{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":18,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":18,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"10d5acbc03d9","filters":{"venue":"Engineering Technology & Applied Science Research"}},"results":[{"id":"W2913733727","doi":"10.48084/etasr.2491","title":"Effect of Polyvinyl Pyrolidone on Morphology and Performance of Cellulose Acetate Based Dialysis Membrane","year":2019,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National University of Science and Technology; University of Waterloo","keywords":"Membrane; Cellulose acetate; Contact angle; Permeation; Chemical engineering; Polyvinyl chloride; Materials science; Urea; Bovine serum albumin; Dialysis tubing; Regenerated cellulose; Synthetic membrane; Chromatography; Polymer chemistry; Polymer; Polyvinyl acetate; Cellulose; Chemistry; Organic chemistry; Composite material; Biochemistry","retraction":null,"screen_n_in":null,"score":{"opus":0.0088847773342674,"gpt":0.2638491618370304,"spread":0.254964384502763,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003170507,0.00017685,0.0003858549,0.001369165,0.00009605144,0.000009568499,0.001087609,0.0002145516,0.0001992516],"category_scores_gemma":[0.0003593028,0.0001510591,0.00003167616,0.00362318,0.002418461,0.00009966815,0.0006402202,0.0005500008,0.000174429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001446997,"about_ca_system_score_gemma":0.00003542316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001989623,"about_ca_topic_score_gemma":9.322815e-7,"domain_scores_codex":[0.9976311,0.00003833181,0.0002804924,0.0005931599,0.0008280532,0.0006289005],"domain_scores_gemma":[0.998577,0.0003856893,0.00009164326,0.0008472937,0.00002845734,0.0000699402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009515269,0.00002808824,0.006090571,0.00008956682,0.000005453685,0.000001505223,0.00002809001,0.01342584,0.9728895,0.0009811987,0.00000853903,0.006356536],"study_design_scores_gemma":[0.0004249832,0.001035213,0.00241551,0.0000206512,0.000005079633,0.000003119602,0.0000165675,0.05114338,0.9447324,0.00004056002,0.00004921943,0.0001132642],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976735,0.00002823948,0.0001461191,0.0002204569,0.0000403523,0.0005361256,0.000002472583,0.0001738752,0.001178886],"genre_scores_gemma":[0.9980959,0.00003049223,0.001684292,0.000004912743,0.000003162271,0.00007113364,8.520405e-7,0.00001512162,0.00009419119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03771754,"threshold_uncertainty_score":0.8910919,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2969068073","doi":"10.48084/etasr.2952","title":"Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms","year":2019,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Prosthetics and Rehabilitation Robotics","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Exoskeleton; Gait; Wearable computer; Artificial intelligence; Machine learning; Gait analysis; Computer science; Animation; Robotics; Robot; Simulation; Physical medicine and rehabilitation","retraction":null,"screen_n_in":null,"score":{"opus":0.0215973592732764,"gpt":0.2909808994105394,"spread":0.269383540137263,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009395074,0.0001555517,0.0001686284,0.001373094,0.0003103987,0.00006479402,0.000372064,0.0001623293,0.00001856635],"category_scores_gemma":[0.000070721,0.0001458484,0.00002035326,0.00339227,0.0003684612,0.000162792,0.0001333008,0.001033204,0.00009994026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003734537,"about_ca_system_score_gemma":0.00008901296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008913637,"about_ca_topic_score_gemma":0.000001440143,"domain_scores_codex":[0.998275,0.00001341063,0.0001407724,0.0003585829,0.0005230737,0.0006891249],"domain_scores_gemma":[0.9992543,0.00009558081,0.00002072785,0.0003331213,0.0001983817,0.00009782114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003334067,0.00001902283,0.0001317523,0.00003520654,0.000009374491,0.000002388035,0.00005092497,0.3909237,0.6051821,0.0005700223,0.000002447095,0.00306981],"study_design_scores_gemma":[0.000187292,0.00009842261,0.0003901823,0.00004805874,0.000003734533,0.00002345157,0.0000736467,0.9001742,0.09852612,0.0002035274,0.00009383482,0.0001775448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9545797,0.00005518809,0.04165189,0.0001451038,0.0001015151,0.0004488323,0.000008045612,0.002601513,0.0004081834],"genre_scores_gemma":[0.9481605,0.00001479447,0.05169553,0.000001589495,0.00001358488,0.00004759511,0.000004547908,0.00003963696,0.0000222126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5092505,"threshold_uncertainty_score":0.5947523,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2889123613","doi":"10.48084/etasr.2135","title":"Industrial Bearing Fault Detection Using Time-Frequency