{"meta":{"query_hash":"7785ef991964","filters":{"venue":"Pattern Analysis and Applications"},"cohort_total":71,"direct_labels_cover":0,"predictions_cover":71,"exported":71,"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/7785ef991964","api":"https://metacan.xera.ac/api/v1/cohort?venue=Pattern+Analysis+and+Applications"},"results":[{"id":"W1605253782","doi":"10.1007/s10044-011-0263-5","title":"Creating ensemble classifiers through order and incremental data selection in a stream","year":2012,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; AdaBoost; Benchmark (surveying); Machine learning; Artificial intelligence; Ensemble learning; Concept drift; Data mining; Selection (genetic algorithm); Stability (learning theory); Class (philosophy); Sensitivity (control systems); Data stream mining; Pattern recognition (psychology); Support vector machine","score_opus":0.04332190781487145,"score_gpt":0.3200904332674533,"score_spread":0.27676852545258185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1605253782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069872625,0.000078868325,0.9289693,0.00014157829,0.0000031144336,0.00011497191,0.000031088915,0.000064783504,0.00072364387],"genre_scores_gemma":[0.9253428,0.0000720439,0.07423819,0.00010042726,0.000028746777,0.000056192282,0.00014330907,0.0000037907078,0.000014478115],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999221,0.000032937325,0.00015950782,0.00033390053,0.00009320428,0.00015941713],"domain_scores_gemma":[0.9993581,0.00004992711,0.00007196072,0.00045176735,0.000019068411,0.00004916763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002343091,0.00008125332,0.00012501466,0.00013088505,0.00011807842,0.00011866396,0.00032453402,0.000030602212,0.00000852872],"category_scores_gemma":[0.000010520639,0.00007785434,0.00001361874,0.0009584913,0.000027975024,0.000676844,0.00041762306,0.00006779657,0.0000028418665],"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":[4.9913507e-7,0.00010518334,0.76083803,0.0000072368553,0.00009981144,1.6384446e-7,0.0004875448,0.0000032644498,0.0010446595,0.0029419432,0.00023979628,0.23423187],"study_design_scores_gemma":[0.00052188936,0.000060686925,0.6605513,0.00003936934,0.0007108991,0.000023595996,0.000994639,0.31603184,0.006392503,0.0020988549,0.011839492,0.0007349345],"about_ca_topic_score_codex":0.0016278051,"about_ca_topic_score_gemma":0.0009238962,"teacher_disagreement_score":0.8554702,"about_ca_system_score_codex":0.000020458332,"about_ca_system_score_gemma":0.000008975711,"threshold_uncertainty_score":0.31748077},"labels":[],"label_agreement":null},{"id":"W1656023708","doi":"10.1007/s10044-015-0492-0","title":"Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm","year":2015,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; Alzheimer's Disease Neuroimaging Initiative; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Synarc; University of Southern California; Medpace; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; University of California, San Francisco; Foundation for the National Institutes of Health","keywords":"Undersampling; Boosting (machine learning); Pattern recognition (psychology); Artificial intelligence; Segmentation; Computer science; Algorithm; Hippocampal formation; Random forest; Medicine","score_opus":0.03224919150146756,"score_gpt":0.3183575597015484,"score_spread":0.28610836820008084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1656023708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039879684,0.000039346927,0.9952041,0.00017473937,0.0000068770323,0.00028906413,0.0000035419653,0.0002564341,0.00003794632],"genre_scores_gemma":[0.30150008,0.000012476124,0.69793195,0.00034122125,0.000024572715,0.00012733469,0.000048643866,0.0000068282784,0.000006894928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989322,0.00006928346,0.00027427272,0.00032331757,0.00024974204,0.00015121326],"domain_scores_gemma":[0.9993424,0.00007455144,0.00013984986,0.00023570294,0.00008548939,0.00012204149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003903086,0.00010163343,0.00018558832,0.00030331776,0.00009400151,0.0001684409,0.00018218847,0.00003115457,0.0000073366505],"category_scores_gemma":[0.000009175912,0.00008866537,0.000030035306,0.0013198918,0.00004242488,0.00027375942,0.00006526636,0.0000755988,0.0000035532694],"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":[0.0000047981166,0.00013124365,0.04564197,0.00001969518,0.00024797695,0.0000072401476,0.0014809988,0.008842665,0.0018856056,0.00006405346,0.00005172752,0.941622],"study_design_scores_gemma":[0.000649152,0.000013760909,0.0018217161,0.000013590132,0.00011200753,0.000004354361,0.0002980626,0.99490297,0.0018555962,0.00019596661,0.000013501565,0.00011932833],"about_ca_topic_score_codex":0.0004836211,"about_ca_topic_score_gemma":0.00009841025,"teacher_disagreement_score":0.9860603,"about_ca_system_score_codex":0.0000714194,"about_ca_system_score_gemma":0.00003971697,"threshold_uncertainty_score":0.3615669},"labels":[],"label_agreement":null},{"id":"W1968620120","doi":"10.1007/s10044-011-0199-9","title":"On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases","year":2011,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Set (abstract data type); Random variable; Partition (number theory); Data mining; Variables; Heuristic; Algorithm; Mathematics; Artificial intelligence; Machine learning; Statistics; Combinatorics","score_opus":0.04062258526704,"score_gpt":0.3130670910758171,"score_spread":0.2724445058087771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968620120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036807512,0.0000124943,0.99349445,0.0017141008,0.000012986676,0.00038029015,0.00046699986,0.0001437934,0.000094147385],"genre_scores_gemma":[0.6105676,0.000008754603,0.388183,0.00053230027,0.000007769428,0.00038576915,0.00030999788,0.0000029470284,0.0000018105229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907714,0.000019434574,0.00023611635,0.00034833007,0.00015251823,0.0001664541],"domain_scores_gemma":[0.99722195,0.00020444598,0.00011337489,0.0023034338,0.00009010555,0.000066705055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019696055,0.00010398982,0.00013114644,0.000293749,0.0001665476,0.00010847986,0.0026890275,0.00003650283,0.000021756367],"category_scores_gemma":[0.0008319565,0.00009870663,0.000039663075,0.0007319656,0.000033002987,0.00042904128,0.0021441232,0.00007231088,0.000036909823],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015164804,0.00015566738,0.009655295,0.00008333822,0.00016371714,6.738538e-7,0.00021818123,0.00007285145,0.0007529515,0.048720308,0.009873833,0.930288],"study_design_scores_gemma":[0.00037589297,0.00017035709,0.023985632,0.000078415746,0.00037056993,0.000001991638,0.00015152991,0.74804,0.12133948,0.0963889,0.008372179,0.0007250142],"about_ca_topic_score_codex":0.00013220691,"about_ca_topic_score_gemma":0.00012025137,"teacher_disagreement_score":0.929563,"about_ca_system_score_codex":0.000025639003,"about_ca_system_score_gemma":0.000019818386,"threshold_uncertainty_score":0.49969268},"labels":[],"label_agreement":null},{"id":"W1973759055","doi":"10.1007/s100440070003","title":"An HMM-MLP Hybrid Model for Cursive Script Recognition","year":2000,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"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; Kyungil University","keywords":"Cursive; Hidden Markov model; Computer science; Pattern recognition (psychology); Artificial intelligence; Speech recognition","score_opus":0.02367950298016299,"score_gpt":0.2816651684398643,"score_spread":0.2579856654597013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973759055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009972707,0.0000337363,0.98824525,0.00044662922,0.0000044855838,0.00042385582,0.00018311503,0.0002140185,0.0004761842],"genre_scores_gemma":[0.9423528,0.00023404787,0.054237295,0.0010210306,0.00006623003,0.0014416287,0.00043188996,0.000010882679,0.00020419943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895346,0.00003084231,0.00023220557,0.00048658694,0.00011930492,0.00017760399],"domain_scores_gemma":[0.9991353,0.000033275704,0.00007654407,0.00048327542,0.00015049738,0.00012106551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016559304,0.00012074232,0.0001818907,0.00022019033,0.00023159265,0.00020314596,0.00034805026,0.00003804344,0.00012542102],"category_scores_gemma":[0.000002818757,0.00012170759,0.00013301187,0.0005037763,0.00003506973,0.00038441026,0.000024485376,0.00006364378,0.00005771587],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011389707,0.00009380476,0.000179321,0.000005465154,0.00008326764,1.9894922e-7,0.00009139929,0.00009309489,0.00033166184,0.00044924,0.00025633356,0.99841505],"study_design_scores_gemma":[0.00014829717,0.000034695713,0.00040666317,0.0000057985544,0.00031217578,0.0000034840843,0.0000148450545,0.94681764,0.0063699884,0.043895997,0.0017699361,0.0002204784],"about_ca_topic_score_codex":0.000052504827,"about_ca_topic_score_gemma":0.00005258145,"teacher_disagreement_score":0.9981946,"about_ca_system_score_codex":0.000016670101,"about_ca_system_score_gemma":0.000014538209,"threshold_uncertainty_score":0.49630916},"labels":[],"label_agreement":null},{"id":"W1974230797","doi":"10.1007/s10044-007-0060-3","title":"Periodicity and stability issues of a chaotic pattern recognition neural network","year":2007,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Chaotic; Artificial neural network; Eigenvalues and eigenvectors; Computer science; Stability (learning theory); Lyapunov exponent; Control theory (sociology); Artificial intelligence; Pattern recognition (psychology); Topology (electrical circuits); Mathematics; Control (management); Machine learning; Physics","score_opus":0.02468098660401145,"score_gpt":0.2704438658288962,"score_spread":0.24576287922488474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974230797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32389578,0.00024650776,0.67482615,0.000738037,0.000007975776,0.00018928,0.000014291483,0.000033335888,0.000048624737],"genre_scores_gemma":[0.9972087,0.00015152649,0.0021397998,0.0002721575,0.000111680194,0.000072717296,0.00003171437,0.0000047697854,0.0000069280545],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988641,0.00003697017,0.00033981906,0.0004120942,0.00013592688,0.00021105447],"domain_scores_gemma":[0.999081,0.000116667616,0.00016047763,0.00044008743,0.0000880795,0.00011365731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036387166,0.00011931984,0.00024237784,0.00009295047,0.00021569837,0.00008686902,0.00021668006,0.000041845487,0.000024748902],"category_scores_gemma":[0.0000035098196,0.000108179294,0.000086946944,0.0009765265,0.00010260016,0.00011949192,0.000131203,0.00009329665,0.000003847255],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001429586,0.000086447784,0.19889279,0.000022802937,0.00009157922,4.8944554e-7,0.00018953605,0.000077345525,0.00045062107,0.00047621122,0.00002976774,0.799681],"study_design_scores_gemma":[0.00023003238,0.00005379228,0.8493076,0.0000150432625,0.00040311535,0.0000069395296,0.00011066322,0.14262736,0.0014178246,0.0040752557,0.0014086516,0.00034374656],"about_ca_topic_score_codex":0.00034144043,"about_ca_topic_score_gemma":0.0006096975,"teacher_disagreement_score":0.7993372,"about_ca_system_score_codex":0.00000832024,"about_ca_system_score_gemma":0.0000051469215,"threshold_uncertainty_score":0.44114235},"labels":[],"label_agreement":null},{"id":"W1977336359","doi":"10.1007/s10044-011-0229-7","title":"Iris recognition using shape-guided approach and game theory","year":2011,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Iris recognition; Pattern recognition (psychology); Computer vision; Eyelash; Biometrics; Mathematics","score_opus":0.10491747848420133,"score_gpt":0.28460965666981397,"score_spread":0.17969217818561264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977336359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037031196,0.0001582157,0.9615756,0.000049385224,0.000008237892,0.00013518172,0.000013524875,0.000046170975,0.0009824623],"genre_scores_gemma":[0.9714202,0.00009758276,0.02810518,0.00020615295,0.000022092598,0.00006190679,0.000039002116,0.0000039761703,0.000043891796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913734,0.00006583605,0.00020061893,0.0003780186,0.000107750755,0.000110438996],"domain_scores_gemma":[0.99934566,0.000032189986,0.00010509005,0.0003536994,0.00007832722,0.00008502857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035076033,0.00008427824,0.00013843786,0.00044197813,0.00015031033,0.00013703013,0.00021285992,0.000045438053,0.00005870574],"category_scores_gemma":[0.000007207387,0.000078042605,0.00006273988,0.0017088993,0.00006270769,0.00016216462,0.000093665054,0.00005879588,0.00001606769],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010974296,0.00018875289,0.016439501,0.000021463426,0.00025424376,3.7461808e-7,0.001297509,0.0000014637798,0.0003524495,0.00952363,0.000037333906,0.97188216],"study_design_scores_gemma":[0.00039667968,0.000018368826,0.26414475,0.0000071144405,0.0010885788,0.000028485181,0.00040382476,0.7017059,0.0011235077,0.027735094,0.0027647393,0.00058298936],"about_ca_topic_score_codex":0.00018132041,"about_ca_topic_score_gemma":0.000007394932,"teacher_disagreement_score":0.9712992,"about_ca_system_score_codex":0.000010182508,"about_ca_system_score_gemma":0.000007908945,"threshold_uncertainty_score":0.3182485},"labels":[],"label_agreement":null},{"id":"W1977559714","doi":"10.1007/s10044-011-0253-7","title":"Joint segmentation and pairing of multispectral chromosome images","year":2011,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Multispectral image; Segmentation; Artificial intelligence; Grayscale; Computer science; Pattern recognition (psychology); Chromosome; Image segmentation; Multispectral pattern recognition; Computer vision; Pixel; Biology; Genetics; Gene","score_opus":0.021112324065961217,"score_gpt":0.2579642343865197,"score_spread":0.2368519103205585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977559714","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5824506,0.00037736192,0.41664594,0.00004812042,0.000004935153,0.00011168706,0.000018190627,0.0000053068393,0.00033782434],"genre_scores_gemma":[0.9981697,0.00040494552,0.0011291954,0.000037823742,0.00002665956,0.000083249484,0.0000666058,0.000004091123,0.00007771258],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99958444,0.00001423201,0.00012560739,0.00018013698,0.000041184543,0.000054408993],"domain_scores_gemma":[0.99970585,0.000001904867,0.000077644305,0.00014930128,0.000029156812,0.000036131412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000048300863,0.000053313484,0.00008077009,0.00006354134,0.000043311615,0.000007892539,0.00003529275,0.000027318665,0.000031694417],"category_scores_gemma":[0.0000018660124,0.00004874505,0.000041750223,0.0001185792,0.000040707997,0.0000028659838,0.000027386315,0.000018661,9.172525e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023068264,0.000033017597,0.07450911,0.000010826515,0.0000961214,4.0609166e-8,0.000082078106,0.0000028658108,0.8901629,0.000041034033,0.000027380132,0.03503236],"study_design_scores_gemma":[0.00011786356,0.00001862481,0.35581377,0.0000024620406,0.0001284397,7.3925105e-7,0.00016056195,0.00013593832,0.6432142,0.00004751254,0.00029950382,0.000060344235],"about_ca_topic_score_codex":0.00006369494,"about_ca_topic_score_gemma":0.000029593804,"teacher_disagreement_score":0.4157191,"about_ca_system_score_codex":0.0000024611222,"about_ca_system_score_gemma":0.0000045330908,"threshold_uncertainty_score":0.19877653},"labels":[],"label_agreement":null},{"id":"W1998227249","doi":"10.1007/s10044-008-0120-3","title":"Towards a measure of biometric feature information","year":2008,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Biometrics; Pattern recognition (psychology); Feature (linguistics); Artificial intelligence; Entropy (arrow of time); Linear discriminant analysis; Population; Measure (data warehouse); Computer science; Mathematics; Facial recognition system; Data mining","score_opus":0.018595720800592883,"score_gpt":0.2433341003508717,"score_spread":0.2247383795502788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998227249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006262507,0.00023059505,0.9920004,0.00069728564,0.000012297787,0.00011218659,0.000027582348,0.000035986624,0.0006211286],"genre_scores_gemma":[0.99515134,0.00016979789,0.0043579205,0.0001492971,0.000012604254,0.000040998566,0.000055411652,0.0000013418186,0.00006128046],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99927175,0.00002141911,0.00021016569,0.00015552751,0.00025784227,0.00008328746],"domain_scores_gemma":[0.99915487,0.000021829243,0.00015216967,0.00039754354,0.0002100247,0.000063561194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016847448,0.0000657441,0.00015025903,0.001581583,0.00013297313,0.00005939566,0.00031993195,0.00004875868,0.000015354384],"category_scores_gemma":[0.000016664126,0.00005769704,0.000099328245,0.010301181,0.00004535037,0.00029327534,0.00006605876,0.000054979795,0.000021918919],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.5908184e-7,0.00011865521,0.039686188,0.00002771546,0.00027250743,2.856227e-7,0.0006927263,0.000006759871,0.00030249194,0.008092633,0.00097398617,0.9498253],"study_design_scores_gemma":[0.0002877578,0.000019275793,0.902093,0.0000033388744,0.00023469298,0.0000120879995,0.00006039111,0.020379594,0.0029034691,0.0004012797,0.07336769,0.0002374128],"about_ca_topic_score_codex":0.00016186593,"about_ca_topic_score_gemma":0.000011616818,"teacher_disagreement_score":0.98888886,"about_ca_system_score_codex":0.000012135167,"about_ca_system_score_gemma":0.000025189174,"threshold_uncertainty_score":0.4949376},"labels":[],"label_agreement":null},{"id":"W2000095052","doi":"10.1007/s10044-009-0170-1","title":"Representation and classification of high-dimensional biomedical spectral data","year":2009,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; National Research Council Institute for Biodiagnostics; University of Alberta","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Dimensionality reduction; Principal component analysis; Computer science; Linear subspace; Perceptron; Classifier (UML); Feature vector; k-nearest neighbors algorithm; Random subspace method; Support vector machine; Subspace topology; Artificial neural network; Mathematics","score_opus":0.03573797126209471,"score_gpt":0.332521243984583,"score_spread":0.2967832727224883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000095052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78627115,0.00048786605,0.21048598,0.0015771359,0.000004431596,0.00006792161,0.0002359564,0.000035819477,0.00083372585],"genre_scores_gemma":[0.99752116,0.0001360772,0.0007012528,0.00006019762,0.000070675494,0.000010612291,0.0013814507,0.0000033834442,0.00011520128],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9991014,0.000008519316,0.00025363427,0.00037383812,0.00016853635,0.00009407365],"domain_scores_gemma":[0.9991704,0.000057880443,0.00013947174,0.00051967066,0.000038215432,0.00007436846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079005746,0.000082186656,0.00022671578,0.00020092052,0.00007617033,0.000024679637,0.00015664626,0.000053781416,0.00037300278],"category_scores_gemma":[0.000016193417,0.00007435754,0.000046728623,0.0010053401,0.00008913078,0.00006869418,0.000046799494,0.000067279645,0.000003901794],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020544732,0.00084489584,0.2149686,0.00006733861,0.00178439,0.0000018521904,0.00009526749,0.000028009717,0.4710004,0.0043524215,0.00096333574,0.30587295],"study_design_scores_gemma":[0.00050611707,0.000029901776,0.91239053,0.000007749125,0.004597095,0.000008940266,0.00022180997,0.027812855,0.05043207,0.0029662596,0.0007561157,0.00027056434],"about_ca_topic_score_codex":0.00018777732,"about_ca_topic_score_gemma":0.000029622579,"teacher_disagreement_score":0.6974219,"about_ca_system_score_codex":0.000010318008,"about_ca_system_score_gemma":0.000009396861,"threshold_uncertainty_score":0.4084119},"labels":[],"label_agreement":null},{"id":"W2000218629","doi":"10.1007/s10044-006-0023-0","title":"A