{"meta":{"query_hash":"f7243cc23602","filters":{"venue":"Machine Learning and Knowledge Extraction"},"cohort_total":51,"direct_labels_cover":0,"predictions_cover":51,"exported":51,"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/f7243cc23602","api":"https://metacan.xera.ac/api/v1/cohort?venue=Machine+Learning+and+Knowledge+Extraction"},"results":[{"id":"W2739573821","doi":"10.3390/make1010002","title":"Learning to Teach Reinforcement Learning Agents","year":2017,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Washington State University; U.S. Department of Agriculture; National Aeronautics and Space Administration; National Science Foundation","keywords":"Reinforcement learning; Advice (programming); Heuristics; Statistic; Action (physics); Variance (accounting); Quality (philosophy); Discounting","score_opus":0.02352967150582671,"score_gpt":0.323069848083718,"score_spread":0.29954017657789134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739573821","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":[0.07430705,0.00027246927,0.91887003,0.00090212235,0.00006875837,0.00012704321,0.00005040497,0.0004694462,0.0049326103],"genre_scores_gemma":[0.9086771,0.00018848703,0.08585128,0.00023048524,0.000052816664,0.00027047767,0.00006334483,0.000041473675,0.0046244315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991597,0.00043174086,0.00003301474,0.00014790523,0.00012786538,0.000099747995],"domain_scores_gemma":[0.9943039,0.0043503316,0.00044752288,0.00029383224,0.00035531973,0.00024898577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017652123,0.00088755845,0.0008937666,0.00032805683,0.00030864013,0.0006809475,0.0014117184,0.0012753432,0.0028543046],"category_scores_gemma":[0.014421762,0.00034789165,0.00034414267,0.000332747,0.0011506857,0.0013166218,0.0008191156,0.0017021941,0.00036414998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001446207,0.0002238285,0.0017628685,0.0001021635,0.00006490502,0.000090612724,0.00016301023,0.9158089,0.0010065057,0.030670678,0.0010324124,0.048929464],"study_design_scores_gemma":[0.000035070192,0.00005955312,0.00011840785,0.0000073207207,0.0000071024656,0.000011608247,0.000011542113,0.9851724,0.00026033598,0.013882348,0.0004296736,0.000004646145],"about_ca_topic_score_codex":0.0030529057,"about_ca_topic_score_gemma":0.0025624693,"teacher_disagreement_score":0.0030529057,"about_ca_system_score_codex":0.0010633427,"about_ca_system_score_gemma":0.0011236003,"threshold_uncertainty_score":0.009548664},"labels":[],"label_agreement":null},{"id":"W2901533770","doi":"10.3390/make1020036","title":"Real-Time Vehicle Make and Model Recognition System","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Support vector machine; License; Artificial intelligence; Set (abstract data type); Feature (linguistics); Random forest; Class (philosophy); Identification (biology); Component (thermodynamics); Feature extraction; Machine learning; Real-time computing; Pattern recognition (psychology); Computer vision","score_opus":0.008471277298213386,"score_gpt":0.2261265976645268,"score_spread":0.2176553203663134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901533770","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":[0.12886572,0.00041968824,0.76891506,0.00036368193,0.00043505285,0.00039307345,0.003049172,0.08123328,0.016325302],"genre_scores_gemma":[0.810238,0.0002544796,0.16361952,0.00025297137,0.000090056004,0.00018561746,0.007045225,0.0005156552,0.017798588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995901,0.000016481967,0.000022411114,0.00012140571,0.0001819268,0.00006769023],"domain_scores_gemma":[0.9995623,0.000027912924,0.00005054291,0.00013772426,0.00018957343,0.000031932057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023306161,0.00061292865,0.00070815807,0.0010836497,0.00036523573,0.00071674923,0.0011196064,0.00054295704,0.006060494],"category_scores_gemma":[0.00075876835,0.0002306569,0.00051973894,0.0004656443,0.00015745158,0.0011635155,0.00075045304,0.00050239085,0.005666581],"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":[0.00055199274,0.0003789448,0.0074339914,0.00019192162,0.000082508326,0.00058525166,0.000112960326,0.031901605,0.12326585,0.001992095,0.03355515,0.79994774],"study_design_scores_gemma":[0.000057267058,0.00033049742,0.013280066,0.000032468524,0.00008839169,0.0013365159,0.00017043632,0.73756903,0.20852216,0.0019293699,0.036512617,0.00017119218],"about_ca_topic_score_codex":0.004085968,"about_ca_topic_score_gemma":0.0032700887,"teacher_disagreement_score":0.006060494,"about_ca_system_score_codex":0.0004023195,"about_ca_system_score_gemma":0.0004654914,"threshold_uncertainty_score":0.020274341},"labels":[],"label_agreement":null},{"id":"W2907210592","doi":"10.3390/make1010018","title":"Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Ontario Power Generation; Ontario Tech University","funders":"","keywords":"Autoregressive integrated moving average; Interface (matter); Computer science; Situation awareness; Time series; Process (computing); Sequence (biology); Autoregressive model; Human–machine interface; Human–machine system; Data mining; Operator (biology); Human error; State (computer science); Series (stratigraphy); Artificial intelligence; Machine learning; Engineering; Econometrics; Algorithm; Reliability engineering; Mathematics","score_opus":0.07389168470611468,"score_gpt":0.4390646777614761,"score_spread":0.36517299305536144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907210592","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":[0.30316406,0.0022825203,0.68531084,0.0008592542,0.00032351277,0.00025488497,0.0008032433,0.003632305,0.00336938],"genre_scores_gemma":[0.88452864,0.0005441071,0.11197495,0.00012662965,0.000067283036,0.00018461194,0.0008693228,0.000121009136,0.0015834406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976412,0.0011874043,0.00019206243,0.00043792443,0.00040733902,0.00013414284],"domain_scores_gemma":[0.9833524,0.012955176,0.00056423544,0.00055356545,0.0023558177,0.00021871514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009664953,0.001295855,0.0010436515,0.0012168139,0.0005639801,0.0011582844,0.0015259678,0.0010938399,0.001874074],"category_scores_gemma":[0.024259036,0.00043721954,0.0008515214,0.0008375527,0.0002640321,0.0016619429,0.00067177,0.0019480981,0.0006177436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014637803,0.0005068589,0.012044836,0.00020586043,0.00043841693,0.00007668922,0.00017257182,0.7943064,0.0028802485,0.0024665657,0.0014030557,0.18403465],"study_design_scores_gemma":[0.000008705556,0.00008065715,0.0007898723,0.000006271023,0.000016393698,0.000006831069,0.00001649065,0.99818146,0.0004575157,0.00028782006,0.00013920898,0.000008868703],"about_ca_topic_score_codex":0.032307185,"about_ca_topic_score_gemma":0.019448303,"teacher_disagreement_score":0.032307185,"about_ca_system_score_codex":0.0009679298,"about_ca_system_score_gemma":0.0015638102,"threshold_uncertainty_score":0.06423825},"labels":[],"label_agreement":null},{"id":"W2954062317","doi":"10.3390/make1030045","title":"Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Université TÉLUQ; Institut National de la Recherche Scientifique","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Statistic; Kinematics; Curse of dimensionality; Artificial intelligence; Pattern recognition (psychology); Computer science; Classifier (UML); Sample size determination; Mathematics; Sample (material); Statistics; Machine learning; Data mining","score_opus":0.009507961157756373,"score_gpt":0.2808299173983609,"score_spread":0.2713219562406045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954062317","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":[0.23856978,0.00068819703,0.7573386,0.00042871461,0.00013237055,0.0001599196,0.00037856665,0.0011490419,0.0011548842],"genre_scores_gemma":[0.7775239,0.00020399748,0.22081725,0.00007312272,0.00007065809,0.00010438456,0.00045752767,0.00005874343,0.000690424],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998245,0.0008167317,0.00018405587,0.00021980736,0.00042394118,0.00011040999],"domain_scores_gemma":[0.9917277,0.0055839075,0.0006025394,0.0005533042,0.0013412436,0.0001912584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004645469,0.0003845039,0.0010200945,0.001852257,0.00030421946,0.000823481,0.0005151635,0.00050592795,0.0007749818],"category_scores_gemma":[0.0148002375,0.00013429459,0.0007281142,0.0018845138,0.0004960314,0.00067499734,0.00057768606,0.0007546756,0.00029950982],"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":[0.001019785,0.0004289802,0.042538688,0.00041341668,0.0003642392,0.00050475704,0.00058973255,0.16868477,0.024682716,0.013435684,0.0056707966,0.7416665],"study_design_scores_gemma":[0.000015332836,0.00023849789,0.00831962,0.0000108140575,0.00001631786,0.00014231475,0.000089067296,0.9832187,0.0035083708,0.0037014775,0.0007060861,0.000033399854],"about_ca_topic_score_codex":0.0032662614,"about_ca_topic_score_gemma":0.0022006596,"teacher_disagreement_score":0.004645469,"about_ca_system_score_codex":0.00046587182,"about_ca_system_score_gemma":0.00097274035,"threshold_uncertainty_score":0.024567842},"labels":[],"label_agreement":null},{"id":"W2977512614","doi":"10.3390/make1040059","title":"Towards Image Classification with Machine Learning Methodologies for Smartphones","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Machine learning; Artificial intelligence; Transfer of learning; Deep learning; Inductive transfer; Android (operating system); Butterfly; Robot learning; Mobile robot","score_opus":0.040413905756543966,"score_gpt":0.36179164142149467,"score_spread":0.3213777356649507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977512614","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":[0.0058392407,0.002098364,0.9882103,0.0005047438,0.00008956046,0.000093720286,0.00009791087,0.0012323451,0.0018337172],"genre_scores_gemma":[0.11313701,0.0021997727,0.88002217,0.00023901067,0.00014921273,0.00017677357,0.00030949104,0.00009853795,0.0036681504],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993666,0.00014973378,0.000044619217,0.00013053749,0.00026886593,0.000039595332],"domain_scores_gemma":[0.998862,0.0003623644,0.00010694864,0.00016955326,0.0004648775,0.000034296474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010120012,0.00074142107,0.0005933704,0.0014988205,0.00026953468,0.0014286968,0.0010864898,0.0011527555,0.0030836249],"category_scores_gemma":[0.003324058,0.00032393116,0.0006400034,0.0011449187,0.0004699803,0.001748454,0.00089050457,0.0012268686,0.0021440338],"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":[0.00006278251,0.00014996917,0.0012523829,0.0003784602,0.00006278897,0.00009874494,0.000089357105,0.06252555,0.019860668,0.022866592,0.0068516266,0.8858011],"study_design_scores_gemma":[0.000009152092,0.00008849225,0.00091231876,0.000068627276,0.000014043604,0.00010293709,0.00006723255,0.9440061,0.012708817,0.029590776,0.012411778,0.00001972485],"about_ca_topic_score_codex":0.002112227,"about_ca_topic_score_gemma":0.0021461996,"teacher_disagreement_score":0.0030836249,"about_ca_system_score_codex":0.000672773,"about_ca_system_score_gemma":0.00060457224,"threshold_uncertainty_score":0.010315716},"labels":[],"label_agreement":null},{"id":"W2986573554","doi":"10.3390/make1040061","title":"Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis","year":2019,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Threat Reduction Agency; Nvidia","keywords":"Thresholding; Pattern recognition (psychology); Linear discriminant analysis; Binary number; Artificial intelligence; Partial least squares regression; Sample (material); Artificial neural network; Computer science; Noise (video); Relevance (law); Mathematics; Machine learning; Chemistry; Chromatography","score_opus":0.024540741268471326,"score_gpt":0.30879774378637936,"score_spread":0.28425700251790803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986573554","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":[0.019693736,0.0001974822,0.9782049,0.000087500506,0.000042086023,0.000034302604,0.00003797973,0.000969441,0.0007325559],"genre_scores_gemma":[0.35063553,0.00012391299,0.6470038,0.00014641735,0.000056122073,0.00010822322,0.00019266657,0.00016810093,0.0015651212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879265,0.00027911935,0.000059524253,0.00037620644,0.0003544402,0.00013795053],"domain_scores_gemma":[0.999006,0.00038337937,0.0001434598,0.00016800547,0.000258236,0.00004101593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001530227,0.0008308657,0.00082305353,0.0013130171,0.00054360717,0.0009458767,0.001380909,0.0014416545,0.0017901286],"category_scores_gemma":[0.0031945899,0.00033588413,0.00063811836,0.0009089949,0.0007426915,0.0015172722,0.0012681372,0.0011647508,0.00091531983],"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":[0.00048659573,0.00035249165,0.0022026245,0.00020871914,0.000105020554,0.00012816221,0.0001522517,0.10950279,0.12145356,0.005980597,0.0020329044,0.75739425],"study_design_scores_gemma":[0.000010568838,0.000047317033,0.0006025553,0.0000093986755,0.000017910448,0.00004626927,0.000021728076,0.96777844,0.026088612,0.004660147,0.00070171274,0.000015336676],"about_ca_topic_score_codex":0.0010723717,"about_ca_topic_score_gemma":0.0016375678,"teacher_disagreement_score":0.0017901286,"about_ca_system_score_codex":0.0005211418,"about_ca_system_score_gemma":0.0004907126,"threshold_uncertainty_score":0.008092701},"labels":[],"label_agreement":null},{"id":"W3005415419","doi":"10.3390/make2010003","title":"Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"University of Regina","keywords":"Random forest; Remote sensing; Pixel; Convolutional neural network; Canopy; Computer science; Lidar; Environmental