Analysis","year":2018,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Bearing (navigation); Vibration; Fault (geology); Time domain; Fault detection and isolation; Frequency domain; Time–frequency analysis; Engineering; Measure (data warehouse); Test bench; Software; Computer science; Electronic engineering; Acoustics; Artificial intelligence; Data mining; Embedded system; Computer vision; Electrical engineering; Physics; Actuator","retraction":null,"screen_n_in":null,"score":{"opus":0.0397845518302875,"gpt":0.349584008420619,"spread":0.3097994565903315,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002700205,0.0002740932,0.000357502,0.006812212,0.0005385145,0.0001257614,0.001412443,0.0004642627,0.00004985131],"category_scores_gemma":[0.0005824535,0.0003003859,0.00007128956,0.01808535,0.000973668,0.0002745453,0.0004526774,0.001473853,0.0001499945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009864571,"about_ca_system_score_gemma":0.0001040532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007082174,"about_ca_topic_score_gemma":0.00002041769,"domain_scores_codex":[0.99669,0.00002005552,0.000366523,0.0006851003,0.0009362676,0.001302027],"domain_scores_gemma":[0.998449,0.0001142202,0.00003578739,0.0009338822,0.0002890129,0.0001781004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002836848,0.00001732362,0.0006410129,0.00001058187,0.00008116616,0.000004569187,0.00005351653,0.01465002,0.9721175,0.001054939,0.00005351831,0.01131294],"study_design_scores_gemma":[0.0001055752,0.00005261114,0.0002755309,0.0000176652,0.00003016864,0.000007113134,0.0000211979,0.4726308,0.5257131,0.0005349473,0.0003760869,0.0002351682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9131104,0.00005482124,0.07948289,0.00005738817,0.000182054,0.0004596269,0.00000561258,0.005290919,0.00135623],"genre_scores_gemma":[0.9794415,0.00001088251,0.02008286,0.000003020894,0.0002285237,0.0001482777,0.000001661103,0.00007131245,0.00001193256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4579808,"threshold_uncertainty_score":0.9999448,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407066574","doi":"10.48084/etasr.9154","title":"Optimal CNN Model for Obstructive Sleep Apnea Detection using Particle Swarm Optimization","year":2025,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Particle swarm optimization; Obstructive sleep apnea; Sleep (system call); Computer science; Particle (ecology); Sleep apnea; Artificial intelligence; Medicine; Machine learning; Anesthesia; Biology; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.03631189765501572,"gpt":0.3535994745882692,"spread":0.3172875769332534,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001900462,0.0001950812,0.0002868406,0.002417204,0.0007026758,0.0000694206,0.0005930759,0.00027822,0.000006753897],"category_scores_gemma":[0.001342982,0.0002018822,0.00005749277,0.006413944,0.001179634,0.0002155539,0.0004868803,0.0009314605,0.000008532206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001347521,"about_ca_system_score_gemma":0.0002731455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006296224,"about_ca_topic_score_gemma":0.000001015496,"domain_scores_codex":[0.996751,0.00001624183,0.0002966808,0.0008188656,0.0008565513,0.001260719],"domain_scores_gemma":[0.9980533,0.0001680796,0.00003955061,0.0006504905,0.0009105481,0.0001780659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000948749,0.000040525,0.0000852655,0.0000464792,0.00002500836,0.000001996211,0.00005784159,0.528505,0.4542076,0.006705564,0.000001020274,0.01022888],"study_design_scores_gemma":[0.0005584466,0.00006706714,0.00008481163,0.00001367935,0.00001533242,0.000008814382,0.0002606886,0.6170008,0.3813816,0.0005052247,0.00001280233,0.00009081123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4200512,0.00004021341,0.5782199,0.0003395166,0.00007416341,0.0009191385,0.000003277747,0.0002396174,0.0001130005],"genre_scores_gemma":[0.808071,0.000002779008,0.1913848,0.00000722372,0.00003520846,0.0004095452,0.000001909622,0.0000289448,0.00005867739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3880198,"threshold_uncertainty_score":0.8232518,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4311890330","doi":"10.48084/etasr.5362","title":"Effectiveness of EPS Bead Size and Cement Proportions on the Strength and Deformation of Light-Weighted Soil","year":2022,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Geotechnical Engineering and Soil Stabilization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Powertech Labs (Canada)","funders":"","keywords":"Bead; Materials science; Cement; Composite material; Brittleness; Deformation (meteorology); Compressive strength; Context (archaeology); Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.01090050376020219,"gpt":0.2467703610940644,"spread":0.2358698573338622,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002008551,0.00009943482,0.0001493192,0.0005683385,0.0002468298,0.00001092045,0.0003012387,0.0000585592,0.00001244748],"category_scores_gemma":[0.0002713091,0.000081649,0.00001374212,0.0020345,0.0003084923,0.00004873772,0.000250317,0.0005112781,6.181909e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001453917,"about_ca_system_score_gemma":0.00003762935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003889795,"about_ca_topic_score_gemma":5.197024e-7,"domain_scores_codex":[0.9987971,0.00001779168,0.0002015993,0.0001950719,0.0004925322,0.0002959268],"domain_scores_gemma":[0.9992114,0.0003535482,0.00002623971,0.0002874227,0.00007884559,0.00004258605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001159832,0.00005404737,0.00004542344,0.0003083507,0.00001628672,5.99487e-7,0.0001960723,0.3021376,0.605499,0.08890444,0.00001416902,0.002812453],"study_design_scores_gemma":[0.0002897445,0.0003023633,0.003979195,0.00009808593,0.000007702546,0.000008255135,0.0006176054,0.488122,0.5036619,0.002474887,0.0002505504,0.0001877348],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965749,0.00006632141,0.002370287,0.0001306111,0.00003654393,0.0004127359,0.00001613997,0.0002281767,0.0001643207],"genre_scores_gemma":[0.9992886,0.00002544148,0.0003284116,5.621082e-7,0.000003210287,0.0003347934,0.000002827987,0.00001294799,0.000003244859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1859844,"threshold_uncertainty_score":0.3329549,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4362586752","doi":"10.48084/etasr.5632","title":"Numerical Simulation and Optimization of Methane Steam Reforming to Maximize H2 Production: A Case Study","year":2023,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Iron and Steelmaking Processes","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Multiphysics; Steam reforming; Hydrogen production; Methane reformer; Process engineering; Methane; Work (physics); Engineering; Hydrogen; Mechanical engineering; Chemistry; Finite element method","retraction":null,"screen_n_in":null,"score":{"opus":0.05155787441155153,"gpt":0.3641341564014521,"spread":0.3125762819899006,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001713969,0.0001149945,0.00017714,0.002038076,0.0001606772,0.00001978689,0.0002815596,0.00008669366,0.000003221362],"category_scores_gemma":[0.0005318591,0.0001147126,0.000009650495,0.007413165,0.0001514445,0.0001279714,0.000194264,0.0003426674,0.00000876248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001331249,"about_ca_system_score_gemma":0.00004299935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009834203,"about_ca_topic_score_gemma":0.000001943982,"domain_scores_codex":[0.9984632,0.000009721755,0.0002116565,0.0003619588,0.0004776911,0.0004757444],"domain_scores_gemma":[0.9993228,0.00008527225,0.00001671548,0.0003334778,0.0001599581,0.00008177751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005394907,0.00001791013,0.00004023417,0.00006665893,0.000006280421,0.00004105406,0.0007186219,0.9619333,0.02858002,0.0003155082,0.000004628397,0.008270353],"study_design_scores_gemma":[0.0001723765,0.0001193596,0.00007787719,0.00002695843,0.000004523725,0.0001087267,0.003897869,0.9506913,0.0444707,0.00003234668,0.0002466805,0.0001512443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542751,0.00005277455,0.04340452,0.0001426447,0.0001258497,0.0006697885,9.840344e-7,0.00116077,0.0001675357],"genre_scores_gemma":[0.992762,0.00001055279,0.006975013,9.547101e-7,0.0000253629,0.0001381546,5.116187e-7,0.00002453553,0.00006293874],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03848685,"threshold_uncertainty_score":0.4677845,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4311890421","doi":"10.48084/etasr.5230","title":"Classification of Macromolecules Based on Amino Acid Sequences Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Word2vec; Embedding; Word embedding; Task (project management); Machine learning; Convolutional neural network; Artificial neural network; Pattern recognition (psychology); Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02226822236046992,"gpt":0.3170238563315601,"spread":0.2947556339710902,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002144333,0.0001168503,0.0001203444,0.0009772428,0.0005609475,0.0000216888,0.0009769717,0.0001075085,0.00001573896],"category_scores_gemma":[0.0005779847,0.0001244527,0.00002914193,0.001902875,0.0006142085,0.000005345057,0.0005661826,0.0008396702,0.000004147762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001726538,"about_ca_system_score_gemma":0.000227961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004568516,"about_ca_topic_score_gemma":5.89185e-7,"domain_scores_codex":[0.998135,0.00004888879,0.0002113076,0.0003701274,0.0007474701,0.0004871541],"domain_scores_gemma":[0.9992093,0.00003844361,0.00008941894,0.0004874167,0.000122108,0.00005333202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001