teacher_disagreement_score":0.009141548,"about_ca_system_score_codex":0.00046694532,"about_ca_system_score_gemma":0.0011015612,"threshold_uncertainty_score":0.018176675},"labels":[],"label_agreement":null},{"id":"W4415915494","doi":"10.54254/2755-2721/2025.ld28901","title":"Unknown Maze Map with Unknown Coordinates Exploration Through HPHS and CvaR Framework","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Robotics and Sensor-Based Localization","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":"Robustness (evolution); CVAR; Cluster analysis; Partition (number theory); Convergence (economics); Scheme (mathematics); Construct (python library); Path (computing); Robot","score_opus":0.005595082225681796,"score_gpt":0.19663445543776115,"score_spread":0.19103937321207934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415915494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014488641,0.00024007756,0.98327994,0.00006970323,0.00001966672,0.000023513434,0.00003385425,0.0002619967,0.0015825162],"genre_scores_gemma":[0.63727665,0.00044923372,0.35732538,0.000094638686,0.00006416515,0.00023139153,0.00020803808,0.000085400156,0.0042650797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995097,0.00012506946,0.000021359676,0.00010868006,0.00016138366,0.00007388224],"domain_scores_gemma":[0.9995882,0.0001818172,0.000059521575,0.000048570408,0.000084077394,0.00003770857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050920696,0.00065917,0.0011084029,0.0007520542,0.00046969677,0.0009603638,0.0014362213,0.0007879456,0.0012980576],"category_scores_gemma":[0.001156896,0.0004204782,0.0010416568,0.00081274944,0.0006638931,0.0011804664,0.0018952643,0.0008541739,0.00022062425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000632019,0.000028278579,0.0005891253,0.00006644425,0.00005341415,0.00012074039,0.0001325922,0.9223192,0.0020177697,0.017804107,0.000696819,0.05610838],"study_design_scores_gemma":[0.000005835347,0.000030434841,0.00008435068,0.0000034138804,0.0000070269743,0.00002880551,0.00001832796,0.9949857,0.0003034643,0.004059754,0.00046574452,0.0000071064305],"about_ca_topic_score_codex":0.0070346748,"about_ca_topic_score_gemma":0.0041235513,"teacher_disagreement_score":0.0070346748,"about_ca_system_score_codex":0.00046305425,"about_ca_system_score_gemma":0.0011605655,"threshold_uncertainty_score":0.013987482},"labels":[],"label_agreement":null},{"id":"W4416108764","doi":"10.54254/2755-2721/2025.29485","title":"A Review of Optimizing SRAM-Based FPGA In-memory Computing","year":2025,"lang":"","type":"review","venue":"Applied and Computational Engineering","topic":"Low-power high-performance VLSI design","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":"Static random-access memory; Field-programmable gate array; Key (lock); Random access memory; Efficient energy use; Power (physics); Convolution (computer science); Random access","score_opus":0.008823725575848753,"score_gpt":0.23197363488440373,"score_spread":0.22314990930855497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416108764","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007375173,0.9893825,0.003574251,0.00026439645,0.00041907348,0.000017763787,0.000087163186,0.000056521825,0.0054608095],"genre_scores_gemma":[0.0034597423,0.9891606,0.0035851349,0.00028624845,0.0003053912,0.000026226344,0.00016603785,0.000020604934,0.0029901525],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998084,0.00002090438,0.000028541472,0.000042160085,0.00008187401,0.000018236533],"domain_scores_gemma":[0.9996518,0.00014505851,0.000042528525,0.000016859889,0.00012702896,0.00001670434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032195944,0.001100958,0.0007815322,0.0015089335,0.00023393566,0.00072232925,0.0009474525,0.000774991,0.0049139415],"category_scores_gemma":[0.00074881106,0.0005257542,0.00050427637,0.0023995952,0.00021684034,0.0012214384,0.00037473577,0.00084614236,0.0027571581],"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":[0.000