science; Satellite imagery; Geography; Artificial intelligence","score_opus":0.01660234135065939,"score_gpt":0.2684972853422704,"score_spread":0.251894943991611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005415419","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":[0.7202118,0.0019467862,0.25333923,0.00042833402,0.00020668136,0.00011540736,0.007755657,0.0076754075,0.008320721],"genre_scores_gemma":[0.93764025,0.00029582664,0.05431587,0.00008267278,0.000026960186,0.000021410979,0.005471426,0.0000705141,0.0020750444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985397,0.000012273963,0.0000069947605,0.00004055129,0.000048418613,0.000037703347],"domain_scores_gemma":[0.9998242,0.000034218767,0.00002769921,0.000028703054,0.000074442534,0.000010767897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021511898,0.00060513715,0.00026786284,0.00087943487,0.0001786627,0.000418409,0.00052149885,0.000492017,0.0014723844],"category_scores_gemma":[0.00063069287,0.00023873034,0.00043621255,0.0008449036,0.0001066946,0.0006045696,0.0002271384,0.00040012563,0.000604334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004058561,0.0004609978,0.029774304,0.00029642982,0.0003995008,0.00041145924,0.00007550091,0.46198592,0.037178203,0.0015809296,0.01151783,0.45591304],"study_design_scores_gemma":[0.000006492725,0.000018705156,0.012352329,0.000012460154,0.000023869563,0.000040988703,0.000017495784,0.9804561,0.005798573,0.0005011694,0.00075710024,0.000014633814],"about_ca_topic_score_codex":0.032376263,"about_ca_topic_score_gemma":0.04623325,"teacher_disagreement_score":0.032376263,"about_ca_system_score_codex":0.00060693204,"about_ca_system_score_gemma":0.00035995516,"threshold_uncertainty_score":0.06437564},"labels":[],"label_agreement":null},{"id":"W3046519084","doi":"10.3390/make2030011","title":"Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter","year":2020,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":34,"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":"Social media; Immigration; Computer science; Word (group theory); Character (mathematics); Task (project management); Voice activity detection; Recall; Precision and recall; Internet privacy; Artificial intelligence; World Wide Web; Speech processing; Political science; Psychology; Linguistics; Law; Cognitive psychology; Engineering","score_opus":0.02308940963608441,"score_gpt":0.2834093222664004,"score_spread":0.260319912630316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046519084","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":[0.9846311,0.00019812756,0.0020363606,0.00044960354,0.00005417782,0.00006917181,0.007943198,0.00043055252,0.0041877055],"genre_scores_gemma":[0.98330057,0.00017507805,0.006074634,0.000103232735,0.0000627257,0.00007768805,0.00803593,0.000023872444,0.0021463044],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944144,0.00018018014,0.0000519301,0.00012363069,0.00011837347,0.00008438368],"domain_scores_gemma":[0.9980627,0.0008125586,0.00040163464,0.00017814492,0.0003781953,0.00016667889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045671168,0.00038362664,0.00027140835,0.0017314102,0.0005141922,0.0005988006,0.00025033063,0.0005881388,0.0007505314],"category_scores_gemma":[0.0025742033,0.00010106284,0.00019207875,0.0010050745,0.0001918075,0.0007667475,0.00045452322,0.00037526488,0.0009909832],"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":[0.000859484,0.00041948975,0.76051337,0.0005471206,0.0001338486,0.0010165883,0.0038593495,0.0037696294,0.036492992,0.00084446446,0.020999987,0.17054372],"study_design_scores_gemma":[0.000025984586,0.0003422024,0.8370907,0.00012626701,0.000109670356,0.0009104105,0.0060616345,0.10094325,0.03041789,0.00082574505,0.023036113,0.00011008984],"about_ca_topic_score_codex":0.010695802,"about_ca_topic_score_gemma":0.0236473,"teacher_disagreement_score":0.010695802,"about_ca_system_score_codex":0.00040379266,"about_ca_system_score_gemma":0.00027381277,"threshold_uncertainty_score":0.021267116},"labels":[],"label_agreement":null},{"id":"W3176714582","doi":"10.3390/make3030027","title":"Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability","year":2021,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":345,"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":"Interpretability; Artificial intelligence; Computer science; Machine learning; Cluster analysis; Classifier (UML); Stability (learning theory); Random forest; Popularity; Pattern recognition (psychology)","score_opus":0.02470034312009067,"score_gpt":0.3228493839746241,"score_spread":0.2981490408545334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176714582","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":[0.032453574,0.0003395478,0.9619036,0.0008001042,0.000033342407,0.0001109647,0.00042987542,0.0020131033,0.0019159948],"genre_scores_gemma":[0.62448514,0.00020525206,0.37179255,0.00038705865,0.0000580462,0.00027145725,0.0014063764,0.00026714394,0.0011269824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99659544,0.0013815943,0.00020551837,0.0008747802,0.00078096456,0.00016167993],"domain_scores_gemma":[0.977437,0.0152647495,0.0014772599,0.0041753594,0.0014032183,0.00024242395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039066155,0.00094225677,0.0006784879,0.0015260122,0.00067378517,0.0017256711,0.0019301608,0.0013946587,0.0037518863],"category_scores_gemma":[0.034384307,0.0004870839,0.0012778504,0.00085160823,0.0015690798,0.0038058027,0.0030504817,0.0025045779,0.0005736784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043935404,0.00028106873,0.017327761,0.0010350163,0.0004306085,0.00062279054,0.0011689607,0.4440414,0.012379056,0.1774213,0.010002047,0.33485067],"study_design_scores_gemma":[0.000032648353,0.000052557047,0.0014013435,0.00004639081,0.00003939721,0.00012449612,0.00008473687,0.8837819,0.0039266585,0.10797905,0.0025053045,0.000025430201],"about_ca_topic_score_codex":0.0014925089,"about_ca_topic_score_gemma":0.002862096,"teacher_disagreement_score":0.0039066155,"about_ca_system_score_codex":0.0012690417,"about_ca_system_score_gemma":0.0013742173,"threshold_uncertainty_score":0.02066046},"labels":[],"label_agreement":null},{"id":"W3187934274","doi":"10.3390/make3030030","title":"Proposing an Ontology Model for Planning Photovoltaic Systems","year":2021,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Photovoltaic System Optimization Techniques","field":"Energy","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":"Concordia University; Université du Québec en Outaouais","funders":"","keywords":"Photovoltaic system; Computer science; Maximum power point tracking; Ontology; Reuse; Controller (irrigation); Systems engineering; Power (physics); Engineering; Electrical engineering","score_opus":0.03439009288818463,"score_gpt":0.33945899330691176,"score_spread":0.30506890041872714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187934274","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":[0.0040256036,0.00028672014,0.98519486,0.0006705264,0.0000808763,0.00023620002,0.0011729042,0.00068257184,0.0076496964],"genre_scores_gemma":[0.07375864,0.001047563,0.91754514,0.00019204826,0.000050719605,0.0005798814,0.0029812965,0.0001484252,0.00369622],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987243,0.0002864228,0.0002286403,0.00024171718,0.00040192503,0.00011699385],"domain_scores_gemma":[0.9992206,0.00027070084,0.000107165644,0.0001418557,0.0002022288,0.00005741193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012894338,0.0007224232,0.0005708043,0.0022813482,0.0010169676,0.0033411407,0.0019811615,0.0015043332,0.002756385],"category_scores_gemma":[0.0024098847,0.0006349286,0.0028160238,0.0025147423,0.001047669,0.0041298047,0.0019491971,0.0016714266,0.00096658757],"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":[0.000084364685,0.0002206992,0.002410956,0.0006471955,0.00017149186,0.0011941998,0.0013226296,0.20273156,0.0060398104,0.63534415,0.007847285,0.14198568],"study_design_scores_gemma":[0.000062253195,0.00006961052,0.00087955216,0.0002692174,0.00016864866,0.00055768003,0.0006346896,0.61394125,0.0037232905,0.22230148,0.15730678,0.00008553287],"about_ca_topic_score_codex":0.025734495,"about_ca_topic_score_gemma":0.026747707,"teacher_disagreement_score":0.025734495,"about_ca_system_score_codex":0.0017990564,"about_ca_system_score_gemma":0.0040502194,"threshold_uncertainty_score":0.051169395},"labels":[],"label_agreement":null},{"id":"W3189505573","doi":"10.3390/make4020017","title":"Missing Data Estimation in Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN)","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Missing data; Computer science; Estimation; Artificial neural network; Temporal database; Position (finance); Graph; Artificial intelligence; Data mining; Pattern recognition (psychology); Machine learning; Theoretical computer science; Engineering; Business","score_opus":0.015742407613713535,"score_gpt":0.2819110839778797,"score_spread":0.26616867636416613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189505573","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":[0.16254225,0.00081631355,0.8322985,0.00051857246,0.00014082932,0.000049249724,0.00059615733,0.0020629456,0.00097501394],"genre_scores_gemma":[0.8953228,0.00027277987,0.10168954,0.00020908237,0.0000497726,0.00004967141,0.001059584,0.00006702978,0.0012798496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996414,0.00008324236,0.000025395695,0.00013278834,0.000064075524,0.000053031035],"domain_scores_gemma":[0.9986725,0.0006047957,0.00020521463,0.00017938013,0.00027747592,0.000060554456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010730042,0.0007947001,0.0007153139,0.0007873733,0.00029799822,0.00045406545,0.0016536893,0.0009780141,0.00065441034],"category_scores_gemma":[0.005117015,0.00035852188,0.000521976,0.00085083523,0.0004158502,0.0019926832,0.0008548626,0.0012620264,0.00020314379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029614003,0.0001152452,0.007893013,0.00010308005,0.000117782656,0.00015444176,0.000083212406,0.77461535,0.0028893184,0.0030457624,0.0023618618,0.20832485],"study_design_scores_gemma":[0.0000037900115,0.000017784234,0.00039505787,0.000005031581,0.000009449523,0.000018768602,0.000007594042,0.9964734,0.0005905671,0.0023290017,0.00014554318,0.0000039566316],"about_ca_topic_score_codex":0.009911447,"about_ca_topic_score_gemma":0.013752627,"teacher_disagreement_score":0.009911447,"about_ca_system_score_codex":0.00085102697,"about_ca_system_score_gemma":0.0007346929,"threshold_uncertainty_score":0.019707501},"labels":[],"label_agreement":null},{"id":"W3195446130","doi":"10.3390/make3030034","title":"A Survey of Machine Learning-Based Solutions for Phishing Website Detection","year":2021,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":172,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phishing; Computer science; The Internet; Computer security; Internet security; World Wide Web; Information security; Security service","score_opus":0.03124810316152414,"score_gpt":0.2902130198347076,"score_spread":0.25896491667318344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195446130","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":[0.035716742,0.3207044,0.6076649,0.0041194092,0.0013003787,0.0005154055,0.0020403508,0.007203656,0.02073484],"genre_scores_gemma":[0.27906597,0.24947229,0.43933895,0.0029982761,0.0026622484,0.0006150919,0.0075891516,0.0005630068,0.017695004],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99712723,0.000498621,0.00027264474,0.0006032208,0.0013556641,0.0001425749],"domain_scores_gemma":[0.9948,0.0024445248,0.00043160733,0.0006045792,0.0016065486,0.00011268721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024247344,0.0018433899,0.002017652,0.00787024,0.0007447099,0.0019557758,0.0020327005,0.0019150212,0.0017471404],"category_scores_gemma":[0.007460431,0.0007765897,0.0016066013,0.0064887214,0.0005209175,0.003285732,0.00095726474,0.0019939295,0.0028894725],"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":[0.00010234045,0.00029666617,0.0059132497,0.0015268612,0.00014861238,0.00008049489,0.00010731439,0.0047528236,0.0022691444,0.002240659,0.01623594,0.9663258],"study_design_scores_gemma":[0.00008395478,0.0011005364,0.033961724,0.003444532,0.0007919745,0.0035249942,0.00095139456,0.5417031,0.043419994,0.028727898,0.34187406,0.0004159761],"about_ca_topic_score_codex":0.002696057,"about_ca_topic_score_gemma":0.002390349,"teacher_disagreement_score":0.00787024,"about_ca_system_score_codex":0.0007811632,"about_ca_system_score_gemma":0.0009512822,"threshold_uncertainty_score":0.012823343},"labels":[],"label_agreement":null},{"id":"W3212509273","doi":"10.3390/make3040045","title":"A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence","year":2021,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"","keywords":"Transparency (behavior); Computer science; Component (thermodynamics); Artificial intelligence; Knowledge management; Management