new approach for breast skin-line estimation in mammograms","year":2006,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"AI in cancer detection","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Line (geometry); Thresholding; Pixel; Artificial intelligence; Boundary (topology); Standard deviation; Computer science; Ground truth; Pattern recognition (psychology); Euclidean distance; Enhanced Data Rates for GSM Evolution; Mathematics; Computer vision; Image (mathematics); Statistics; Geometry; Mathematical analysis","score_opus":0.011499080979121171,"score_gpt":0.25949574408666826,"score_spread":0.2479966631075471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000218629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000939812,0.000036721587,0.9980251,0.0004390515,0.000006794536,0.00027553586,0.000012569804,0.000044828997,0.00021956311],"genre_scores_gemma":[0.8434645,0.000005339221,0.15576082,0.00005047687,0.00007865959,0.00041263577,0.00007266324,0.000004410675,0.00015047287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926907,0.000011679199,0.00018670424,0.00032726297,0.00009164571,0.000113652415],"domain_scores_gemma":[0.99953187,0.000026517266,0.000077435776,0.00029515228,0.00003160962,0.000037425845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011692825,0.00007389422,0.00012281128,0.00020693183,0.000075067335,0.000108170425,0.0001849799,0.00003244232,0.0000071469285],"category_scores_gemma":[9.601315e-7,0.00007293262,0.000069268965,0.0011859282,0.00001173237,0.000119979835,0.000034903143,0.00004005561,0.000004666676],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.572065e-7,0.000051804538,0.007946063,0.000008815132,0.000031718562,4.5198835e-8,0.000026214795,0.014486176,0.00005661791,0.0017968079,0.000075638214,0.9755191],"study_design_scores_gemma":[0.00018626348,0.000005926938,0.064022176,0.000001755983,0.00008660383,0.000003447768,0.0000073317037,0.9303793,0.00020805853,0.0044724797,0.00053175876,0.00009491246],"about_ca_topic_score_codex":0.0016290658,"about_ca_topic_score_gemma":0.00069302006,"teacher_disagreement_score":0.97542423,"about_ca_system_score_codex":0.000034570592,"about_ca_system_score_gemma":0.000013117675,"threshold_uncertainty_score":0.29741058},"labels":[],"label_agreement":null},{"id":"W2004374514","doi":"10.1007/s10044-014-0428-0","title":"Omnifont text recognition of printed cursive scripts via HMMs, compact lossless features, and soft data clustering","year":2014,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Cosmetic and Laser Surgery","funders":"","keywords":"Cursive; Computer science; Vector quantization; Pattern recognition (psychology); Artificial intelligence; Optical character recognition; Speech recognition; Feature vector; Cluster analysis; Hidden Markov model","score_opus":0.03277821513927734,"score_gpt":0.2844556664686302,"score_spread":0.2516774513293528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004374514","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015608222,0.0001814242,0.99722016,0.0005441756,0.000008026626,0.00018246834,0.000059047354,0.0000613359,0.00018257082],"genre_scores_gemma":[0.99421364,0.00013968037,0.005168319,0.00014212704,0.000029831448,0.000024503768,0.00022259438,0.0000050864896,0.00005418705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990265,0.000053064774,0.00024316108,0.00042207888,0.00014177606,0.000113418086],"domain_scores_gemma":[0.99875885,0.00006538184,0.00019738343,0.0007739165,0.00012989117,0.00007454872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024740736,0.00010594114,0.00021911922,0.00016804368,0.00012208112,0.000121782236,0.0004843946,0.00004272315,0.000009351459],"category_scores_gemma":[0.0000145343965,0.00009319043,0.00004508996,0.00055471034,0.00007622551,0.00022141289,0.00023701506,0.00008420397,0.0000056277654],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002385276,0.00008523879,0.0060696416,0.000047024863,0.000145166,1.8425631e-7,0.000110623885,0.000003143165,0.0058474406,0.0004831494,0.00014851983,0.9870575],"study_design_scores_gemma":[0.0004901368,0.000079287354,0.31822193,0.00008758543,0.00081815256,0.000027819424,0.0001238328,0.62667525,0.03806612,0.0049827215,0.009816978,0.00061019947],"about_ca_topic_score_codex":0.00027859656,"about_ca_topic_score_gemma":0.00011850633,"teacher_disagreement_score":0.99265283,"about_ca_system_score_codex":0.000010713623,"about_ca_system_score_gemma":0.000008629415,"threshold_uncertainty_score":0.38001955},"labels":[],"label_agreement":null},{"id":"W2007769114","doi":"10.1007/s10044-006-0028-8","title":"Rotation invariant feature extraction using Ridgelet and Fourier transforms","year":2006,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pattern recognition (psychology); Fourier transform; Artificial intelligence; Invariant (physics); Short-time Fourier transform; Feature extraction; Fractional Fourier transform; Mathematics; Rotation (mathematics); S transform; Gaussian; Noise (video); Computer science; Computer vision; Fourier analysis; Wavelet transform; Image (mathematics); Physics; Mathematical analysis; Wavelet; Discrete wavelet transform","score_opus":0.012482872432951531,"score_gpt":0.2647739473047341,"score_spread":0.25229107487178254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007769114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008203891,0.00012716402,0.99005127,0.0011344063,0.0000051412544,0.00017327757,0.000009039079,0.0000620069,0.00023380495],"genre_scores_gemma":[0.97973543,0.00008437921,0.019771324,0.000108582084,0.00005358058,0.00007403343,0.000042204247,0.0000043005994,0.00012618677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933517,0.000020507818,0.0001535764,0.00027375977,0.000120420904,0.00009653962],"domain_scores_gemma":[0.9995642,0.000022150922,0.00009025056,0.00022160144,0.00006176354,0.000040069765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001276083,0.000084546504,0.000114971815,0.00016372949,0.000203535,0.00019078396,0.00010598005,0.000049281498,0.000008111181],"category_scores_gemma":[0.0000016462521,0.00007182891,0.00005359395,0.00074007845,0.000030934294,0.00028400504,0.000020152675,0.00006946083,0.0000023011169],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024739113,0.00013776828,0.015555074,0.000031937387,0.00015268824,0.0000015287076,0.00014571635,0.000049559876,0.060394783,0.034195118,0.00012247436,0.8892109],"study_design_scores_gemma":[0.00033129758,0.000027819628,0.2626364,0.000012751327,0.0006477035,0.000032679764,0.00006475607,0.6520754,0.059816763,0.01060605,0.013265684,0.0004826803],"about_ca_topic_score_codex":0.00027113422,"about_ca_topic_score_gemma":0.00006951041,"teacher_disagreement_score":0.9715315,"about_ca_system_score_codex":0.000016886528,"about_ca_system_score_gemma":0.000009644179,"threshold_uncertainty_score":0.29290977},"labels":[],"label_agreement":null},{"id":"W2007932135","doi":"10.1007/s10044-004-0226-1","title":"Feature dimensionality reduction for the verification of handwritten numerals","year":2004,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"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; Royal Society of Canada","keywords":"Pattern recognition (psychology); Dimensionality reduction; Artificial intelligence; Computer science; Linear discriminant analysis; Feature extraction; Principal component analysis; Classifier (UML); Curse of dimensionality; Artificial neural network; Discriminant","score_opus":0.015898302070796747,"score_gpt":0.2761278753260146,"score_spread":0.2602295732552179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007932135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003058822,0.00017789277,0.98782086,0.008389909,0.000010956371,0.00042029683,0.00002794319,0.000055862492,0.00003747069],"genre_scores_gemma":[0.9779797,0.00012839737,0.020945627,0.0001395474,0.000045870813,0.0006380694,0.000056204623,0.0000038036271,0.00006277806],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993561,0.000020849619,0.00016314437,0.0002550841,0.000121530094,0.00008332966],"domain_scores_gemma":[0.99914443,0.000065343,0.00014143653,0.00044137886,0.00017438894,0.000033023956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024365527,0.000072109935,0.00013633422,0.000104350496,0.00022530518,0.000059031907,0.0002471161,0.00004119085,0.00000516431],"category_scores_gemma":[0.000007713475,0.000052471507,0.00012235394,0.0006758588,0.00005393073,0.000117836455,0.000039630948,0.000051671974,0.000002981738],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005486295,0.00025019323,0.001777284,0.000047412403,0.0006820756,1.0666234e-7,0.00036487047,0.00025057988,0.030892689,0.076319866,0.0007700649,0.8886394],"study_design_scores_gemma":[0.0016882187,0.00018221326,0.18006907,0.000075988275,0.0029878085,0.000031816864,0.00043949025,0.031301547,0.5536984,0.18346389,0.04510172,0.00095980027],"about_ca_topic_score_codex":0.00010739252,"about_ca_topic_score_gemma":0.000023063714,"teacher_disagreement_score":0.97492087,"about_ca_system_score_codex":0.000016166889,"about_ca_system_score_gemma":0.000015841042,"threshold_uncertainty_score":0.2139726},"labels":[],"label_agreement":null},{"id":"W2010748082","doi":"10.1007/s10044-013-0323-0","title":"Beyond hybrid generative discriminative learning: spherical data classification","year":2013,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Discriminative model; Categorization; Generative grammar; Computer science; Probabilistic logic; Artificial intelligence; Generative model; Machine learning; Pattern recognition (psychology); Contrast (vision)","score_opus":0.04171890366918496,"score_gpt":0.3063761530275938,"score_spread":0.26465724935840884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010748082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00088464416,0.00010016832,0.9945978,0.0022493207,0.0000107386595,0.00024236177,0.000018146646,0.00005247438,0.001844368],"genre_scores_gemma":[0.73065585,0.00006561622,0.267897,0.00033307832,0.0000778642,0.00027535265,0.00025689695,0.000006595575,0.00043171723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868536,0.0001430809,0.00021573386,0.0006462266,0.00015000222,0.00015957658],"domain_scores_gemma":[0.9986328,0.00008660464,0.00012627692,0.0009341678,0.00010100405,0.00011918538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021037718,0.00012523813,0.00020459724,0.00009296722,0.00023566374,0.00028210107,0.00070006197,0.000031024923,0.000070183705],"category_scores_gemma":[0.000012783433,0.000102171034,0.000060894814,0.0006414891,0.00006431146,0.00041982968,0.00033484623,0.00013830134,0.00005437113],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.999688e-7,0.00006391964,0.002657709,0.0000037047655,0.0001637916,3.340386e-7,0.00017805662,0.0000144609985,0.0010416289,0.030962216,0.00076512824,0.9641489],"study_design_scores_gemma":[0.00010632558,0.000025406796,0.032957073,0.000002135755,0.00031147982,0.0000029246569,0.00009871204,0.9284629,0.000810872,0.03179048,0.0051737013,0.0002579893],"about_ca_topic_score_codex":0.00014750945,"about_ca_topic_score_gemma":0.000026310892,"teacher_disagreement_score":0.96389085,"about_ca_system_score_codex":0.000014097488,"about_ca_system_score_gemma":0.000015251505,"threshold_uncertainty_score":0.41664138},"labels":[],"label_agreement":null},{"id":"W2012547968","doi":"10.1007/s10044-012-0311-9","title":"Geometric algorithm for dominant point extraction from shape contour","year":2012,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Algorithm; Position (finance); Point (geometry); Set (abstract data type); Regular polygon; Mathematics; Heuristic; Computer science; Convex hull; Artificial intelligence; Iterative closest point; Tree (set theory); Pattern recognition (psychology); Point cloud; Combinatorics; Geometry","score_opus":0.019509206955667845,"score_gpt":0.29756651149045865,"score_spread":0.27805730453479083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012547968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010191693,0.00044575197,0.9977084,0.00025940916,0.000015822872,0.00020300057,0.00004544557,0.00010446472,0.00019852968],"genre_scores_gemma":[0.7727058,0.000034901983,0.22644454,0.00017423838,0.00009967078,0.00042303372,0.000048349983,0.000005675739,0.00006377119],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999248,0.000010482188,0.00018554747,0.0002708974,0.00010956932,0.0001755053],"domain_scores_gemma":[0.999253,0.00011402754,0.00012566152,0.0003271212,0.000086712425,0.00009348179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019289654,0.00009238255,0.00016209192,0.00029287476,0.00011899301,0.00029109587,0.00026864812,0.000033605447,0.0000144456035],"category_scores_gemma":[0.000010946965,0.00008238523,0.00009948726,0.0009771571,0.000021383084,0.0006967016,0.00009013666,0.00004336163,0.000015040189],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.0465188e-7,0.00008645954,0.0027776957,0.0000027986007,0.000073899326,8.059948e-8,0.000032685544,3.2051784e-7,0.0005736597,0.00028855415,0.00012673817,0.9960369],"study_design_scores_gemma":[0.0005163247,0.000054943644,0.09331124,0.000019078547,0.0011830351,0.000009203433,0.00009321655,0.79097754,0.059296615,0.015941016,0.037819818,0.0007779459],"about_ca_topic_score_codex":0.00013788993,"about_ca_topic_score_gemma":0.000009445469,"teacher_disagreement_score":0.995259,"about_ca_system_score_codex":0.000020676143,"about_ca_system_score_gemma":0.000006743851,"threshold_uncertainty_score":0.33595723},"labels":[],"label_agreement":null},{"id":"W2023711680","doi":"10.1007/s10044-005-0012-8","title":"Estimation of generalized entropies with sample spacing","year":2005,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Statistical Mechanics and Entropy","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Sample (material); Mathematics; Statistics; Pattern recognition (psychology); Artificial intelligence; Chromatography; Computer science; Chemistry","score_opus":0.007552408055228806,"score_gpt":0.2515704940165722,"score_spread":0.24401808596134342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023711680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07898986,0.000014768641,0.9204973,0.00013184207,0.0000015281825,0.00007113198,0.000100392586,0.0000059628705,0.0001872239],"genre_scores_gemma":[0.98279864,0.000004553192,0.016898086,0.000024109864,0.00005850765,0.000060563634,0.00012786571,0.0000040886666,0.000023597382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960625,0.000007658649,0.00012716634,0.00011673572,0.00006640753,0.000075774195],"domain_scores_gemma":[0.9997022,0.00004441404,0.00007096452,0.000118264696,0.000025804313,0.000038335696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000026547832,0.00005836774,0.0001364531,0.000053829677,0.000060056573,0.00002152292,0.000035067216,0.000007793622,0.0003611018],"category_scores_gemma":[0.0000011452044,0.00004580841,0.000043165808,0.00020674482,0.000017972881,0.000027569298,0.000011563786,0.000024578765,0.0000048677716],"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":[0.0000024766703,0.00006551843,0.06625498,0.000006289062,0.00037976538,2.5470937e-8,0.00006691545,0.004985777,0.00020786704,0.31746808,0.000017183162,0.6105451],"study_design_scores_gemma":[0.0004415597,0.000023801487,0.016279375,0.000007320214,0.0011338159,1.8779673e-7,0.00014209487,0.96430224,0.0035328576,0.011813822,0.0021504008,0.00017255115],"about_ca_topic_score_codex":0.00058076385,"about_ca_topic_score_gemma":0.000025665591,"teacher_disagreement_score":0.95931643,"about_ca_system_score_codex":0.0000040562354,"about_ca_system_score_gemma":0.0000040588693,"threshold_uncertainty_score":0.39538115},"labels":[],"label_agreement":null},{"id":"W2036578773","doi":"10.1007/s10044-012-0307-5","title":"Bayesian classification and unsupervised learning for isolating weeds in row crops","year":2012,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Agriculture and Agri-Food Canada; Université Laval","funders":"Agriculture and Agri-Food Canada","keywords":"Pattern recognition (psychology); Artificial intelligence; Weed; Cluster analysis; Classifier (UML); Computer science; Unsupervised learning; Bayesian probability; Feature extraction; Mathematics; Machine learning; Agronomy; Biology","score_opus":0.011682615328639396,"score_gpt":0.24093317298651284,"score_spread":0.22925055765787344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036578773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49124992,0.000055372384,0.5072352,0.00039004761,0.000004347655,0.00022280401,0.0000021508135,0.000016808626,0.0008233495],"genre_scores_gemma":[0.99701303,0.00002473454,0.002661454,0.00006586673,0.000043468965,0.00003336937,0.000040025836,0.000005801244,0.00011222562],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99940133,0.000029577757,0.0001465629,0.00019948531,0.00007219152,0.00015085937],"domain_scores_gemma":[0.9996991,0.000050197825,0.00006595397,0.00011062343,0.0000062688036,0.00006786034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017476047,0.00007481974,0.00010956591,0.00005174592,0.00018410305,0.000041075316,0.000047080444,0.00004367978,0.000048341175],"category_scores_gemma":[0.000009622485,0.00006257163,0.000036918376,0.0004735117,0.000043426997,0.0000970524,0.000033812306,0.00007446435,0.000010319883],"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":[5.3987554e-7,0.000024242301,0.8882805,0.0000043095874,0.000017891598,1.6965167e-8,0.0002901211,0.0004780243,0.011736063,0.000024179632,0.00001412246,0.099129975],"study_design_scores_gemma":[0.00007782486,0.0000048280153,0.86202806,0.0000026224923,0.00009856428,7.6818606e-7,0.0002824272,0.1354872,0.0001399771,0.000039871684,0.001756344,0.000081514496],"about_ca_topic_score_codex":0.00035715505,"about_ca_topic_score_gemma":0.0005401872,"teacher_disagreement_score":0.5057631,"about_ca_system_score_codex":0.000030907562,"about_ca_system_score_gemma":8.943616e-7,"threshold_uncertainty_score":0.2551597},"labels":[],"label_agreement":null},{"id":"W2040405999","doi":"10.1007/s10044-012-0303-9","title":"Efficient steganalysis of images: learning is good for anticipation","year":2012,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Steganalysis; Steganography; Artificial intelligence; Pattern recognition (psychology); Computer science; Anticipation (artificial intelligence); Embedding; Mathematics; Computer