251032,0.00002019198,0.0009363109,0.0000160632,0.000003604183,0.000001080536,0.00003000417,0.1991641,0.7950245,0.002874878,0.000004147342,0.001912676],"study_design_scores_gemma":[0.0001320353,0.0003088968,0.0005222572,0.000008124502,0.000002162347,0.00001110462,0.000428118,0.5300649,0.4672057,0.00009051675,0.001104562,0.0001216083],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9663767,0.00007546297,0.03224988,0.0001641761,0.00005131475,0.0002138346,0.000003979403,0.00008162266,0.000783004],"genre_scores_gemma":[0.9798132,0.000006217098,0.02002646,0.00001174498,0.00001722874,0.00007635143,0.00001370837,0.00001723781,0.0000178721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3309009,"threshold_uncertainty_score":0.5075034,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4311890480","doi":"10.48084/etasr.5248","title":"Assessment of Shear Strength Models of Reinforced Concrete Columns","year":2022,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Structural Behavior of Reinforced Concrete","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Eurocode; Reinforced concrete; Structural engineering; Shear strength (soil); Shear (geology); Building code; Empirical modelling; Experimental data; Mathematics; Geotechnical engineering; Materials science; Geology; Computer science; Engineering; Statistics; Composite material; Simulation","retraction":null,"screen_n_in":null,"score":{"opus":0.02496488712403978,"gpt":0.3057912838052219,"spread":0.2808263966811821,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001572535,0.0001918076,0.0003849795,0.001990982,0.0002539564,0.00001737661,0.001716005,0.0001266069,0.00008610215],"category_scores_gemma":[0.00008069425,0.0002212929,0.00005825467,0.004478237,0.0008553084,0.0001661296,0.0008844284,0.001291558,0.000002047825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006225473,"about_ca_system_score_gemma":0.0003065919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002048303,"about_ca_topic_score_gemma":1.180509e-7,"domain_scores_codex":[0.9967423,0.00001403692,0.0004661063,0.0003688225,0.001536548,0.0008721781],"domain_scores_gemma":[0.9986321,0.0001390197,0.0000649124,0.0008374685,0.0002065074,0.0001200333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002629752,2.04448e-7,0.00001465864,0.00003069868,0.00001012338,0.000002327866,0.00005387455,0.3787127,0.5540693,0.06645979,0.00001655842,0.0006270761],"study_design_scores_gemma":[0.0002415695,0.0001241003,0.0001126715,0.0000130822,0.000005328314,0.00001055035,0.0003594383,0.6384774,0.3601244,0.0002299332,0.0001414997,0.0001600094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859113,0.00008608381,0.00950686,0.00003511772,0.0001660566,0.0005676636,0.00004176979,0.0006090608,0.003076099],"genre_scores_gemma":[0.9800494,0.00001978196,0.01958103,0.000001439512,0.00001001898,0.0002426621,0.000004363988,0.00003707286,0.00005421339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2597647,"threshold_uncertainty_score":0.9024062,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4404979278","doi":"10.48084/etasr.9133","title":"AI-Driven Energy Efficiency Optimizations in mHealth Applications: A Comprehensive Review on User Behavior Prediction and System Performance","year":2024,"lang":"en","type":"review","venue":"Engineering Technology & Applied Science Research","topic":"Green IT and Sustainability","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Sheridan College","funders":"Prince Sultan University","keywords":"mHealth; Popularity; Computer science; Energy consumption; Scheduling (production processes); Generalization; Machine learning; Artificial intelligence; Health care; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03644977022195307,"gpt":0.3570817180916102,"spread":0.3206319478696571,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001241939,0.0004137671,0.00093707,0.003561491,0.000280322,0.00006898656,0.0009479343,0.0004398508,0.000002186317],"category_scores_gemma":[0.00005268164,0.0003718138,0.00006929326,0.008680914,0.0005136968,0.0001106302,0.0003630701,0.001927142,0.00003431767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002004603,"about_ca_system_score_gemma":0.0004502003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005672866,"about_ca_topic_score_gemma":0.000001652539,"domain_scores_codex":[0.996832,0.00003766015,0.0006735513,0.0009020083,0.0006334947,0.0009213527],"domain_scores_gemma":[0.9985509,0.0001245125,0.0000466638,0.0009093778,0.0002144366,0.0001540979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001779753,0.00009064168,0.00001385415,0.2180012,0.00003547825,0.00003229467,0.00004917074,0.02489011,0.00003677421,0.03614879,0.0003643794,0.7203355],"study_design_scores_gemma":[0.0001577664,0.00012909,0.00002404464,0.03983527,0.0002249391,0.0001405841,0.000120756,0.1958977,0.00001893345,0.00004410895,0.7627238,0.0006829847],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000115668,0.9907634,0.004686933,0.00006625193,0.0001283868,0.00289677,0.00003525698,0.001170684,0.0001366983],"genre_scores_gemma":[0.006822778,0.9836814,0.0006968213,0.000004829373,0.00003960023,0.008641696,0.00002055251,0.00007181357,0.00002049727],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7623594,"threshold_uncertainty_score":0.9998734,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4399274848","doi":"10.48084/etasr.7094","title":"Modified