science; Engineering","score_opus":0.06261654919714393,"score_gpt":0.3471028790874849,"score_spread":0.28448632989034095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212509273","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":[0.00069115986,0.0007014357,0.9881384,0.001748591,0.000047920144,0.00023262399,0.000070747126,0.0003529328,0.008016218],"genre_scores_gemma":[0.04639134,0.0008480969,0.94829726,0.00034760713,0.000088749315,0.0008584992,0.00022294302,0.0001566815,0.0027889165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9946791,0.0027949386,0.00043291168,0.0008018102,0.0010507962,0.00024045407],"domain_scores_gemma":[0.992945,0.0041285967,0.00046791142,0.0014753467,0.0007157877,0.00026733684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009689177,0.0018182539,0.0009687893,0.0051108287,0.0021171977,0.007351148,0.0046952986,0.0029068608,0.0086122705],"category_scores_gemma":[0.013379147,0.0013672711,0.003529538,0.0026967225,0.012451225,0.009100869,0.0052109743,0.004286016,0.0019206528],"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":[0.000011994249,0.000019021334,0.00019054463,0.00018704601,0.000039001192,0.000078812154,0.00056108745,0.006377116,0.00027698762,0.9787972,0.0009336033,0.012527629],"study_design_scores_gemma":[0.000019139703,0.000025100762,0.00017027401,0.00018208043,0.000041539755,0.00009593299,0.00018238768,0.036369666,0.0005621983,0.9185824,0.043744065,0.00002532597],"about_ca_topic_score_codex":0.0053220005,"about_ca_topic_score_gemma":0.0051533096,"teacher_disagreement_score":0.009689177,"about_ca_system_score_codex":0.0049122116,"about_ca_system_score_gemma":0.0051551927,"threshold_uncertainty_score":0.051241934},"labels":[],"label_agreement":null},{"id":"W4225492836","doi":"10.3390/make4020015","title":"An Attention-Based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Western University","funders":"Government of Canada","keywords":"Autoencoder; Computer science; Artificial intelligence; Thresholding; Anomaly detection; Pattern recognition (psychology); Feature (linguistics); Multivariate statistics; Feature learning; Machine learning; Data mining; Deep learning; Image (mathematics)","score_opus":0.006973311791659286,"score_gpt":0.2750364673034,"score_spread":0.2680631555117407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225492836","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":[0.036681466,0.00036484937,0.96093774,0.00013994178,0.000060341776,0.00002600148,0.00006564259,0.0007503338,0.00097361434],"genre_scores_gemma":[0.73186684,0.00043567325,0.26210925,0.00026622013,0.00007954011,0.0001000016,0.00048048672,0.00010517023,0.004556723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997881,0.00003200632,0.000014288769,0.000076125165,0.000057960046,0.00003154766],"domain_scores_gemma":[0.9996902,0.00013508763,0.000035099714,0.00003116755,0.000092367634,0.000016083803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005490858,0.0005528669,0.00044457827,0.0003393301,0.00017656088,0.0003129274,0.00080275343,0.0005725891,0.00080535887],"category_scores_gemma":[0.0012399931,0.00027017994,0.00054262544,0.00037050454,0.0003297947,0.00064375985,0.00056555565,0.0009603899,0.0002898755],"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":[0.00020879105,0.00017667204,0.002222972,0.00011298634,0.00014517768,0.00020340724,0.00013552996,0.4016849,0.06098436,0.004888937,0.0026947903,0.5265415],"study_design_scores_gemma":[0.0000027125855,0.0000230216,0.00039394485,0.000004413626,0.0000102260465,0.00002668923,0.000004339671,0.99499583,0.0036330805,0.0006068039,0.00029408635,0.000004788073],"about_ca_topic_score_codex":0.006097776,"about_ca_topic_score_gemma":0.008662082,"teacher_disagreement_score":0.006097776,"about_ca_system_score_codex":0.00038133317,"about_ca_system_score_gemma":0.0006076753,"threshold_uncertainty_score":0.012124538},"labels":[],"label_agreement":null},{"id":"W4283076847","doi":"10.3390/make4020026","title":"Fairness and Explanation in AI-Informed Decision Making","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":148,"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":"Austrian Science Fund","keywords":"Transparency (behavior); Perspective (graphical); Perception; Fairness measure; Affect (linguistics); Computer science; Reciprocal; Psychology; Social psychology; Artificial intelligence; Computer security","score_opus":0.024374707503598263,"score_gpt":0.4105602472728633,"score_spread":0.386185539769265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283076847","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":[0.37914875,0.009874974,0.456475,0.039778028,0.0006111389,0.00062987924,0.00028891815,0.00028185794,0.11291155],"genre_scores_gemma":[0.97386885,0.000689102,0.02382415,0.0006837025,0.00007496207,0.00011278515,0.00003695426,0.000023223469,0.00068630476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9332348,0.054398213,0.0026152118,0.002559388,0.0053541837,0.0018382606],"domain_scores_gemma":[0.79200524,0.17250495,0.01669459,0.009221413,0.007223197,0.0023506181],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047959648,0.000826514,0.00082043227,0.0025077832,0.0023249413,0.005740111,0.0013607084,0.0031342695,0.003730444],"category_scores_gemma":[0.14161375,0.0005391131,0.0012799071,0.0013818055,0.013892955,0.0077424,0.0048160697,0.0031106554,0.0003266191],"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":[0.0006144887,0.00034787433,0.035416376,0.0013228272,0.00057294616,0.0004840016,0.02567865,0.037075523,0.0012010034,0.77305126,0.00208219,0.122152865],"study_design_scores_gemma":[0.000118014475,0.00014785642,0.008732536,0.0006760828,0.00013046281,0.00019703053,0.0033703065,0.027680263,0.0010094445,0.9473458,0.010474641,0.00011748265],"about_ca_topic_score_codex":0.0039630774,"about_ca_topic_score_gemma":0.0022912933,"teacher_disagreement_score":0.047959648,"about_ca_system_score_codex":0.004004998,"about_ca_system_score_gemma":0.0049302313,"threshold_uncertainty_score":0.25363785},"labels":[],"label_agreement":null},{"id":"W4285595287","doi":"10.3390/make4030032","title":"Input/Output Variables Selection in Data Envelopment Analysis: A Shannon Entropy Approach","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Dalhousie University","funders":"","keywords":"Data envelopment analysis; RDM; Entropy (arrow of time); Computer science; Econometrics; Information Criteria; Data mining; Mathematical optimization; Statistics; Mathematics; Model selection; Artificial intelligence","score_opus":0.067863478137799,"score_gpt":0.3723473594554385,"score_spread":0.3044838813176395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285595287","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":[0.012560462,0.0003595499,0.9857223,0.00007692714,0.0000095734595,0.00007267649,0.00004810949,0.000037473532,0.0011129401],"genre_scores_gemma":[0.5494065,0.001197113,0.4473333,0.00006368274,0.00006591808,0.000558239,0.00024591136,0.00003849095,0.0010908464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99603575,0.0020355461,0.00032496013,0.00024179452,0.0012315449,0.00013043162],"domain_scores_gemma":[0.99644744,0.0027020623,0.00021872524,0.00015093473,0.00044470074,0.000036147827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060461406,0.0011846821,0.0014293323,0.0038765767,0.0006128542,0.0020398865,0.0006149008,0.00060541206,0.00071792165],"category_scores_gemma":[0.009089082,0.00044740096,0.001244218,0.0037437128,0.0008532291,0.0018527119,0.0013833506,0.00082388526,0.00014443701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011293899,0.00014059547,0.0042066583,0.0005954771,0.00027499444,0.00019832586,0.00036312194,0.7301299,0.006286449,0.05701282,0.00053820555,0.2001404],"study_design_scores_gemma":[0.000007786688,0.00005201182,0.0011323072,0.00004895125,0.000029403836,0.000034581615,0.000049046248,0.97969353,0.0024333762,0.015736429,0.0007576579,0.000024943742],"about_ca_topic_score_codex":0.0022444457,"about_ca_topic_score_gemma":0.0013719224,"teacher_disagreement_score":0.0060461406,"about_ca_system_score_codex":0.0010822219,"about_ca_system_score_gemma":0.0014196058,"threshold_uncertainty_score":0.031975448},"labels":[],"label_agreement":null},{"id":"W4297236464","doi":"10.3390/make4040041","title":"Comparison of Imputation Methods for Missing Rate of Perceived Exertion Data in Rugby","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Sports Performance and Training","field":"Medicine","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":"Canadian Sport Centre Pacific; University of Victoria","funders":"","keywords":"Imputation (statistics); Missing data; Statistics; Random forest; Perceived exertion; Mean squared error; Elastic net regularization; Computer science; Regression; Mathematics; Medicine; Artificial intelligence","score_opus":0.08415591073145363,"score_gpt":0.48443963850910543,"score_spread":0.4002837277776518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297236464","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":[0.30127004,0.0039520306,0.68044806,0.0022708338,0.00039615016,0.0013103818,0.005675829,0.0010168758,0.0036598565],"genre_scores_gemma":[0.71553385,0.0018137099,0.26867878,0.0005352825,0.00012820294,0.0023354995,0.008698902,0.00029478705,0.0019809867],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95305616,0.03807664,0.0026944317,0.0022554316,0.003314704,0.00060257426],"domain_scores_gemma":[0.83368826,0.13553768,0.00831172,0.012497572,0.00941373,0.00055103865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.080860585,0.0007823417,0.0015219765,0.0018518638,0.0008718445,0.002170274,0.0024664586,0.0011545434,0.0028794308],"category_scores_gemma":[0.18969026,0.00055180455,0.0031652716,0.0026992834,0.0005723973,0.001704879,0.0017230725,0.0017771865,0.0010434148],"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":[0.005287213,0.0008463054,0.36582598,0.0023768307,0.0047691907,0.0003880182,0.0033303988,0.05488402,0.0009083286,0.012233143,0.01542672,0.5337239],"study_design_scores_gemma":[0.0009976986,0.003231938,0.32529333,0.004488685,0.0031348087,0.0019157294,0.004562386,0.59189856,0.004554871,0.035691086,0.023739692,0.0004911376],"about_ca_topic_score_codex":0.0049277404,"about_ca_topic_score_gemma":0.005659997,"teacher_disagreement_score":0.080860585,"about_ca_system_score_codex":0.00077178696,"about_ca_system_score_gemma":0.0022345358,"threshold_uncertainty_score":0.42763674},"labels":[],"label_agreement":null},{"id":"W4307765015","doi":"10.3390/make4040048","title":"Lottery Ticket Structured Node Pruning for Tabular Datasets","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Data Stream Mining 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 Windsor","funders":"","keywords":"Pruning; Computer science; Inference; Reduction (mathematics); Range (aeronautics); Ticket; Artificial neural network; Node (physics); Iterative method; Machine learning; Artificial intelligence; Data mining; Algorithm; Mathematics","score_opus":0.014513956100050959,"score_gpt":0.30008244950784685,"score_spread":0.2855684934077959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307765015","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":[0.4372236,0.0018146603,0.54248554,0.00051113043,0.00036492702,0.00043818855,0.0019777007,0.010317807,0.004866422],"genre_scores_gemma":[0.5457704,0.00047875257,0.4440085,0.00031603558,0.00005725381,0.00032237798,0.0055822954,0.0007206968,0.0027436563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985476,0.00047172382,0.00014098061,0.00035372237,0.00033595818,0.00015007395],"domain_scores_gemma":[0.99233425,0.004378277,0.0003529914,0.0016596953,0.0010864179,0.00018833327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003801591,0.0009946887,0.0011910456,0.0013934791,0.0010126565,0.0013161416,0.0022009853,0.0010009711,0.0016989181],"category_scores_gemma":[0.012914521,0.000403495,0.0010032811,0.0017249673,0.0005950221,0.0026925285,0.0011182906,0.0016622389,0.00064810883],"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":[0.001020192,0.00061285496,0.015689148,0.0005720713,0.00051312055,0.0002936702,0.0005885607,0.47040635,0.011482981,0.005586072,0.012612978,0.4806221],"study_design_scores_gemma":[0.000041824944,0.00034266128,0.0020800221,0.00005337099,0.000060596758,0.00013422687,0.00015563642,0.979451,0.009332082,0.005037429,0.0032825707,0.000028574488],"about_ca_topic_score_codex":0.0052683163,"about_ca_topic_score_gemma":0.013384454,"teacher_disagreement_score":0.0052683163,"about_ca_system_score_codex":0.0008354743,"about_ca_system_score_gemma":0.0010850199,"threshold_uncertainty_score":0.020104945},"labels":[],"label_agreement":null},{"id":"W4308718476","doi":"10.3390/make4040047","title":"Actionable Explainable AI (AxAI): A Practical Example with Aggregation Functions for Adaptive Classification and Textual Explanations for Interpretable Machine Learning","year":2022,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":25,"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":"Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR; Ministerstvo školstva, vedy, výskumu a športu Slovenskej republiky; Austrian Science Fund","keywords":"Class (philosophy); Range (aeronautics); Domain (mathematical analysis); Function (biology); Space (punctuation); Artificial intelligence; Computer science; Binary classification; Mathematics; Machine learning; Support vector machine","score_opus":0.04673742173121412,"score_gpt":0.3243509899479155,"score_spread":0.27761356821670136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308718476","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":[0.0035271992,0.00026038484,0.9880344,0.0020737085,0.000084216095,0.000061016846,0.00011466383,0.0009671768,0.004877221],"genre_scores_gemma":[0.13999619,0.00028919312,0.85309666,0.0005020984,0.0001294298,0.00022907252,0.0002182103,0.00029791376,0.005241145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980003,0.0010376782,0.000117481955,0.0003439569,0.0004078947,0.000092664886],"domain_scores_gemma":[0.99507695,0.0035012492,0.00022732158,0.000634141,0.00045735872,0.00010297958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031835458,0.0010603186,0.0004962443,0.0010487839,0.0008915773,0.0020973145,0.0017518571,0.002452341,0.0076751336],"category_scores_gemma":[0.012020787,0.00030041495,0.0014707855,0.0010581369,0.00282321,0.0035412954,0.0024321666,0.0032672891,0.0013653486],"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":[0.00015990458,0.00009369303,0.0010860908,0.00036375396,0.00005562575,0.00072241283,0.0018893274,0.02890288,0.005368422,0.8119368,0.010229073,0.13919197],"study_design_scores_gemma":[0.000033179644,0.000065100896,0.00045012805,0.000083589766,0.000038575196,0.00046356348,0.00026498354,0.29250488,0.0036671192,0.66162306,0.04076117,0.00004479024],"about_ca_topic_score_codex":0.0020759949,"about_ca_topic_score_gemma":0.0025863687,"teacher_disagreement_score":0.0076751336,"about_ca_system_score_codex":0.0011296656,"about_ca_system_score_gemma":0.0008442562,"threshold_uncertainty_score":0.025675893},"labels":[],"label_agreement":null},{"id":"W4313575157","doi":"10.3390/make5010004","title":"IPPT4KRL: Iterative Post-Processing Transfer for Knowledge Representation Learning","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Graph Neural Networks","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":"Royal Bank of Canada","funders":"","keywords":"Embedding; Computer science; Benchmarking; Representation (politics); Artificial intelligence; Graph; Machine learning; Feature learning; Knowledge representation and reasoning; Iterative refinement; Task (project management); Transfer of learning; Theoretical computer science","score_opus":0.026325188613280908,"score_gpt":0.34010690381531417,"score_spread":0.31378171520203324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313575157","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":[0.0074163033,0.00025451553,0.9788887,0.00021186925,0.00005680504,0.00019563668,0.00035418355,0.011349173,0.0012728423],"genre_scores_gemma":[0.14455932,0.00029968095,0.84428847,0.00043062092,0.00006759842,0.0005594438,0.00427688,0.0009715716,0.004546342],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997997,0.00049301866,0.0001281661,0.0006364699,0.0005641043,0.00018121816],"domain_scores_gemma":[0.99585307,0.0014705237,0.00033604205,0.001427233,0.00078880275,0.00012419524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002494018,0.0021831538,0.0016233295,0.0024906145,0.0010638037,0.0016469965,0.004916675,0.0023495439,0.007737998],"category_scores_gemma":[0.009500747,0.00089343294,0.0019299485,0.0030020087,0.0013639617,0.0059695146,0.0048189736,0.0035701017,0.0050475765],"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":[0.00017059631,0.00037808673,0.0010553615,0.00037699883,0.00013742551,0.00022652882,0.00027080838,0.09734774,0.008021025,0.008191563,0.01689179,0.86693203],"study_design_scores_gemma":[0.000035724617,0.000101421065,0.00032849115,0.00002428909,0.00003755494,0.000089644214,0.000081511454,0.96384585,0.008334089,0.022536706,0.004561932,0.000022748787],"about_ca_topic_score_codex":0.0053628534,"about_ca_topic_score_gemma":0.01043509,"teacher_disagreement_score":0.007737998,"about_ca_system_score_codex":0.0015245774,"about_ca_system_score_gemma":0.002281024,"threshold_uncertainty_score":0.025886178},"labels":[],"label_agreement":null},{"id":"W4321231457","doi":"10.3390/make5010015","title":"Can Principal Component Analysis Be Used to Explore the Relationship of Rowing Kinematics and Force Production in Elite Rowers during a Step Test? A Pilot Study","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Sports Performance and Training","field":"Medicine","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":"Canadian Sport Centre Pacific; Simon Fraser University; University of Victoria","funders":"","keywords":"Rowing; Kinematics; Physical medicine and rehabilitation; Elbow; Stroke (engine); Physical therapy; Mathematics; Simulation; Medicine; Engineering; Physics; Surgery; Mechanical engineering","score_opus":0.08999170683810115,"score_gpt":0.35666462255622855,"score_spread":0.2666729157181274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321231457","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":[0.93805915,0.0012064672,0.056342762,0.00079780264,0.0001596858,0.00040928327,0.0004665662,0.0002788611,0.0022794118],"genre_scores_gemma":[0.9663609,0.00082295993,0.031233607,0.00013577408,0.000085416716,0.00033502007,0.00032531345,0.00004945474,0.0006515161],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99731785,0.0017469483,0.00016783486,0.00026330404,0.00035069842,0.0001532687],"domain_scores_gemma":[0.99227554,0.00495424,0.00061226153,0.0007920179,0.0010713957,0.00029462596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008587503,0.0008950649,0.0010828292,0.0015991712,0.00040265237,0.00098588,0.0005057797,0.00057843677,0.0017193567],"category_scores_gemma":[0.018095352,0.00034437302,0.0007366226,0.0021285485,0.00055774394,0.0008834802,0.00036467038,0.000528187,0.00063584483],"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":[0.0009255737,0.00049021695,0.68776286,0.000249688,0.0006946535,0.00017142713,0.0011249024,0.0014667948,0.00763558,0.00035227506,0.0015562631,0.29756972],"study_design_scores_gemma":[0.000046223093,0.0010339249,0.9861107,0.00004670489,0.0001675774,0.00009265586,0.0007338078,0.008935075,0.0007553575,0.0006732681,0.00136877,0.000036007852],"about_ca_topic_score_codex":0.005497473,"about_ca_topic_score_gemma":0.008386256,"teacher_disagreement_score":0.008587503,"about_ca_system_score_codex":0.00021143048,"about_ca_system_score_gemma":0.000837809,"threshold_uncertainty_score":0.04541564},"labels":[],"label_agreement":null},{"id":"W4321371507","doi":"10.3390/make5010016","title":"A Novel Pipeline Age Evaluation: Considering Overall Condition Index and Neural Network Based on Measured Data","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Structural Integrity and Reliability Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Corrosion; Pipeline transport; Artificial neural network; Downtime; Reliability (semiconductor); Cathodic protection; Submarine pipeline; Environmental science; Fossil fuel; Pipeline (software); Petroleum engineering; Engineering; Reliability engineering; Computer science; Materials science; Metallurgy; Geotechnical engineering; Environmental engineering; Waste management; Artificial intelligence; Mechanical engineering","score_opus":0.05312106828459529,"score_gpt":0.32163520580308463,"score_spread":0.26851413751848935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321371507","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":[0.23365352,0.0011663926,0.7582881,0.00022096257,0.00015227184,0.0001648327,0.00073231355,0.0010948877,0.004526787],"genre_scores_gemma":[0.9567989,0.00038946225,0.039853577,0.000031963493,0.000091936105,0.0000998194,0.00048322178,0.000029253382,0.0022218619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941874,0.00006946326,0.000055575325,0.00019966584,0.0001942537,0.00006247954],"domain_scores_gemma":[0.9993631,0.0001371792,0.00014340889,0.00003708757,0.0002774566,0.0000417233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067202357,0.0010942254,0.0007373435,0.002282093,0.00028473017,0.0009885131,0.0007098077,0.0011355733,0.0011086114],"category_scores_gemma":[0.0018921413,0.00024521,0.0005024945,0.0011885863,0.0003188824,0.0020174254,0.0004955617,0.00045224105,0.00033388587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049578986,0.00035791923,0.062438,0.00028374803,0.0002125507,0.0003917531,0.00010622727,0.5734239,0.022379369,0.0019894266,0.0017994622,0.33612183],"study_design_scores_gemma":[0.000003216011,0.00008391771,0.007111453,0.000009265358,0.000026584445,0.000053643187,0.000015312195,0.98982084,0.0021246779,0.00041905127,0.0003181746,0.000013869609],"about_ca_topic_score_codex":0.0061079566,"about_ca_topic_score_gemma":0.004431274,"teacher_disagreement_score":0.0061079566,"about_ca_system_score_codex":0.0006424182,"about_ca_system_score_gemma":0.00042296306,"threshold_uncertainty_score":0.012144804},"labels":[],"label_agreement":null},{"id":"W4323565362","doi":"10.3390/make5010017","title":"Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":25,"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":"Ministero della Salute; Università degli Studi di Siena","keywords":"Usability; Computer science; Perception; Relevance (law); Transparency (behavior); Reading (process); Data science; Human–computer interaction; Psychology; Computer security","score_opus":0.023069008272530413,"score_gpt":0.32517115363827465,"score_spread":0.30210214536574426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323565362","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":[0.9940241,0.00009880865,0.003941295,0.000096319105,0.000012361481,0.0002993114,0.00005982187,0.00009673271,0.0013712468],"genre_scores_gemma":[0.98123175,0.00019039602,0.016502485,0.00017454967,0.000032053704,0.00038390618,0.0001362574,0.00006631852,0.0012822276],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9953597,0.002970885,0.00036307095,0.00058991805,0.0005099457,0.00020644382],"domain_scores_gemma":[0.8519643,0.13787408,0.0029571385,0.0040623313,0.0019913113,0.0011508225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058028507,0.0006856596,0.00064180285,0.00073348725,0.00080875424,0.0017109026,0.0012095527,0.0013684201,0.0050471355],"category_scores_gemma":[0.07343791,0.0005233086,0.00045907305,0.0004840767,0.0012608687,0.002045909,0.0017323955,0.0012831783,0.00076039304],"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":[0.0126481075,0.038498037,0.066828616,0.007799187,0.0006095338,0.0037857408,0.30137885,0.009964858,0.1603177,0.0033965018,0.0048033725,0.38996947],"study_design_scores_gemma":[0.005873472,0.12966585,0.37724683,0.0019393902,0.0021063692,0.0055346335,0.11876612,0.10684808,0.19561511,0.01600786,0.039244886,0.0011514695],"about_ca_topic_score_codex":0.00078097935,"about_ca_topic_score_gemma":0.0010083268,"teacher_disagreement_score":0.0058028507,"about_ca_system_score_codex":0.00039971693,"about_ca_system_score_gemma":0.0003874473,"threshold_uncertainty_score":0.030688822},"labels":[],"label_agreement":null},{"id":"W4366390417","doi":"10.3390/make5020024","title":"Lottery Ticket Search on Untrained Models with Applied Lottery Sample Selection","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lottery; Computer science; Ticket; Fraction (chemistry); Machine learning; Sample (material); Set (abstract data type); Selection (genetic algorithm); Artificial intelligence; Artificial neural network; Process (computing); Mathematics; Statistics; Computer security","score_opus":0.08505546188253313,"score_gpt":0.3895661560022754,"score_spread":0.3045106941197423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366390417","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":[0.32492206,0.0012498444,0.6622949,0.0012387198,0.00012787827,0.00037259716,0.0013091913,0.003950629,0.0045340983],"genre_scores_gemma":[0.80457085,0.00018809852,0.1899,0.00044504262,0.000043146098,0.00027268365,0.002039108,0.0001617333,0.0023793126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902594,0.0004814,0.00007481707,0.00021256828,0.000116725896,0.00008864503],"domain_scores_gemma":[0.9943541,0.004056579,0.00028851442,0.00079867046,0.00038516626,0.00011693492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029424757,0.0011148178,0.0018032995,0.0016385175,0.00060704607,0.001475312,0.0021281221,0.0015172701,0.004079306],"category_scores_gemma":[0.010932511,0.00064993824,0.0013414628,0.0012441373,0.00074636744,0.0029376224,0.0016692383,0.0017558293,0.00074818806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049429416,0.0003490573,0.008041674,0.00020351198,0.00017926382,0.00014165431,0.00016572318,0.81752455,0.0014222867,0.005556208,0.0028543128,0.16306752],"study_design_scores_gemma":[0.000020157002,0.0000579461,0.0003189599,0.000013863868,0.000010190065,0.000013408839,0.000025810226,0.99476236,0.00038456448,0.004098306,0.00028805184,0.0000064402057],"about_ca_topic_score_codex":0.0050044777,"about_ca_topic_score_gemma":0.009167262,"teacher_disagreement_score":0.0050044777,"about_ca_system_score_codex":0.0012446595,"about_ca_system_score_gemma":0.0013873832,"threshold_uncertainty_score":0.015561461},"labels":[],"label_agreement":null},{"id":"W4385424060","doi":"10.3390/make5030045","title":"Efficient Latent Space Compression for Lightning-Fast Fine-Tuning and Inference of Transformer-Based Models","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","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":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Computer science; Inference; Embedding; Encoder; Transformer; Fine-tuning; Artificial intelligence; Pattern recognition (psychology); Voltage","score_opus":0.02609713780614908,"score_gpt":0.2920949468936843,"score_spread":0.26599780908753523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385424060","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":[0.012590326,0.00013780817,0.9820832,0.000100696336,0.00003725896,0.000028466702,0.00012621288,0.0038571102,0.0010390001],"genre_scores_gemma":[0.5563567,0.00019840353,0.43735006,0.0002367984,0.00005148825,0.00011720756,0.0010906145,0.0009869941,0.0036117467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954385,0.00012550628,0.00002752638,0.00011486303,0.00013005188,0.000058284186],"domain_scores_gemma":[0.9989203,0.00056731934,0.00005893376,0.00027105573,0.00014734881,0.000035102432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009533523,0.0008460589,0.00067906355,0.00050288526,0.00031434326,0.0007721749,0.001122715,0.0006765272,0.0046621347],"category_scores_gemma":[0.004690786,0.0005048393,0.0007203165,0.00041101113,0.00051211606,0.0015967435,0.0011861468,0.0019321819,0.0018893542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033083555,0.0001324589,0.0015432962,0.00014824622,0.000098908036,0.00021174159,0.0001599078,0.58193314,0.030577824,0.016176937,0.0059102555,0.36277652],"study_design_scores_gemma":[0.0000070972433,0.000016127904,0.00007990569,0.000004404249,0.0000057324137,0.000024062661,0.000009633314,0.98839146,0.0059960796,0.0047490653,0.0007121281,0.000004276992],"about_ca_topic_score_codex":0.004817622,"about_ca_topic_score_gemma":0.008797215,"teacher_disagreement_score":0.004817622,"about_ca_system_score_codex":0.0006503695,"about_ca_system_score_gemma":0.0009453335,"threshold_uncertainty_score":0.01559639},"labels":[],"label_agreement":null},{"id":"W4386135062","doi":"10.3390/make5030057","title":"Comparing