065529646,0.00008135045,0.00027232245,0.01928192,0.000089227375,0.00013746577,0.000047704485,0.0034377805,0.0063464805,0.009266803,0.030704489,0.9302688],"study_design_scores_gemma":[0.0000111915215,0.00020747219,0.00059908786,0.002430449,0.00013001503,0.00070964283,0.000031262673,0.0015832912,0.004200285,0.0025580758,0.9875049,0.0000342822],"about_ca_topic_score_codex":0.0010159754,"about_ca_topic_score_gemma":0.0011950925,"teacher_disagreement_score":0.0049139415,"about_ca_system_score_codex":0.00037145062,"about_ca_system_score_gemma":0.00076066225,"threshold_uncertainty_score":0.016438842},"labels":[],"label_agreement":null},{"id":"W4416108786","doi":"10.54254/2755-2721/2026.ka29339","title":"A Comprehensive Review of Non-Fluorinated Durable Water-Repellent and Stain-Resistant Coatings","year":2025,"lang":"","type":"review","venue":"Applied and Computational Engineering","topic":"Surface Modification and Superhydrophobicity","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Durability; Mechanism (biology); Lotus effect; Sustainable development; Resist; Sustainability; Environmentally friendly","score_opus":0.012691660639974857,"score_gpt":0.2499158472780099,"score_spread":0.23722418663803505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416108786","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026527082,0.9963856,0.0002829198,0.00016781843,0.00030894307,0.000009616845,0.000073856914,0.000019245186,0.0024867859],"genre_scores_gemma":[0.000927383,0.99704415,0.0003635045,0.0001214556,0.00014793196,0.000011879136,0.00008799453,0.0000031694965,0.0012925185],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99980503,0.000023045981,0.000029110606,0.00004089978,0.00008081608,0.00002118969],"domain_scores_gemma":[0.9997807,0.00009530406,0.000038256065,0.000009052784,0.000055862572,0.000020737132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038232232,0.0012129915,0.0010438473,0.002775391,0.0003461343,0.0008045944,0.00065097533,0.00078971847,0.0072337426],"category_scores_gemma":[0.000608974,0.00038466245,0.0005946109,0.0030795757,0.00022620993,0.0012781224,0.00053526834,0.0012050736,0.0027740942],"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":[0.00006244105,0.00011906388,0.0001700514,0.06251129,0.00012397887,0.0002116221,0.00011084287,0.0007318617,0.008194762,0.007022928,0.06561752,0.8551237],"study_design_scores_gemma":[0.000005557255,0.00008238614,0.00048983545,0.0031172575,0.00008726258,0.0004536798,0.000029741575,0.000084481624,0.00082534296,0.00074295886,0.9940653,0.00001615913],"about_ca_topic_score_codex":0.0012808206,"about_ca_topic_score_gemma":0.0022108424,"teacher_disagreement_score":0.0072337426,"about_ca_system_score_codex":0.00043552095,"about_ca_system_score_gemma":0.0013199244,"threshold_uncertainty_score":0.024199247},"labels":[],"label_agreement":null},{"id":"W4416125924","doi":"10.54254/2755-2721/2026.tj29489","title":"On-Policy Vs. Off-Policy Reinforcement Learning in ConnectX: Seat-Stratified Performance and the Role of Action Masking","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Pooling; Robustness (evolution); Generalization; Masking (illustration); Reinforcement learning; Reinforcement; Lever; Set (abstract data type)","score_opus":0.005874809224640797,"score_gpt":0.2240839369894798,"score_spread":0.218209127764839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416125924","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8116808,0.0034294794,0.16169363,0.0014713464,0.00044467882,0.00053851726,0.00041720286,0.0057301885,0.014594057],"genre_scores_gemma":[0.9770817,0.00018209132,0.019553246,0.000363614,0.000033203854,0.0001692114,0.00028527848,0.00014451225,0.00218714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984433,0.0005934987,0.000075852004,0.00035863637,0.00025748403,0.00027130067],"domain_scores_gemma":[0.9947015,0.0032764017,0.00042168083,0.00063566724,0.00040598947,0.00055877777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052040406,