Equivalent Compression Stress Block for Normal-Strength Concrete Flexural Design using Energy Modeling","year":2024,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Structural Behavior of Reinforced Concrete","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Flexural strength; Structural engineering; Compression (physics); Block (permutation group theory); Stress (linguistics); Materials science; Composite material; Mathematics; Engineering; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.0753670900005694,"gpt":0.3343656345626682,"spread":0.2589985445620988,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001032412,0.0003777039,0.0003463481,0.002145676,0.000423923,0.0002427913,0.001368143,0.0003552221,0.000008699656],"category_scores_gemma":[0.00009762292,0.0003741737,0.00008242408,0.002598605,0.0004423345,0.0003481265,0.0004434865,0.0009925927,0.000007921812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007522756,"about_ca_system_score_gemma":0.0002125545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000180565,"about_ca_topic_score_gemma":2.961518e-7,"domain_scores_codex":[0.996323,0.00001385674,0.0004170331,0.0007321793,0.0008750099,0.001638958],"domain_scores_gemma":[0.9986401,0.0002568644,0.00002142455,0.0006497758,0.0002001777,0.0002316938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000742646,2.228332e-7,3.70743e-7,0.0000647336,0.0000133966,0.000007638651,0.00003404226,0.519845,0.4676817,0.00721591,0.00001563209,0.005113895],"study_design_scores_gemma":[0.0001544862,0.00003490664,3.849682e-7,0.0001284545,0.00001091852,0.00002226963,0.00006741279,0.6301138,0.3688294,0.0002374897,0.0001487066,0.0002517568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3534062,0.0009122072,0.6420491,0.00004428983,0.0004906445,0.0005527291,0.00002779054,0.002314633,0.0002024973],"genre_scores_gemma":[0.9494465,0.00005920267,0.04993225,0.000002248938,0.0001145282,0.0002892543,0.000007256956,0.00009556585,0.00005318002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5960404,"threshold_uncertainty_score":0.999871,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411122614","doi":"10.48084/etasr.10868","title":"AI-Driven Automated Helmet Detection in Underground Coal Mines using Attention-Enhanced Vision Transformer","year":2025,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Coal mining; Transformer; Mining engineering; Artificial intelligence; Engineering; Computer vision; Coal; Computer science; Forensic engineering; Waste management; Electrical engineering; Voltage","retraction":null,"screen_n_in":null,"score":{"opus":0.05296954035041436,"gpt":0.502491755669282,"spread":0.4495222153188676,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004351633,0.0001701301,0.0002846516,0.004763077,0.001715514,0.00002835988,0.0006944404,0.0004795906,0.00003123006],"category_scores_gemma":[0.0006409538,0.0001667603,0.00003317048,0.009494124,0.0006095077,0.0002749016,0.0002435738,0.00202017,0.00008167389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001835518,"about_ca_system_score_gemma":0.00167704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001948634,"about_ca_topic_score_gemma":0.0004051193,"domain_scores_codex":[0.9960268,0.000133043,0.0005781694,0.0006473496,0.0009569963,0.001657641],"domain_scores_gemma":[0.9981232,0.0007015592,0.00004876955,0.0004370812,0.0005294818,0.0001599095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001502979,0.00008742412,0.004220756,0.0003720447,0.000009502027,0.000004017377,0.0001374921,0.004840989,0.9688849,0.01026666,0.0001014213,0.01092453],"study_design_scores_gemma":[0.002038799,0.0002191352,0.1027601,0.0008131171,0.000006723204,0.000003732931,0.001610677,0.8420075,0.04445822,0.004156545,0.001572944,0.0003525269],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9073216,0.00007568064,0.08434487,0.003711791,0.0004453068,0.001953217,0.000007210759,0.0007613382,0.001379026],"genre_scores_gemma":[0.9968577,0.0000430068,0.002196628,0.00004899059,0.00004313735,0.0005883957,0.000005673232,0.00001925025,0.0001972437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9244266,"threshold_uncertainty_score":0.9995841,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411122872","doi":"10.48084/etasr.10758","title":"Transfer Learning Approach using Simulated Induction Motor Bearing Data: A Comparative Analysis of SE-ResNet and its Hybrid