the Performance of Machine Learning Algorithms in the Automatic Classification of Psychotherapeutic Interactions in Avatar Therapy","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut national de psychiatrie légale Philippe-Pinel; Université de Montréal; Institut Universitaire en Santé Mentale de Québec","funders":"Eli Lilly Canada; Fonds de Recherche du Québec - Santé; Otsuka Canada Pharmaceutical; Eli Lilly and Company","keywords":"Computer science; Machine learning; Artificial intelligence; Support vector machine; Perceptron; Naive Bayes classifier; Classifier (UML); Decision tree; Artificial neural network","score_opus":0.08972079733234087,"score_gpt":0.42494684368794694,"score_spread":0.33522604635560604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386135062","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":[0.7177726,0.0035292604,0.2664831,0.0009542821,0.00041629464,0.00062898104,0.0017661733,0.0036770352,0.0047722002],"genre_scores_gemma":[0.83159864,0.0004455356,0.16457343,0.00013470475,0.000063832595,0.00031423717,0.0017535945,0.00008251584,0.0010336104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951361,0.0022205373,0.000578287,0.000998854,0.0007383514,0.00032790052],"domain_scores_gemma":[0.9845732,0.012039101,0.0006997777,0.000685565,0.0017705809,0.00023182847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007324148,0.0012653514,0.0009983934,0.004286755,0.00053490186,0.0020662525,0.0011133469,0.0020908695,0.0012215833],"category_scores_gemma":[0.016917935,0.00020875665,0.0009780453,0.002096264,0.0004018578,0.0013748766,0.0008860192,0.0014454273,0.0009252848],"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":[0.0015836756,0.0010843985,0.04849672,0.0005066428,0.00045119066,0.00014300687,0.000422303,0.070181005,0.0054533454,0.0008903152,0.004124421,0.866663],"study_design_scores_gemma":[0.0000538204,0.00059006375,0.021625193,0.00013852911,0.00010856768,0.00016808718,0.0004697046,0.962665,0.010553428,0.002099878,0.0014744849,0.000053241332],"about_ca_topic_score_codex":0.004267491,"about_ca_topic_score_gemma":0.0033394054,"teacher_disagreement_score":0.007324148,"about_ca_system_score_codex":0.0012988546,"about_ca_system_score_gemma":0.0010823961,"threshold_uncertainty_score":0.038734257},"labels":[],"label_agreement":null},{"id":"W4386776962","doi":"10.3390/make5030060","title":"Gradient-Based Neural Architecture Search: A Comprehensive Evaluation","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Rashtriya Uchchatar Shiksha Abhiyan; Canadian Institute for Advanced Research","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Artificial neural network; Architecture; Gradient descent; Machine learning; Resource (disambiguation); Deep learning","score_opus":0.04902472630016129,"score_gpt":0.3505566359418409,"score_spread":0.3015319096416796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386776962","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":[0.15409279,0.2222328,0.5507085,0.0035063156,0.001422379,0.0012500746,0.0023994327,0.013092755,0.051295113],"genre_scores_gemma":[0.56325877,0.046131667,0.37148628,0.0012690343,0.0005304408,0.00064338214,0.0054897703,0.0018170875,0.009373533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707234,0.0008538896,0.00022349901,0.00038182572,0.0013015469,0.00016697211],"domain_scores_gemma":[0.9948561,0.0030538652,0.00022403264,0.0005184349,0.0011483852,0.00019919152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050881486,0.002565529,0.0025502453,0.0027579109,0.00063031854,0.0014873594,0.00309009,0.0023869288,0.0031661233],"category_scores_gemma":[0.013148604,0.0006955529,0.0014804165,0.0021688938,0.0008354895,0.002761894,0.0013235487,0.0018196097,0.0010387136],"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":[0.000664299,0.00037823446,0.003724216,0.0020424915,0.00082660065,0.00009263126,0.00005975171,0.39353278,0.0015980783,0.007228148,0.016760036,0.5730927],"study_design_scores_gemma":[0.00011885294,0.0007485653,0.0016861177,0.0003169838,0.00025417222,0.00018507389,0.000046483106,0.9820545,0.0024610865,0.0043450748,0.0077457335,0.000037402802],"about_ca_topic_score_codex":0.008583334,"about_ca_topic_score_gemma":0.008057074,"teacher_disagreement_score":0.008583334,"about_ca_system_score_codex":0.001767427,"about_ca_system_score_gemma":0.0023367016,"threshold_uncertainty_score":0.026909053},"labels":[],"label_agreement":null},{"id":"W4387331439","doi":"10.3390/make5040069","title":"Entropy-Aware Time-Varying Graph Neural Networks with Generalized Temporal Hawkes Process: Dynamic Link Prediction in the Presence of Node Addition and Deletion","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"University of Toronto","keywords":"Computer science; Graph; Entropy (arrow of time); Point process; Theoretical computer science; Representation (politics); Node (physics); Artificial intelligence; Dynamic network analysis; Enhanced Data Rates for GSM Evolution; Mathematics","score_opus":0.014607877925904228,"score_gpt":0.273304304026124,"score_spread":0.25869642610021976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387331439","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":[0.20856641,0.0005992905,0.7882678,0.00046352262,0.00006357061,0.000038364484,0.00014093592,0.000566823,0.0012931965],"genre_scores_gemma":[0.96105623,0.00020133775,0.037020043,0.00012879755,0.000054739532,0.000040438263,0.00020096044,0.00005329204,0.001244072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970526,0.00008211665,0.000012425208,0.000104250714,0.000052070776,0.00004388472],"domain_scores_gemma":[0.9982437,0.0011569423,0.00023658015,0.00011717901,0.00015481046,0.00009079537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011147705,0.00066048896,0.0008962598,0.0008106344,0.0003419239,0.0007267472,0.0016370774,0.0011006084,0.00067100505],"category_scores_gemma":[0.00441992,0.00044160662,0.00051733037,0.000818401,0.0008048456,0.0019360874,0.0008281075,0.0012886445,0.00013030415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049059458,0.0000335554,0.001156521,0.000023073488,0.000030917345,0.000052898456,0.00003369322,0.9742303,0.0008902955,0.004575966,0.00032752953,0.018596143],"study_design_scores_gemma":[9.2134604e-7,0.0000025079034,0.00005017376,6.3242135e-7,0.00000140423,0.0000022364222,9.631491e-7,0.9984357,0.000071705465,0.0014173907,0.000015355767,0.0000010539999],"about_ca_topic_score_codex":0.008263313,"about_ca_topic_score_gemma":0.008597775,"teacher_disagreement_score":0.008263313,"about_ca_system_score_codex":0.00091467856,"about_ca_system_score_gemma":0.0006164361,"threshold_uncertainty_score":0.016430438},"labels":[],"label_agreement":null},{"id":"W4388945936","doi":"10.3390/make5040085","title":"FCIoU: A Targeted Approach for Improving Minority Class Detection in Semantic Segmentation Systems","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intersection (aeronautics); Segmentation; Class (philosophy); Function (biology); Computer science; Terrain; Artificial intelligence; Machine learning; Geography; Cartography","score_opus":0.01779713589971847,"score_gpt":0.29313076773951846,"score_spread":0.2753336318398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388945936","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":[0.059428237,0.0006227452,0.9305131,0.00040455427,0.000078362864,0.00018871021,0.0002892813,0.0050231167,0.0034518933],"genre_scores_gemma":[0.58786035,0.00034266262,0.40344003,0.00060765515,0.00014769504,0.000311493,0.0022204462,0.000980933,0.0040887375],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99657214,0.00080795074,0.00018401802,0.00074193784,0.0012870545,0.00040690138],"domain_scores_gemma":[0.99690825,0.0012064992,0.00027922713,0.000517415,0.0009257333,0.00016280572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005943196,0.002003849,0.0017015929,0.0029086096,0.001455785,0.002974856,0.003489939,0.0022503415,0.0021631678],"category_scores_gemma":[0.009496884,0.00045503344,0.0011086114,0.0017816544,0.001475839,0.004894416,0.004033955,0.0023691773,0.00129012],"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":[0.00083503575,0.0005565504,0.007302984,0.00023233505,0.00023058572,0.00016326497,0.0005390923,0.18771413,0.022995932,0.01576601,0.010730097,0.7529341],"study_design_scores_gemma":[0.00001965087,0.0001850413,0.0010491684,0.000023872122,0.000041174735,0.00010450676,0.00016400397,0.96582586,0.020965585,0.0079000965,0.0036969583,0.000024023195],"about_ca_topic_score_codex":0.0042853463,"about_ca_topic_score_gemma":0.005039921,"teacher_disagreement_score":0.005943196,"about_ca_system_score_codex":0.0018312539,"about_ca_system_score_gemma":0.0021111972,"threshold_uncertainty_score":0.03143102},"labels":[],"label_agreement":null},{"id":"W4389203808","doi":"10.3390/make5040089","title":"Analysing Semi-Supervised ConvNet Model Performance with Computation Processes","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Preprocessor; Machine learning; Computation; Classifier (UML); Artificial neural network; Supervised learning; Training set; Data pre-processing; Algorithm","score_opus":0.02850362207233759,"score_gpt":0.3039833642872269,"score_spread":0.2754797422148893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389203808","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":[0.7720916,0.0017068619,0.21446413,0.0008772105,0.0002642204,0.00032536068,0.00096009515,0.003358987,0.0059514856],"genre_scores_gemma":[0.94471145,0.00023821267,0.05087262,0.00018363951,0.000025038324,0.00020360216,0.0017521335,0.00020290805,0.001810423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978734,0.0007812147,0.00018435797,0.0005064067,0.00048134063,0.00017326395],"domain_scores_gemma":[0.99138355,0.0046724845,0.00059848995,0.0014597895,0.0017431267,0.0001426574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006205587,0.0014526256,0.0007533299,0.000722584,0.0005774736,0.0017679813,0.0016987603,0.0013002991,0.0016228063],"category_scores_gemma":[0.020881047,0.00048183315,0.0008476707,0.00061254087,0.00096732925,0.0021925902,0.0012346497,0.0020583596,0.0008666748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082971196,0.0002892304,0.010623068,0.00029396015,0.00026825097,0.00011226397,0.0002245345,0.8393636,0.008239565,0.0024260005,0.0027970136,0.13453284],"study_design_scores_gemma":[0.000011725464,0.0001256765,0.0013542146,0.000020391148,0.000019735864,0.00003473855,0.000031533098,0.9907889,0.0062339725,0.00092757295,0.0004374092,0.0000140687835],"about_ca_topic_score_codex":0.008868588,"about_ca_topic_score_gemma":0.011707269,"teacher_disagreement_score":0.008868588,"about_ca_system_score_codex":0.0015981437,"about_ca_system_score_gemma":0.0013943625,"threshold_uncertainty_score":0.032818675},"labels":[],"label_agreement":null},{"id":"W4392957858","doi":"10.3390/make6010032","title":"Analyzing the Impact of Oncological Data at Different Time Points and Tumor Biomarkers on Artificial Intelligence Predictions for Five-Year Survival in Esophageal Cancer","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Esophageal Cancer Research and Treatment","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":"Institute of Cancer Research","funders":"","keywords":"Esophageal cancer; Cancer; Medicine; Oncology; Artificial intelligence; Internal medicine; Computer science","score_opus":0.05788579022705994,"score_gpt":0.4176247959946425,"score_spread":0.35973900576758255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392957858","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":[0.9900137,0.0005026572,0.008358048,0.00018614926,0.000017624703,0.000018062237,0.00047729004,0.00006865731,0.00035779315],"genre_scores_gemma":[0.9975992,0.00007498004,0.0015228639,0.00002498642,0.0000098137,0.0000096566155,0.00062883255,0.000004319521,0.00012533666],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937505,0.00029183333,0.00006036916,0.00012918166,0.00008753731,0.0000560452],"domain_scores_gemma":[0.99593097,0.0029588614,0.000534652,0.00021283045,0.00024699882,0.0001157987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025815547,0.00060718815,0.00032216642,0.001093508,0.0001457105,0.0006551092,0.00038041835,0.00039695192,0.00060317334],"category_scores_gemma":[0.0061908835,0.00011076138,0.00057674147,0.00060630625,0.00023691065,0.00059916684,0.00042188246,0.0005451607,0.00020955036],"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":[0.0006576143,0.00019651094,0.916502,0.00006851626,0.00031096596,0.00010301825,0.000047569574,0.039440665,0.001267105,0.000078203186,0.0003485082,0.040979344],"study_design_scores_gemma":[0.00003480542,0.0010453303,0.5477605,0.000042951506,0.00026161698,0.0002859189,0.0001453933,0.4450391,0.004000631,0.0007322812,0.0006187237,0.000032701093],"about_ca_topic_score_codex":0.0020291351,"about_ca_topic_score_gemma":0.0021783537,"teacher_disagreement_score":0.0025815547,"about_ca_system_score_codex":0.00043127267,"about_ca_system_score_gemma":0.0003807478,"threshold_uncertainty_score":0.013652742},"labels":[],"label_agreement":null},{"id":"W4394956176","doi":"10.3390/make6020041","title":"Enhancing Legal Sentiment Analysis: A Convolutional Neural Network–Long Short-Term Memory Document-Level Model","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Term (time); Convolutional neural network; Computer science; Long short term memory; Sentiment analysis; Natural language processing; Artificial intelligence; Information