{"meta":{"query_hash":"7154b7183c67","filters":{"topic":"Biometric Identification and Security"},"cohort_total":394,"direct_labels_cover":0,"predictions_cover":394,"exported":394,"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/7154b7183c67","api":"https://metacan.xera.ac/api/v1/cohort?topic=Biometric+Identification+and+Security"},"results":[{"id":"W1007063632","doi":"","title":"Dealing with data complexity: on neural networks and fusion in biometric research","year":2010,"lang":"en","type":"article","venue":"Journal of Medical Informatics & Technologies","topic":"Biometric Identification and Security","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":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Artificial neural network; Sensor fusion; Artificial intelligence; Biometric data","score_opus":0.16776780735986369,"score_gpt":0.3931490647114482,"score_spread":0.22538125735158454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1007063632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6296557,0.0006122672,0.35186532,0.01685215,0.00046318403,0.00017163838,0.0000027325677,0.00017899246,0.00019800267],"genre_scores_gemma":[0.9640084,0.000925538,0.03492682,0.000109423476,0.000023326358,7.180456e-7,0.0000018727098,0.0000024719589,0.0000014398307],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974901,0.000038074286,0.00059169356,0.000098905344,0.0015487908,0.00023239995],"domain_scores_gemma":[0.99832386,0.00047834427,0.00028206233,0.0006045843,0.00021220952,0.000098948025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005620463,0.00007770741,0.00018859887,0.0028639194,0.00011456945,0.00025137162,0.0028681273,0.0002483657,0.00000699427],"category_scores_gemma":[0.0018464444,0.000049371607,0.000014462602,0.0038292455,0.00051365496,0.00064285,0.0013278432,0.001997369,0.000002014189],"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":[0.000014370797,0.00009100206,0.0022588416,0.000028191827,0.000009666291,0.00004196731,0.0002321557,0.000019500638,0.0000140110305,0.040014714,0.00075043924,0.95652515],"study_design_scores_gemma":[0.00055076106,0.00028250754,0.0030638126,0.000108170374,0.0000025672246,0.0003676623,0.0010185719,0.98576874,0.00006937394,0.0031482044,0.0055252607,0.00009438918],"about_ca_topic_score_codex":0.000018067823,"about_ca_topic_score_gemma":0.00003992854,"teacher_disagreement_score":0.98574924,"about_ca_system_score_codex":0.000022741731,"about_ca_system_score_gemma":0.00007394269,"threshold_uncertainty_score":0.8677686},"labels":[],"label_agreement":null},{"id":"W103107239","doi":"10.1007/978-3-642-31298-4_3","title":"Multibiometric System Using Level Set Method and Particle Swarm Optimization","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"University of Waterloo","funders":"","keywords":"Particle swarm optimization; Computer science; Set (abstract data type); Multi-swarm optimization; Mathematical optimization; Algorithm; Mathematics; Programming language","score_opus":0.0881056531450336,"score_gpt":0.31104825135603764,"score_spread":0.22294259821100404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W103107239","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":[0.00012434243,0.0012529203,0.9965236,0.000120304474,0.0014469441,0.00029728713,0.0000114732875,0.00011633671,0.00010678188],"genre_scores_gemma":[0.21759439,0.000031529533,0.78194696,0.000175633,0.00016335568,0.0000022673753,0.0000047201065,0.0000160688,0.00006510651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715275,0.00008186473,0.0004585211,0.0010551662,0.00076526986,0.00048640597],"domain_scores_gemma":[0.99797696,0.00034287933,0.00030283755,0.0008787748,0.00027747743,0.00022105704],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019483754,0.00032058044,0.00036494285,0.001956395,0.0002905502,0.0006593274,0.001305588,0.00026910414,0.000006443748],"category_scores_gemma":[0.00012196027,0.00030282376,0.000067558365,0.0031448903,0.00022649774,0.0006022688,0.00090185367,0.0003219367,0.000014022425],"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":[0.0000033337285,0.00004002773,0.00015367632,0