vision","score_opus":0.014753644223842417,"score_gpt":0.2871244033667694,"score_spread":0.272370759142927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040405999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025353326,0.00015205913,0.97401386,0.00010513203,0.0000054198103,0.00016519919,0.000013439316,0.000058616108,0.00013295993],"genre_scores_gemma":[0.97412646,0.0000317221,0.025526015,0.00005265151,0.000024752022,0.00017625952,0.00001929307,0.0000041726375,0.000038671697],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925643,0.000029223445,0.00022666292,0.00021040553,0.00011318308,0.00016410858],"domain_scores_gemma":[0.99928904,0.00007431051,0.00017990098,0.00031185974,0.00008955514,0.00005536272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026465525,0.0000844002,0.00019515146,0.00031223372,0.00015971139,0.000038025846,0.00019691103,0.000029975834,0.000004594652],"category_scores_gemma":[0.000004938039,0.00007452547,0.00019047718,0.0010616713,0.00003057359,0.000094664036,0.00006171945,0.000046430334,0.0000014452121],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025645945,0.0003216477,0.6756158,0.000076965734,0.00091073074,6.9892735e-8,0.0016581205,0.0023399,0.0042299363,0.01173718,0.00003440973,0.30307266],"study_design_scores_gemma":[0.00046992226,0.00010828936,0.22100407,0.000032324417,0.0034226016,0.0000020674718,0.00026132792,0.6446123,0.1157972,0.003583988,0.0099784825,0.00072741066],"about_ca_topic_score_codex":0.000026775668,"about_ca_topic_score_gemma":0.0000027481037,"teacher_disagreement_score":0.94877315,"about_ca_system_score_codex":0.0000068151194,"about_ca_system_score_gemma":0.000003338876,"threshold_uncertainty_score":0.30390605},"labels":[],"label_agreement":null},{"id":"W2043650904","doi":"10.1007/s10044-014-0367-9","title":"Topology-oriented self-organizing maps: a survey","year":2014,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Self-organizing map; Resource (disambiguation); Artificial intelligence; Tree (set theory); Asset (computer security); Architecture; Data science; Artificial neural network; Geography; Computer security; Mathematics","score_opus":0.010837478049906275,"score_gpt":0.24406133211178974,"score_spread":0.23322385406188345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043650904","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011020382,0.000042053976,0.98678064,0.0013746104,0.000015166977,0.00014659479,0.00001383453,0.00015576504,0.0004509298],"genre_scores_gemma":[0.99298435,0.00004460121,0.0058318023,0.0007067077,0.00010601983,0.00015090288,0.00007746513,0.0000065771856,0.00009155541],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99895865,0.00008430174,0.00021353149,0.0004475878,0.000106127845,0.00018978251],"domain_scores_gemma":[0.9989082,0.00015294687,0.000098149845,0.0006487733,0.000081461156,0.0001104749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028123482,0.00011113231,0.0001914227,0.0001294081,0.0002898671,0.00011780591,0.0003988565,0.0000408629,0.000016415594],"category_scores_gemma":[0.0000056239282,0.000100249395,0.000072539646,0.0016838626,0.000033736993,0.000089708556,0.000160086,0.00008471658,0.000052936255],"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":[0.0000010712102,0.0003389427,0.470563,0.000016737553,0.0006366822,6.1302933e-7,0.0003015233,0.00021792033,0.00077319366,0.16894324,0.0021578746,0.3560492],"study_design_scores_gemma":[0.00033373976,0.000038007653,0.6372274,0.000004857608,0.0004565043,0.0000071516265,0.00003099479,0.21151052,0.000616576,0.005393099,0.14383017,0.0005509831],"about_ca_topic_score_codex":0.00027138417,"about_ca_topic_score_gemma":0.0003214223,"teacher_disagreement_score":0.981964,"about_ca_system_score_codex":0.0000103950015,"about_ca_system_score_gemma":0.000007518366,"threshold_uncertainty_score":0.40880516},"labels":[],"label_agreement":null},{"id":"W2047848293","doi":"10.1007/s10044-006-0036-8","title":"A novel look-ahead optimization strategy for trie-based approximate string matching","year":2006,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Trie; Computer science; String (physics); String searching algorithm; Matching (statistics); Pattern recognition (psychology); Artificial intelligence; Algorithm; Pattern matching; Mathematics; Data structure; Statistics","score_opus":0.020681177201560992,"score_gpt":0.2668556260348765,"score_spread":0.24617444883331552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047848293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096644636,0.000059526476,0.99826884,0.00017956902,0.00000783965,0.000284815,0.000090400616,0.00007368875,0.00006885765],"genre_scores_gemma":[0.7591498,0.00000789971,0.2399257,0.00009361569,0.0000816887,0.00030743284,0.00038308086,0.00000854148,0.00004224807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990889,0.000010986949,0.00023638445,0.000384019,0.00012275735,0.00015698717],"domain_scores_gemma":[0.9993103,0.000059230068,0.00012999488,0.00038522817,0.000065515764,0.000049692717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013669883,0.000108911176,0.00016409661,0.00016556536,0.00026945243,0.00029754112,0.00027064868,0.000036209716,0.000007070279],"category_scores_gemma":[0.000001408954,0.0000979971,0.000087897235,0.0005752218,0.00001632923,0.00020691605,0.00006827917,0.00004572707,0.0000014931687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023682371,0.0001923257,0.0012669571,0.0000306982,0.00007387656,2.4813136e-7,0.000019027755,0.86123115,0.0012645071,0.015762169,0.000042753334,0.1201139],"study_design_scores_gemma":[0.00030500724,0.000009517796,0.0016903357,0.0000060680977,0.00010818823,6.0532784e-7,0.000012207481,0.9957336,0.00043334498,0.0012071751,0.00036912042,0.0001248157],"about_ca_topic_score_codex":0.00045278916,"about_ca_topic_score_gemma":0.0000641115,"teacher_disagreement_score":0.75834316,"about_ca_system_score_codex":0.000014089534,"about_ca_system_score_gemma":0.000018440289,"threshold_uncertainty_score":0.3996206},"labels":[],"label_agreement":null},{"id":"W2048541556","doi":"10.1007/s10044-014-0382-x","title":"Discriminative learning of generative models: large margin multinomial mixture models for document classification","year":2014,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Discriminative model; Generative model; Artificial intelligence; Computer science; Margin (machine learning); Multinomial distribution; Generative grammar; Pattern recognition (psychology); Machine learning; Mixture model; Support vector machine; Mathematics; Statistics","score_opus":0.029579852196899705,"score_gpt":0.2820173542255078,"score_spread":0.2524375020286081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048541556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023005512,0.00007001024,0.99509895,0.0015547411,0.000008433798,0.0003865246,0.000019525352,0.00010044101,0.00046082598],"genre_scores_gemma":[0.97688335,0.000074216565,0.021779831,0.000077047574,0.000028889364,0.0008431073,0.00010593,0.0000066151583,0.0002010046],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883395,0.00006525381,0.00031998134,0.00045826857,0.00015394375,0.0001686096],"domain_scores_gemma":[0.9989088,0.00012416637,0.00030180105,0.00044934414,0.00016804539,0.000047814785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000294901,0.00013476326,0.00024909465,0.0002530944,0.00024269415,0.000107702916,0.00039662633,0.000069986774,0.0000053827303],"category_scores_gemma":[0.00001641545,0.000116977986,0.00013169911,0.00053321075,0.000059528298,0.00035826617,0.00012366932,0.00009125947,0.0000022746024],"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":[0.0000026548607,0.00010108172,0.0014654397,0.00002071497,0.00016551891,1.8385824e-8,0.0008248245,0.0051655895,0.0012918435,0.7823889,0.00007590415,0.20849751],"study_design_scores_gemma":[0.0002559019,0.000022894887,0.0022491855,0.0000036735555,0.00014812915,1.13936316e-7,0.00032056266,0.9414019,0.0021810767,0.05207989,0.0012059778,0.00013070689],"about_ca_topic_score_codex":0.000025235408,"about_ca_topic_score_gemma":0.000026378688,"teacher_disagreement_score":0.9745828,"about_ca_system_score_codex":0.00002742094,"about_ca_system_score_gemma":0.000011876653,"threshold_uncertainty_score":0.4770224},"labels":[],"label_agreement":null},{"id":"W2051699617","doi":"10.1007/s10044-007-0095-5","title":"Estimation of distributions involving unobservable events: the case of optimal search with unknown Target Distributions","year":2007,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Diffusion and Search Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Unobservable; Object (grammar); Computer science; Bin; Function (biology); Set (abstract data type); Probability distribution; Task (project management); Joint probability distribution; Distribution (mathematics); Data mining; Probability density function; Algorithm; Mathematics; Theoretical computer science; Artificial intelligence; Statistics; Econometrics","score_opus":0.00948003686215223,"score_gpt":0.2830994655818897,"score_spread":0.2736194287197375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051699617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46522623,0.00006331571,0.53424215,0.000067094756,0.000001564837,0.00010642832,0.0002578953,0.0000022657275,0.000033050434],"genre_scores_gemma":[0.9963726,0.00007899579,0.0018952152,0.000016542226,0.000015069746,0.000036883408,0.0015114264,0.000005109395,0.00006815013],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993123,0.000034323464,0.00023243911,0.00017848378,0.00009864427,0.00014383184],"domain_scores_gemma":[0.99929976,0.000038698996,0.000097698336,0.00032396184,0.0001720966,0.00006777021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034350174,0.000078613404,0.00012730944,0.00006499411,0.00020788018,0.0000134706,0.00011538957,0.00004384976,0.000017163386],"category_scores_gemma":[0.000013381023,0.00005618637,0.0000834227,0.00059947395,0.00014734999,0.00000552263,0.00007773716,0.00006408142,6.5314794e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018974848,0.003008522,0.43438554,0.0003435144,0.0046911826,0.000028871113,0.00073251204,0.07650894,0.21837798,0.058996096,0.00021710759,0.20252],"study_design_scores_gemma":[0.0019787755,0.00061638694,0.27824557,0.00006943267,0.0030672231,0.00020165504,0.004391712,0.53005594,0.17275614,0.0010288458,0.0066553983,0.0009329265],"about_ca_topic_score_codex":0.0003467376,"about_ca_topic_score_gemma":0.00089068594,"teacher_disagreement_score":0.53234696,"about_ca_system_score_codex":0.000010642869,"about_ca_system_score_gemma":0.00003254836,"threshold_uncertainty_score":0.22912137},"labels":[],"label_agreement":null},{"id":"W2051778481","doi":"10.1007/s10044-014-0374-x","title":"Combining local and global learners in the pairwise multiclass classification","year":2014,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Pairwise comparison; Pattern recognition (psychology); Artificial intelligence; Benchmark (surveying); Multiclass classification; Margin (machine learning); Computer science; k-nearest neighbors algorithm; Class (philosophy); Binary classification; Machine learning; Binary number; Mathematics; Support vector machine","score_opus":0.019829353799847137,"score_gpt":0.2793350011628899,"score_spread":0.25950564736304277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051778481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007274337,0.00003113118,0.9889192,0.002992606,0.000004390379,0.00017531954,0.0000073713236,0.000073693285,0.0005219711],"genre_scores_gemma":[0.99440265,0.000045406763,0.0046104486,0.0006519158,0.000011645193,0.00022906423,0.00003807384,0.0000026962298,0.000008103394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900615,0.00013146554,0.00021495066,0.0003678149,0.00014894159,0.00013065748],"domain_scores_gemma":[0.9990707,0.00012563306,0.00010154647,0.0006212188,0.00003363961,0.000047268546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050913164,0.00009014181,0.00013394967,0.00010838951,0.00013888309,0.00018194134,0.00045442875,0.000041388786,0.0000017618559],"category_scores_gemma":[0.000015803975,0.00007023349,0.000034967987,0.0010354816,0.00011248465,0.00015566449,0.00008912387,0.00009058878,0.000008378316],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.36007e-7,0.000069795904,0.15959902,0.0000075172698,0.000031722022,1.9582157e-7,0.000287925,0.00003979593,0.00020051563,0.13024205,0.00007874761,0.7094421],"study_design_scores_gemma":[0.00013862876,0.0000116209985,0.460923,0.0000035617027,0.0000554484,0.0000022477175,0.00029911037,0.5313051,0.000075101125,0.0029616342,0.004114705,0.000109849854],"about_ca_topic_score_codex":0.0001599816,"about_ca_topic_score_gemma":0.00022405613,"teacher_disagreement_score":0.9871283,"about_ca_system_score_codex":0.000027294302,"about_ca_system_score_gemma":0.000007981476,"threshold_uncertainty_score":0.28640383},"labels":[],"label_agreement":null},{"id":"W2054767096","doi":"10.1007/s10044-006-0032-z","title":"Breadth-first search strategies for trie-based syntactic pattern recognition","year":2006,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Trie; Computer science; Computation; Prefix; String (physics); Heuristic; Representation (politics); Algorithm; Element (criminal law); Dynamic programming; Data structure; Theoretical computer science; Pattern recognition (psychology); Mathematics; Artificial intelligence","score_opus":0.02461615076739881,"score_gpt":0.2738431311713298,"score_spread":0.249226980403931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054767096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062466403,0.00006668633,0.99214107,0.00084368204,0.000013536778,0.00030670784,0.00017684704,0.00007223095,0.00013257403],"genre_scores_gemma":[0.98993194,0.000023478222,0.0086551495,0.00018030661,0.000116904055,0.00044802998,0.0006136992,0.00000768337,0.000022814676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989454,0.000028382108,0.00022754533,0.00043680237,0.00016950394,0.0001923747],"domain_scores_gemma":[0.9991153,0.00015650672,0.00009233112,0.00045976913,0.000111737354,0.00006437319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001608286,0.00012051838,0.00017770534,0.00020566478,0.0003442424,0.00043497066,0.00030515486,0.00004459034,0.000027026474],"category_scores_gemma":[0.0000018491191,0.0001067724,0.00011615652,0.00061890885,0.00003112468,0.00029648136,0.00006608079,0.0000641689,0.00002164333],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003770628,0.0002821013,0.013750054,0.0000687064,0.00013923999,0.0000013693862,0.00005755384,0.001014238,0.00028074346,0.0017448142,0.00045401597,0.98220336],"study_design_scores_gemma":[0.0006569474,0.000058098187,0.04629894,0.000026330501,0.00043992375,0.000003012147,0.000103190854,0.93721414,0.0013888218,0.007179364,0.006237799,0.00039344135],"about_ca_topic_score_codex":0.0016433345,"about_ca_topic_score_gemma":0.000711787,"teacher_disagreement_score":0.9836853,"about_ca_system_score_codex":0.00001857851,"about_ca_system_score_gemma":0.00003236026,"threshold_uncertainty_score":0.43540522},"labels":[],"label_agreement":null},{"id":"W2058372030","doi":"10.1007/s10044-002-0182-6","title":"MDL estimation of a dense map of relative depth and 3D motion from a temporal sequence of images","year":2003,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Optical flow; Mathematics; Minimum description length; Motion estimation; Motion (physics); Boundary (topology); Sequence (biology); Algorithm; Stereoscopy; Depth map; Artificial intelligence; Computer vision; Computer science; Image (mathematics); Mathematical analysis","score_opus":0.017931220511754564,"score_gpt":0.2959869581346372,"score_spread":0.27805573762288266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058372030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039027173,0.00022452044,0.9604909,0.000081616934,0.0000028582958,0.00008224211,0.000024239192,0.0000073426054,0.000059118396],"genre_scores_gemma":[0.8516253,0.000032775664,0.14828981,0.000015347703,0.0000018092722,0.000010993623,0.000012116336,0.0000017757775,0.000010047947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993766,0.000039072635,0.0002463904,0.00018890768,0.0000978354,0.000051175484],"domain_scores_gemma":[0.9993281,0.0000693282,0.0002567367,0.00023124342,0.00008454861,0.000030082285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010382183,0.000057967394,0.00017581011,0.00012986612,0.000038321396,0.000012834098,0.0000847757,0.00001669061,0.0000072426833],"category_scores_gemma":[0.00001622796,0.00005250085,0.000042780935,0.0003971545,0.00007328153,0.0002114669,0.000033824694,0.000033033684,8.748002e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022464856,0.0001248592,0.301003,0.00005179259,0.00019457212,4.1343122e-7,0.000871148,0.0014978076,0.022015981,0.006040073,0.000007145046,0.66819096],"study_design_scores_gemma":[0.0003399511,0.00003269318,0.18758918,0.00005415364,0.00032558187,0.0000018043954,0.00016423472,0.75466824,0.040410463,0.016181208,0.00007267896,0.00015980961],"about_ca_topic_score_codex":0.00025859475,"about_ca_topic_score_gemma":0.000030263815,"teacher_disagreement_score":0.81259817,"about_ca_system_score_codex":0.0000055101277,"about_ca_system_score_gemma":0.000008732625,"threshold_uncertainty_score":0.21409225},"labels":[],"label_agreement":null},{"id":"W2059414344","doi":"10.1007/s10044-014-0432-4","title":"An investigation of implicit features in compression-based learning for comparing webpages","year":2014,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Feature (linguistics); Feature selection; Web page; Compression (physics); Task (project management); Phishing; Pattern recognition (psychology); Artificial intelligence; Cover (algebra); Feature vector; Image compression; Space (punctuation); Data compression; Selection (genetic algorithm); Information retrieval; Data mining; Image (mathematics); The Internet; Image processing; World Wide Web","score_opus":0.01604752687691857,"score_gpt":0.27934949015732446,"score_spread":0.2633019632804059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059414344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2436714,0.000019756679,0.75588316,0.00018788234,0.000005349561,0.00017552893,0.0000015441626,0.000036002704,0.000019387408],"genre_scores_gemma":[0.9742092,0.000001935479,0.0254674,0.00009190795,0.000028155499,0.00014779493,0.00004633752,0.000004180041,0.000003079586],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902606,0.000101867336,0.0002596145,0.00037382072,0.00011227965,0.00012635913],"domain_scores_gemma":[0.99904096,0.00024837043,0.00015064515,0.00041385784,0.00007741524,0.000068778696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037621387,0.00009354721,0.00026687374,0.00026231556,0.00012311773,0.00007915077,0.00035677457,0.000037215083,0.0000026323453],"category_scores_gemma":[0.000011784659,0.000087118824,0.0000734987,0.0006510003,0.00004658522,0.000093771814,0.000070412796,0.00008486659,7.4699744e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001117913,0.000060753104,0.81003594,0.000031390624,0.000028842567,4.9145072e-8,0.00019624058,0.0635607,0.0027468547,0.0019676287,0.0000043397486,0.121366166],"study_design_scores_gemma":[0.000114660106,0.000019367984,0.40714282,0.000004921411,0.000029834662,1.809793e-7,0.000011272095,0.59005594,0.0011047114,0.0014104453,0.000046207504,0.000059631137],"about_ca_topic_score_codex":0.0003206887,"about_ca_topic_score_gemma":0.00038281616,"teacher_disagreement_score":0.73053783,"about_ca_system_score_codex":0.000013679478,"about_ca_system_score_gemma":0.000011611595,"threshold_uncertainty_score":0.35526025},"labels":[],"label_agreement":null},{"id":"W2060556149","doi":"10.1007/s10044-008-0111-4","title":"On Bayesian analysis of a finite generalized Dirichlet mixture via a Metropolis-within-Gibbs sampling","year":2008,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Concordia University","funders":"","keywords":"Dirichlet distribution; Gibbs sampling; Conjugate prior; Mathematics; Hierarchical Dirichlet process; Metropolis–Hastings algorithm; Generalized Dirichlet distribution; Latent Dirichlet allocation; Kernel (algebra); Applied mathematics; Bayesian probability; Pattern recognition (psychology); Computer science; Artificial intelligence; Algorithm; Statistics; Posterior probability; Markov chain Monte Carlo; Topic model; Dirichlet's energy; Combinatorics; Mathematical