0.0016407798,0.0012891521,0.0004161379,0.00042048038,0.001036377,0.0017484066,0.0019420664,0.0032744482],"category_scores_gemma":[0.0145564135,0.00038943175,0.0004908455,0.00019908856,0.0015794089,0.00188698,0.00179073,0.003095128,0.0007815962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0062381923,0.0015624235,0.012918712,0.0005536396,0.00029155178,0.00016944595,0.00020019858,0.8075521,0.009735447,0.0058474327,0.0051567648,0.14977404],"study_design_scores_gemma":[0.00038256004,0.0028338225,0.0026766395,0.00008542307,0.00006876909,0.000073039744,0.00007999157,0.9808255,0.0066702035,0.0048766662,0.0013802337,0.000047223395],"about_ca_topic_score_codex":0.0040902467,"about_ca_topic_score_gemma":0.0044500753,"teacher_disagreement_score":0.0052040406,"about_ca_system_score_codex":0.0010949684,"about_ca_system_score_gemma":0.0025284512,"threshold_uncertainty_score":0.027521908},"labels":[],"label_agreement":null},{"id":"W4416389681","doi":"10.54254/2755-2721/2026.ka29730","title":"Study on the Temperature Change Trends and Influencing Factors in Changsha","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario","funders":"","keywords":"Urban heat island; Urbanization; Climate change; Sustainability; Energy consumption; Global warming; Urban climate; Population; Current (fluid)","score_opus":0.01334208010487294,"score_gpt":0.2100273349575895,"score_spread":0.19668525485271654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416389681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943797,0.00073333184,0.0007420437,0.00032788358,0.00005845767,0.000015384054,0.0011096231,0.00003170986,0.0026018368],"genre_scores_gemma":[0.99785185,0.00024245102,0.00019395063,0.000047520538,0.000022073973,0.000010880936,0.0010127033,0.000005079527,0.00061342417],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998467,0.000016825876,0.000011674466,0.000046364075,0.000034210923,0.000044145152],"domain_scores_gemma":[0.99973637,0.00006095264,0.000041211835,0.000015471795,0.00010860979,0.00003737858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031930677,0.00022874473,0.00017109793,0.00097257126,0.00035783643,0.0004233642,0.00021307277,0.00020699562,0.0008719442],"category_scores_gemma":[0.00037921913,0.000097959375,0.0003423375,0.0020956753,0.00014988442,0.00039064183,0.00021775339,0.00035253807,0.00009690689],"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":[0.000056080473,0.00004822384,0.97403514,0.00011603356,0.00007741283,0.00021607685,0.0006460177,0.0024672162,0.001470171,0.0006422772,0.002089972,0.018135317],"study_design_scores_gemma":[0.0000028764646,0.000024475237,0.9904461,0.000020005791,0.00004781898,0.00006153233,0.0011048042,0.0042851106,0.00046232768,0.0001687275,0.0033655078,0.000010765567],"about_ca_topic_score_codex":0.051372655,"about_ca_topic_score_gemma":0.08053795,"teacher_disagreement_score":0.051372655,"about_ca_system_score_codex":0.0009746235,"about_ca_system_score_gemma":0.0011367693,"threshold_uncertainty_score":0.10214722},"labels":[],"label_agreement":null},{"id":"W4416389698","doi":"10.54254/2755-2721/2026.ka29605","title":"Study on Carbonation of Ultramafic Tailings","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","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":"Carbonation; Ultramafic rock; Tailings; Olivine; Mineralization (soil science); Periclase","score_opus":0.006761409826797882,"score_gpt":0.22967133078279972,"score_spread":0.22290992095600184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416389698","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99648833,0.00067687395,0.0011297938,0.000036181318,0.0000070583696,0.00001609114,0.00014815012,0.000011313339,0.0014863575],"genre_scores_gemma":[0.9975394,0.00052764255,0.000699832,0.0000141789515,0.000004197118,0.0000051111742,0.00012644228,0.000004372527,0.0010789469],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998555,0.0000078399835,0.0000068315194,0.000037334685,0.000059691676,0.00003279273],"domain_