.00013211452,0.000018082972,0.000013659087,0.0011353914,0.15360963,0.00032632786,0.0182444,0.000007073887,0.8263163],"study_design_scores_gemma":[0.00017930113,0.00002683861,0.00016616672,0.00008621732,0.000012919165,0.00007873898,4.4549148e-7,0.99590033,0.001783412,0.0010891256,0.00032101787,0.0003554851],"about_ca_topic_score_codex":0.00005383223,"about_ca_topic_score_gemma":0.0000067945452,"teacher_disagreement_score":0.8422907,"about_ca_system_score_codex":0.0003445288,"about_ca_system_score_gemma":0.00020254176,"threshold_uncertainty_score":0.99994236},"labels":[],"label_agreement":null},{"id":"W11332215","doi":"10.1016/j.jnca.2010.03.019","title":"Special issue on: Recent advances and future directions in biometrics personal identification","year":2010,"lang":"en","type":"article","venue":"Journal of Network and Computer Applications","topic":"Biometric Identification and Security","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":"University of Waterloo","funders":"","keywords":"Computer science; Biometrics; Identification (biology); Data science; Computer security","score_opus":0.011090957049451913,"score_gpt":0.2596469420534914,"score_spread":0.24855598500403953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W11332215","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":[0.01500424,0.014495025,0.93291456,0.022424268,0.012196468,0.0007254184,0.00001367227,0.00008393144,0.002142398],"genre_scores_gemma":[0.15662974,0.22222374,0.3548094,0.003485099,0.26183665,0.00019492419,0.000044745044,0.00006197831,0.00071375346],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989682,0.00004295291,0.00039222877,0.0002283594,0.00023162978,0.00013663279],"domain_scores_gemma":[0.9990792,0.00011934175,0.00027346527,0.00019590413,0.00021237499,0.00011969118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005000745,0.000094428404,0.00014968052,0.00063014793,0.00020647871,0.00024840693,0.0003154862,0.00007675549,0.00002140397],"category_scores_gemma":[0.000009677479,0.00008526933,0.00003868578,0.0026673586,0.00006141173,0.00033545605,0.00006570204,0.0003388903,0.000007261168],"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":[0.0000042959146,0.00011795062,0.00062225456,0.0000067725146,0.0000076095907,0.0000011772589,0.00028886317,0.000007945149,0.000014088503,0.014215607,0.008072111,0.9766413],"study_design_scores_gemma":[0.00019587585,0.00004298241,0.043562014,0.000005278565,0.0000055645314,0.000073143536,0.000024236933,0.0053092903,0.000007653978,0.0020063594,0.94868326,0.00008434535],"about_ca_topic_score_codex":0.000001169945,"about_ca_topic_score_gemma":0.000013481882,"teacher_disagreement_score":0.97655696,"about_ca_system_score_codex":0.0000229831,"about_ca_system_score_gemma":0.000038412833,"threshold_uncertainty_score":0.34771824},"labels":[],"label_agreement":null},{"id":"W114022766","doi":"10.1007/978-3-642-31125-3_15","title":"Axis-Parallel Dimension Reduction for Biometric Research","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"University of Calgary","funders":"","keywords":"Computer science; Dimensionality reduction; Biometrics; Fingerprint (computing); Chaotic; Reduction (mathematics); Artificial neural network; Curse of dimensionality; Dimension (graph theory); Pattern recognition (psychology); Artificial intelligence; Mathematics","score_opus":0.0882276989861538,"score_gpt":0.33905918759727266,"score_spread":0.25083148861111887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W114022766","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":[0.000066094035,0.002304808,0.9907641,0.0011740929,0.0039753634,0.00083250337,0.0000071708673,0.00013744512,0.000738431],"genre_scores_gemma":[0.25102586,0.0003700173,0.74439645,0.00034306778,0.0014452277,0.00006025357,0.00003497351,0.00005296416,0.0022711914],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99505204,0.000081709215,0.0005573254,0.001597432,0.0017477889,0.0009636823],"domain_scores_gemma":[0.99609685,0.0007525078,0.00025927328,0.0016079036,0.0010167729,0.00026668172],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004622217,0.00036