analysis","score_opus":0.02449743636263198,"score_gpt":0.29227753959725356,"score_spread":0.2677801032346216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060556149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010341658,0.00019610253,0.9885517,0.00034040282,0.0000151008235,0.00017035492,0.00006227932,0.000052545107,0.0002698934],"genre_scores_gemma":[0.765613,0.000104937666,0.23325339,0.00071664905,0.00003925332,0.00008973679,0.00007052508,0.000009472137,0.00010302998],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809474,0.00015225512,0.0005284224,0.00066930585,0.0003119336,0.00024337006],"domain_scores_gemma":[0.9980926,0.00023539516,0.00033097196,0.0010408186,0.00012554092,0.00017471057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034791182,0.00022651178,0.0007208004,0.0012376133,0.0002833618,0.000067715104,0.00053850847,0.000093740135,0.000050520877],"category_scores_gemma":[0.000019690584,0.00018786751,0.00054255564,0.0058547896,0.00008317703,0.00010080497,0.00011511987,0.0001526562,0.000004342291],"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":[0.000036611178,0.0015408226,0.09275573,0.000109801906,0.034787375,0.000026751924,0.0065066684,0.036771234,0.008125968,0.3863713,0.00047086625,0.43249688],"study_design_scores_gemma":[0.00030182305,0.000040812854,0.020291528,0.0000071278087,0.0052171824,0.0000042821503,0.000012796656,0.9595532,0.0013455288,0.012156906,0.0006611017,0.0004077376],"about_ca_topic_score_codex":0.00043881751,"about_ca_topic_score_gemma":0.00020581672,"teacher_disagreement_score":0.92278194,"about_ca_system_score_codex":0.000022412496,"about_ca_system_score_gemma":0.00002451405,"threshold_uncertainty_score":0.7661015},"labels":[],"label_agreement":null},{"id":"W2077649826","doi":"10.1007/s10044-005-0252-7","title":"A multi-population genetic algorithm for robust and fast ellipse detection","year":2005,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Ellipse; Artificial intelligence; Hough transform; Genetic algorithm; Pattern recognition (psychology); Computer science; Cluster analysis; Algorithm; Noise (video); Population; Image (mathematics); Computer vision; Mathematics; Machine learning","score_opus":0.011331711183350794,"score_gpt":0.25184918953435753,"score_spread":0.24051747835100673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077649826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023499448,0.00011619914,0.9969772,0.0001288838,0.000006853999,0.00028922438,0.000006906629,0.00011680844,0.000007986734],"genre_scores_gemma":[0.61235434,0.000082966595,0.38690218,0.00011861283,0.000079557765,0.00037489415,0.000009223162,0.000005071328,0.00007319336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993317,0.00001651381,0.00016960708,0.0003096398,0.00006599057,0.00010654694],"domain_scores_gemma":[0.9995631,0.000023016228,0.00007536021,0.00023062254,0.00005729317,0.000050582763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000086540975,0.00008492874,0.000116341645,0.00021955455,0.00022763485,0.00013898741,0.00010189245,0.000040053135,0.0000029637379],"category_scores_gemma":[0.0000022578174,0.000082666375,0.00006581249,0.00045726218,0.000016154056,0.00016090045,0.00003929677,0.000042886666,0.0000030930287],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.0976512e-7,0.000022218595,0.00088536757,0.0000034686568,0.000033363314,5.0029037e-8,0.00004013155,0.00011941142,0.00052535115,0.0000059881204,0.000003749651,0.9983607],"study_design_scores_gemma":[0.00013978963,0.000019337072,0.04580134,0.0000013512048,0.00013566866,0.0000039576325,0.000014272619,0.93777746,0.014675422,0.0001335212,0.0011793947,0.000118463955],"about_ca_topic_score_codex":0.00021091707,"about_ca_topic_score_gemma":0.000483841,"teacher_disagreement_score":0.9982422,"about_ca_system_score_codex":0.00001955084,"about_ca_system_score_gemma":0.0000028570526,"threshold_uncertainty_score":0.3371037},"labels":[],"label_agreement":null},{"id":"W2092353981","doi":"10.1007/s10044-003-0191-0","title":"A brief taxonomy and ranking of creative prototype reduction schemes","year":2003,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; sort; Ranking (information retrieval); Reduction (mathematics); Taxonomy (biology); Set (abstract data type); Data reduction; Artificial intelligence; Class (philosophy); Machine learning; Data mining; Pattern recognition (psychology); Information retrieval; Mathematics","score_opus":0.020255414103223807,"score_gpt":0.2641137965063549,"score_spread":0.2438583824031311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092353981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012462382,0.00013089963,0.985604,0.00019269112,0.000003087445,0.00026958462,0.000003665085,0.000021590226,0.0013121049],"genre_scores_gemma":[0.9871511,0.000058027636,0.012326886,0.000019966714,0.00001097035,0.00034735922,0.000019145242,0.0000021325511,0.00006442167],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99942297,0.00005161445,0.00014288789,0.00025044222,0.00007012237,0.00006195241],"domain_scores_gemma":[0.9994854,0.000023231703,0.00012370963,0.00028036116,0.000052810898,0.00003451736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017822208,0.000057839825,0.00011729762,0.00012788776,0.00010927941,0.00005881875,0.000093349634,0.000020454245,0.000013899711],"category_scores_gemma":[0.0000138639725,0.000052417712,0.00003096321,0.0005751369,0.000040923424,0.00011569213,0.000027403717,0.00004961284,0.0000017888586],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000241212,0.000095957,0.15121584,0.0000379984,0.00025212293,1.02171306e-7,0.0003149908,0.000036360823,0.0022786523,0.11053375,0.000024293819,0.7352075],"study_design_scores_gemma":[0.0011598384,0.00017128423,0.56719446,0.00005254282,0.0012323504,0.000028063398,0.0006240052,0.1213181,0.019485507,0.006902289,0.28102988,0.0008016918],"about_ca_topic_score_codex":0.0001362164,"about_ca_topic_score_gemma":0.000010945281,"teacher_disagreement_score":0.9746887,"about_ca_system_score_codex":0.0000050087124,"about_ca_system_score_gemma":0.000010899738,"threshold_uncertainty_score":0.21375322},"labels":[],"label_agreement":null},{"id":"W2092619501","doi":"10.1007/s10044-007-0094-6","title":"Ensemble of HMM classifiers based on the clustering validity index for a handwritten numeral recognizer","year":2007,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hidden Markov model; Codebook; Pattern recognition (psychology); Computer science; Vector quantization; Artificial intelligence; Cluster analysis; Linde–Buzo–Gray algorithm; Speech recognition","score_opus":0.03782214546971913,"score_gpt":0.2895338379360944,"score_spread":0.25171169246637526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092619501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059954072,0.0000070470987,0.9914974,0.0012231272,0.000011096669,0.00046926865,0.000023803657,0.00006762745,0.0007052043],"genre_scores_gemma":[0.9860153,0.000008165718,0.012495621,0.0009725033,0.000040124723,0.000383738,0.000020457432,0.0000072403095,0.00005683419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896896,0.000045525547,0.0003021486,0.000322058,0.00017608421,0.00018520853],"domain_scores_gemma":[0.99868083,0.00045603153,0.00016572849,0.0004849117,0.00014336528,0.0000691479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082085875,0.00011182463,0.0002038149,0.00026860714,0.00019743969,0.00008809115,0.0003548658,0.000058385896,0.00002600683],"category_scores_gemma":[0.000022325587,0.000084333675,0.00018500509,0.0007991619,0.00005323017,0.00007389409,0.00006248289,0.00009022156,0.000004259269],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017215585,0.00022731141,0.029026786,0.0000464071,0.00026296175,6.556921e-7,0.00018305088,0.000085668005,0.0025451074,0.0017919798,0.0005735855,0.9652393],"study_design_scores_gemma":[0.000744908,0.00017272538,0.050167315,0.000050619743,0.0005061055,0.0000026730397,0.00015113484,0.8583084,0.07400125,0.008575822,0.006818311,0.0005007749],"about_ca_topic_score_codex":0.0000855956,"about_ca_topic_score_gemma":0.0002220875,"teacher_disagreement_score":0.9800199,"about_ca_system_score_codex":0.000019886362,"about_ca_system_score_gemma":0.000016207332,"threshold_uncertainty_score":0.34390274},"labels":[],"label_agreement":null},{"id":"W2093156537","doi":"10.1007/s10044-007-0088-4","title":"Hierarchical clustering of subpopulations with a dissimilarity based on the likelihood ratio statistic: application to clustering massive data sets","year":2007,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Cluster analysis; Mathematics; Statistic; Hierarchical clustering; Multinomial distribution; Poisson distribution; Multivariate normal distribution; Statistics; Data mining; Computer science; Multivariate statistics","score_opus":0.03091776160389113,"score_gpt":0.3354578783523037,"score_spread":0.3045401167484125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093156537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015964806,0.000006336986,0.9946455,0.0027464363,0.0000063554335,0.0006715439,0.00019591785,0.00004629654,0.000085128275],"genre_scores_gemma":[0.8774816,0.000002919872,0.12178784,0.00027622897,0.000028731383,0.0002365725,0.0001654137,0.000011341687,0.000009360572],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981647,0.000069931826,0.00036023904,0.0006531398,0.00045602146,0.00029597184],"domain_scores_gemma":[0.997193,0.00054330507,0.00015921425,0.0017765979,0.0001427892,0.00018512788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006673663,0.00015545153,0.00022399469,0.00031526593,0.00034913587,0.00013764443,0.0010329782,0.00003784699,0.000008308174],"category_scores_gemma":[0.00004234431,0.00011448043,0.00004171809,0.0017423165,0.000081221144,0.00015561971,0.0005849554,0.00018884521,0.0000049594005],"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":[0.000040042985,0.00028452463,0.018225608,0.00006918236,0.00027430104,0.000004575236,0.00046723435,0.1810209,0.0011944149,0.0030801853,0.000029932897,0.7953091],"study_design_scores_gemma":[0.00013186384,0.000042989446,0.06852243,0.000014588523,0.000074913885,0.000001242111,0.000053974327,0.93037015,0.00020211091,0.00029372633,0.00016255288,0.00012947967],"about_ca_topic_score_codex":0.00026763152,"about_ca_topic_score_gemma":0.0026313695,"teacher_disagreement_score":0.8758851,"about_ca_system_score_codex":0.0000480169,"about_ca_system_score_gemma":0.000039002098,"threshold_uncertainty_score":0.4668376},"labels":[],"label_agreement":null},{"id":"W2144837664","doi":"10.1007/s10044-011-0256-4","title":"Expectation-maximization algorithms for inference in Dirichlet processes mixture","year":2011,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Markov chain Monte Carlo; Computer science; Mixture model; Expectation–maximization algorithm; Algorithm; Inference; Dirichlet distribution; Bayes' theorem; Model selection; Artificial intelligence; Pattern recognition (psychology); Machine learning; Mathematics; Maximum likelihood; Bayesian probability","score_opus":0.03564797153838353,"score_gpt":0.30369508603931766,"score_spread":0.2680471145009341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144837664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006604475,0.00014517413,0.9984118,0.00017757856,0.000008423762,0.00028138148,0.000011444715,0.000035226338,0.00026855923],"genre_scores_gemma":[0.66423917,0.00008867033,0.33478352,0.00015116375,0.000025293895,0.00063922576,0.000028266786,0.0000044381964,0.000040264273],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992263,0.000028427461,0.00019635189,0.0003472795,0.0000763661,0.00012530533],"domain_scores_gemma":[0.99932545,0.00012941536,0.0000921759,0.00027309207,0.00012857112,0.000051300616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013632097,0.00009308243,0.0001657396,0.00022606632,0.000087187254,0.000065009386,0.00025732702,0.000044387987,0.000010221974],"category_scores_gemma":[0.000030555944,0.0000819688,0.00004773494,0.001349798,0.000019101763,0.00018976105,0.00004063888,0.000045532048,0.0000017208824],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022675965,0.0002081592,0.057348367,0.00007053511,0.00013415601,6.520544e-7,0.0039211484,0.00008269483,0.00012158289,0.030439273,0.000029749732,0.9076414],"study_design_scores_gemma":[0.0008895488,0.00009637821,0.17954133,0.00004346821,0.0006461685,0.0000028790457,0.00021902786,0.6122115,0.0055080713,0.19756405,0.0022856835,0.0009918314],"about_ca_topic_score_codex":0.00008423576,"about_ca_topic_score_gemma":0.00024449185,"teacher_disagreement_score":0.9066496,"about_ca_system_score_codex":0.000008363189,"about_ca_system_score_gemma":0.000024528983,"threshold_uncertainty_score":0.33425906},"labels":[],"label_agreement":null},{"id":"W2150409896","doi":"10.1007/s10044-006-0044-8","title":"On poem recognition","year":2006,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Poetry; Lyrics; Computer science; Fuzzy logic; Field (mathematics); Artificial intelligence; Literature; Art; Mathematics","score_opus":0.014736266615565718,"score_gpt":0.24381536547690044,"score_spread":0.22907909886133473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150409896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012679439,0.00003185915,0.9810527,0.0018025056,0.000004480947,0.00007864759,0.0000046990594,0.00020884004,0.0041368064],"genre_scores_gemma":[0.9979672,0.000029439869,0.0013176828,0.0002407636,0.000020532008,0.00015292031,0.00004526664,0.0000020817752,0.00022413852],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994586,0.0000099136205,0.00012241154,0.0002414654,0.00008751408,0.00008010717],"domain_scores_gemma":[0.9995054,0.00003793932,0.00006259294,0.00034626835,0.000028096449,0.00001969964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000052351283,0.0000588982,0.000078160214,0.00021470492,0.00011331465,0.0001202775,0.00020472224,0.000028144994,0.000029027568],"category_scores_gemma":[0.0000023756209,0.000051866056,0.00005139247,0.00076717214,0.000023048591,0.000089394554,0.00004015203,0.00003997536,0.0001244696],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.1728865e-7,0.0000988627,0.013141346,0.000002549382,0.000051535462,2.5055726e-7,0.000010622096,0.000020000467,0.00041383086,0.23885067,0.0010778258,0.74633217],"study_design_scores_gemma":[0.00035158795,0.000038963073,0.38077918,0.00000707989,0.00030330766,0.000002088785,0.000048678146,0.029226497,0.009945336,0.55545557,0.023344168,0.0004975338],"about_ca_topic_score_codex":0.00006194532,"about_ca_topic_score_gemma":0.000029077508,"teacher_disagreement_score":0.9852877,"about_ca_system_score_codex":0.00000969931,"about_ca_system_score_gemma":0.0000029478629,"threshold_uncertainty_score":0.21150364},"labels":[],"label_agreement":null},{"id":"W2151096815","doi":"10.1007/pl00010988","title":"Endocardial Boundary E timation and Tracking in Echocardiographic Images using Deformable Template and Markov Random Fields","year":2001,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Heart Institute; Computer Research Institute of Montréal","funders":"Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Artificial intelligence; Markov random field; Segmentation; Image segmentation; Pattern recognition (psychology); Active contour model; Hidden Markov model; Prior probability; Computer science; Computer vision; Tracking (education); Point distribution model; Level set (data structures); Boundary (topology); Mathematics; Bayesian probability","score_opus":0.013309599970584101,"score_gpt":0.28822004179571764,"score_spread":0.2749104418251335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151096815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08100115,0.00036333705,0.9180935,0.00015453038,0.0000061451888,0.00019407719,0.0000040184796,0.000042815955,0.00014041898],"genre_scores_gemma":[0.9730347,0.0011928962,0.025442401,0.00020406673,0.000022896344,0.00007713143,0.000015317102,0.0000037888537,0.000006794769],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991832,0.000056142773,0.00023228172,0.00027080273,0.0001268941,0.00013067412],"domain_scores_gemma":[0.99956083,0.00007225628,0.00007637218,0.00019045277,0.000030762676,0.00006933863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033666057,0.00008864965,0.00019987891,0.00037422526,0.00016753616,0.00033136804,0.00010143076,0.00003975085,0.000008950024],"category_scores_gemma":[0.0000074910254,0.000083186096,0.000055329616,0.00078261964,0.00006782107,0.00050519226,0.00007636919,0.000086977314,7.393505e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034839827,0.000022851948,0.1830606,0.000022009028,0.00012387603,0.0000047318986,0.00021221326,0.00006948624,0.0011407206,0.00003653605,0.000021665624,0.8152818],"study_design_scores_gemma":[0.0020905784,0.000040613573,0.42314714,0.000059911803,0.0006845383,0.00010415706,0.00019167636,0.5629509,0.0052943616,0.0042521483,0.00062034494,0.0005636056],"about_ca_topic_score_codex":0.00031884073,"about_ca_topic_score_gemma":0.00006398409,"teacher_disagreement_score":0.8926511,"about_ca_system_score_codex":0.000011238607,"about_ca_system_score_gemma":0.0000086656255,"threshold_uncertainty_score":0.33922303},"labels":[],"label_agreement":null},{"id":"W2159760851","doi":"10.1007/s10044-005-0020-8","title":"Concealed weapon detection and visualization in a synthesized image","year":2005,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Army Research Office","keywords":"Artificial intelligence; Computer vision; Computer science; Image fusion; Visualization; Pixel; Image (mathematics); Cluster analysis; Process (computing); Feature detection (computer vision); Pattern recognition (psychology); Image processing","score_opus":0.004138618208844329,"score_gpt":0.25241919397226115,"score_spread":0.2482805757634168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159760851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09582854,0.00015635403,0.9034508,0.000037742113,0.0000018001401,0.00015942857,0.000005896847,0.0001526483,0.00020678788],"genre_scores_gemma":[0.99647176,0.0005250661,0.0026735202,0.000033209813,0.000024271198,0.0002325403,0.000012549741,0.000010449894,0.000016609963],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995687,0.000013183787,0.00015853692,0.00013839584,0.000045166482,0.0000760099],"domain_scores_gemma":[0.99978054,0.000030937546,0.000027485694,0.000112303445,0.00001802819,0.000030726173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006179493,0.00007234532,0.00012863273,0.00020991372,0.000040864503,0.00002767068,0.000031140185,0.000030718606,0.000028303815],"category_scores_gemma":[0.000005806297,0.0000752509,0.000024197734,0.00044578357,0.00002179807,0.00010441289,0.000012912747,0.000045445664,0.000004332817],"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":[0.0000015980933,0.000024260897,0.004357172,0.000022354528,0.00005052278,2.6106696e-7,0.00010199453,0.00038560847,0.20746279,0.000054219683,0.000008619318,0.7875306],"study_design_scores_gemma":[0.00035412217,0.000010949192,0.04913335,0.000016224096,0.0002583241,0.0000028581337,0.00014151946,0.7877989,0.1573455,0.00032069915,0.0043216236,0.00029594533],"about_ca_topic_score_codex":0.000028923243,"about_ca_topic_score_gemma":0.00037053783,"teacher_disagreement_score":0.9007773,"about_ca_system_score_codex":0.000023498176,"about_ca_system_score_gemma":0.0000010817129,"threshold_uncertainty_score":0.3068643},"labels":[],"label_agreement":null},{"id":"W2171498274","doi":"10.1007/s10044-002-0169-3","title":"Large vocabulary off-line handwriting recognition: A survey","year":2003,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":165,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Vocabulary; Lexicon; Handwriting; Intelligent