scores_gemma":[0.99990916,0.000024487423,0.00001724125,0.000006374222,0.00003298743,0.000009646041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011106344,0.00019163036,0.00020252704,0.00030345502,0.00029751108,0.00018185437,0.00027219122,0.00026112882,0.0011054992],"category_scores_gemma":[0.00017440725,0.000071624374,0.00022585629,0.0003037604,0.00013549133,0.0003185339,0.00014236002,0.00030759288,0.000120147924],"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":[0.00013536919,0.000074978874,0.0039220313,0.00032352083,0.000022722139,0.0002283757,0.00009040238,0.0030479862,0.9828063,0.0004538083,0.00012516667,0.00876928],"study_design_scores_gemma":[0.000009988141,0.0003060091,0.019329254,0.000013595289,0.00001669693,0.00015382952,0.00013380309,0.011781411,0.96564007,0.00018889226,0.002414284,0.000012094379],"about_ca_topic_score_codex":0.0066217757,"about_ca_topic_score_gemma":0.011598138,"teacher_disagreement_score":0.0066217757,"about_ca_system_score_codex":0.0003047812,"about_ca_system_score_gemma":0.00027835666,"threshold_uncertainty_score":0.013166487},"labels":[],"label_agreement":null},{"id":"W4416390058","doi":"10.54254/2755-2721/2026.tj29614","title":"Experimental Research on Stock Trend Analysis Based on News Sentiment Labeling","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Stock market; Stock (firearms); Experimental data; Data set; Set (abstract data type); Sentiment analysis; Stock market prediction; Time series","score_opus":0.09919270174017271,"score_gpt":0.42672986566205934,"score_spread":0.32753716392188664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416390058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91157496,0.0010240606,0.075989604,0.0005325009,0.00027800867,0.00016291045,0.001519813,0.0015943141,0.0073238397],"genre_scores_gemma":[0.9257718,0.0004977059,0.0679366,0.000087034816,0.00008570826,0.00010949257,0.0035822673,0.00007242499,0.001856897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852955,0.00053351204,0.000146606,0.00028364296,0.0004085518,0.00009811828],"domain_scores_gemma":[0.99460214,0.0028935932,0.000392403,0.00057141035,0.0014219264,0.00011836494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029380636,0.00076298433,0.0004986996,0.0012667279,0.00045471193,0.00072539,0.00055959437,0.0004907683,0.0017397733],"category_scores_gemma":[0.009526138,0.00020961792,0.00047000268,0.001548629,0.0002886733,0.0016247133,0.00028590855,0.00050771935,0.00045846627],"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":[0.0023646464,0.0030294287,0.083057344,0.0009786565,0.0004984981,0.0004629073,0.00054569176,0.0992681,0.08199041,0.0046066046,0.012896811,0.71030086],"study_design_scores_gemma":[0.00008840635,0.0005119493,0.028926244,0.000033364762,0.00013050705,0.00013808737,0.00025752716,0.92519104,0.04030275,0.0017178754,0.0026639283,0.00003833334],"about_ca_topic_score_codex":0.0047335806,"about_ca_topic_score_gemma":0.0034971552,"teacher_disagreement_score":0.0047335806,"about_ca_system_score_codex":0.00044851156,"about_ca_system_score_gemma":0.00036118712,"threshold_uncertainty_score":0.015538156},"labels":[],"label_agreement":null},{"id":"W4416390090","doi":"10.54254/2755-2721/2026.tj29620","title":"Deep Learning and Natural Language Processing Research: Technological Evolution and Frontier Exploration of Hallucination Problems","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Mental Health via Writing","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Meaning (existential); Representation (politics); Frontier; Credibility; Deep learning; Control (management); Natural