372844,0.00040524147,0.007924832,0.0006648869,0.0007206626,0.0030841655,0.0004693527,0.000025669533],"category_scores_gemma":[0.00035323756,0.00033717154,0.00015400724,0.0065634483,0.0008132832,0.0008238756,0.0011796093,0.0008550368,0.00014650311],"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":[0.000009111381,0.00008074984,0.000010585487,0.00005536435,0.000009053421,0.00000423977,0.0005497753,0.00041249773,0.0009237928,0.06734315,0.00045864627,0.93014306],"study_design_scores_gemma":[0.0010571483,0.0006353213,0.00068161526,0.00035244922,0.000022011674,0.0002016115,0.0000011484075,0.42550367,0.009070297,0.44893706,0.111696556,0.0018411083],"about_ca_topic_score_codex":0.000034190296,"about_ca_topic_score_gemma":0.0000070553656,"teacher_disagreement_score":0.92830193,"about_ca_system_score_codex":0.0005830305,"about_ca_system_score_gemma":0.00041297515,"threshold_uncertainty_score":0.99990803},"labels":[],"label_agreement":null},{"id":"W11477303","doi":"10.1201/9781315220444","title":"Biometric Inverse Problems","year":2018,"lang":"en","type":"book","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Fingerprint (computing); Artificial intelligence; Orientation (vector space); Iris recognition; Pattern recognition (psychology); Computer vision; Algorithm; Mathematics","score_opus":0.036873764057740996,"score_gpt":0.24019503495878808,"score_spread":0.20332127090104707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W11477303","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[9.765181e-7,0.00025101166,0.22918195,0.00023828544,0.0012735749,0.00020884551,0.0000075883813,0.00034250534,0.76849526],"genre_scores_gemma":[0.000025084102,0.00009363605,0.019568924,0.00052074715,0.00019599809,0.000008295797,0.000046791574,0.00001335077,0.9795272],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982767,0.00003310879,0.00031434125,0.0006125314,0.0005249808,0.00023832801],"domain_scores_gemma":[0.9982107,0.00006758138,0.00019743094,0.0011067075,0.0002724995,0.00014507493],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00043735898,0.00020509041,0.00022197822,0.0033458876,0.00009045181,0.00035229506,0.0017902848,0.00034085495,0.00079925626],"category_scores_gemma":[0.00007133892,0.00018014647,0.0001230769,0.0038294734,0.0001125829,0.0002591886,0.00045970225,0.00019539971,0.008164179],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.6296909e-7,0.000024448911,0.000002034219,0.000033328368,0.000016423572,0.0000021891888,0.00005392179,2.930347e-8,0.0000039292577,0.08689782,0.8987942,0.014171529],"study_design_scores_gemma":[0.000091824324,0.00003468709,0.000023013323,0.000014019779,0.000005960032,0.0000046718283,7.4773226e-7,0.0016652619,0.000035112338,0.01656037,0.98132205,0.00024227874],"about_ca_topic_score_codex":0.000018758094,"about_ca_topic_score_gemma":0.000015181897,"teacher_disagreement_score":0.2110319,"about_ca_system_score_codex":0.00017903122,"about_ca_system_score_gemma":0.0003886417,"threshold_uncertainty_score":0.9926081},"labels":[],"label_agreement":null},{"id":"W148418788","doi":"10.1007/978-3-642-21596-4_12","title":"Fingerprint Verification Using Rotation Invariant Feature Codes","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Thresholding; Pattern recognition (psychology); Fingerprint (computing); Principal component analysis; Curse of dimensionality; Dimensionality reduction; Rotation (mathematics); Invariant (physics); Gabor filter; Discriminant; Computer vision; Feature extraction; Image (mathematics); Mathematics","score_opus":0.04325197266084318,"score_gpt":0.2612803189287392,"score_spread":0.21802834626789602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W148418788","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":[0.0001572882,0.00034541672,0.99495,0.00049679796,0.0020908162,0.00036237467,0.0000060833563,0.00014103139,0.0014501669],"genre_scores_gemma":[0.23844904,0.00008646379,0.75971454,0.00085837994,0.00040981642,0.000009814237,0.0000225349,0.00003174147,0.00041768208],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969205,0.000051950286,0.00044258477,0.0013510546,0.00081896957,0.0004149068],"domain_scores_gemma":[0.99747515,0.00016583003,0.00042661588,0.0013908428,0.00039993707,0.000141653],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010762204,0.0003730302,0.00035451644,0.0014725835,0.00030763316,0.000695789,0.002475226,0.00040110553,0.00003230827],"category_scores_gemma":[0.00013319032,0.00036102388,0.000103136386,0.0013754264,0.000402505,0.00070452027,0.0006526154,0.0006786928,0.000060393395],"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":[0.000008238671,0.00007098484,0.000068686684,0.00006403081,0.000018448736,0.000026532949,0.002583841,0.0035492443,0.0019214791,0.18178894,0.000068523244,0.8098311],"study_design_scores_gemma":[0.00024443012,0.000077087905,0.0014975362,0.00027247047,0.000016263713,0.00008318518,3.0760094e-7,0.81299394,0.0049712136,0.17371367,0.0052438113,0.0008860525],"about_ca_topic_score_codex":0.00008566443,"about_ca_topic_score_gemma":0.000049393144,"teacher_disagreement_score":0.8094447,"about_ca_system_score_codex":0.00039343003,"about_ca_system_score_gemma":0.0005562031,"threshold_uncertainty_score":0.9998842},"labels":[],"label_agreement":null},{"id":"W1504520402","doi":"10.1007/978-3-642-02611-9_45","title":"Automated Multimodal Biometrics Using Face and Ear","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"Biometrics; Computer science; Face (sociological concept); Artificial intelligence; Facial recognition system; Speech recognition; Computer vision; Three-dimensional face recognition; Pattern recognition (psychology); Face detection","score_opus":0.031123738744381794,"score_gpt":0.2803288383741673,"score_spread":0.2492050996297855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1504520402","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":[0.00054367055,0.00090306386,0.9958985,0.00042713623,0.0010705814,0.00028943634,0.000007718748,0.00045331565,0.0004065726],"genre_scores_gemma":[0.21193095,0.00010476775,0.7864584,0.0009931595,0.00017226503,0.0000015010376,0.000006973613,0.000024597912,0.00030742298],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965302,0.000036418038,0.00048049982,0.0014217817,0.0009921497,0.0005389702],"domain_scores_gemma":[0.9979159,0.00027339635,0.00028213987,0.0010356111,0.0002709042,0.0002220103],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009129298,0.00041675323,0.00043525628,0.00408577,0.00030393552,0.001012995,0.0021895962,0.0003996232,0.000007623758],"category_scores_gemma":[0.0001662556,0.00039973733,0.00008169568,0.0042402395,0.00057876133,0.00060222513,0.0009163509,0.0005498644,0.000025066785],"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":[0.0000017781695,0.000037505153,0.000046008852,0.000025938947,0.000007718973,0.00004376416,0.0004899511,0.0030732942,0.00041990442,0.004365803,0.000023046507,0.9914653],"study_design_scores_gemma":[0.00020502205,0.00006521787,0.00081755844,0.00008087549,0.0000059130143,0.00007612467,1.1830422e-7,0.9849724,0.00060717773,0.011107713,0.0015817912,0.00048005502],"about_ca_topic_score_codex":0.000051492956,"about_ca_topic_score_gemma":0.000009092725,"teacher_disagreement_score":0.9909852,"about_ca_system_score_codex":0.00028291944,"about_ca_system_score_gemma":0.0003486547,"threshold_uncertainty_score":0.99984545},"labels":[],"label_agreement":null},{"id":"W1504994924","doi":"10.1007/978-3-540-25948-0_103","title":"Feature-Level Fusion for Effective Palmprint Authentication","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Hamming distance; Fusion; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Biometrics; Code (set theory); Authentication (law); Gabor filter; Feature extraction; Computer