retrieval; Artificial neural network; Recurrent neural network","score_opus":0.03887216470533473,"score_gpt":0.37624500442286146,"score_spread":0.33737283971752674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394956176","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":[0.3023582,0.0024475474,0.6547086,0.004117571,0.0007313054,0.00020202687,0.0020138565,0.0038209867,0.029599933],"genre_scores_gemma":[0.9236564,0.00088229537,0.058922548,0.00052546826,0.00012942875,0.00009046947,0.0017517463,0.00007393162,0.013967858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999889,0.000016180615,0.000006682809,0.00003008981,0.000029776913,0.000028189714],"domain_scores_gemma":[0.9998337,0.000040097275,0.000024018073,0.000012283199,0.000079847814,0.000009979069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037005305,0.00057932176,0.0002603167,0.0005325967,0.00025297084,0.0007165475,0.0006502495,0.00059805374,0.0017370064],"category_scores_gemma":[0.0009739621,0.00018512052,0.00043301005,0.0004370054,0.00023907884,0.0008286163,0.00036795312,0.0009687232,0.000779098],"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":[0.00031232258,0.000396392,0.010946618,0.00016720087,0.00022003513,0.0003276204,0.00022002612,0.38609478,0.028377268,0.015026395,0.023181047,0.5347304],"study_design_scores_gemma":[0.000003610785,0.000015717687,0.0006796024,0.000008562869,0.000018776962,0.000012428118,0.000008632894,0.99500614,0.001570728,0.0016173429,0.001054188,0.00000427479],"about_ca_topic_score_codex":0.02468364,"about_ca_topic_score_gemma":0.025092373,"teacher_disagreement_score":0.02468364,"about_ca_system_score_codex":0.0011635575,"about_ca_system_score_gemma":0.0009786931,"threshold_uncertainty_score":0.049079955},"labels":[],"label_agreement":null},{"id":"W4396952634","doi":"10.3390/make6020050","title":"Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Software Engineering Research","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Vulnerability (computing); Computer science; Vulnerability assessment; Psychology; Computer security","score_opus":0.013586231344363547,"score_gpt":0.335502881454395,"score_spread":0.32191665011003145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396952634","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":[0.27414528,0.0019797136,0.70560503,0.0014201853,0.00005689888,0.00021779953,0.0021941138,0.010071525,0.004309393],"genre_scores_gemma":[0.8171662,0.00043156123,0.1777037,0.00016113873,0.000045374178,0.00013362145,0.0030650874,0.000218504,0.0010748412],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998415,0.00044868444,0.00009813744,0.00037959538,0.0005330267,0.00012556292],"domain_scores_gemma":[0.9928197,0.0042267526,0.001075027,0.00095651636,0.0007921553,0.00012983417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022889639,0.0014811984,0.0006300101,0.0069135586,0.0004084099,0.0013320276,0.0012208702,0.0009808732,0.0012028464],"category_scores_gemma":[0.012420796,0.00031740134,0.0012699891,0.0023093775,0.0006934075,0.0023093598,0.0016805329,0.0014770854,0.00042292575],"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":[0.00038334052,0.0005380044,0.14869222,0.0010698581,0.0007513557,0.00046020086,0.00096604053,0.17049345,0.017703673,0.013633948,0.00937629,0.6359315],"study_design_scores_gemma":[0.000021255259,0.00014580014,0.01715273,0.00008170461,0.00018664722,0.0002442089,0.00020003309,0.95082855,0.0065606907,0.02084693,0.003695152,0.000036261732],"about_ca_topic_score_codex":0.0046898467,"about_ca_topic_score_gemma":0.008111654,"teacher_disagreement_score":0.0069135586,"about_ca_system_score_codex":0.0010164377,"about_ca_system_score_gemma":0.0014512858,"threshold_uncertainty_score":0.012105346},"labels":[],"label_agreement":null},{"id":"W4398349452","doi":"10.3390/make6020052","title":"Locally-Scaled Kernels and Confidence Voting","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Mitacs","keywords":"Voting; Computer science; Political science; Law; Politics","score_opus":0.011671512419377554,"score_gpt":0.29089840322773763,"score_spread":0.2792268908083601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398349452","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":[0.03668299,0.0007836402,0.95883185,0.0002051597,0.000060477774,0.00005739205,0.00008195864,0.00077165436,0.0025248397],"genre_scores_gemma":[0.8676642,0.00024327975,0.12903999,0.0001021479,0.00009607436,0.00007027563,0.00039004188,0.00014211847,0.002251858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994193,0.0019827294,0.0003548677,0.0012303806,0.0018578687,0.0003811227],"domain_scores_gemma":[0.9869221,0.0061151553,0.0014342418,0.002696259,0.0025747558,0.000257446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061482526,0.00063378253,0.0017414627,0.0021745625,0.0005660213,0.0022284354,0.0024105476,0.0015196169,0.0016980012],"category_scores_gemma":[0.034458276,0.00037388082,0.0007355091,0.001920078,0.0014826447,0.0033660287,0.0018968162,0.0013082961,0.00086140406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059167854,0.00017461176,0.0059298603,0.00020483424,0.00019249656,0.00011355223,0.00019682408,0.4678968,0.0065094726,0.078358285,0.0034006042,0.43643096],"study_design_scores_gemma":[0.000012589295,0.000060710612,0.0009259572,0.000013479731,0.000014442123,0.000080993006,0.000019900825,0.97118706,0.0023453531,0.024300655,0.0010125183,0.000026393893],"about_ca_topic_score_codex":0.0032083094,"about_ca_topic_score_gemma":0.0016697159,"teacher_disagreement_score":0.0061482526,"about_ca_system_score_codex":0.0013727909,"about_ca_system_score_gemma":0.0008844736,"threshold_uncertainty_score":0.032515466},"labels":[],"label_agreement":null},{"id":"W4404638618","doi":"10.3390/make6040130","title":"Node-Centric Pruning: A Novel Graph Reduction Approach","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Scalability; Pruning; Graph; Theoretical computer science; Node (physics); Distributed computing; Artificial intelligence; Machine learning; Engineering","score_opus":0.015621257278360324,"score_gpt":0.2815210943851965,"score_spread":0.2658998371068362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404638618","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":[0.010451692,0.00033065284,0.98563784,0.0002168921,0.00006188591,0.00014271063,0.00012978322,0.00066707894,0.0023614317],"genre_scores_gemma":[0.12928249,0.00048004513,0.8632178,0.0002517523,0.00010678544,0.00021454677,0.00095528294,0.00043579369,0.00505564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988759,0.00023618744,0.00005655647,0.0002318282,0.00050242414,0.000097161945],"domain_scores_gemma":[0.99761236,0.0008779297,0.00023106775,0.000572106,0.0006014292,0.00010509497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000894694,0.0012105434,0.001020602,0.0027029715,0.0011295441,0.0009895171,0.002061981,0.00094673637,0.0018191922],"category_scores_gemma":[0.004817695,0.00044161058,0.001083307,0.001900011,0.00089216366,0.0016992157,0.0015771894,0.0014261792,0.0009948262],"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":[0.00019462754,0.00030065925,0.0036702624,0.0005430981,0.000208021,0.0008019591,0.0005259933,0.19760351,0.05333295,0.06265429,0.019435404,0.6607292],"study_design_scores_gemma":[0.000041230935,0.0001779668,0.0012431118,0.00007794727,0.0001548268,0.00093428,0.00020535273,0.8833663,0.023002027,0.06351399,0.027241528,0.000041412688],"about_ca_topic_score_codex":0.0034557218,"about_ca_topic_score_gemma":0.008519548,"teacher_disagreement_score":0.0034557218,"about_ca_system_score_codex":0.00066680333,"about_ca_system_score_gemma":0.0017199519,"threshold_uncertainty_score":0.0068712234},"labels":[],"label_agreement":null},{"id":"W4407192178","doi":"10.3390/make7010012","title":"Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Interpretability; Artificial intelligence; Pixel; Computer science; Medicine; Machine learning","score_opus":0.0202131925955474,"score_gpt":0.36563903158435757,"score_spread":0.3454258389888102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407192178","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":[0.14906275,0.004877331,0.83384,0.0021954041,0.00017190602,0.00028817958,0.0007669099,0.0035608055,0.0052366816],"genre_scores_gemma":[0.80794346,0.0013214367,0.18720469,0.00051138,0.000117235526,0.00015167007,0.0010855495,0.00042113604,0.0012434146],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997143,0.001126594,0.00021179263,0.0005408206,0.0008364818,0.00014132954],"domain_scores_gemma":[0.98285043,0.012732183,0.0012081052,0.0016794441,0.0012797829,0.00025008505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008689731,0.0013761118,0.0010176295,0.0026752546,0.00039242205,0.0037526086,0.0019422199,0.0018548585,0.0023063747],"category_scores_gemma":[0.040564477,0.00043757397,0.0014400999,0.0011997301,0.001477224,0.0030506847,0.0024896918,0.0021153693,0.00053250714],"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":[0.0015295177,0.0002281074,0.022617104,0.0013488629,0.0006238683,0.00051177293,0.0011069356,0.42775124,0.013166021,0.020306913,0.004212826,0.50659686],"study_design_scores_gemma":[0.000036497026,0.00024097045,0.004203,0.00011828871,0.00011101434,0.00038035747,0.00017849484,0.96697325,0.006161729,0.019518007,0.002038522,0.000039869996],"about_ca_topic_score_codex":0.0039447434,"about_ca_topic_score_gemma":0.0035783383,"teacher_disagreement_score":0.008689731,"about_ca_system_score_codex":0.0016254134,"about_ca_system_score_gemma":0.0012010456,"threshold_uncertainty_score":0.045956254},"labels":[],"label_agreement":null},{"id":"W4409634738","doi":"10.3390/make7020038","title":"Knowledge Graphs and Their Reciprocal Relationship with Large Language Models","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reciprocal; Computer science; Natural language processing; Linguistics; Philosophy","score_opus":0.012439613366926514,"score_gpt":0.2888355388840247,"score_spread":0.2763959255170982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409634738","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":[0.027917614,0.011286687,0.9357208,0.0076922188,0.00011250321,0.00021076374,0.0019124703,0.0010089662,0.014137923],"genre_scores_gemma":[0.5117346,0.008978617,0.47090626,0.0014070123,0.0001578463,0.0005730933,0.003303247,0.00038602797,0.0025533196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98649746,0.00889144,0.00075506995,0.0017270953,0.0019366868,0.000192338],"domain_scores_gemma":[0.8921665,0.09501352,0.0045610094,0.005408082,0.0024898597,0.00036098465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010493859,0.00083755795,0.0006631226,0.0071775755,0.0010991624,0.0076288525,0.0018402,0.0014227119,0.0035113497],"category_scores_gemma":[0.07100824,0.0009174017,0.0014864017,0.006897273,0.0045200326,0.015949203,0.004348648,0.0026008044,0.0006117054],"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":[0.00006533291,0.00004947754,0.005397399,0.0016312088,0.00041313202,0.00062609185,0.003676945,0.034314986,0.0011037786,0.8095995,0.00428866,0.13883348],"study_design_scores_gemma":[0.000011788221,0.000018438359,0.0011751181,0.0005666244,0.00014733388,0.00028831197,0.0007565053,0.058057453,0.0009245766,0.90631217,0.031693414,0.000048163667],"about_ca_topic_score_codex":0.006526254,"about_ca_topic_score_gemma":0.010139037,"teacher_disagreement_score":0.010493859,"about_ca_system_score_codex":0.0027481446,"about_ca_system_score_gemma":0.002698765,"threshold_uncertainty_score":0.055497527},"labels":[],"label_agreement":null},{"id":"W4410240055","doi":"10.3390/make7020042","title":"Leveraging Failure Modes and Effect Analysis for Technical Language Processing","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec; Université du Québec à Trois-Rivières","keywords":"Computer science","score_opus":0.008437007984600979,"score_gpt":0.30890788592712254,"score_spread":0.3004708779425216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410240055","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":[0.014141909,0.00044937318,0.97552574,0.00041804477,0.000061072686,0.00013231148,0.0013975297,0.00435074,0.0035233381],"genre_scores_gemma":[0.33495963,0.0008992415,0.65339696,0.00021995875,0.00020394879,0.0003699585,0.0060134735,0.0008684467,0.003068414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736696,0.00082152133,0.00025749634,0.00084793795,0.0006153192,0.000090760885],"domain_scores_gemma":[0.9869251,0.008828377,0.0010596054,0.0015924637,0.0015059432,0.000088487206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034380255,0.0014273082,0.0006332989,0.008875934,0.0007670292,0.001987778,0.00132872,0.0009781092,0.00285937],"category_scores_gemma":[0.016852869,0.00043749603,0.0017207668,0.0028630637,0.00089656806,0.0036916146,0.0018956728,0.0013530306,0.0021175817],"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":[0.00014749347,0.00019743302,0.014249036,0.0014317005,0.00028882475,0.0011995974,0.0030181424,0.09176915,0.03419409,0.028256424,0.0115462,0.81370187],"study_design_scores_gemma":[0.000026461668,0.00014575307,0.012674768,0.00034581823,0.00029867628,0.0009364537,0.00095349445,0.7881492,0.041860428,0.09425662,0.060172826,0.0001796069],"about_ca_topic_score_codex":0.0045136437,"about_ca_topic_score_gemma":0.0056321747,"teacher_disagreement_score":0.008875934,"about_ca_system_score_codex":0.0011716305,"about_ca_system_score_gemma":0.0014669446,"threshold_uncertainty_score":0.018182218},"labels":[],"label_agreement":null},{"id":"W4413160539","doi":"10.3390/make7030082","title":"Multilayer Perceptron Mapping of Subjective Time Duration onto Mental Imagery Vividness and Underlying Brain Dynamics: A Neural Cognitive Modeling Approach","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Neural dynamics and brain