language","score_opus":0.02585996813292053,"score_gpt":0.33331711557023763,"score_spread":0.3074571474373171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416390090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04127844,0.10296574,0.67971146,0.13397181,0.0007787026,0.000103337145,0.00022749105,0.00046915511,0.040493865],"genre_scores_gemma":[0.6032121,0.07376884,0.30325356,0.006449242,0.0025272435,0.00027709935,0.00027129156,0.00024180193,0.009998784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979113,0.0011991967,0.000111835376,0.0003058895,0.00036417507,0.00010770098],"domain_scores_gemma":[0.9852469,0.012221189,0.00029764217,0.0011372052,0.00085869903,0.00023842187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008192694,0.00060559704,0.0006908031,0.0013514477,0.0007335925,0.0042970083,0.0013798795,0.0026658676,0.0023620022],"category_scores_gemma":[0.019195456,0.0005280353,0.0005510671,0.0015166578,0.008609596,0.0108506745,0.0032973613,0.00570677,0.00052317395],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010087854,0.00009596183,0.0012813532,0.0004849014,0.0000702826,0.0000901092,0.000977114,0.014706813,0.0010996376,0.7973016,0.0042433157,0.17954804],"study_design_scores_gemma":[0.000023578305,0.000051734554,0.0003291497,0.0001756612,0.000010869136,0.0000801334,0.0002527409,0.05101035,0.0011381245,0.9265547,0.02034397,0.000029174189],"about_ca_topic_score_codex":0.0023124008,"about_ca_topic_score_gemma":0.0015029022,"teacher_disagreement_score":0.008192694,"about_ca_system_score_codex":0.002082379,"about_ca_system_score_gemma":0.0017569403,"threshold_uncertainty_score":0.04332757},"labels":[],"label_agreement":null},{"id":"W4416390170","doi":"10.54254/2755-2721/2025.29791","title":"The Industrial-Level Effects of Climate Change: Evidence from the Health Industry, Wheat Industry, Potatoes Industry, and Corns Industry","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Climate change; Productivity; Agriculture; Extreme weather; Crop productivity; Adverse weather; Food industry; Agricultural productivity; Production (economics)","score_opus":0.05747259271683687,"score_gpt":0.270994951945712,"score_spread":0.21352235922887514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416390170","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8885842,0.072151765,0.0018212863,0.005941342,0.0003020426,0.000048462738,0.0034472204,0.00003142075,0.02767233],"genre_scores_gemma":[0.9548829,0.041570973,0.0004399854,0.0006588276,0.00019838894,0.00001285209,0.0017021246,0.00001207273,0.0005219055],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992779,0.00023272399,0.00006039306,0.00014233569,0.00016487829,0.00012179064],"domain_scores_gemma":[0.99425733,0.0025960999,0.0015791657,0.000324892,0.00092288654,0.00031956015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014252823,0.0003359868,0.00024846476,0.0014979537,0.0005328308,0.0011239969,0.0003877941,0.00046636057,0.0029019448],"category_scores_gemma":[0.0038062762,0.00011393753,0.0008637416,0.002795335,0.00067975675,0.0006855083,0.0010376484,0.0008562502,0.0003540311],"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":[0.00054984825,0.0002713695,0.8978473,0.0021170408,0.0013932092,0.00058771955,0.0012061436,0.0021084626,0.00070631446,0.0023053563,0.0049962667,0.08591103],"study_design_scores_gemma":[0.000014414384,0.00013088691,0.9805157,0.0005830887,0.0005079087,0.00014831062,0.0026800577,0.00043545917,0.00046341016,0.0009190186,0.013582766,0.000018958626],"about_ca_topic_score_codex":0.016126834,"about_ca_topic_score_gemma":0.025085922,"teacher_disagreement_score":0.016126834,"about_ca_system_score_codex":0.00050508644,"about_ca_system_score_gemma":0.00072208483,"threshold_uncertainty_score":0.032065928},"labels":[],"label_agreement":null},{"id":"W4416718609","doi":"10.54254/2755-2721/2025.ld29993","title":"Stacking Outperforms in Debiased Neural Collaborative Filtering: A Comparative Study of IPS-Weighted NCF and Tree-Based Models for Exposure-Biased CTR Prediction","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Collaborative filtering; Recommender system; Convolutional neural network; Artificial neural network; Deep learning; Embedding; Transformer; Deep neural networks; Representation (politics)","score_opus":0.025511543922224714,"score_gpt":0.25648334138361256,"score_spread":0.23097179746138785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416718609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32314613,0.014189462,0.64219606,0.0022344762,0.00071244885,0.00023046069,0.0008920869,0.0032046793,0.013194208],"genre_scores_gemma":[0.94039655,0.002006816,0.051482834,0.00038680583,0.00022884356,0.000075346084,0.00070876174,0.00012400831,0.004590107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979971,0.0006294453,0.00012602034,0.0004901379,0.00047382928,0.0002834341],"domain_scores_gemma":[0.9850284,0.01030714,0.00065091084,0.0015002257,0.002101643,0.00041169737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063021095,0.0016404134,0.002423308,0.0015636166,0.001118527,0.0016977523,0.0025875065,0.0028022618,0.002892194],"category_scores_gemma":[0.020919628,0.0005906369,0.0012685964,0.0014613054,0.0008772614,0.004298547,0.0011667056,0.0023138402,0.0008982439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007576948,0.00032410547,0.007043773,0.00023040918,0.00030421323,0.00013720532,0.00021260932,0.78431076,0.00091641565,0.01305022,0.0044097477,0.18830292],"study_design_scores_gemma":[0.000009169849,0.00006757448,0.00029122093,0.000011543468,0.00002584644,0.000017174081,0.000010179286,0.9975727,0.00018345295,0.001523398,0.00027737397,0.000010311945],"about_ca_topic_score_codex":0.039770406,"about_ca_topic_score_gemma":0.030575342,"teacher_disagreement_score":0.039770406,"about_ca_system_score_codex":0.0018939783,"about_ca_system_score_gemma":0.0024277477,"threshold_uncertainty_score":0.07907784},"labels":[],"label_agreement":null},{"id":"W4416951889","doi":"10.54254/2755-2721/2026.ka30152","title":"Impact of Climate Change on Agriculture and Countermeasures","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Climate change; Agriculture; Sustainability; Political economy of climate change; Food security; Diversification (marketing strategy); Agricultural diversification; Agricultural productivity; Food systems","score_opus":0.01649150094880738,"score_gpt":0.2409706941566965,"score_spread":0.22447919320788912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416951889","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095377795,0.23567808,0.054175172,0.16335474,0.016484667,0.00024395129,0.0010300839,0.0005600981,0.43309548],"genre_scores_gemma":[0.8109104,0.14927404,0.015957564,0.010976504,0.0028521158,0.00016284607,0.00025183902,0.00010382368,0.009510838],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99808866,0.0008286949,0.00010251119,0.00019116724,0.00059818354,0.00019070892],"domain_scores_gemma":[0.99746835,0.0012104841,0.00039445926,0.00028130226,0.0005213028,0.00012421129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029898707,0.0007158598,0.00043662355,0.0009804617,0.0011222392,0.0043159625,0.00085722865,0.0021244122,0.00398578],"category_scores_gemma":[0.0060113864,0.00015203361,0.0005410518,0.0012629884,0.0022091886,0.0026958417,0.0023957256,0.0018300661,0.00056173594],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013574233,0.00019540437,0.016512144,0.0037087423,0.00046417187,0.0010691745,0.0024407033,0.045471326,0.005824533,0.48499307,0.04152444,0.3976605],"study_design_scores_gemma":[0.000024307314,0.00031878529,0.020469824,0.0035295635,0.00017683618,0.0006257697,0.00777744,0.008143698,0.004006177,0.31432182,0.6404639,0.00014179609],"about_ca_topic_score_codex":0.0027510999,"about_ca_topic_score_gemma":0.0030069477,"teacher_disagreement_score":0.0043159625,"about_ca_system_score_codex":0.0018392411,"about_ca_system_score_gemma":0.0029455002,"threshold_uncertainty_score":0.015812159},"labels":[],"label_agreement":null},{"id":"W4417195420","doi":"10.54254/2755-2721/2026.30562","title":"Design and Failure Mechanism Analysis of Fire Resistance Test for Fire-Resistant Oil Booms","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Transportation of Ontario","funders":"","keywords":"Boom; Oil boom; Fire resistance; Firefighting; Failure mechanism; Test method; Fire