vision; Algorithm","score_opus":0.025385588412356736,"score_gpt":0.2673124641116527,"score_spread":0.24192687569929597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1504994924","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":[0.000054213262,0.0002497222,0.99264324,0.0027483178,0.0023663389,0.0012696545,0.0000185312,0.00014789974,0.00050208735],"genre_scores_gemma":[0.28003886,0.00004213215,0.7172246,0.0010337638,0.00042923828,0.000086836684,0.00003713823,0.00003576259,0.0010716692],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967778,0.00003014201,0.00034195516,0.0015265443,0.0008390665,0.0004845458],"domain_scores_gemma":[0.99727094,0.0004789353,0.00031420012,0.0012875146,0.0004930785,0.00015535661],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011377641,0.00040071868,0.00038424123,0.0013862053,0.00033922616,0.0005875653,0.0025308211,0.00040742455,0.000012339327],"category_scores_gemma":[0.00022279941,0.00036443537,0.00018135789,0.001129717,0.00040176456,0.0004005859,0.00076600333,0.00053831295,0.000051577776],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006301753,0.00004595425,0.000009529865,0.000064795546,0.0000095526375,0.0000056589947,0.0007451171,0.0007736464,0.0005005508,0.15828268,0.00004519269,0.83951104],"study_design_scores_gemma":[0.0009101691,0.0002744728,0.003197181,0.0004245686,0.000021398524,0.00003783611,2.2847263e-7,0.28357655,0.0092647355,0.6898729,0.011374278,0.0010456964],"about_ca_topic_score_codex":0.000016873577,"about_ca_topic_score_gemma":0.000030803796,"teacher_disagreement_score":0.83846533,"about_ca_system_score_codex":0.00064914563,"about_ca_system_score_gemma":0.00050834124,"threshold_uncertainty_score":0.99988073},"labels":[],"label_agreement":null},{"id":"W1506397497","doi":"10.5539/cis.v8n3p155","title":"Authentication systems: principles and threats","year":2015,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Biometrics; Security token; Computer security; Authentication (law); Confidentiality; Identity (music)","score_opus":0.06492891137933132,"score_gpt":0.2790403191516236,"score_spread":0.21411140777229226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506397497","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":[0.029648885,0.00020548684,0.9663324,0.00049202994,0.0006156354,0.00013151305,0.0000012554949,0.00009289562,0.0024799465],"genre_scores_gemma":[0.97753686,0.0000574843,0.022046722,0.00027673543,0.000024391897,0.000005118205,0.0000026937064,8.153603e-7,0.00004918641],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991253,0.000017360526,0.00020993836,0.00015696441,0.00036838718,0.00012207386],"domain_scores_gemma":[0.9991161,0.000022074,0.00009175811,0.00025582244,0.000338456,0.00017574064],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010436217,0.00005775948,0.00006453153,0.00041476454,0.00018337271,0.0013405419,0.00040555594,0.000023615296,4.7482234e-7],"category_scores_gemma":[0.00005484777,0.000048145106,0.000007009187,0.0010451858,0.00018759875,0.008370481,0.00030615053,0.000037807637,0.000036915655],"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":[9.974015e-7,0.000011095819,0.00083734334,0.000021162346,0.0000016666216,1.6224035e-7,0.0056333886,0.000047522604,0.00002135669,0.85385174,0.00040767883,0.13916591],"study_design_scores_gemma":[0.00019341504,0.000031285108,0.036929227,0.000008642805,0.0000012278241,0.000036440204,0.00010728231,0.92044526,0.0000751,0.0008611995,0.041214403,0.00009651145],"about_ca_topic_score_codex":0.000008513792,"about_ca_topic_score_gemma":1.5232322e-7,"teacher_disagreement_score":0.94788796,"about_ca_system_score_codex":0.000029645043,"about_ca_system_score_gemma":0.00010808628,"threshold_uncertainty_score":0.99969614},"labels":[],"label_agreement":null},{"id":"W1511102755","doi":"10.1109/isba.2015.7126360","title":"Prior resemblance probability of users for multimodal biometrics rank fusion","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Rank (graph theory); Artificial intelligence; Face (sociological concept); Identity (music); Key (lock); Pattern recognition (psychology); Machine learning; Mathematics; Computer