function","field":"Neuroscience","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":"Children's Hospital of Eastern Ontario; Carleton University","funders":"","keywords":"Cognition; Duration (music); Dynamics (music); Mental image; Psychology; Multilayer perceptron; Artificial intelligence; Neural correlates of consciousness; Cognitive psychology; Artificial neural network; Neuroimaging; Computer science; Neuroscience; Art","score_opus":0.028333824630308542,"score_gpt":0.3081942580353396,"score_spread":0.2798604334050311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413160539","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":[0.34146458,0.00046130348,0.655013,0.0004424646,0.000065066095,0.00007042109,0.00032897113,0.0008004988,0.0013536254],"genre_scores_gemma":[0.9474367,0.00016351198,0.050695896,0.000045276436,0.000018491575,0.00009086027,0.00024191698,0.00002166276,0.0012856696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972457,0.00010046235,0.00001674882,0.000086442065,0.000032416236,0.00003930667],"domain_scores_gemma":[0.9990005,0.0006574531,0.000104393934,0.00007631252,0.00012871716,0.00003255973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001306852,0.0006991133,0.0005310228,0.000670569,0.00024070231,0.0008661222,0.000730479,0.0006316162,0.0012907266],"category_scores_gemma":[0.0040941215,0.00039548636,0.0011236693,0.0004316064,0.00028854702,0.0008756992,0.00053271273,0.0012579605,0.00029316984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034026,0.00014182429,0.0066461856,0.00007595834,0.00021145426,0.000118797696,0.00020760437,0.9149351,0.0062369583,0.0024966528,0.0004898039,0.06809943],"study_design_scores_gemma":[0.000001881569,0.0000130718545,0.00065382436,0.0000026782736,0.0000067102815,0.000006569156,0.000006165351,0.99808633,0.00029923784,0.0008889857,0.000031223706,0.0000032300418],"about_ca_topic_score_codex":0.009013002,"about_ca_topic_score_gemma":0.0051302644,"teacher_disagreement_score":0.009013002,"about_ca_system_score_codex":0.00090197346,"about_ca_system_score_gemma":0.00049706566,"threshold_uncertainty_score":0.01792109},"labels":[],"label_agreement":null},{"id":"W4413794792","doi":"10.3390/make7030089","title":"AlzheimerRAG: Multimodal Retrieval-Augmented Generation for Clinical Use Cases","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Generative grammar; Search engine indexing; Artificial intelligence; Information retrieval; Machine learning; Natural language processing; Data science","score_opus":0.10801962975602644,"score_gpt":0.4138889138627881,"score_spread":0.3058692841067616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413794792","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":[0.08725765,0.002669536,0.7681709,0.0023808398,0.00038406797,0.002319104,0.009175791,0.11412626,0.013515873],"genre_scores_gemma":[0.35193938,0.0007953241,0.6267868,0.001176724,0.00014617268,0.0012146238,0.010257106,0.0017384379,0.0059454027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810445,0.0010166836,0.00015142829,0.0002927264,0.0003604613,0.00007414134],"domain_scores_gemma":[0.9940295,0.004434654,0.00026217423,0.0007296685,0.00037322892,0.00017075482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027931507,0.0014416332,0.00049593975,0.00218814,0.0003140015,0.0011881115,0.0015196819,0.0016630627,0.014134541],"category_scores_gemma":[0.013634463,0.00037421813,0.0010211413,0.00087108265,0.00058362575,0.0014356984,0.002461324,0.00084160635,0.00372514],"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":[0.0015656723,0.00062272855,0.0064058383,0.0017048954,0.00032143758,0.003427352,0.0018126997,0.030946888,0.035464305,0.0063010724,0.082361355,0.8290658],"study_design_scores_gemma":[0.0009597381,0.0013849704,0.0060074544,0.0003768716,0.00034843228,0.0056071137,0.0014389462,0.748071,0.06357861,0.04330418,0.12868418,0.00023852833],"about_ca_topic_score_codex":0.0018347332,"about_ca_topic_score_gemma":0.0031538624,"teacher_disagreement_score":0.014134541,"about_ca_system_score_codex":0.00056971645,"about_ca_system_score_gemma":0.0006524527,"threshold_uncertainty_score":0.047284782},"labels":[],"label_agreement":null},{"id":"W4414093251","doi":"10.3390/make7030097","title":"A Review of Large Language Models for Automated Test Case Generation","year":2025,"lang":"en","type":"review","venue":"Machine Learning and Knowledge Extraction","topic":"Software Testing and Debugging 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":"Ontario Tech University","funders":"","keywords":"Test (biology); Natural language generation; Natural language; Focus (optics); Software; Test case","score_opus":0.04220589820370538,"score_gpt":0.4041485293505684,"score_spread":0.361942631146863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414093251","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":[0.00034392145,0.99131846,0.005605451,0.00039829532,0.00013996143,0.00011854121,0.00022561126,0.00010980053,0.0017399681],"genre_scores_gemma":[0.0035134288,0.9855099,0.009345259,0.00034387107,0.000090933594,0.00023533645,0.00048289468,0.00003908633,0.00043923577],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967181,0.001130211,0.0007648747,0.00032122326,0.0009795275,0.00008604234],"domain_scores_gemma":[0.9754411,0.020293072,0.0013368505,0.0005224252,0.002246443,0.00016009308],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005450453,0.0016742153,0.0020156233,0.009180949,0.00043118303,0.0017293008,0.0025860686,0.0015119043,0.005492438],"category_scores_gemma":[0.023244804,0.0009896924,0.0025464564,0.008276055,0.0006627603,0.0027701173,0.0011512729,0.001370236,0.0023476153],"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":[0.00007015371,0.00007878715,0.0003899224,0.09907699,0.00027035305,0.00014644524,0.0001924858,0.0019673593,0.0007962887,0.004041777,0.014465775,0.8785036],"study_design_scores_gemma":[0.00006334857,0.00036333612,0.002806632,0.1358625,0.0022642065,0.0015474217,0.00026462437,0.002977339,0.0023476894,0.007150427,0.8442238,0.00012869928],"about_ca_topic_score_codex":0.0048487633,"about_ca_topic_score_gemma":0.0073524527,"teacher_disagreement_score":0.009180949,"about_ca_system_score_codex":0.001642625,"about_ca_system_score_gemma":0.0059868223,"threshold_uncertainty_score":0.028825104},"labels":[],"label_agreement":null},{"id":"W4414240954","doi":"10.3390/make7030101","title":"CRISP-NET: Integration of the CRISP-DM Model with Network Analysis","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","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":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Dalhousie University","keywords":"Identification (biology); Process (computing); Field (mathematics); Adaptation (eye); Personalization; Data integration; Software; Software development; Software development process","score_opus":0.007368779355266472,"score_gpt":0.2965423148062663,"score_spread":0.28917353545099983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414240954","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":[0.0017943024,0.00015821266,0.9916128,0.00041659758,0.00004053287,0.00012074514,0.0003165018,0.000331215,0.005209129],"genre_scores_gemma":[0.057635885,0.00057639164,0.9369749,0.00017928093,0.00007088051,0.0005020693,0.0008147968,0.00017009498,0.0030756458],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958209,0.0020383126,0.00031855048,0.0006015315,0.001118938,0.00010183165],"domain_scores_gemma":[0.99165064,0.0055735703,0.0005807696,0.00074490166,0.0012166363,0.00023341588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064220144,0.00092938944,0.00078059355,0.0034072255,0.000836541,0.004327198,0.002844658,0.0011484851,0.008652501],"category_scores_gemma":[0.017226446,0.000580951,0.0015023629,0.0030373484,0.0012392298,0.0040535773,0.0029028237,0.0018178177,0.0016850235],"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":[0.00013711544,0.00013645484,0.0026644343,0.0009287251,0.00022874583,0.00031504538,0.000650824,0.36057797,0.0013198543,0.46305367,0.006230993,0.16375625],"study_design_scores_gemma":[0.00002323716,0.00005164035,0.00048222297,0.00018426873,0.00004059638,0.00012307367,0.00014182994,0.7359797,0.00084001134,0.23552018,0.026575034,0.0000382618],"about_ca_topic_score_codex":0.0052261935,"about_ca_topic_score_gemma":0.006954773,"teacher_disagreement_score":0.008652501,"about_ca_system_score_codex":0.0019658904,"about_ca_system_score_gemma":0.0029830048,"threshold_uncertainty_score":0.033963263},"labels":[],"label_agreement":null},{"id":"W4415299179","doi":"10.3390/make7040121","title":"Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Provincial Health Services Authority; University of British Columbia","funders":"","keywords":"Task (project management); Language model; Selection (genetic algorithm); Exploit; Health care; Language understanding; Task analysis","score_opus":0.07084545954893032,"score_gpt":0.3752476515817091,"score_spread":0.30440219203277874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415299179","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":[0.3671596,0.0080051925,0.57939184,0.0065967967,0.00049268827,0.0006141945,0.0015651269,0.024910044,0.011264445],"genre_scores_gemma":[0.83429736,0.0008828363,0.15506376,0.0019375508,0.00009620319,0.0003591032,0.0020165036,0.0010581267,0.004288551],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820864,0.0009829272,0.000091698996,0.00045533775,0.00012124636,0.00014004261],"domain_scores_gemma":[0.995357,0.0035734852,0.00016587783,0.00037854913,0.00032828285,0.00019681019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037839455,0.001576215,0.00087113486,0.0005919966,0.000362248,0.0016640486,0.0017611226,0.0014777129,0.0040885494],"category_scores_gemma":[0.016858723,0.00057334604,0.00073872914,0.0003585084,0.00061550125,0.0030040701,0.0017653451,0.0029491659,0.0030302443],"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":[0.0016287207,0.00077864225,0.012296095,0.0011230733,0.00030739076,0.0004102984,0.0009228894,0.16594414,0.038341306,0.0036791912,0.024310961,0.7502574],"study_design_scores_gemma":[0.00019173641,0.00053178094,0.0024885524,0.0002194222,0.00016573016,0.00024861374,0.0005369284,0.9524067,0.022916641,0.011391174,0.0088290945,0.00007356532],"about_ca_topic_score_codex":0.005972823,"about_ca_topic_score_gemma":0.010546065,"teacher_disagreement_score":0.005972823,"about_ca_system_score_codex":0.0008824739,"about_ca_system_score_gemma":0.0019690495,"threshold_uncertainty_score":0.020011604},"labels":[],"label_agreement":null},{"id":"W4415357126","doi":"10.3390/make7040124","title":"SemiSeg-CAW: Semi-Supervised Segmentation of Ultrasound Images by Leveraging Class-Level Information and an Adaptive Multi-Loss Function","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Medical Image Segmentation 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":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Weighting; Scale-space segmentation; Feature (linguistics); Pattern recognition (psychology); Image segmentation; Segmentation-based object categorization; Dependency (UML)","score_opus":0.017657013095751087,"score_gpt":0.31087791044407603,"score_spread":0.29322089734832496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415357126","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":[0.016928675,0.00028102397,0.97831464,0.00013788053,0.000030876115,0.000094580806,0.00018154448,0.0034202049,0.0006104863],"genre_scores_gemma":[0.3283687,0.00033952875,0.6619058,0.00047821796,0.00011389944,0.00040595257,0.0019412189,0.0012731188,0.0051735556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904305,0.00019927525,0.00005067511,0.0003831542,0.00024393068,0.00007991626],"domain_scores_gemma":[0.9979824,0.0007122065,0.00024158806,0.0005138451,0.00044421075,0.000105681705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021977487,0.0015413007,0.0015528832,0.0016277605,0.0006224176,0.0014738244,0.0031701655,0.0021931108,0.0015422322],"category_scores_gemma":[0.0040294663,0.00096584146,0.0013355757,0.0010679496,0.001228704,0.0025721889,0.0022341053,0.0018424637,0.0010174642],"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":[0.0005853976,0.00033854987,0.0027754032,0.00033597788,0.0003342132,0.0002586584,0.00033054405,0.29139015,0.0714638,0.0065053604,0.011582645,0.6140994],"study_design_scores_gemma":[0.000012140053,0.00006550806,0.00058212987,0.000011667326,0.000023356795,0.00010187051,0.000015844262,0.9824338,0.011348676,0.0036782932,0.0017042872,0.000022414504],"about_ca_topic_score_codex":0.004936882,"about_ca_topic_score_gemma":0.010329503,"teacher_disagreement_score":0.004936882,"about_ca_system_score_codex":0.0008306919,"about_ca_system_score_gemma":0.0020557872,"threshold_uncertainty_score":0.011622906},"labels":[],"label_agreement":null},{"id":"W4416295981","doi":"10.3390/make7040147","title":"Adaptive Multi-View Hypergraph Learning for Cross-Condition Bearing Fault Diagnosis","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Robustness (evolution); Hypergraph; Feature (linguistics); Fault (geology); Feature learning; Fusion; Similarity (geometry)","score_opus":0.014321793020525355,"score_gpt":0.3441445669387899,"score_spread":0.3298227739182646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416295981","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":[0.03108688,0.00060163985,0.96606255,0.0002493175,0.000034180754,0.00003877837,0.00017653633,0.0011418686,0.0006082339],"genre_scores_gemma":[0.83552074,0.000403796,0.16032422,0.00036885715,0.00009804998,0.0000815097,0.00119525,0.00014119802,0.0018663994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949646,0.000121636534,0.000021212867,0.00019429419,0.00010259013,0.00006377545],"domain_scores_gemma":[0.9989766,0.0005130636,0.00012167128,0.00015609438,0.00016752648,0.00006503948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078802934,0.0013090102,0.0011023339,0.001972409,0.00039912976,0.0007546588,0.0017176822,0.0015976584,0.0012555599],"category_scores_gemma":[0.002778551,0.00047263745,0.0011959972,0.0013384058,0.00069747784,0.0017964263,0.0014518566,0.0017042656,0.00041023997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020185208,0.00019297583,0.0043862937,0.00014091941,0.00021023881,0.00030725665,0.00020426796,0.5824847,0.009663734,0.005890116,0.0047204974,0.39159706],"study_design_scores_gemma":[0.000005387046,0.000026955864,0.0003486128,0.000005441623,0.000016699027,0.00004086055,0.000017083234,0.99167705,0.0012289077,0.0062882584,0.00033825534,0.0000064675373],"about_ca_topic_score_codex":0.005870145,"about_ca_topic_score_gemma":0.00720436,"teacher_disagreement_score":0.005870145,"about_ca_system_score_codex":0.0008730806,"about_ca_system_score_gemma":0.0006955744,"threshold_uncertainty_score":0.01167196},"labels":[],"label_agreement":null},{"id":"W4416322339","doi":"10.3390/make7040148","title":"Model-Aware Automatic Benchmark Generation with Self-Error Instructions for Data-Driven Models","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","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":"Artificial Intelligence in Medicine (Canada)","funders":"Ministero dello Sviluppo Economico","keywords":"Benchmark (surveying); Data point; Generative grammar; Regression; Data-driven; Data modeling; Experimental data","score_opus":0.05107415529911185,"score_gpt":0.3329024960587661,"score_spread":0.28182834075965424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416322339","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":[0.05816122,0.00019593221,0.9079359,0.00026530694,0.00012621726,0.00038817083,0.0007132466,0.030352851,0.0018611475],"genre_scores_gemma":[0.43688512,0.00008564488,0.55539566,0.0002043761,0.000034045876,0.0009066618,0.0027027398,0.0027100197,0.0010757187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966349,0.0012322704,0.00026099294,0.0007181685,0.0009015727,0.0002521956],"domain_scores_gemma":[0.9845962,0.008336271,0.0008724747,0.0036558777,0.002189752,0.00034951678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00427002,0.0017663368,0.000996988,0.0013692962,0.00054906955,0.0017980961,0.003218587,0.0012611239,0.0035967492],"category_scores_gemma":[0.026143383,0.0007611934,0.0010604332,0.001020143,0.00095944473,0.0019763233,0.0021543477,0.0027638425,0.0011525665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005736863,0.0008044968,0.01109877,0.00042635106,0.00016399875,0.00032792307,0.00038183547,0.6966406,0.01838717,0.0201851,0.014314185,0.23669581],"study_design_scores_gemma":[0.000023444174,0.000038889735,0.00020793687,0.000009860445,0.0000069400126,0.000015809868,0.000013817113,0.990234,0.0047290726,0.00404211,0.0006690916,0.000008977969],"about_ca_topic_score_codex":0.0038660069,"about_ca_topic_score_gemma":0.0068046753,"teacher_disagreement_score":0.00427002,"about_ca_system_score_codex":0.0013713541,"about_ca_system_score_gemma":0.002878155,"threshold_uncertainty_score":0.022582293},"labels":[],"label_agreement":null},{"id":"W4416363878","doi":"10.3390/make7040149","title":"Explainable Recommendation of Software Vulnerability Repair Based on Metadata Retrieval and Multifaceted LLMs","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metadata; Context (archaeology); Code (set theory); Vulnerability (computing); Knowledge base; Transparency (behavior); Artifact (error); Robustness (evolution)","score_opus":0.016644298966465746,"score_gpt":0.31910019328848227,"score_spread":0.3024558943220165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416363878","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":[0.41160142,0.009780501,0.5341796,0.0026200535,0.00027212448,0.0014112483,0.010383626,0.01808071,0.011670631],"genre_scores_gemma":[0.62049836,0.0011356279,0.3625057,0.0004976943,0.00011112541,0.0004361845,0.011032006,0.00030283973,0.0034804957],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969055,0.0008284186,0.00033223425,0.00074183295,0.0010109857,0.0001809801],"domain_scores_gemma":[0.9891894,0.0059969155,0.0008936132,0.0016204177,0.0019462517,0.0003534024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023073272,0.0014034295,0.0010761239,0.0056548417,0.00066073926,0.0019731855,0.0013981182,0.0016922954,0.0022265732],"category_scores_gemma":[0.022180663,0.0003977864,0.0014193314,0.0027742116,0.00039311082,0.0033388645,0.0012502744,0.0015731466,0.0014126134],"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":[0.0011493722,0.0010845863,0.08016481,0.003009677,0.00079332094,0.0006449288,0.0022550859,0.03142967,0.023807315,0.004521388,0.028964486,0.8221753],"study_design_scores_gemma":[0.00031678323,0.0014976994,0.06559817,0.00094651245,0.0013609335,0.0010973356,0.002883364,0.8346069,0.029598333,0.01675474,0.04494271,0.00039653006],"about_ca_topic_score_codex":0.011485492,"about_ca_topic_score_gemma":0.03551168,"teacher_disagreement_score":0.011485492,"about_ca_system_score_codex":0.0009270068,"about_ca_system_score_gemma":0.0020468545,"threshold_uncertainty_score":0.022837281},"labels":[],"label_agreement":null},{"id":"W4416723482","doi":"10.3390/make7040154","title":"Low-SNR Northern Right Whale Upcall Detection and Classification Using Passive Acoustic Monitoring to Reduce Adverse Human–Whale Interactions","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Marine animal studies overview","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":"Dalhousie University","funders":"","keywords":"Support vector machine; Feature extraction; Classifier (UML); Pattern recognition (psychology); Right whale; Underwater; Whale","score_opus":0.0195422294831908,"score_gpt":0.31584453593367134,"score_spread":0.29630230645048056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416723482","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":[0.65824854,0.0016786471,0.32482296,0.0005147183,0.00029638712,0.00011448382,0.0004279976,0.0023635034,0.011532833],"genre_scores_gemma":[0.9765329,0.00029533482,0.019285819,0.00012593094,0.000068107984,0.000044685054,0.0003275237,0.000039840932,0.0032798976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997948,0.000030151605,0.0000114899585,0.0000682375,0.000049297407,0.000046064488],"domain_scores_gemma":[0.9997193,0.00009742091,0.000042256594,0.00002723344,0.00009089456,0.000022771777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041403066,0.00073132035,0.00048132293,0.00046158917,0.0002176548,0.0005228404,0.000648648,0.00045996532,0.0010480678],"category_scores_gemma":[0.0011854379,0.00018038196,0.00028050688,0.0001510356,0.00020668027,0.00065908016,0.0007279592,0.00049842946,0.00052435463],"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":[0.00070628343,0.0003888603,0.040949143,0.00029549657,0.00014318424,0.00056702155,0.00031084,0.070472725,0.080898955,0.0015536154,0.0057108854,0.798003],"study_design_scores_gemma":[0.00001781159,0.00022969698,0.020364894,0.00004871223,0.00008827984,0.00019726742,0.00021916878,0.95206,0.022846026,0.0013789958,0.0025158876,0.00003337693],"about_ca_topic_score_codex":0.003651392,"about_ca_topic_score_gemma":0.005998447,"teacher_disagreement_score":0.003651392,"about_ca_system_score_codex":0.00021432892,"about_ca_system_score_gemma":0.000484286,"threshold_uncertainty_score":0.007260263},"labels":[],"label_agreement":null},{"id":"W4416904921","doi":"10.3390/make7040157","title":"SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Digital Imaging for Blood Diseases","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 Calgary","funders":"","keywords":"Deep learning; Lyme disease; Preprocessor; Generalizability theory; Borrelia burgdorferi; Disease; Robustness (evolution)","score_opus":0.019082248411816355,"score_gpt":0.29651888788893216,"score_spread":0.27743663947711583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416904921","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":[0.23011255,0.004984203,0.73403585,0.0011465322,0.0005970028,0.00033645166,0.003811339,0.017725836,0.007250254],"genre_scores_gemma":[0.799923,0.0013484521,0.17789513,0.0010451848,0.00015148471,0.00022715253,0.008161143,0.00034478368,0.01090365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997867,0.000038505743,0.00001014392,0.000078462464,0.000045128632,0.000041126586],"domain_scores_gemma":[0.99983764,0.000055560882,0.000017300708,0.000021190137,0.0000501118,0.000018253351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006153982,0.0015117755,0.0005758604,0.0010627416,0.00021338869,0.00067565317,0.0014322686,0.0009642021,0.0016704593],"category_scores_gemma":[0.0008803902,0.00034000687,0.0008813382,0.00050324993,0.00028729692,0.0010125316,0.0009343913,0.00102515,0.00061451556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004285012,0.00049618585,0.006803087,0.00024284986,0.0003511609,0.0003387742,0.00008834674,0.530871,0.016411103,0.0027735846,0.018503826,0.42269158],"study_design_scores_gemma":[0.000009024411,0.000049713184,0.00036377643,0.000011947698,0.000016025811,0.000029226772,0.000009164899,0.99505746,0.0023240207,0.0011086114,0.0010129972,0.000008122624],"about_ca_topic_score_codex":0.011808241,"about_ca_topic_score_gemma":0.018481202,"teacher_disagreement_score":0.011808241,"about_ca_system_score_codex":0.00089031324,"about_ca_system_score_gemma":0.0008257488,"threshold_uncertainty_score":0.023479044},"labels":[],"label_agreement":null},{"id":"W7116670301","doi":"10.3390/make8010002","title":"Enhancing GNN Explanations for Malware Detection with Dual Subgraph Matching","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Malware Detection 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 New Brunswick","funders":"","keywords":"Malware; Discriminative model; Benchmark (surveying); Generalization; Matching (statistics); Dual (grammatical number); Subgraph isomorphism problem; Control flow graph","score_opus":0.006989772379721288,"score_gpt":0.2894999140025139,"score_spread":0.2825101416227926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116670301","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":[0.05707824,0.00021258731,0.93842006,0.00041080028,0.000025297604,0.00009839809,0.00026965977,0.0022935162,0.0011914253],"genre_scores_gemma":[0.6597739,0.00022123514,0.3357144,0.00023945335,0.00003919445,0.00013961976,0.0013473287,0.00019957425,0.002325339],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994349,0.00014440928,0.000025752784,0.00020616625,0.00014194119,0.00004689718],"domain_scores_gemma":[0.99812883,0.0010127573,0.00024887326,0.00030150102,0.00025669232,0.00005129485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077721267,0.0008530558,0.00048869324,0.0019503782,0.0004986623,0.0005971868,0.0013400766,0.0012166933,0.002352181],"category_scores_gemma":[0.00581494,0.00032079947,0.0009978318,0.00094632787,0.0007004908,0.0019619518,0.0012848801,0.0011465423,0.00035304023],"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":[0.0002051924,0.00028112292,0.01927138,0.00029037497,0.00019216673,0.0005982095,0.0005791673,0.4343651,0.013308694,0.040288944,0.005895229,0.4847245],"study_design_scores_gemma":[0.000008847647,0.000029020113,0.00091121026,0.000014748645,0.000023220984,0.00009124981,0.000036520727,0.9696214,0.002294751,0.025618834,0.001341632,0.000008498011],"about_ca_topic_score_codex":0.0057993694,"about_ca_topic_score_gemma":0.011473556,"teacher_disagreement_score":0.0057993694,"about_ca_system_score_codex":0.0009796194,"about_ca_system_score_gemma":0.0010416936,"threshold_uncertainty_score":0.011531234},"labels":[],"label_agreement":null},{"id":"W7117764744","doi":"10.3390/make8010006","title":"Research Frontiers in Machine Learning &amp; Knowledge Extraction","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Eusko Jaurlaritza; Austrian Science Fund","keywords":"Transparency (behavior); Underpinning; Software deployment; Cornerstone; Applications of artificial intelligence; Embedding; Domain (mathematical analysis); Knowledge integration","score_opus":0.04255175253055986,"score_gpt":0.3903736406979469,"score_spread":0.347821888167387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117764744","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":[0.00803364,0.19524018,0.5139451,0.14637578,0.0019320685,0.00025871434,0.00054842787,0.0010254433,0.13264067],"genre_scores_gemma":[0.36214015,0.18826935,0.40794924,0.014452582,0.0055069993,0.0007389559,0.0011943922,0.00047016898,0.019278197],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9908298,0.00482765,0.00045599596,0.0012334138,0.002264648,0.00038841652],"domain_scores_gemma":[0.9560737,0.03590004,0.00088675733,0.0035722281,0.0027925244,0.00077468366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014815186,0.0010768381,0.0011045729,0.0046850764,0.0018998493,0.011484653,0.0028964698,0.005876476,0.009339812],"category_scores_gemma":[0.020903472,0.0006724233,0.001228535,0.005374362,0.0141480435,0.018659964,0.004443045,0.006519648,0.0033066517],"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":[0.00002806736,0.00007040733,0.00072914484,0.00082997786,0.000030074116,0.00008435813,0.0004874163,0.0017498243,0.00037318864,0.8558108,0.009313404,0.13049327],"study_design_scores_gemma":[0.000010599328,0.000027829985,0.00035468853,0.0008023502,0.000012463824,0.00016557369,0.00047959748,0.008232647,0.0007055192,0.8568971,0.13227288,0.000038820715],"about_ca_topic_score_codex":0.0021008544,"about_ca_topic_score_gemma":0.0012009729,"teacher_disagreement_score":0.014815186,"about_ca_system_score_codex":0.004247347,"about_ca_system_score_gemma":0.006677959,"threshold_uncertainty_score":0.07835108},"labels":[],"label_agreement":null}]}