character recognition; Handwriting recognition; Key (lock); Natural language processing; Word recognition; Artificial intelligence; Software deployment; Speech recognition; Process (computing); Word (group theory); Line (geometry); Character recognition; Feature extraction; Linguistics; Reading (process); Software engineering","score_opus":0.028867063532360654,"score_gpt":0.2801902910180454,"score_spread":0.2513232274856847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171498274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008993214,0.00029643538,0.9886625,0.00019254551,0.000009720437,0.00022095905,0.0001007471,0.00019622789,0.0013276865],"genre_scores_gemma":[0.9871905,0.00029756213,0.011000307,0.00072061585,0.000040435752,0.00033804306,0.0002766949,0.000009940047,0.0001259019],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985718,0.0001979766,0.00032909616,0.000488173,0.00017380154,0.00023916125],"domain_scores_gemma":[0.99890274,0.00015044645,0.00012746174,0.00049103925,0.00020532757,0.00012295914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008861933,0.00014159038,0.00024722278,0.00029969084,0.00028362699,0.00021849383,0.0002708131,0.000062854786,0.00016877943],"category_scores_gemma":[0.000044432403,0.00014151297,0.00012360263,0.0016419969,0.00003197829,0.00020348858,0.00008314208,0.00011923997,0.00009977875],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010107457,0.00029955586,0.079983465,0.000020690619,0.0003678484,0.0000040958257,0.00014409971,0.0000021556516,0.00024213559,0.0031979093,0.0003027577,0.9154343],"study_design_scores_gemma":[0.004461076,0.00040037127,0.50224423,0.00022899736,0.003329195,0.00022955927,0.0008154893,0.12535413,0.098517716,0.07393754,0.18511939,0.0053622825],"about_ca_topic_score_codex":0.00008495145,"about_ca_topic_score_gemma":0.0004634061,"teacher_disagreement_score":0.9781973,"about_ca_system_score_codex":0.0000147540895,"about_ca_system_score_gemma":0.000021029453,"threshold_uncertainty_score":0.57707316},"labels":[],"label_agreement":null},{"id":"W2175858202","doi":"10.1007/s10044-015-0494-y","title":"A biologically inspired framework for contour detection","year":2015,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision","score_opus":0.04008665302426928,"score_gpt":0.3272895018711384,"score_spread":0.28720284884686914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2175858202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074046094,0.000038506292,0.9980201,0.00069870346,0.000011119607,0.00027458128,0.0000065386535,0.00014555003,0.000064423635],"genre_scores_gemma":[0.7428777,0.000015990234,0.25516102,0.0011090565,0.000053820724,0.0007418012,0.000015590693,0.0000026199464,0.00002238406],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99935424,0.000029034914,0.00016650639,0.00025292241,0.00010196243,0.00009535087],"domain_scores_gemma":[0.9993157,0.00009852043,0.000085115396,0.00026465527,0.00011173065,0.0001242698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022597464,0.000062615676,0.00012617563,0.000109554734,0.000077990575,0.00009529354,0.00022831725,0.000050595616,0.0000080476],"category_scores_gemma":[0.00006440814,0.000051164305,0.00006463046,0.0005632084,0.000033557295,0.00008736714,0.000056223063,0.0000489352,0.000008508363],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013048457,0.00004696953,0.003574867,0.000004291493,0.00009103711,1.7605598e-7,0.00009699698,0.0000038850944,0.0013120022,0.005481867,0.00009480273,0.9892918],"study_design_scores_gemma":[0.0019545536,0.00065866794,0.0803435,0.000035767724,0.0013672442,0.0000102327695,0.000556463,0.31828275,0.14705245,0.4203304,0.028015342,0.0013926021],"about_ca_topic_score_codex":0.000057749516,"about_ca_topic_score_gemma":0.000033179644,"teacher_disagreement_score":0.9878992,"about_ca_system_score_codex":0.000015942582,"about_ca_system_score_gemma":0.000010603062,"threshold_uncertainty_score":0.20864199},"labels":[],"label_agreement":null},{"id":"W2211701988","doi":"10.1007/s10044-015-0518-7","title":"Detecting and quantifying extended landscape structure with spatial co-occurrence surfaces","year":2015,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Adjacency list; Land cover; Generalization; Class (philosophy); Scale (ratio); Computer science; Adjacency matrix; Spatial distribution; Land use; Pattern recognition (psychology); Remote sensing; Artificial intelligence; Cartography; Mathematics; Geography; Algorithm; Theoretical computer science; Ecology","score_opus":0.01975147407544417,"score_gpt":0.259430073540451,"score_spread":0.23967859946500683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2211701988","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9798143,0.000078808356,0.019695928,0.0000549549,0.0000044279377,0.00009558363,0.000068348934,0.000017602453,0.00017007689],"genre_scores_gemma":[0.9996951,0.000024010427,0.00013479503,0.000036615573,0.000020863932,0.000018995403,0.00006285926,0.0000032577602,0.0000034824702],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993847,0.000018824385,0.00010739245,0.0002501474,0.00012670597,0.00011224358],"domain_scores_gemma":[0.99965245,0.000022186347,0.00007517456,0.00014195427,0.0000083891255,0.00009982404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009545447,0.00008492207,0.00013536337,0.000034528042,0.00015583824,0.00008015956,0.000074558906,0.00002577859,0.0001554976],"category_scores_gemma":[0.0000015560537,0.000057248042,0.000016000447,0.00027001603,0.000018527033,0.000090275724,0.000046065208,0.00004658074,0.0000152685],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023722685,0.000008706337,0.9791526,0.000009066429,0.000049217535,4.007766e-7,0.00018128869,0.0006451524,0.0001613336,0.0000011069138,0.000009508512,0.019779243],"study_design_scores_gemma":[0.00034786295,0.000039418763,0.93347967,0.000009992095,0.00045388204,0.000009521774,0.0007846407,0.06260525,0.0007936154,0.00008684269,0.0011291258,0.00026016732],"about_ca_topic_score_codex":0.0015955551,"about_ca_topic_score_gemma":0.019880211,"teacher_disagreement_score":0.061960094,"about_ca_system_score_codex":0.0000073921847,"about_ca_system_score_gemma":0.0000029404846,"threshold_uncertainty_score":0.99800444},"labels":[],"label_agreement":null},{"id":"W2256856437","doi":"10.1007/s10044-016-0536-0","title":"Direction histogram: novel discriminative global feature for Thai offline handwritten OCR","year":2016,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Histogram; Feature (linguistics); Computer science; Discriminative model; Handwriting; Convolutional neural network; Feature extraction; Character (mathematics); Pixel; Artificial neural network; Image (mathematics); Mathematics","score_opus":0.01614470884737868,"score_gpt":0.2822022742602904,"score_spread":0.2660575654129117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2256856437","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005393646,0.00008483028,0.99235815,0.00563338,0.000019521352,0.00041523192,0.0002687961,0.00021614,0.00046457353],"genre_scores_gemma":[0.94220823,0.00010545718,0.0546906,0.000455443,0.00012305069,0.0013600278,0.000083926134,0.000009691273,0.00096359436],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989414,0.000022978258,0.00019618828,0.0005045498,0.00014674192,0.00018813023],"domain_scores_gemma":[0.9990888,0.000092042086,0.00013654657,0.0004017417,0.0001834845,0.00009733551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001540689,0.000146626,0.00022719758,0.00016087102,0.00021025834,0.000120289216,0.00031992208,0.00006931691,0.000015109264],"category_scores_gemma":[0.000015824375,0.00010222797,0.00018811287,0.00082956377,0.00006588111,0.00022843701,0.000096577605,0.000045880377,0.0000099768495],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002072848,0.00012010459,0.0049193786,0.000009673436,0.00018806472,1.6087851e-7,0.000050819664,2.9042806e-7,0.003027243,0.005482236,0.0007486505,0.9854513],"study_design_scores_gemma":[0.0053941696,0.00063529203,0.3769559,0.0002417918,0.0048163747,0.00007271613,0.00035079767,0.059945222,0.058464967,0.10031483,0.38935563,0.0034523152],"about_ca_topic_score_codex":0.00009179428,"about_ca_topic_score_gemma":0.00027853655,"teacher_disagreement_score":0.981999,"about_ca_system_score_codex":0.00007182564,"about_ca_system_score_gemma":0.000014250156,"threshold_uncertainty_score":0.41687354},"labels":[],"label_agreement":null},{"id":"W2309123164","doi":"10.1007/s10044-016-0535-1","title":"The design of absorbing Bayesian pursuit algorithms and the formal analyses of their ε-optimality","year":2016,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Mathematical proof; Bayesian probability; Conjugate prior; Algorithm; Convergence (economics); Prior probability; Computer science; Discretization; Mathematics; Artificial intelligence","score_opus":0.024461076743623637,"score_gpt":0.29278379505628027,"score_spread":0.26832271831265664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2309123164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030480183,0.00038150154,0.9943008,0.0020205744,0.0000056566782,0.00013974149,0.000009712973,0.000014303551,0.00007971089],"genre_scores_gemma":[0.993435,0.00032273596,0.0060536894,0.00004281132,0.000025069545,0.00005915442,0.0000013030469,0.0000031080538,0.00005712035],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990435,0.00016065151,0.00028596117,0.00021147342,0.00015841902,0.00013999626],"domain_scores_gemma":[0.99839675,0.0007486959,0.00023828814,0.000503051,0.0000716806,0.00004154066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010671798,0.000091910086,0.00023044196,0.00007859446,0.00030833553,0.000082970524,0.0005016924,0.000021566946,0.0000051243032],"category_scores_gemma":[0.000018724502,0.000036934387,0.000120187644,0.0005251199,0.000259152,0.00010196436,0.00016987137,0.000055908513,7.0297415e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004180259,0.0000243516,0.0081706485,0.0000073112615,0.00043681663,8.322575e-8,0.00027761687,0.00039930287,0.00080221874,0.0066649914,0.000007878355,0.9832046],"study_design_scores_gemma":[0.00057029194,0.00003462413,0.04338437,0.000014299628,0.00044568628,0.000003249282,0.00014124688,0.9460732,0.0032930418,0.005436002,0.00045886327,0.00014517408],"about_ca_topic_score_codex":0.00033901402,"about_ca_topic_score_gemma":0.000023815302,"teacher_disagreement_score":0.99038696,"about_ca_system_score_codex":0.0000039249576,"about_ca_system_score_gemma":0.000010941268,"threshold_uncertainty_score":0.23714988},"labels":[],"label_agreement":null},{"id":"W2405162173","doi":"10.1007/s10044-016-0554-y","title":"A comparative study of the performance of local feature-based pattern recognition algorithms","year":2016,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Noise (video); Algorithm; Feature (linguistics); Pattern recognition (psychology); Transformation (genetics); Artificial intelligence; Similarity (geometry); Image (mathematics); Point (geometry); Mathematics","score_opus":0.02512775064174129,"score_gpt":0.2977534430160425,"score_spread":0.27262569237430123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405162173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19611748,0.00002214903,0.8034375,0.00013181198,0.0000036782317,0.00023687241,0.000018283394,0.000017143846,0.00001508632],"genre_scores_gemma":[0.99796426,0.000022456567,0.0018209259,0.000055271634,0.0000093101635,0.000108901055,0.0000032128362,0.0000024353624,0.000013203909],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992928,0.00005059327,0.00019778879,0.00021889448,0.00016187655,0.00007804184],"domain_scores_gemma":[0.9991107,0.00007624674,0.00020356587,0.0004360368,0.00014750018,0.000025971069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010458192,0.00007946964,0.00020673066,0.00010269259,0.00007491522,0.000012828895,0.00031454046,0.00002161925,0.000005237995],"category_scores_gemma":[0.00000205824,0.000043857188,0.000076485456,0.0008229165,0.00008328634,0.00011693577,0.000084885905,0.000047240253,0.0000018976102],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020837847,0.00022836115,0.07699275,0.000010045087,0.000101976686,8.471022e-8,0.00015708203,0.000020240745,0.001287593,0.000009376168,0.000010226769,0.9211802],"study_design_scores_gemma":[0.0008224803,0.00045066286,0.44113997,0.00008064522,0.00048599346,0.0000012545626,0.0002956708,0.066110276,0.489742,0.0003749481,0.00023050397,0.00026558872],"about_ca_topic_score_codex":0.000055259214,"about_ca_topic_score_gemma":0.000056016812,"teacher_disagreement_score":0.9209146,"about_ca_system_score_codex":0.000011188102,"about_ca_system_score_gemma":0.000010637297,"threshold_uncertainty_score":0.17884442},"labels":[],"label_agreement":null},{"id":"W2577832877","doi":"10.1007/s10044-016-0591-6","title":"Fuzzy-based algorithm for Fongbe continuous speech segmentation","year":2017,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Segmentation; Speech segmentation; Computer science; Speech recognition; Domain (mathematical analysis); Syllable; Fuzzy logic; Set (abstract data type); Algorithm; Pattern recognition (psychology); Artificial intelligence; Mathematics","score_opus":0.01613108445661661,"score_gpt":0.29333128591238367,"score_spread":0.27720020145576707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577832877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002245862,0.00007626354,0.99595016,0.0010800664,0.000017383081,0.00023914492,0.000024472049,0.00004599121,0.00032064534],"genre_scores_gemma":[0.58123344,0.000019371071,0.41747034,0.00053352705,0.00012243286,0.0003949291,0.000062442414,0.000007132402,0.00015640138],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999254,0.000008812019,0.0001513904,0.00032903888,0.000111986505,0.00014480228],"domain_scores_gemma":[0.99905044,0.000038146565,0.00020252199,0.00054797187,0.000094089824,0.00006683794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014554782,0.00008766565,0.00015708075,0.00010714731,0.00068195,0.00066276337,0.00044345556,0.00002912064,0.000005469286],"category_scores_gemma":[0.000005772788,0.00008121703,0.00010213579,0.00018595657,0.000037244245,0.00021146308,0.00005932908,0.00003556097,0.000009303899],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.4997186e-7,0.000026919128,0.009966811,0.0000061570236,0.000063138774,3.6859916e-7,0.000016923597,0.0000047927647,0.00067907805,0.00009368377,0.000040952546,0.98910093],"study_design_scores_gemma":[0.0021854416,0.00009824136,0.08056776,0.000031341595,0.0013527937,0.0000047264148,0.0001512354,0.54752517,0.3416764,0.013963004,0.011526861,0.00091704406],"about_ca_topic_score_codex":0.00006570506,"about_ca_topic_score_gemma":0.0000834629,"teacher_disagreement_score":0.98818386,"about_ca_system_score_codex":0.000011346424,"about_ca_system_score_gemma":0.000018332757,"threshold_uncertainty_score":0.63910425},"labels":[],"label_agreement":null},{"id":"W2792572138","doi":"10.1007/s10044-018-0680-9","title":"Gene clustering with hidden Markov model optimized by PSO algorithm","year":2018,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cluster analysis; Hidden Markov model; Pattern recognition (psychology); Computer science; Sequence (biology); Artificial intelligence; Similarity (geometry); Particle swarm optimization; Hierarchical clustering; Correlation clustering; CURE data clustering algorithm; Single-linkage clustering; Canopy clustering algorithm; Algorithm; Data mining; Biology; Genetics","score_opus":0.008168033288264294,"score_gpt":0.25285345360270695,"score_spread":0.24468542031444265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792572138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019987606,0.00018913642,0.9789253,0.00026162938,0.000005433616,0.00013323508,0.00005812564,0.0000115480725,0.0004279821],"genre_scores_gemma":[0.9607555,0.0003256975,0.035618804,0.00044323038,0.00016415726,0.0003085431,0.00055457035,0.000016348191,0.0018131203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992791,0.000017233873,0.00013378644,0.00036327698,0.00008732691,0.000119299686],"domain_scores_gemma":[0.99940526,0.000002585132,0.00007636669,0.00035865724,0.00007756442,0.00007955747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059988884,0.00010395998,0.00011897286,0.000060570484,0.00013715758,0.000039417027,0.00011519641,0.00005645204,0.00003765376],"category_scores_gemma":[0.0000011181991,0.00008670505,0.000054283024,0.00023173839,0.000066310015,0.0000031903692,0.00005600313,0.000033331256,0.000005500421],"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":[0.000028559716,0.000081604805,0.0026283066,0.0000068433346,0.00047044887,1.2832692e-7,0.000045834313,0.00032816676,0.44844985,0.0000032142523,0.0041299653,0.54382706],"study_design_scores_gemma":[0.0014385327,0.00015578761,0.0037660003,0.000009741999,0.0010023552,0.0000071715804,0.00017134784,0.5934256,0.36215675,0.000051083265,0.037157737,0.0006579353],"about_ca_topic_score_codex":0.000030421768,"about_ca_topic_score_gemma":0.00005359933,"teacher_disagreement_score":0.9433065,"about_ca_system_score_codex":0.0000066997927,"about_ca_system_score_gemma":0.000015636739,"threshold_uncertainty_score":0.3535729},"labels":[],"label_agreement":null},{"id":"W2889064910","doi":"10.1007/s10044-018-0747-7","title":"Gabor filter-based texture for ancient degraded document image binarization","year":2018,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"Ministère de l'Enseignement Supérieur et de la Recherche","keywords":"Artificial intelligence; Gabor filter; Computer science; Pattern recognition (psychology); Preprocessor; Computer vision; Filter (signal processing); Image (mathematics)","score_opus":0.012818523463252837,"score_gpt":0.2781765914422702,"score_spread":0.26535806797901734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889064910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020105531,0.000043964046,0.9957403,0.0013574774,0.000012375533,0.0004959494,0.000042082385,0.0001656101,0.00013167906],"genre_scores_gemma":[0.92345375,0.0000117617865,0.07386218,0.0012090118,0.00008930282,0.0011555157,0.00013212675,0.000007753473,0.000078613244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900943,0.000028896919,0.00022097111,0.00042681737,0.00014177713,0.00017211647],"domain_scores_gemma":[0.99902296,0.000043468448,0.00012099775,0.0004649233,0.00026151186,0.00008614618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018004855,0.000115892966,0.0001571154,0.00028137254,0.00025867697,0.0002479608,0.00031018653,0.00004600693,0.00005437628],"category_scores_gemma":[0.000008886224,0.000103195205,0.00011671964,0.0010702292,0.00006694743,0.00016226614,0.00005838905,0.000042338124,0.000028421731],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042770575,0.0002744178,0.005323446,0.00005079264,0.0002316987,5.810098e-7,0.00028206708,0.0000059980903,0.016528968,0.0056232326,0.0020130044,0.96966153],"study_design_scores_gemma":[0.0014617173,0.00047477783,0.04214451,0.000060982038,0.001220588,0.0000049700648,0.000078810066,0.50602853,0.3199775,0.01759402,0.10979874,0.0011548783],"about_ca_topic_score_codex":0.000035100904,"about_ca_topic_score_gemma":0.00006300864,"teacher_disagreement_score":0.96850663,"about_ca_system_score_codex":0.00002541123,"about_ca_system_score_gemma":0.000021555443,"threshold_uncertainty_score":0.42081782},"labels":[],"label_agreement":null},{"id":"W2910497671","doi":"10.1007/s10044-018-00767-y","title":"A nonparametric Bayesian learning model using accelerated variational inference and feature selection","year":2019,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Feature selection; Artificial intelligence; Nonparametric statistics; Bayesian