security","score_opus":0.09630601291370634,"score_gpt":0.3167083030286391,"score_spread":0.2204022901149328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1511102755","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":[0.12960674,0.00009000845,0.867694,0.0008843094,0.00045434438,0.00053739676,0.000009990934,0.000099333956,0.00062383234],"genre_scores_gemma":[0.7135491,0.000006929495,0.2859253,0.00006104976,0.000018358596,0.000017935623,0.0000048783063,0.0000032677221,0.0004131931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987624,0.000052609954,0.00029629166,0.0003247187,0.00039067704,0.00017328929],"domain_scores_gemma":[0.99832284,0.00018395329,0.00012341038,0.00059014966,0.0006489992,0.00013062608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001397958,0.00007622082,0.00014955671,0.0006205998,0.000052299416,0.0000635809,0.00071909185,0.000074620635,0.0000067784918],"category_scores_gemma":[0.0012663284,0.00006284114,0.000067991416,0.003874382,0.000060032722,0.0002873602,0.00016814345,0.00004804924,0.000015007401],"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":[0.00034984938,0.0027922627,0.032050256,0.0006165741,0.00007129377,0.0000026313019,0.0047293236,0.00008570544,0.018147748,0.24377474,0.06591623,0.6314634],"study_design_scores_gemma":[0.007936585,0.00085996016,0.0779422,0.000031073123,0.000022708533,0.000007637299,0.00025323816,0.6233791,0.11176226,0.037021212,0.13988034,0.0009036497],"about_ca_topic_score_codex":0.00011806721,"about_ca_topic_score_gemma":0.000014309786,"teacher_disagreement_score":0.63055974,"about_ca_system_score_codex":0.000061456674,"about_ca_system_score_gemma":0.00012805918,"threshold_uncertainty_score":0.25625876},"labels":[],"label_agreement":null},{"id":"W1511389334","doi":"","title":"Biometric technologies and applications","year":2007,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Identification (biology); Focus (optics); Computer security; Data science; Biology","score_opus":0.01795172642647677,"score_gpt":0.2656345267468119,"score_spread":0.24768280032033516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1511389334","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":[0.00090978434,0.000528212,0.98862165,0.0007280485,0.000039468425,0.00008412752,3.614066e-7,0.0005481018,0.0085402485],"genre_scores_gemma":[0.91190726,0.0000755288,0.08722886,0.00009887861,0.000008650698,0.000008954624,7.5212506e-7,0.0000013609053,0.00066976243],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99954903,0.0000030114081,0.00009102199,0.00016299076,0.000093068425,0.000100853336],"domain_scores_gemma":[0.9995641,0.00006176286,0.000023119104,0.00028282613,0.00004075767,0.000027443148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030914278,0.000034405442,0.00003683587,0.001126372,0.00007042673,0.0000859337,0.00034774755,0.000042669373,0.000005722777],"category_scores_gemma":[0.000036810085,0.000028906525,0.000010026618,0.0054620155,0.000049089333,0.00012716526,0.0001360645,0.000037389458,0.000053419048],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.245955e-8,0.000013320389,0.00037106822,0.0000014892928,0.0000010074422,2.3275142e-7,0.0000094631805,5.403323e-9,0.00022412706,0.3570128,0.0003025097,0.6420639],"study_design_scores_gemma":[0.00018820088,0.000025596513,0.052187964,0.0000012297246,0.0000026165606,0.000026751612,0.00034183133,0.0012888062,0.02213348,0.032371175,0.89121,0.00022235066],"about_ca_topic_score_codex":0.000009713563,"about_ca_topic_score_gemma":0.000003955495,"teacher_disagreement_score":0.91099745,"about_ca_system_score_codex":0.000010149105,"about_ca_system_score_gemma":0.0000063917046,"threshold_uncertainty_score":0.26243174},"labels":[],"label_agreement":null},{"id":"W1515847688","doi":"10.1007/978-3-540-79187-4_17","title":"A Robust Authentication System Using Multiple Biometrics","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Biometric Identification and Security","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":"University of Calgary","funders":"","keywords":"Biometrics; Voting; Computer science; Linear discriminant analysis; Artificial intelligence; Majority rule; Pattern recognition (psychology); Authentication (law); Signature recognition; Optimal distinctiveness theory; Machine learning; Data mining; Computer