protection; Combustion; Fossil fuel","score_opus":0.013940990158197535,"score_gpt":0.2556349333284164,"score_spread":0.2416939431702189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417195420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26407412,0.00016907396,0.7314487,0.00009983957,0.000043790482,0.0002862912,0.00018550415,0.0014894003,0.0022032377],"genre_scores_gemma":[0.9478821,0.00006865664,0.050209913,0.000020367264,0.000008453981,0.00021111363,0.00013696232,0.000043097763,0.0014191887],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992099,0.00012030835,0.00004434194,0.0001809083,0.0003619293,0.00008274028],"domain_scores_gemma":[0.9989361,0.00023647258,0.00016720576,0.00011161996,0.00046884216,0.00007975856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000770491,0.0008280859,0.0005653977,0.0009319902,0.0003573312,0.00048422973,0.00150895,0.0006344331,0.0020561072],"category_scores_gemma":[0.0014874763,0.00042129352,0.0006249058,0.0002600984,0.00041371287,0.00062933017,0.00037564462,0.00034362942,0.00036799486],"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":[0.0012192362,0.00033765263,0.017420625,0.00046686846,0.00011248977,0.00071805634,0.00031886174,0.28925467,0.59042287,0.00333437,0.000987409,0.09540688],"study_design_scores_gemma":[0.000059656068,0.0010417198,0.0073757917,0.000015590855,0.000060841496,0.00020957043,0.000068022884,0.89274347,0.09658145,0.0004158733,0.0013794382,0.000048633523],"about_ca_topic_score_codex":0.0029184406,"about_ca_topic_score_gemma":0.0024234715,"teacher_disagreement_score":0.0029184406,"about_ca_system_score_codex":0.00063048105,"about_ca_system_score_gemma":0.000665583,"threshold_uncertainty_score":0.0068784356},"labels":[],"label_agreement":null},{"id":"W4417196025","doi":"10.54254/2755-2721/2025.ld30577","title":"Robotics Vision Sensor Technology and Its Current State of Development","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"CCD and CMOS Imaging Sensors","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":"Robotics; Machine vision; State (computer science); Sensor fusion; Ranging; Artificial vision; Robot","score_opus":0.004359580986156217,"score_gpt":0.21218398806299757,"score_spread":0.20782440707684136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417196025","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004902984,0.91234696,0.04027403,0.008528954,0.0014023914,0.000054725955,0.00010635961,0.0003402771,0.032043334],"genre_scores_gemma":[0.06552215,0.8777538,0.04420859,0.0025335515,0.0020540662,0.00009847019,0.00025608542,0.00009106275,0.0074822255],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979962,0.0003990277,0.00015960986,0.00035335394,0.000943897,0.00014790638],"domain_scores_gemma":[0.99686974,0.0014026169,0.00024212163,0.00019634441,0.001084428,0.00020486648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039466773,0.00059761043,0.0007289689,0.0014956156,0.0005453889,0.003083978,0.0016939677,0.0021004286,0.004276387],"category_scores_gemma":[0.0033304826,0.00052099506,0.00052663643,0.0017416924,0.0018990807,0.0048453105,0.0012787196,0.0021005883,0.0024160245],"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":[0.000099827834,0.00010072552,0.0008038489,0.003890725,0.00003497716,0.00007814592,0.00022141966,0.0018305768,0.003994043,0.07943058,0.013896404,0.8956187],"study_design_scores_gemma":[0.000012971022,0.0004530427,0.0016738212,0.0025058412,0.00006454094,0.00074347196,0.00040621698,0.004636061,0.006062315,0.035307996,0.9480412,0.000092498514],"about_ca_topic_score_codex":0.0011617977,"about_ca_topic_score_gemma":0.0006265147,"teacher_disagreement_score":0.004276387,"about_ca_system_score_codex":0.0013142138,"about_ca_system_score_gemma":0.0025783316,"threshold_uncertainty_score":0.020872295},"labels":[],"label_agreement":null},{"id":"W4417444870","doi":"10.54254/2755-2721/2025.30585","title":"Optimization of Parameter Allocation System for LoRaWAN","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"IoT