Variants","year":2025,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Nuclear Waste Management Organization","funders":"","keywords":"Bearing (navigation); Induction motor; Artificial intelligence; Engineering; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.08310310238690373,"gpt":0.3968767961050079,"spread":0.3137736937181042,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002680713,0.0002275707,0.0005234088,0.006020787,0.000267499,0.00006326231,0.001155656,0.0001968351,0.000005098714],"category_scores_gemma":[0.0003071864,0.000247003,0.00003318763,0.010743,0.0003775291,0.0003920433,0.0006142723,0.00128379,0.000001034806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002490135,"about_ca_system_score_gemma":0.00008377049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004726536,"about_ca_topic_score_gemma":0.00000248182,"domain_scores_codex":[0.9976684,0.00004062707,0.0003761882,0.0007170062,0.0005239832,0.0006738032],"domain_scores_gemma":[0.9987924,0.0002127076,0.0000247346,0.0007010126,0.0001867012,0.00008242855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004813017,0.00002512557,0.0001831785,0.00006620744,0.0001798859,0.000001228662,0.0001066444,0.5347332,0.4612208,0.002786891,0.000005948976,0.0006860452],"study_design_scores_gemma":[0.0001294525,0.00001986933,0.0008954081,0.00004292929,0.00008462712,0.000002480778,0.0001216458,0.8237752,0.1746422,0.00006242015,0.00007351451,0.0001502531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9002678,0.0003302121,0.09759621,0.00002552619,0.00003949938,0.0005129182,0.00002599026,0.0008623623,0.0003394398],"genre_scores_gemma":[0.9916964,0.00006541293,0.008138497,0.000001532557,0.00001113687,0.00003807497,0.00001918238,0.00002049551,0.000009310766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.289042,"threshold_uncertainty_score":0.9999982,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401296614","doi":"10.48084/etasr.7681","title":"Evaluating Surface Water Quality of Euphrates River in Al-Najaf Al-Ashraf, Iraq with Water Quality Index (WQI)","year":2024,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Turbidity; Water quality; Environmental science; Hydrology (agriculture); Pollution; Surface water; Environmental engineering; Index (typography); Water resource management; Oceanography; Engineering; Geology; Geotechnical engineering; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.0943815735220446,"gpt":0.4323394962297326,"spread":0.337957922707688,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01794081,0.0002470708,0.0003547929,0.00068539,0.0002712931,0.00009693428,0.00111568,0.0001900591,0.0003302839],"category_scores_gemma":[0.0001299915,0.0001611429,0.00004296395,0.002631014,0.002462705,0.0003802013,0.001185188,0.001125246,0.0003995266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008063092,"about_ca_system_score_gemma":0.000103554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007774477,"about_ca_topic_score_gemma":0.00007083525,"domain_scores_codex":[0.9947991,0.0001975754,0.0006347316,0.0009953241,0.001995475,0.001377811],"domain_scores_gemma":[0.9987755,0.000219869,0.000040789,0.0007619181,0.0000620333,0.0001399314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003336758,0.00007543201,0.008295479,0.00007509607,0.00001268509,0.000008492449,0.001857793,0.04222142,0.9420759,0.003778447,0.00008996449,0.001475937],"study_design_scores_gemma":[0.0005257875,0.0001659374,0.03030936,0.0001001686,0.000004690256,0.0000074056,0.0006636109,0.02870515,0.9311873,0.005537147,0.002327724,0.0004656842],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990197,0.00003812752,0.003366627,0.004834032,0.00008226471,0.0004932632,0.000008634521,0.0002681194,0.0007119474],"genre_scores_gemma":[0.9941331,0.000008678599,0.005421482,0.00005075607,0.000009700877,0.00008693501,0.000004554511,0.00002348178,0.0002613261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02201388,"threshold_uncertainty_score":0.9073941,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4362585011","doi":"10.48084/etasr.5683","title":"Multi-level Association Rule Mining for the Discovery of Strong Underrepresented Patterns","year":2023,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Tanzania; Production (economics); Business; Dairy farming; Agriculture; Milk production; Agricultural science; Population; Association rule learning; Cluster (spacecraft); Agricultural economics; Geography; Environmental health; Economics; Environmental science; Environmental planning; Biology; Animal science; Medicine; Computer science; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.1066213340430877,"gpt":0.3716883813356935,"spread":0.2650670472926058,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003418226,0.00009421234,0.0001286918,0.0008758432,0.0004984982,0.0001632738,0.002328839,0.00009179176,6.364149e-7],"category_scores_gemma":[0.0008124271,0.00007756141,0.0000321768,0.005139689,0.0002257697,0.0003177723,0.00121143,0.0003260667,0.00001360469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002005798,"about_ca_system_score_gemma":0.0001637399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002089753,"about_ca_topic_score_gemma":0.000005263516,"domain_scores_codex":[0.9978863,0.000009993442,0.0002047823,0.0004938364,0.0006864602,0.0007186552],"domain_scores_gemma":[0.9978781,0.0009035244,0.00007151929,0.000900614,0.0002020768,0.00004421333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005194061,0.0001699246,0.002934913,0.00008346915,0.00009632719,0.000004337327,0.001117412,0.01521788,0.4913053,0.2801319,0.001750883,0.2071824],"study_design_scores_gemma":[0.0002019687,0.00002284767,0.0143377,0.00002145737,0.00000291503,0.000001439946,0.0007183712,0.9575026,0.0260112,0.0007185553,0.0003574424,0.0001034779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05567008,0.00002095489,0.9408087,0.002495504,0.0001180808,0.0004061883,0.00005960443,0.000385739,0.00003512861],"genre_scores_gemma":[0.8792473,0.00001847705,0.1199466,0.000003958301,0.00002781636,0.0004904626,0.000005794937,0.0000111839,0.0002483712],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9422848,"threshold_uncertainty_score":0.4327601,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4404978855","doi":"10.48084/etasr.7442","title":"Application of Artificial Intelligence in the Identification of Banana Bunch Top Virus (BBTV) in Mozambique","year":2024,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"PEST analysis; Infestation; Crop; Artificial intelligence; Biology; Computer science; Agronomy; Horticulture","retraction":null,"screen_n_in":null,"score":{"opus":0.02579886981110907,"gpt":0.3036727404137287,"spread":0.2778738706026196,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003393219,0.00007548182,0.0001204415,0.0003698019,0.0000692063,0.00003532783,0.0009629937,0.0001259447,0.00000628413],"category_scores_gemma":[0.0001681567,0.0000308451,0.00002337044,0.008712367,0.0003541831,0.00008555078,0.0001186329,0.0004284212,0.00001340975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007296884,"about_ca_system_score_gemma":0.00003017413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002259255,"about_ca_topic_score_gemma":0.0003524271,"domain_scores_codex":[0.9984782,0.00002314741,0.0003689717,0.0003399649,0.000465423,0.0003242297],"domain_scores_gemma":[0.9994638,0.0002502958,0.00003565409,0.0001425349,0.00008677955,0.0000209162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002488636,0.00003974832,0.0001882719,0.00001240376,8.802393e-7,0.00000117576,0.0001546121,0.000370944,0.8446482,0.0694415,0.000008705412,0.08513109],"study_design_scores_gemma":[0.00001453687,0.00006737926,0.02105728,0.00004463558,0.000001730444,0.000003694817,0.001495725,0.02842427,0.9311044,0.01698399,0.0007003025,0.0001020258],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953352,0.0002160505,0.002061041,0.00177785,0.00004182527,0.0004060535,0.000005357216,0.00005637884,0.0001003083],"genre_scores_gemma":[0.9995908,0.00003763835,0.0001730402,0.000003801972,0.00003529233,0.0001500884,0.000003311721,7.881166e-7,0.000005196528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08645625,"threshold_uncertainty_score":0.4186004,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411122947","doi":"10.48084/etasr.10735","title":"CViTLNN: A Hybrid Approach based on Vision Transformer and Liquid Neural Network for COVID-19 Detection","year":2025,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Artificial neural network; Transformer; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Artificial intelligence; Computer science; Pattern recognition (psychology); Engineering; Virology; Electrical engineering; Medicine; Internal medicine; Infectious disease (medical specialty); Voltage","retraction":null,"screen_n_in":null,"score":{"opus":0.03370811819360123,"gpt":0.3777620534262094,"spread":0.3440539352326082,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003290448,0.0001914197,0.0002883141,0.002450335,0.0006546509,0.00005502973,0.0004122217,0.0002029806,0.000003805736],"category_scores_gemma":[0.001913764,0.0001803397,0.00005099473,0.00417125,0.0007949309,0.00006612955,0.000109939,0.0008648638,0.000003144086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000818486,"about_ca_system_score_gemma":0.0007076587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001726932,"about_ca_topic_score_gemma":0.00000349514,"domain_scores_codex":[0.9973527,0.00001843244,0.0002330325,0.0008529391,0.0006091205,0.0009337364],"domain_scores_gemma":[0.9982541,0.0007132402,0.00002439654,0.0005815287,0.0001827575,0.000243972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001581611,0.0003655649,0.0001256521,0.001132346,0.00003347761,0.00002052504,0.00009400147,0.1837691,0.