security; Psychology","score_opus":0.3431434100700742,"score_gpt":0.3597396008448261,"score_spread":0.016596190774751884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515847688","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":[0.000050163555,0.0075290003,0.98286563,0.00011747031,0.0018295917,0.00039452512,0.000041900523,0.0001745565,0.0069971606],"genre_scores_gemma":[0.53827965,0.0050839926,0.41945884,0.00023861702,0.00040312234,0.00006413589,0.00020614233,0.000099072204,0.03616645],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997139,0.000054265074,0.0009476845,0.0007110096,0.0008727436,0.00027532503],"domain_scores_gemma":[0.99685466,0.0010071122,0.00051493605,0.00053442316,0.0010087366,0.00008012656],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051610393,0.00035102436,0.00048480774,0.002515454,0.00029013917,0.000107249056,0.0010545409,0.00022690171,0.000010703725],"category_scores_gemma":[0.00035542547,0.00037134392,0.00015100758,0.0017152162,0.0003939098,0.0002516004,0.0005386336,0.0003395771,0.00020220593],"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":[0.0000078696385,0.00011224048,0.00008134896,0.00045949811,0.0002548624,0.00009364111,0.0038628052,0.08075895,0.0000029687305,0.87880874,0.0017222337,0.03383483],"study_design_scores_gemma":[0.00010406072,0.00003065999,0.00008203342,0.0004194859,0.000021113845,0.00012517702,0.00030588152,0.9721906,0.00001722683,0.016519576,0.009654391,0.0005297816],"about_ca_topic_score_codex":0.000028448701,"about_ca_topic_score_gemma":0.000009186468,"teacher_disagreement_score":0.8914317,"about_ca_system_score_codex":0.000932523,"about_ca_system_score_gemma":0.0002144088,"threshold_uncertainty_score":0.9998739},"labels":[],"label_agreement":null},{"id":"W1535203525","doi":"10.1007/11608288_65","title":"Iris Recognition with Support Vector Machines","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":46,"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; Concordia University","keywords":"Artificial intelligence; Computer science; Iris recognition; Support vector machine; Pattern recognition (psychology); Thresholding; Eyelash; Hough transform; IRIS (biosensor); Canny edge detector; Computer vision; Classifier (UML); Artificial neural network; Edge detection; Biometrics; Image processing; Image (mathematics)","score_opus":0.02317202760250498,"score_gpt":0.246878880862528,"score_spread":0.22370685326002301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1535203525","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":[0.000086713844,0.0001886734,0.99177796,0.0015885076,0.0011586657,0.00030209066,0.000015861011,0.00020417605,0.0046773404],"genre_scores_gemma":[0.09052068,0.00011686922,0.9018851,0.0035318846,0.0010635754,0.000019538476,0.000069360554,0.000054805543,0.0027381913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965995,0.000029574248,0.00043859484,0.0013812465,0.0010621524,0.000488948],"domain_scores_gemma":[0.9978174,0.000203672,0.00029026764,0.0011461545,0.00036093462,0.00018158686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007285492,0.0004198819,0.00038276686,0.0015176802,0.00022702519,0.0007000032,0.00233365,0.0002703593,0.00018615574],"category_scores_gemma":[0.000059475973,0.00034789953,0.00009442179,0.0014629911,0.0005038705,0.0007348814,0.00050754996,0.0006298156,0.0002757771],"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":[0.000005129956,0.000035069603,0.000039465955,0.000020340378,0.000008155102,0.00004105611,0.00034869707,0.00023291653,0.00003816576,0.0029098408,0.00012940545,0.99619174],"study_design_scores_gemma":[0.0020087168,0.0013604126,0.004127071,0.0008659533,0.00007009068,0.0011065961,5.16304e-7,0.64867795,0.0045943987,0.17258228,0.15990523,0.004700806],"about_ca_topic_score_codex":0.000030716586,"about_ca_topic_score_gemma":0.00012920887,"teacher_disagreement_score":0.99149096,"about_ca_system_score_codex":0.00024954558,"about_ca_system_score_gemma":0.0004986051,"threshold_uncertainty_score":0.9998973},"labels":[],"label_agreement":null},{"id":"W1535586468","doi":"","title":"Iris recognition using genetic algorithms and asymmetrical SVMs","year":2010,"lang":"en","type":"article","venue":"Machine Graphics & Vision International Journal archive","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Iris recognition; Biometrics; Computer