inference; Inference; Computer science; Selection (genetic algorithm); Pattern recognition (psychology); Machine learning; Model selection; Feature (linguistics); Bayesian probability; Mathematics; Econometrics","score_opus":0.02184809992247997,"score_gpt":0.29730838640841345,"score_spread":0.27546028648593346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910497671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018653905,0.00006742631,0.9806695,0.00020651506,0.000006654512,0.00016310342,0.0000033030237,0.000044224274,0.00018534815],"genre_scores_gemma":[0.7159106,0.000027481894,0.28376576,0.000115117604,0.000018124794,0.000016670978,0.000010425276,0.000004224599,0.0001315777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908614,0.000064158194,0.00014415977,0.00042945502,0.00013347474,0.00014259231],"domain_scores_gemma":[0.9994358,0.000079155034,0.000108680855,0.00019953151,0.00009789026,0.000078947465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020910258,0.00011423204,0.00019212389,0.00034905868,0.00020253315,0.0002529499,0.00015389829,0.00007415227,0.000014565537],"category_scores_gemma":[0.000008888158,0.00010554752,0.000052445153,0.0019371111,0.000013913057,0.00023352931,0.00008353684,0.00017266441,0.0000033389797],"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":[0.00000330347,0.00010528067,0.22014585,0.000041402633,0.00049965794,4.2616503e-7,0.000535994,0.10921013,0.009320337,0.05534911,0.000014988771,0.6047735],"study_design_scores_gemma":[0.00009573801,0.000009001047,0.015455829,0.000003304006,0.00012554639,0.000002937427,0.0000036611298,0.9768555,0.00010378936,0.0071707238,0.000049697006,0.00012425781],"about_ca_topic_score_codex":0.0000719622,"about_ca_topic_score_gemma":0.000017785082,"teacher_disagreement_score":0.8676454,"about_ca_system_score_codex":0.000019620269,"about_ca_system_score_gemma":0.000033143406,"threshold_uncertainty_score":0.43041033},"labels":[],"label_agreement":null},{"id":"W2913645927","doi":"10.1007/s10044-019-00790-7","title":"Applying adversarial auto-encoder for estimating human walking gait abnormality index","year":2019,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Abnormality; Point cloud; Point (geometry); Gait; Perspective (graphical); Adversarial system; Pattern recognition (psychology); Index (typography)","score_opus":0.011512577433787878,"score_gpt":0.25600821914488375,"score_spread":0.24449564171109586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913645927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06832945,0.000027614778,0.92966944,0.000022450497,0.00002760838,0.00046024026,0.00004332112,0.00014469387,0.0012751545],"genre_scores_gemma":[0.9954381,0.000006804047,0.0030205592,0.000085005915,0.00015431562,0.00090496097,0.0002580087,0.000018598615,0.000113661976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908894,0.000013905847,0.00030907302,0.0002700648,0.00012090083,0.00019713654],"domain_scores_gemma":[0.9994939,0.000060635866,0.000075730815,0.00023717951,0.00005533838,0.00007719992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018157142,0.00014378043,0.0002884408,0.00023698437,0.00021106,0.00010584692,0.00010748118,0.00006161696,0.0003015173],"category_scores_gemma":[0.0000038431795,0.00014720375,0.00022500477,0.00051917695,0.000016973427,0.000098760815,0.000027125168,0.00010185425,0.000039367802],"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":[0.0000033781414,0.00010998393,0.29556653,0.0004413757,0.0031340378,7.0041324e-7,0.00041154618,0.120723516,0.006580988,0.00058688695,0.00017547308,0.57226557],"study_design_scores_gemma":[0.00030608676,0.0000045824904,0.015649484,0.0000116102765,0.0008430485,7.400011e-7,0.00014651628,0.98003566,0.00018249816,0.0003871291,0.0022029078,0.00022972464],"about_ca_topic_score_codex":0.00010551759,"about_ca_topic_score_gemma":0.0001236586,"teacher_disagreement_score":0.92710865,"about_ca_system_score_codex":0.00002690967,"about_ca_system_score_gemma":0.000004235215,"threshold_uncertainty_score":0.6002795},"labels":[],"label_agreement":null},{"id":"W2927045701","doi":"10.1007/s10044-019-00812-4","title":"Multi-task non-negative matrix factorization for visual object tracking","year":2019,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Video tracking; Computer science; Artificial intelligence; Object (grammar); Tracking (education); Subspace topology; Pattern recognition (psychology); Task (project management); Non-negative matrix factorization; Computer vision; Particle filter; Matrix decomposition; Eye tracking; Matrix (chemical analysis); Algorithm; Kalman filter","score_opus":0.020608213181781136,"score_gpt":0.34333266829081394,"score_spread":0.3227244551090328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927045701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025597006,0.000034918554,0.9736537,0.000111350695,0.000036518584,0.0004449467,0.000023198414,0.0000560298,0.00004237831],"genre_scores_gemma":[0.9528219,0.000020480786,0.04665314,0.000091252594,0.000057380228,0.00018627419,0.00004599425,0.0000080875325,0.00011545154],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990066,0.00004662284,0.00021797138,0.0004390788,0.000121441095,0.00016824961],"domain_scores_gemma":[0.99909914,0.0002522082,0.00014127017,0.00033332442,0.00011929889,0.000054737357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003045006,0.00011725944,0.00023788144,0.0001906818,0.00015600353,0.00018798729,0.00024264978,0.000046657777,0.000009634552],"category_scores_gemma":[0.000015488917,0.00010654631,0.00014570095,0.00088005693,0.00001539371,0.0002149051,0.000052506733,0.000061683815,0.000026871428],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033954138,0.00017182637,0.5397327,0.000060724386,0.0005902168,3.2448028e-7,0.00094591663,0.0011886449,0.015127016,0.0009398717,0.000014285244,0.44122508],"study_design_scores_gemma":[0.00051648094,0.000042483225,0.48226696,0.000008604161,0.00019717636,8.0913867e-7,0.00007596208,0.50945216,0.00546159,0.00069865235,0.0009991028,0.0002800035],"about_ca_topic_score_codex":0.000088820154,"about_ca_topic_score_gemma":0.0000883494,"teacher_disagreement_score":0.92722493,"about_ca_system_score_codex":0.000017126424,"about_ca_system_score_gemma":0.000016128684,"threshold_uncertainty_score":0.43448323},"labels":[],"label_agreement":null},{"id":"W2933135745","doi":"10.1007/s10044-019-00817-z","title":"On enhancing the deadlock-preventing object migration automaton using the pursuit paradigm","year":2019,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Deadlock; Computer science; Cellular automaton; Automaton; Object (grammar); Property (philosophy); Field (mathematics); Distributed computing; Theoretical computer science; Learning automata; Artificial intelligence; State (computer science); Algorithm; Mathematics","score_opus":0.014976528653831232,"score_gpt":0.27659150646443487,"score_spread":0.2616149778106036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2933135745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068242095,0.000049239563,0.92863315,0.002415669,0.000015384783,0.00035451207,0.0000017457346,0.000043753415,0.00024443323],"genre_scores_gemma":[0.9977152,0.000027336733,0.001294697,0.0006705836,0.000027211181,0.00008639224,0.000008932323,0.0000048056586,0.00016485307],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990185,0.00009951776,0.000205759,0.00027549363,0.00024562347,0.00015509101],"domain_scores_gemma":[0.9990474,0.0001841495,0.00012244393,0.0005705638,0.00003836801,0.000037070073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005168678,0.00008658897,0.000113774804,0.00010487447,0.00045069415,0.00045492974,0.0005096526,0.000024456001,0.000036726615],"category_scores_gemma":[0.000007230513,0.00005084204,0.00009361264,0.0009803895,0.000024072111,0.00013034052,0.00011352192,0.00010010594,0.00004057179],"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":[0.00000421934,0.00033844396,0.09823907,0.00008037083,0.0013820976,0.0000011276154,0.0061377087,0.32984632,0.012862718,0.22074984,0.00036041587,0.3299977],"study_design_scores_gemma":[0.000064092266,0.000009213102,0.0062464154,0.000008461308,0.00008257101,0.0000014869366,0.00007698382,0.9903165,0.0007460854,0.0013345822,0.0010259763,0.00008759564],"about_ca_topic_score_codex":0.0001792765,"about_ca_topic_score_gemma":0.00029786993,"teacher_disagreement_score":0.9294731,"about_ca_system_score_codex":0.00001980579,"about_ca_system_score_gemma":0.000020333466,"threshold_uncertainty_score":0.4386898},"labels":[],"label_agreement":null},{"id":"W2946689049","doi":"10.1007/s10044-020-00901-9","title":"Spatio-temporal adversarial learning for detecting unseen falls","year":2020,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute; University Health Network","funders":"","keywords":"Autoencoder; Adversarial system; Classifier (UML); Deep learning; Rendering (computer graphics); Modalities; Class (philosophy); Adversarial machine learning; Feature learning","score_opus":0.03176615309929862,"score_gpt":0.2679380683893751,"score_spread":0.2361719152900765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946689049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010350581,0.000023486347,0.98778456,0.0012657179,0.00001576143,0.00032872744,0.000012522029,0.0001167792,0.000101848455],"genre_scores_gemma":[0.99588674,0.0000036878218,0.0032157137,0.00033583835,0.00020281323,0.00025596947,0.000055108587,0.0000068151953,0.0000373379],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990476,0.00005576361,0.00023115479,0.0004054078,0.00012468592,0.00013541465],"domain_scores_gemma":[0.99923956,0.00019244726,0.0001710645,0.00019362858,0.00009343476,0.00010984341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001746721,0.000096652046,0.00021514294,0.00010698012,0.00024649865,0.00019356258,0.00023889959,0.000036485297,0.000015696003],"category_scores_gemma":[0.000033847442,0.00009992331,0.00014771412,0.00074953516,0.000014620934,0.0001889793,0.00009375659,0.000090145106,0.000027451455],"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":[0.000004453317,0.00002855445,0.12818995,0.000028592125,0.00032408966,4.937931e-7,0.0011181226,0.00023409746,0.0005080523,0.00025008846,0.000042490912,0.869271],"study_design_scores_gemma":[0.00078232726,0.00008915424,0.0121007925,0.000010459052,0.000526776,0.0000028096556,0.00047633055,0.9279351,0.0016130446,0.00031813062,0.055693798,0.00045122742],"about_ca_topic_score_codex":0.0002351621,"about_ca_topic_score_gemma":0.000314986,"teacher_disagreement_score":0.98553616,"about_ca_system_score_codex":0.000013712446,"about_ca_system_score_gemma":0.000017029517,"threshold_uncertainty_score":0.40747544},"labels":[],"label_agreement":null},{"id":"W3090026837","doi":"10.1007/s10044-020-00917-1","title":"Efficient integration of generative topic models into discriminative classifiers using robust probabilistic kernels","year":2020,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Artificial intelligence; Dirichlet distribution; Pattern recognition (psychology); Decision boundary; Latent Dirichlet allocation; Computer science; Generative model; Support vector machine; Mathematics; Classifier (UML); Prior probability; Machine learning; Bayesian probability; Topic model; Generative grammar","score_opus":0.07222202404860935,"score_gpt":0.2936769322880273,"score_spread":0.221454908239418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090026837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008966197,0.000057890615,0.9892574,0.0012314655,0.000005120789,0.00028511815,0.000009688141,0.000049511273,0.00013760547],"genre_scores_gemma":[0.96012086,0.000016918199,0.03955846,0.00015336543,0.000023412558,0.000092069014,0.000015785365,0.000003947689,0.000015162475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990318,0.000056412962,0.00028697858,0.00036504903,0.0001668407,0.00009294687],"domain_scores_gemma":[0.9992505,0.00003835445,0.00018126692,0.000259113,0.00019207515,0.00007869931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000997291,0.00010734277,0.00020953566,0.000119140466,0.00012444524,0.00007542097,0.00026351277,0.000036475714,0.0000069585285],"category_scores_gemma":[0.000014930596,0.00008843645,0.00009565858,0.0011071877,0.000091714915,0.00010632974,0.000094447976,0.00007330823,0.0000014837544],"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":[0.00000798789,0.00042968584,0.0013697762,0.00017471139,0.00059052446,0.0000014065512,0.016662672,0.09469941,0.10335223,0.27652308,0.000027486742,0.50616103],"study_design_scores_gemma":[0.00005020502,0.000019840267,0.0006796833,0.0000060486573,0.00015609406,3.0899318e-7,0.00024352188,0.9758483,0.018810863,0.0040725116,0.000016423297,0.00009619216],"about_ca_topic_score_codex":0.000098696044,"about_ca_topic_score_gemma":0.000014223276,"teacher_disagreement_score":0.9511547,"about_ca_system_score_codex":0.0000385729,"about_ca_system_score_gemma":0.000027332728,"threshold_uncertainty_score":0.36063337},"labels":[],"label_agreement":null},{"id":"W3172375040","doi":"10.1007/s10044-021-00996-8","title":"Ridgelet moment invariants for robust pattern recognition","year":2021,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Zernike polynomials; Moment (physics); Invariant (physics); Pattern recognition (psychology); Artificial intelligence; Mathematics; Image moment; Hermite polynomials; Wavelet; Computer science; Image processing; Image (mathematics); Pure mathematics; Physics","score_opus":0.03897978337354041,"score_gpt":0.270058623955221,"score_spread":0.2310788405816806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172375040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034446965,0.000082966406,0.9964816,0.002469188,0.000012557535,0.00023257866,0.00008116889,0.00008806048,0.0002074087],"genre_scores_gemma":[0.9338437,0.0006481511,0.060542732,0.0019509952,0.00014677433,0.0016534086,0.0007680387,0.000014583719,0.00043162817],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990745,0.000032532906,0.00022503041,0.00040850765,0.00012302394,0.00013636434],"domain_scores_gemma":[0.99913204,0.00005243874,0.00010331763,0.0004244304,0.00021797366,0.000069780406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001552468,0.00009232192,0.00015884922,0.000121309255,0.0001713253,0.00019991871,0.00021235064,0.00004031812,0.00004934001],"category_scores_gemma":[0.0000075180133,0.00008867514,0.0001181204,0.0008550215,0.000019992443,0.00013647355,0.00008390821,0.00005048471,0.000025257084],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.9526958e-7,0.000095705574,0.0018858155,0.000017403609,0.00013258545,6.7652104e-7,0.00003994412,0.00000375297,0.0011131101,0.0010477751,0.00020802833,0.9954548],"study_design_scores_gemma":[0.0011992891,0.00010953617,0.08448331,0.000052686133,0.0016907114,0.000027894986,0.00024183464,0.58284533,0.23125917,0.023301508,0.07344239,0.0013463142],"about_ca_topic_score_codex":0.00003339206,"about_ca_topic_score_gemma":0.000038403912,"teacher_disagreement_score":0.9941085,"about_ca_system_score_codex":0.000020229598,"about_ca_system_score_gemma":0.00002236025,"threshold_uncertainty_score":0.3616067},"labels":[],"label_agreement":null},{"id":"W3198387749","doi":"10.1007/s10044-021-01023-6","title":"Batch and online variational learning of hierarchical Dirichlet process mixtures of multivariate Beta distributions in medical applications","year":2021,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Interpretability; Latent Dirichlet allocation; Computer science; Cluster analysis; Machine learning; Artificial intelligence; Multivariate statistics; Health care; Process (computing); Task (project management); Hierarchical clustering; Data mining; Topic model","score_opus":0.011169136132250323,"score_gpt":0.31091202498829634,"score_spread":0.299742888856046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198387749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008518641,0.0003017513,0.9890569,0.0017744033,0.0000038374046,0.00014451651,0.00010464431,0.000013853351,0.00008143803],"genre_scores_gemma":[0.9210544,0.00016046088,0.07825914,0.00006885496,0.000031526113,0.00014972004,0.00025213227,0.0000041428525,0.00001962609],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986493,0.00014388887,0.0004265633,0.00039213515,0.00025946248,0.00012864944],"domain_scores_gemma":[0.9989432,0.00030173405,0.00016277295,0.00029339787,0.00018524371,0.00011364623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000346045,0.00009851401,0.000317498,0.00016895267,0.00009980915,0.000029855919,0.00024957038,0.00008279944,0.000023185894],"category_scores_gemma":[0.000052343916,0.00009170989,0.000078736215,0.0015022522,0.00009746453,0.00007117795,0.00015677573,0.0002059488,3.009715e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027895335,0.0009519847,0.08469644,0.00012627934,0.00039230048,0.0000026191958,0.0005842773,0.0002801572,0.002347483,0.48868817,0.000005833188,0.42192167],"study_design_scores_gemma":[0.00069520756,0.000026561054,0.37313786,0.000050891715,0.00047423408,0.00001608083,0.00006242499,0.55780476,0.0029257222,0.063324094,0.0011614873,0.0003206388],"about_ca_topic_score_codex":0.00009511895,"about_ca_topic_score_gemma":0.00012835016,"teacher_disagreement_score":0.9125357,"about_ca_system_score_codex":0.000008004838,"about_ca_system_score_gemma":0.000091128255,"threshold_uncertainty_score":0.37398204},"labels":[],"label_agreement":null},{"id":"W4246412339","doi":"10.1007/bf02683995","title":"Feature dimensionality reduction for the verification of handwritten numerals","year":2004,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pattern recognition (psychology); Artificial intelligence; Dimensionality reduction; Computer science; Linear discriminant analysis; Feature extraction; Principal component analysis; Classifier (UML); Curse of dimensionality; Discriminant; Artificial neural network","score_opus":0.015898302070796747,"score_gpt":0.2761278753260146,"score_spread":0.2602295732552179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246412339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003058822,0.00017789277,0.98782086,0.008389909,0.000010956371,0.00042029683,0.00002794319,0.000055862492,0.00003747069],"genre_scores_gemma":[0.9779797,0.00012839737,0.020945627,0.0001395474,0.000045870813,0.0006380694,0.000056204623,0.0000038036271,0.00006277806],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993561,0.000020849619,0.00016314437,0.0002550841,0.000121530094,0.00008332966],"domain_scores_gemma":[0.99914443,0.000065343,0.00014143653,0.00044137886,0.00017438894,0.000033023956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024365527,0.000072109935,0.00013633422,0.000104350496,0.00022530518,0.000059031907,0.0002471161,0.00004119085,0.00000516431],"category_scores_gemma":[0.000007713475,0.000052471507,0.00012235394,0.0006758588,0.00005393073,0.000117836455,0.000039630948,0.000051671974,0.000002981738],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005486295,0.00025019323,0.001777284,0.000047412403,0.0006820756,1.0666234e-7,0.00036487047,0.00025057988,0.030892689,0.076319866,0.0007700649,0.8886394],"study_design_scores_gemma":[0.0016882187,0.00018221326,0.18006907,0.000075988275,0.0029878085,0.000031816864,0.00043949025,0.031301547,0.5536984,0.18346389,0.04510172,0.00095980027],"about_ca_topic_score_codex":0.00010739252,"about_ca_topic_score_gemma":0.000023063714,"teacher_disagreement_score":0.97492087,"about_ca_system_score_codex":0.000016166889,"about_ca_system_score_gemma":0.000015841042,"threshold_uncertainty_score":0.2139726},"labels":[],"label_agreement":null},{"id":"W4249000472","doi":"10.1007/s10044-005-0247-4","title":"Estimation of generalized