7357392,0.008689056,0.004069866,0.06437956],"study_design_scores_gemma":[0.001759623,0.001196697,0.0003030524,0.0001394181,0.00002556217,0.00002075484,0.00006265689,0.8404248,0.1259203,0.0005919455,0.02935061,0.0002045122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.243562,0.0002315133,0.6846322,0.06679597,0.0002136271,0.003194833,0.000008489615,0.001109251,0.0002520696],"genre_scores_gemma":[0.9850698,0.00001890737,0.0117687,0.002147461,0.0000611093,0.0008697077,0.000004422739,0.00002593013,0.00003403573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7415077,"threshold_uncertainty_score":0.735404,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4411123035","doi":"10.48084/etasr.10222","title":"Classification of IPv6 Transition Mechanisms using Multiple-Criteria Decision-Making","year":2025,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"RFID technology advancements","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Transition (genetics); Computer science; Data mining; Artificial intelligence; Mathematics; Biology; Genetics","retraction":null,"screen_n_in":null,"score":{"opus":0.02956619122295252,"gpt":0.3611767484727844,"spread":0.3316105572498318,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001533498,0.0001951332,0.0002685237,0.004343425,0.0002761068,0.00002925378,0.001162587,0.0003552778,0.00001228923],"category_scores_gemma":[0.000536243,0.0002244193,0.00003301939,0.006915272,0.0006752307,0.0002201114,0.0002547621,0.0007889785,0.00001137304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006274525,"about_ca_system_score_gemma":0.0001099321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001689188,"about_ca_topic_score_gemma":0.00000198696,"domain_scores_codex":[0.9976922,0.00001067507,0.0004150612,0.0005097169,0.0005826491,0.0007897458],"domain_scores_gemma":[0.9986637,0.0002414823,0.00003608711,0.0007892098,0.0002228171,0.00004676245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009273127,0.00002119511,0.00004088781,0.00005714754,0.00001444868,0.000002311393,0.00003839678,0.04184728,0.8851966,0.04865487,0.00001582555,0.02410172],"study_design_scores_gemma":[0.0002160337,0.00001839336,0.0004173207,0.0002020762,0.000005727718,0.000004777904,0.0002018426,0.6273057,0.3440344,0.02736759,0.00008626869,0.0001398833],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3931569,0.00009912584,0.6051471,0.0000773007,0.0001992755,0.000318386,0.000004488219,0.0007643194,0.0002330519],"genre_scores_gemma":[0.8432162,0.00002102882,0.1566115,0.000003246568,0.000008303159,0.0001098394,0.000001177707,0.0000257384,0.000002909195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5854584,"threshold_uncertainty_score":0.9151555,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414882152","doi":"10.48084/etasr.12761","title":"A Response-by-Retrieval Chatbot for Enhancing Horticulture Extension Services in Tanzania","year":2025,"lang":"en","type":"article","venue":"Engineering Technology & Applied Science Research","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"International Development Research Centre","keywords":"Chatbot; Credibility; Key (lock); Government (linguistics); Revenue; Encoder; Software deployment; Language model","retraction":null,"screen_n_in":null,"score":{"opus":0.01262882439108442,"gpt":0.3300004618410143,"spread":0.3173716374499299,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004558764,0.0001856491,0.0002362368,0.002753365,0.0004945891,0.0002089663,0.003333318,0.0002644104,0.00000276667],"category_scores_gemma":[0.0007740311,0.0001814005,0.00003411393,0.01069171,0.0002865447,0.0004909754,0.00131337,0.0009401462,0.00002738233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006700137,"about_ca_system_score_gemma":0.000371412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001836876,"about_ca_topic_score_gemma":0.00004658797,"domain_scores_codex":[0.9968394,0.00003452245,0.0003504537,0.0009814495,0.0006857143,0.001108475],"domain_scores_gemma":[0.9976795,0.0006695942,0.00004930215,0.001062983,0.0004427184,0.00009588173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005538697,0.00003422645,0.00001889739,0.00003304089,0.000004712747,0.000004801941,0.0002757839,0.0007146111,0.9614474,0.0351655,0.0002475348,0.001998177],"study_design_scores_gemma":[0.0004433206,0.0001251944,0.0009823177,0.0002500442,0.000002686823,0.00001402151,0.0009215809,0.1834873,0.7949141,0.008646968,0.009937049,0.0002754691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4673111,0.0002829567,0.513831,0.01625091,0.0003823595,0.0009656182,0.000004562351,0.0008055805,0.0001659079],"genre_scores_gemma":[0.9550427,0.00001230468,0.0443257,0.0001263684,0.00001415455,0.0002724963,9.297818e-7,0.00001273076,0.000192629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4877316,"threshold_uncertainty_score":0.7397299,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}