Networks and Protocols","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":"Network packet; Process (computing); Bandwidth (computing); Transmission (telecommunications); Wireless; Bandwidth allocation; Range (aeronautics); Power (physics); Bit error rate","score_opus":0.005971228788134177,"score_gpt":0.2092339533693362,"score_spread":0.20326272458120204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417444870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072659336,0.0010236079,0.9152038,0.00024843102,0.000057287707,0.00014494255,0.000067545465,0.0003686927,0.0102264015],"genre_scores_gemma":[0.8994249,0.0004981488,0.097019464,0.000056379653,0.000021699341,0.0002065699,0.00009483832,0.000058506033,0.0026194456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943,0.00020499343,0.000025532374,0.00010079525,0.00013909335,0.0000996068],"domain_scores_gemma":[0.9995617,0.00017141538,0.0000842042,0.000026579011,0.00013096741,0.000025109364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072386593,0.0009662354,0.0006108106,0.0006282527,0.00074294495,0.0010351592,0.0006438255,0.0005025139,0.0016992966],"category_scores_gemma":[0.0019674553,0.00033310248,0.00036073808,0.0005021809,0.0004605023,0.0010220513,0.00076670287,0.0005510358,0.00029871476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060162893,0.00002673748,0.0007779919,0.00007075089,0.000022845887,0.00005053571,0.00006868813,0.96734536,0.0033531056,0.0035654379,0.00052619434,0.024132121],"study_design_scores_gemma":[0.000018398237,0.0000628655,0.00028374276,0.000010580182,0.000015608293,0.000033480846,0.000056998917,0.99480164,0.001568948,0.001941901,0.0011921775,0.000013678917],"about_ca_topic_score_codex":0.0048591304,"about_ca_topic_score_gemma":0.0041946624,"teacher_disagreement_score":0.0048591304,"about_ca_system_score_codex":0.0009080583,"about_ca_system_score_gemma":0.0010692689,"threshold_uncertainty_score":0.0096616745},"labels":[],"label_agreement":null},{"id":"W4417444878","doi":"10.54254/2755-2721/2025.30672","title":"Stock Price Prediction Report","year":2025,"lang":"","type":"article","venue":"Applied and Computational Engineering","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Stock (firearms); Stock price; Stock market; Cost price; Time series; Predictive modelling","score_opus":0.02748597767590628,"score_gpt":0.317244347370659,"score_spread":0.2897583696947527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417444878","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034463912,0.0038114667,0.015180225,0.00566326,0.004516829,0.0016110954,0.77635896,0.006873745,0.15152055],"genre_scores_gemma":[0.08349268,0.0026664825,0.013029425,0.001107367,0.0012817571,0.0010920942,0.79870766,0.00044068036,0.09818185],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997334,0.00017448021,0.00035740668,0.0002787061,0.0017239427,0.00013153537],"domain_scores_gemma":[0.98414224,0.0013954416,0.0010176817,0.0011496743,0.011745705,0.0005492575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030564563,0.0011107902,0.0007621777,0.004870541,0.00053944084,0.0016960776,0.0013231019,0.0007607757,0.050325982],"category_scores_gemma":[0.013160025,0.00025452694,0.00055187766,0.0033842884,0.00013820374,0.0013554895,0.00069255324,0.0012542205,0.056103032],"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":[0.00026412174,0.0001790976,0.025655808,0.00021017434,0.000094939656,0.000109325956,0.00004151572,0.001723833,0.00054965756,0.0021719693,0.8141682,0.15483125],"study_design_scores_gemma":[0.0002073363,0.0003396316,0.11098388,0.00037764688,0.00016659543,0.00025361113,0.00015225266,0.014388377,0.0049763015,0.003614639,0.86440706,0.00013264109],"about_ca_topic_score_codex":0.01793002,"about_ca_topic_score_gemma":0.012186083,"teacher_disagreement_score":0.050325982,"about_ca_system_score_codex":0.00087495026,"about_ca_system_score_gemma":0.002043171,"threshold_uncertainty_score":0.1683572},"labels":[],"label_agreement":null}]}