entropies with sample spacing","year":2005,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Statistical Mechanics and Entropy","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"Ostravská Univerzita v Ostravě","keywords":"Mathematics; Kernel density estimation; Rényi entropy; Nonparametric statistics; Entropy (arrow of time); Tsallis entropy; Computation; Kernel (algebra); Density estimation; Joint entropy; Applied mathematics; Statistics; Principle of maximum entropy; Algorithm; Discrete mathematics; Tsallis statistics","score_opus":0.007552408055228806,"score_gpt":0.2515704940165722,"score_spread":0.24401808596134342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249000472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07898986,0.000014768641,0.9204973,0.00013184207,0.0000015281825,0.00007113198,0.000100392586,0.0000059628705,0.0001872239],"genre_scores_gemma":[0.98279864,0.000004553192,0.016898086,0.000024109864,0.00005850765,0.000060563634,0.00012786571,0.0000040886666,0.000023597382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960625,0.000007658649,0.00012716634,0.00011673572,0.00006640753,0.000075774195],"domain_scores_gemma":[0.9997022,0.00004441404,0.00007096452,0.000118264696,0.000025804313,0.000038335696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000026547832,0.00005836774,0.0001364531,0.000053829677,0.000060056573,0.00002152292,0.000035067216,0.000007793622,0.0003611018],"category_scores_gemma":[0.0000011452044,0.00004580841,0.000043165808,0.00020674482,0.000017972881,0.000027569298,0.000011563786,0.000024578765,0.0000048677716],"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":[0.0000024766703,0.00006551843,0.06625498,0.000006289062,0.00037976538,2.5470937e-8,0.00006691545,0.004985777,0.00020786704,0.31746808,0.000017183162,0.6105451],"study_design_scores_gemma":[0.0004415597,0.000023801487,0.016279375,0.000007320214,0.0011338159,1.8779673e-7,0.00014209487,0.96430224,0.0035328576,0.011813822,0.0021504008,0.00017255115],"about_ca_topic_score_codex":0.00058076385,"about_ca_topic_score_gemma":0.000025665591,"teacher_disagreement_score":0.95931643,"about_ca_system_score_codex":0.0000040562354,"about_ca_system_score_gemma":0.0000040588693,"threshold_uncertainty_score":0.39538115},"labels":[],"label_agreement":null},{"id":"W4285494617","doi":"10.1007/s10044-022-01091-2","title":"User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution","year":2022,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Noma; Computer science; Reinforcement learning; Task (project management); Learning automata; Power (physics); Artificial intelligence; Salient; Channel (broadcasting); Scheme (mathematics); Theoretical computer science; Automaton; Distributed computing; Mathematics; Computer network","score_opus":0.011462736819629763,"score_gpt":0.22583079359516406,"score_spread":0.2143680567755343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285494617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0735218,0.00056484324,0.9252085,0.00017845443,0.0000063901325,0.00026232968,0.00000757943,0.00018808627,0.000062029365],"genre_scores_gemma":[0.99786556,0.00016421628,0.00039227807,0.0000371458,0.000004440449,0.0013530215,0.00014898986,0.000013357305,0.00002101869],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931395,0.000024349474,0.000249868,0.00018102047,0.000109137756,0.00012164399],"domain_scores_gemma":[0.9995189,0.000047195084,0.000065545,0.0003177137,0.00002492326,0.000025767295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001392195,0.00009845745,0.00015608741,0.00034493554,0.00021270373,0.000035216486,0.0001371921,0.000033384676,0.00002016093],"category_scores_gemma":[0.0000056491567,0.00011444509,0.000032502037,0.000759956,0.000028077822,0.000072743416,0.00010423072,0.00018158993,0.0000016077032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.860895e-7,0.000023555918,0.01445265,0.000024045743,0.00006058558,9.0056766e-8,0.000110930094,0.9762702,0.0033889588,0.0004233507,0.000004867921,0.0052397954],"study_design_scores_gemma":[0.00022522999,0.000011794028,0.010748403,0.000007932177,0.00004928829,7.16665e-7,0.00082107773,0.98549724,0.00022784437,0.0000124191965,0.0022667358,0.00013131433],"about_ca_topic_score_codex":0.00014096127,"about_ca_topic_score_gemma":0.00008146225,"teacher_disagreement_score":0.9248162,"about_ca_system_score_codex":0.00010862057,"about_ca_system_score_gemma":0.000005145424,"threshold_uncertainty_score":0.46669352},"labels":[],"label_agreement":null},{"id":"W4308989374","doi":"10.1007/s10044-022-01119-7","title":"Event prediction with rough-fuzzy sets","year":2022,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Event (particle physics); Boundary (topology); Computer science; Artificial intelligence; Set (abstract data type); Motion (physics); Object (grammar); Trajectory; Path (computing); Rough set; Computer vision; Mathematics; Pattern recognition (psychology)","score_opus":0.00993615515284763,"score_gpt":0.23308550194158337,"score_spread":0.22314934678873574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308989374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007563091,0.00009079659,0.98946315,0.0012331633,0.0000131342695,0.00016686785,0.000047482023,0.00006901689,0.0013533107],"genre_scores_gemma":[0.9965343,0.000023518369,0.002322537,0.00041389102,0.000027413309,0.00052431086,0.00007247626,0.000003882894,0.00007766457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991408,0.00003795423,0.00014097577,0.0003400002,0.00021632542,0.00012391784],"domain_scores_gemma":[0.9993964,0.000018119512,0.00007619274,0.00042597853,0.000023692035,0.000059604725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001330467,0.000077758916,0.00011892563,0.00013036738,0.000446946,0.00009757779,0.00029854832,0.000012131429,0.000048584734],"category_scores_gemma":[5.383458e-7,0.00006267733,0.00006194686,0.0011232485,0.000018294908,0.0000971245,0.00018793595,0.00008523807,0.000008286189],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004494589,0.0004570263,0.19736496,0.000013774859,0.00079909986,0.0000072885114,0.0008727056,0.026561668,0.0000688669,0.021607488,0.0011490134,0.7510936],"study_design_scores_gemma":[0.0005652571,0.0002525406,0.3810475,0.0000035872556,0.0007861269,0.00004182214,0.0002773151,0.5367186,0.000041379782,0.009775329,0.0700116,0.00047894794],"about_ca_topic_score_codex":0.00012778424,"about_ca_topic_score_gemma":0.00004986162,"teacher_disagreement_score":0.98897123,"about_ca_system_score_codex":0.000025035793,"about_ca_system_score_gemma":0.000012390947,"threshold_uncertainty_score":0.34375924},"labels":[],"label_agreement":null},{"id":"W4319304668","doi":"10.1007/s10044-023-01131-5","title":"Learning automata-based partitioning algorithms for stochastic grouping problems with non-equal partition sizes","year":2023,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Partition (number theory); Theoretical computer science; Heuristic; Key (lock); Context (archaeology); Reinforcement learning; Learning automata; Constraint (computer-aided design); Automaton; Artificial intelligence; Algorithm; Mathematics","score_opus":0.027467271016067208,"score_gpt":0.28809909852615057,"score_spread":0.26063182751008335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319304668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013968818,0.0000080721275,0.9968182,0.0008999391,0.000008022047,0.0005016487,0.000012596156,0.0003146783,0.000039951603],"genre_scores_gemma":[0.9793949,0.000012349428,0.0181501,0.00012477706,0.00004014195,0.0018288803,0.00034023405,0.000011850342,0.00009676142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988839,0.000037174977,0.00022018366,0.00040055608,0.00020198262,0.00025622206],"domain_scores_gemma":[0.9992489,0.00016363109,0.00011827832,0.00024299225,0.00012799368,0.00009816889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035271695,0.00011180972,0.00017516568,0.00030856338,0.0005476651,0.0003113092,0.00018329517,0.000032232158,0.0000111753425],"category_scores_gemma":[0.000012216039,0.000098679964,0.00007371755,0.001770124,0.000040360643,0.00021091958,0.000051354542,0.000083470535,0.000025004523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016370049,0.00005472893,0.00380621,0.000049778075,0.00014299994,4.336607e-7,0.00034328454,0.93579316,0.00012399083,0.0015616285,0.000034859564,0.058087297],"study_design_scores_gemma":[0.0003512198,0.000072693314,0.0034148316,0.00002150657,0.000105868574,5.057619e-7,0.00006536572,0.9949371,0.00006585431,0.0005055325,0.00031925974,0.00014024643],"about_ca_topic_score_codex":0.000041627893,"about_ca_topic_score_gemma":0.00005624179,"teacher_disagreement_score":0.9786681,"about_ca_system_score_codex":0.000016579652,"about_ca_system_score_gemma":0.000028349057,"threshold_uncertainty_score":0.42122525},"labels":[],"label_agreement":null},{"id":"W4321788863","doi":"10.1007/s10044-023-01148-w","title":"Bounded multivariate generalized Gaussian mixture model using ICA and IVA","year":2023,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Independent component analysis; Mixture model; Bounded function; Computer science; Gaussian; Pattern recognition (psychology); Multivariate statistics; Component analysis; Artificial intelligence; Principal component analysis; Covariance; Independence (probability theory); Cluster analysis; Component (thermodynamics); Mathematics; Algorithm; Statistics; Machine learning","score_opus":0.03369455221902559,"score_gpt":0.3169119121925454,"score_spread":0.2832173599735198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321788863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04361698,0.00003046918,0.9543582,0.0014481447,0.0000036869194,0.00015458479,0.000012803308,0.00023483488,0.00014027322],"genre_scores_gemma":[0.93909127,0.00008103739,0.059943814,0.0005483836,0.000021374639,0.00009778292,0.00003713469,0.000007411354,0.00017176784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991507,0.000046474543,0.0001792594,0.00036489012,0.00012094592,0.00013774331],"domain_scores_gemma":[0.99936825,0.000028182945,0.00008259718,0.0003981499,0.000041696636,0.00008112302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018894256,0.000101537735,0.00017015972,0.00029208144,0.00023998303,0.0002669043,0.00020985819,0.000052955143,0.0000048384372],"category_scores_gemma":[0.000003291634,0.000092191236,0.000065424276,0.0012311527,0.000033621604,0.00014851682,0.00015197281,0.000071103015,0.0000065741997],"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":[0.000008915109,0.00044131573,0.029468205,0.00012159146,0.0023835015,0.000010299728,0.013677466,0.052570768,0.10081252,0.45682833,0.0015669934,0.3421101],"study_design_scores_gemma":[0.00009975646,0.0000029668904,0.004264294,0.0000024506173,0.00010255272,0.0000011515236,0.00001545208,0.98278,0.00071059196,0.011289407,0.0006147574,0.00011662535],"about_ca_topic_score_codex":0.00020191676,"about_ca_topic_score_gemma":0.00007603077,"teacher_disagreement_score":0.9302092,"about_ca_system_score_codex":0.000010520192,"about_ca_system_score_gemma":0.000016485796,"threshold_uncertainty_score":0.37594494},"labels":[],"label_agreement":null},{"id":"W4364377325","doi":"10.1007/s10044-023-01163-x","title":"The object migration automata: its field, scope, applications, and future research challenges","year":2023,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Automaton; Artificial intelligence; Scope (computer science); Benchmark (surveying); Cellular automaton; Theoretical computer science; Field (mathematics); Data science; Machine learning; Programming language; Mathematics","score_opus":0.04986487048192982,"score_gpt":0.34974039894459075,"score_spread":0.2998755284626609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364377325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000835621,0.007859424,0.90121055,0.086433716,0.000026067668,0.0011620983,0.000015065259,0.00031557205,0.0021418976],"genre_scores_gemma":[0.5935253,0.39297432,0.0030148996,0.000685978,0.0006796337,0.0065032006,0.00015860931,0.000026832779,0.0024312574],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987214,0.0001357699,0.00019328907,0.00041218146,0.00030426873,0.00023307187],"domain_scores_gemma":[0.99869674,0.00036095252,0.000054871063,0.0005836037,0.00020582015,0.00009802563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095691043,0.000086549815,0.00011600301,0.0003171431,0.0010079668,0.00045593758,0.00043517916,0.00005382834,0.000007837865],"category_scores_gemma":[0.000014037815,0.000063723885,0.000044235916,0.0025175456,0.000054737764,0.00017202746,0.00023417322,0.00014979644,0.00008425052],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.066368e-7,0.000028211403,0.0005426543,0.000025740359,0.00009163413,3.011491e-7,0.00070631236,0.00005797021,0.0000548785,0.043009203,0.0010896254,0.9543928],"study_design_scores_gemma":[0.00022073912,0.00004861006,0.023601033,0.000009136992,0.000073507916,0.0000028616266,0.0016166555,0.29392186,0.00028739305,0.004279941,0.6756969,0.0002413782],"about_ca_topic_score_codex":0.000043015458,"about_ca_topic_score_gemma":0.00051524636,"teacher_disagreement_score":0.9541514,"about_ca_system_score_codex":0.0000105191775,"about_ca_system_score_gemma":0.000025136816,"threshold_uncertainty_score":0.7752568},"labels":[],"label_agreement":null},{"id":"W4392231600","doi":"10.1007/s10044-024-01213-y","title":"Big topic modeling based on a two-level hierarchical latent Beta-Liouville allocation for large-scale data and parameter streaming","year":2024,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scale (ratio); BETA (programming language); Big data; Artificial intelligence; Data mining; Physics","score_opus":0.07047955069751044,"score_gpt":0.3082657168572723,"score_spread":0.23778616615976184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392231600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019407535,0.00016703438,0.9778864,0.0020895302,0.000022341846,0.00022821693,0.00011551848,0.00005713856,0.000026306536],"genre_scores_gemma":[0.9437569,0.000023965138,0.055400796,0.0003139851,0.00009089994,0.0001786945,0.00019187258,0.000007075337,0.000035840327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987924,0.000024409688,0.00022079647,0.0006624481,0.00013825607,0.00016166749],"domain_scores_gemma":[0.9989305,0.00015670735,0.00003104828,0.0007762495,0.000032675165,0.0000727867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027777816,0.00010517808,0.0001575357,0.00021056918,0.0001573113,0.00027119898,0.00033379224,0.000034838074,0.0000036664912],"category_scores_gemma":[0.0000067503997,0.00009114296,0.00006446286,0.0003740189,0.00001154909,0.00011166928,0.0001789995,0.00008745331,0.000002397111],"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":[0.0000016432612,0.00009122588,0.0055446913,0.00007008598,0.00021091767,7.1462796e-7,0.0002923077,0.034291983,0.00013998011,0.010987646,0.000013397923,0.94835544],"study_design_scores_gemma":[0.00013643208,0.000009642027,0.00072757766,0.000016385784,0.00019859157,5.031838e-7,0.000015008239,0.9969414,0.000036947506,0.0012067616,0.00060723285,0.000103527884],"about_ca_topic_score_codex":0.0001028923,"about_ca_topic_score_gemma":0.000267485,"teacher_disagreement_score":0.9626494,"about_ca_system_score_codex":0.000014701392,"about_ca_system_score_gemma":0.000020988002,"threshold_uncertainty_score":0.37167022},"labels":[],"label_agreement":null},{"id":"W4392242266","doi":"10.1007/s10044-024-01214-x","title":"Adaptive spectral graph wavelets for collaborative filtering","year":2024,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Graph; Wavelet; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science","score_opus":0.023550959285316392,"score_gpt":0.2798330482072035,"score_spread":0.2562820889218871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392242266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047430574,0.00040399595,0.9970401,0.0007920892,0.000024034774,0.0003295829,0.00005194482,0.00015768674,0.0007262747],"genre_scores_gemma":[0.97061944,0.00007569476,0.028183108,0.00007291111,0.000075595555,0.00084220344,0.000016230886,0.0000057516368,0.00010906351],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999303,0.000016727285,0.00015388153,0.00033938402,0.00006792563,0.00011910212],"domain_scores_gemma":[0.99955726,0.00006743482,0.000037281716,0.00024050617,0.000050285373,0.000047262565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011999368,0.00008780587,0.00015202955,0.00021722227,0.0001192146,0.00029288995,0.00018801248,0.000025634481,0.0000066339503],"category_scores_gemma":[8.964766e-7,0.00007398287,0.00011392436,0.0010441857,0.000016008353,0.00013861238,0.000052905936,0.000044556844,0.0000043667364],"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":[0.0000013173589,0.000057837162,0.000921761,0.00007474907,0.0013603963,0.0000034078198,0.0010945569,0.000036900765,0.0015300451,0.3063908,0.0022869152,0.6862413],"study_design_scores_gemma":[0.0003227381,0.0002374306,0.008794947,0.00009910955,0.000982685,0.00001764517,0.00055075326,0.7449738,0.024192663,0.085812524,0.13300309,0.0010126431],"about_ca_topic_score_codex":0.00006666482,"about_ca_topic_score_gemma":0.000082352526,"teacher_disagreement_score":0.9701451,"about_ca_system_score_codex":0.000014919217,"about_ca_system_score_gemma":0.000012979872,"threshold_uncertainty_score":0.30169338},"labels":[],"label_agreement":null},{"id":"W4400936793","doi":"10.1007/s10044-024-01306-8","title":"Causal generative explainers using counterfactual inference: a case study on the Morpho-MNIST dataset","year":2024,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Counterfactual thinking; Artificial intelligence; Machine learning; Computer science; Generative grammar; Classifier (UML); Inference; Interpretability; MNIST database; Generative model; Pattern recognition (psychology); Artificial neural network; Psychology","score_opus":0.08872213112094182,"score_gpt":0.3729687433924339,"score_spread":0.28424661227149206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400936793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25369635,0.00005222173,0.7450216,0.00056747627,0.000022362106,0.00029912274,0.0002624891,0.000041441228,0.000036951886],"genre_scores_gemma":[0.99883884,0.000011338244,0.0003019091,0.00040471394,0.000070174145,0.00024809278,0.00008878558,0.0000071963236,0.000028940194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867034,0.000106948624,0.00025731683,0.00055078726,0.00022458145,0.00019004257],"domain_scores_gemma":[0.9987242,0.00033903695,0.000057768135,0.0007412119,0.000060439834,0.00007733525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034851307,0.00015736904,0.00016519238,0.00021498126,0.0005396509,0.00089318963,0.00041006578,0.000026699423,0.000060814422],"category_scores_gemma":[0.00001287395,0.000108914166,0.000060677383,0.0013455566,0.00008088781,0.00023292222,0.00018555956,0.00015449189,0.00007705175],"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":[0.00002374371,0.0044928407,0.057995573,0.0001500707,0.012734523,0.008330988,0.12115723,0.06562826,0.017546767,0.30563965,0.015114665,0.3911857],"study_design_scores_gemma":[0.00004846331,0.00008378532,0.00026428793,0.000010403154,0.0004940514,0.00010339731,0.011852855,0.98032534,0.0021200841,0.0005159533,0.0038951826,0.0002862082],"about_ca_topic_score_codex":0.003279159,"about_ca_topic_score_gemma":0.0020453,"teacher_disagreement_score":0.91469705,"about_ca_system_score_codex":0.000046481713,"about_ca_system_score_gemma":0.000045249068,"threshold_uncertainty_score":0.8613048},"labels":[],"label_agreement":null},{"id":"W4401536395","doi":"10.1007/s10044-024-01318-4","title":"Multilinear principal component analysis-based tensor decomposition for fabric weave pattern recognition from high-dimensional streaming data","year":2024,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Principal component analysis; Pattern recognition (psychology); Dimensionality reduction; Local binary patterns; Tensor (intrinsic definition); Multilinear map; Image (mathematics); Histogram; Mathematics","score_opus":0.03777358483708904,"score_gpt":0.29570965215031475,"score_spread":0.2579360673132257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401536395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30518854,0.00013568654,0.69107676,0.000045263514,0.00007386715,0.00035092884,0.0029653667,0.00015735692,0.0000062374456],"genre_scores_gemma":[0.9830095,0.000014652158,0.0011927417,0.00003482695,0.00038872406,0.00031028545,0.015012791,0.000026491187,0.000009950545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985926,0.000043284577,0.00042242795,0.000566102,0.00020047998,0.00017511524],"domain_scores_gemma":[0.9990132,0.000246604,0.0000743535,0.00049058715,0.00008167016,0.00009358258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000225317,0.0001950736,0.00036839006,0.00059238897,0.00017387386,0.00019219505,0.0001253243,0.00011356208,0.00010017056],"category_scores_gemma":[0.000005673987,0.00018239628,0.00023949149,0.0010571152,0.000017467852,0.00011503928,0.000040036895,0.00013577787,0.00003757865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014922711,0.0001199732,0.0077351695,0.00009310477,0.005226951,0.000003150875,0.000049010432,0.06984391,0.0052226204,0.0000036958652,0.00016668407,0.9115208],"study_design_scores_gemma":[0.00029235758,0.000015302941,0.020761248,0.000033747274,0.004391389,7.1194637e-7,0.000026899079,0.9709424,0.0022327437,0.000016487269,0.0010743305,0.00021241356],"about_ca_topic_score_codex":0.0014100439,"about_ca_topic_score_gemma":0.00061879837,"teacher_disagreement_score":0.9113084,"about_ca_system_score_codex":0.000059424085,"about_ca_system_score_gemma":0.000010900209,"threshold_uncertainty_score":0.74379045},"labels":[],"label_agreement":null},{"id":"W4401586093","doi":"10.1007/s10044-024-01322-8","title":"Prediction of rare events in the operation of household equipment using co-evolving time series","year":2024,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Series (stratigraphy); Computer science; Rare events; Probabilistic logic; Bernoulli's principle; Process (computing); Time series; Data mining; Bernoulli distribution; Bernoulli trial; Pattern recognition (psychology); Artificial intelligence; Machine learning; Statistics; Mathematics; Random variable","score_opus":0.03189501422677714,"score_gpt":0.2571539260109598,"score_spread":0.22525891178418267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401586093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09110123,0.00029152777,0.9081394,0.00015523868,0.0000068846357,0.0001290168,0.000044592376,0.000016815331,0.000115275834],"genre_scores_gemma":[0.99864256,0.0000407385,0.0011814903,0.000015440779,0.000022365917,0.000027877995,0.00003788066,0.0000032641294,0.000028361601],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992163,0.000042006115,0.00030450604,0.00019581526,0.00016410256,0.00007726664],"domain_scores_gemma":[0.99957097,0.000036958972,0.0000904768,0.00025200503,0.00003369971,0.00001591953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032368212,0.00006525829,0.00015805018,0.00021871463,0.00008468171,0.00006802911,0.000196934,0.000021588972,0.000021718064],"category_scores_gemma":[0.000002651971,0.00004778406,0.000092043854,0.0010628266,0.000024522677,0.0002638397,0.00005491099,0.000045546607,0.0000013995924],"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":[0.000008220082,0.0005807277,0.5128112,0.00058163423,0.0027570885,0.000005113493,0.012207829,0.1187185,0.07064835,0.016811896,0.00021855297,0.2646509],"study_design_scores_gemma":[0.000043827135,0.000018576535,0.04240489,0.000036134843,0.00027089647,0.000002635318,0.00018961258,0.95538116,0.0012085951,0.00018957302,0.00019647194,0.000057659443],"about_ca_topic_score_codex":0.00017546605,"about_ca_topic_score_gemma":0.000039186027,"teacher_disagreement_score":0.90754133,"about_ca_system_score_codex":0.000015000415,"about_ca_system_score_gemma":0.00001342192,"threshold_uncertainty_score":0.19485773},"labels":[],"label_agreement":null},{"id":"W4402640531","doi":"10.1007/s10044-024-01341-5","title":"Hidden Markov models with multivariate bounded asymmetric student’s t-mixture model emissions","year":2024,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algoma University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Bounded function; Hidden Markov model; Mixture model; Markov chain; Multivariate analysis; Mathematics; Pattern recognition (psychology); Markov model; Artificial intelligence; Computer science; Statistics; Econometrics; Mathematical analysis","score_opus":0.017264055253476147,"score_gpt":0.29756395414912024,"score_spread":0.2802998988956441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402640531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045223007,0.0008572061,0.9940324,0.001336142,0.000018335348,0.00030325065,0.000029638739,0.00019686604,0.0027739648],"genre_scores_gemma":[0.6385155,0.00013207736,0.359714,0.00028324334,0.000050005507,0.00024513877,0.00002179415,0.00001592195,0.0010223298],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982677,0.00007014927,0.0002738278,0.0008053082,0.00031349127,0.00026952114],"domain_scores_gemma":[0.99875057,0.00010512922,0.00007075034,0.00077385653,0.00008462168,0.00021508276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003073285,0.00022708728,0.00031514405,0.0005669724,0.0002712531,0.0006471955,0.0005819217,0.00008775643,0.000010703214],"category_scores_gemma":[0.0000029005462,0.00016301771,0.00017043829,0.0028133844,0.00004040934,0.0003482147,0.00021223658,0.00021896046,0.000010010824],"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":[0.0000018643159,0.00013585675,0.00056248566,0.000036669422,0.000920927,0.000007877168,0.0007413796,0.0013819288,0.00023261551,0.20336021,0.00028770984,0.79233044],"study_design_scores_gemma":[0.00012088989,0.000011957132,0.0010961074,0.000018419156,0.0005953154,0.000005906628,0.000013719886,0.9470674,0.000068093716,0.050221194,0.0005466352,0.00023436258],"about_ca_topic_score_codex":0.00009444694,"about_ca_topic_score_gemma":0.000053782005,"teacher_disagreement_score":0.94568545,"about_ca_system_score_codex":0.000033460703,"about_ca_system_score_gemma":0.00006526403,"threshold_uncertainty_score":0.66476697},"labels":[],"label_agreement":null},{"id":"W4406848163","doi":"10.1007/s10044-025-01411-2","title":"Separability and scatteredness (S&S) ratio-based efficient SVM regularization parameter, kernel, and kernel parameter selection","year":2025,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; McGill University","funders":"","keywords":"Kernel (algebra); Pattern recognition (psychology); Mathematics; Artificial intelligence; Regularization (linguistics); Support vector machine; Radial basis function kernel; Kernel method; Selection (genetic algorithm); Applied mathematics; Computer science; Combinatorics","score_opus":0.013679103178572296,"score_gpt":0.2721746611905991,"score_spread":0.2584955580120268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406848163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29542127,0.00007925391,0.70330274,0.0008093686,0.000016123238,0.00026864622,0.000010354214,0.000054720214,0.00003753377],"genre_scores_gemma":[0.9934025,0.00004625789,0.005354874,0.0006211972,0.000015436839,0.00029961564,0.00009004598,0.000005234756,0.00016483307],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858475,0.00010990665,0.0003068909,0.0006774384,0.00015208397,0.0001689334],"domain_scores_gemma":[0.99903077,0.00020938086,0.00012491836,0.0004210231,0.0001214968,0.00009242649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025895805,0.00016400816,0.00024398947,0.00031698556,0.0003418148,0.0003628378,0.0001490617,0.00008726699,0.000015231546],"category_scores_gemma":[0.00002932131,0.00014957662,0.00007286925,0.0011388074,0.000093830895,0.00014962556,0.00010301239,0.0000964652,0.0000066927723],"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":[0.000027982802,0.0007438242,0.41578194,0.00029145417,0.00062927324,6.212679e-7,0.00060446915,0.0067835567,0.012321021,0.004076758,0.0004642024,0.5582749],"study_design_scores_gemma":[0.00042470387,0.000020803054,0.13243565,0.00003313082,0.00041012408,0.000002017073,0.00003222581,0.85703623,0.005070315,0.0032646877,0.001014529,0.00025555465],"about_ca_topic_score_codex":0.00012944217,"about_ca_topic_score_gemma":0.00009015838,"teacher_disagreement_score":0.8502527,"about_ca_system_score_codex":0.000025204088,"about_ca_system_score_gemma":0.000024514786,"threshold_uncertainty_score":0.6099557},"labels":[],"label_agreement":null},{"id":"W4408298201","doi":"10.1007/s10044-025-01439-4","title":"Fedpartwhole: federated domain generalization via consistent part-whole hierarchies","year":2025,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"","keywords":"Generalization; Computer science; Domain (mathematical analysis); Pattern recognition (psychology); Artificial intelligence; Theoretical computer science; Mathematics","score_opus":0.009203864239641242,"score_gpt":0.24442495977706422,"score_spread":0.235221095537423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408298201","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015053082,0.0002854936,0.98209125,0.0014566615,0.000034532328,0.00015715593,0.000010059191,0.0000975796,0.0008142145],"genre_scores_gemma":[0.9958801,0.000042265205,0.0020312942,0.0008060529,0.000059732018,0.00014412127,0.00013457584,0.00000538496,0.0008964736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855095,0.00009821304,0.0004257197,0.0005348239,0.00017193408,0.0002183751],"domain_scores_gemma":[0.9992712,0.000055514483,0.00014059355,0.0003162592,0.0001373336,0.00007911407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023873092,0.00016468228,0.00030759448,0.0003780475,0.00047926418,0.00047328393,0.00032674178,0.000056668265,0.00003600573],"category_scores_gemma":[0.000005117736,0.00014799644,0.00019418934,0.0018430379,0.000064563756,0.00011665466,0.00014360562,0.00009802449,0.000027188744],"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":[0.0000042818715,0.0004556373,0.03564646,0.00006984874,0.004016523,0.000006501672,0.0010634359,0.014860338,0.0012313852,0.5678717,0.00235563,0.37241828],"study_design_scores_gemma":[0.00020144868,0.000010861666,0.0061242813,0.000017525119,0.00049455726,0.0000020640068,0.0000840693,0.96811944,0.00030716212,0.0006280171,0.023777394,0.00023316989],"about_ca_topic_score_codex":0.00006815174,"about_ca_topic_score_gemma":0.00054930657,"teacher_disagreement_score":0.98082703,"about_ca_system_score_codex":0.000027251283,"about_ca_system_score_gemma":0.000024097593,"threshold_uncertainty_score":0.603512},"labels":[],"label_agreement":null},{"id":"W4409446385","doi":"10.1007/s10044-025-01465-2","title":"Diffusion-based data augmentation and hierarchical CLIP for real estate image annotation","year":2025,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TransAlta (Canada); Vector Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Annotation; Computer science; Artificial intelligence; Pattern recognition (psychology); Image (mathematics); Diffusion; Computer vision; Physics","score_opus":0.020928141178853797,"score_gpt":0.33145289597028876,"score_spread":0.310524754791435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409446385","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026872566,0.000019527402,0.99334115,0.0030710017,0.0000074472578,0.0003615868,0.000097834396,0.00008834679,0.00032586887],"genre_scores_gemma":[0.91290396,0.0007984687,0.08351682,0.00061950687,0.000034768927,0.0006186851,0.0013003512,0.0000069161497,0.00020048638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909776,0.000036125424,0.00022124994,0.00044544513,0.00009935949,0.0001000447],"domain_scores_gemma":[0.99902177,0.00017342923,0.00009122584,0.00057002634,0.00009566465,0.00004786777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002327902,0.00008341125,0.00013099382,0.0002238824,0.00023489592,0.00021355305,0.0003336472,0.000032448286,0.0000034045509],"category_scores_gemma":[0.000015100913,0.000075593496,0.00004086568,0.0007638654,0.00006385425,0.00020465469,0.00013436547,0.000048126585,0.0000013853098],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004019862,0.00006773415,0.0066036545,0.00003359411,0.000061056,1.1019179e-7,0.00005275284,0.0000018325572,0.0046208086,0.00651819,0.00011017381,0.9819261],"study_design_scores_gemma":[0.00039261463,0.000022876178,0.09414338,0.000009097727,0.00033798098,3.0334866e-7,0.00004626553,0.88959527,0.005154093,0.003395628,0.0067438935,0.00015857072],"about_ca_topic_score_codex":0.00013398669,"about_ca_topic_score_gemma":0.000056400495,"teacher_disagreement_score":0.9817675,"about_ca_system_score_codex":0.000010874694,"about_ca_system_score_gemma":0.000029842431,"threshold_uncertainty_score":0.30826133},"labels":[],"label_agreement":null},{"id":"W4409923782","doi":"10.1007/s10044-025-01474-1","title":"Enhancing out-of-distribution learning in computer vision through dominant feature masking","year":2025,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Masking (illustration); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Computer science; Distribution (mathematics); Computer vision; Mathematics; Art; Visual arts","score_opus":0.009145509653189155,"score_gpt":0.2835948923947317,"score_spread":0.2744493827415425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409923782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047936984,0.00013429114,0.9938107,0.00078549894,0.000033106324,0.00012023425,0.0000023378125,0.000035184865,0.00028492964],"genre_scores_gemma":[0.9855886,0.000058934413,0.013891575,0.00016885797,0.000022892347,0.000034648176,0.00005286995,0.000002900475,0.00017872012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999062,0.00007445631,0.00026769986,0.0003324569,0.00012262729,0.00014076548],"domain_scores_gemma":[0.9994364,0.00009099152,0.00014319489,0.0002421652,0.000059901005,0.00002734609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002888141,0.000094542615,0.000218143,0.00019612786,0.00017041275,0.000109644876,0.00020442292,0.00005143495,0.0000068284075],"category_scores_gemma":[0.000010383895,0.00008978311,0.00008803055,0.0013441978,0.000027813778,0.00016300897,0.00013190652,0.00016925116,0.000005473002],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040236646,0.00011761946,0.04365905,0.00007023333,0.0002066925,0.0000027306285,0.0018716459,0.01693538,0.0054940553,0.021411223,0.00009526213,0.9101321],"study_design_scores_gemma":[0.00044544792,0.000031449945,0.14217342,0.000117262345,0.00015716079,0.0000010162931,0.00023635993,0.8307192,0.0028344367,0.0012570663,0.021795759,0.00023145083],"about_ca_topic_score_codex":0.00007031079,"about_ca_topic_score_gemma":0.00019623226,"teacher_disagreement_score":0.9807949,"about_ca_system_score_codex":0.000030405105,"about_ca_system_score_gemma":0.00001816603,"threshold_uncertainty_score":0.36612493},"labels":[],"label_agreement":null},{"id":"W4416529539","doi":"10.1007/s10044-025-01544-4","title":"Function approximations valid in both time and frequency domains using legendre moments","year":2025,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Control Systems and Identification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Legendre polynomials; Legendre wavelet; Legendre function; Frequency domain; Domain (mathematical analysis); Singularity; Method of moments (probability theory); Function (biology)","score_opus":0.007861278710084074,"score_gpt":0.23003910194530638,"score_spread":0.2221778232352223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416529539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26027042,0.00050200726,0.7379378,0.00006930471,0.000017459279,0.00027120725,0.000031367093,0.000046651636,0.0008537829],"genre_scores_gemma":[0.99930465,0.00005473113,0.00014857236,0.000019985604,0.00002425394,0.00016716101,0.00007798544,0.0000061175415,0.00019653072],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999427,0.000016866541,0.00023176015,0.00017325142,0.000060478476,0.00009061786],"domain_scores_gemma":[0.99972445,0.000016227388,0.000034379842,0.00017612881,0.00001992945,0.000028866367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010703511,0.00007911252,0.00015218559,0.0003443265,0.00009918536,0.00008085907,0.000044903623,0.000037658512,0.000022562188],"category_scores_gemma":[0.0000016749278,0.00008385966,0.000040083778,0.00071612326,0.000013542057,0.0000892696,0.000013910482,0.00004675447,0.000007258515],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055362416,0.00028259933,0.54540104,0.00043787193,0.0029679309,9.906637e-7,0.00069238036,0.033935774,0.21798256,0.011001504,0.00036150278,0.1869303],"study_design_scores_gemma":[0.00032734926,0.0000035059334,0.37696713,0.000024954566,0.000751566,7.2556946e-7,0.0001021646,0.6191651,0.00014451043,0.0012564828,0.0010846324,0.00017186937],"about_ca_topic_score_codex":0.0004256693,"about_ca_topic_score_gemma":0.0005489894,"teacher_disagreement_score":0.73903424,"about_ca_system_score_codex":0.000042584343,"about_ca_system_score_gemma":0.0000049594996,"threshold_uncertainty_score":0.34196976},"labels":[],"label_agreement":null},{"id":"W7117368275","doi":"10.1007/s10044-025-01559-x","title":"Partial derivative regularized knowledge transformation for deep neural networks generalization","year":2025,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Overfitting; Regularization (linguistics); Jacobian matrix and determinant; Deep learning; Generalization; Deep neural networks; Artificial neural network; Pooling","score_opus":0.012757568290011675,"score_gpt":0.284981592304012,"score_spread":0.2722240240140003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117368275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00072553335,0.0002579503,0.9963192,0.0015398961,0.000028537455,0.0008209109,0.000008803904,0.00011711349,0.00018208551],"genre_scores_gemma":[0.98212063,0.00007912791,0.01456938,0.00051570986,0.00008920592,0.0022917194,0.00023712187,0.0000073278384,0.00008974744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989164,0.00004797998,0.00034039665,0.000422079,0.00007187126,0.00020128436],"domain_scores_gemma":[0.9990918,0.00013418273,0.00012237609,0.00044192135,0.00014611208,0.000063574895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011744481,0.00013939188,0.0002148381,0.00021878026,0.0004344345,0.00012932412,0.00033059306,0.00005937253,0.000003889315],"category_scores_gemma":[0.000005651761,0.00013653681,0.00013464851,0.0021350707,0.00004481432,0.00025620934,0.00006668711,0.00006895266,0.0000019564363],"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":[0.0000052978617,0.00009441442,0.0015089833,0.000021693158,0.0002748606,5.344109e-8,0.00027497212,0.08079669,0.0005695222,0.21170634,0.00009415544,0.704653],"study_design_scores_gemma":[0.00026880775,0.000008167049,0.0027266433,0.0000025166314,0.0002563519,3.640773e-7,0.000013773389,0.98622817,0.00068582635,0.004905634,0.004775111,0.0001286282],"about_ca_topic_score_codex":0.000011107314,"about_ca_topic_score_gemma":0.00012625243,"teacher_disagreement_score":0.9817498,"about_ca_system_score_codex":0.000028619317,"about_ca_system_score_gemma":0.00001084855,"threshold_uncertainty_score":0.556781},"labels":[],"label_agreement":null}]}