science; Support vector machine; Artificial intelligence; IRIS (biosensor); Pattern recognition (psychology); Preprocessor; Feature selection; Fitness function; Feature extraction; Machine learning; Genetic algorithm","score_opus":0.021833631796148445,"score_gpt":0.30500601693740687,"score_spread":0.2831723851412584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1535586468","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":[0.1634565,0.00014555304,0.83156115,0.0019462601,0.0025080256,0.000081151105,0.000046822362,0.000037077258,0.00021749003],"genre_scores_gemma":[0.61970896,0.0005121083,0.3785954,0.0006734355,0.0004445707,0.0000028214138,0.000028351848,0.000013158402,0.000021223757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804443,0.00013112776,0.000434592,0.00035015118,0.0008387287,0.00020094693],"domain_scores_gemma":[0.99860317,0.00022398304,0.00026202181,0.00021440617,0.00044287526,0.00025351913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066441856,0.00015095306,0.0001380656,0.0018634745,0.00031590805,0.00080599793,0.0009380872,0.00007986431,0.00006282095],"category_scores_gemma":[0.00028324168,0.00013200707,0.00012633023,0.00092010375,0.00012620652,0.00037642036,0.00035613473,0.00089162955,0.000021742137],"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":[0.00003190556,0.0003120876,0.0062415856,0.000005790308,0.00011384573,0.0001262238,0.0005210036,0.000012211797,0.00799781,0.018044572,0.00084526377,0.9657477],"study_design_scores_gemma":[0.0010239638,0.00013787836,0.12941864,0.00003219995,0.000021477463,0.0030023153,0.000016647538,0.68561655,0.00031365006,0.15907475,0.020986829,0.00035509752],"about_ca_topic_score_codex":0.00012894862,"about_ca_topic_score_gemma":0.000018539045,"teacher_disagreement_score":0.9653926,"about_ca_system_score_codex":0.00002644989,"about_ca_system_score_gemma":0.000056687,"threshold_uncertainty_score":0.7772256},"labels":[],"label_agreement":null},{"id":"W1537451443","doi":"10.5772/19453","title":"Fingerprint Spoof Detection By NIR Optical Analysis","year":2011,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Biometric Identification and Security","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":"Algonquin College; National Research Council Canada; Institute for Microstructural Sciences","funders":"","keywords":"Fingerprint (computing); Computer science; Pattern recognition (psychology); Artificial intelligence","score_opus":0.027026830830006305,"score_gpt":0.23551754763241972,"score_spread":0.2084907168024134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1537451443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000030791583,0.0000877353,0.63322645,0.00003828119,0.00032437322,0.000112846756,0.000010065436,0.00020146396,0.365968],"genre_scores_gemma":[0.51007426,0.000022135226,0.0039560683,0.00020095846,0.00007479233,0.00002191713,0.000018680565,0.000028811537,0.48560235],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99818057,0.000020157058,0.00044871352,0.00069988036,0.00042219425,0.00022849045],"domain_scores_gemma":[0.99826944,0.00005653587,0.00024452351,0.0010732986,0.0002100189,0.00014618054],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003402788,0.000276006,0.00037904235,0.0012815745,0.00009724504,0.00019992217,0.0010958912,0.0004792013,0.00025161784],"category_scores_gemma":[0.0000357963,0.00027453955,0.00039331114,0.00022030348,0.00013339304,0.00006999364,0.00036432623,0.0005377234,0.00046172086],"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":[0.000011709089,0.00004720759,0.0000042287943,0.000025791252,0.00081777596,0.000026543263,0.00038303528,8.5951584e-8,0.009630997,0.23262845,0.0008043304,0.7556198],"study_design_scores_gemma":[0.00015574347,0.00010446218,0.000055904668,0.00003533432,0.00041048846,0.000023061388,0.0000041081053,0.00083643844,0.23052607,0.061000023,0.706048,0.0008003654],"about_ca_topic_score_codex":0.00008705857,"about_ca_topic_score_gemma":0.000070783775,"teacher_disagreement_score":0.75481945,"about_ca_system_score_codex":0.00013540835,"about_ca_system_score_gemma":0.00004953055,"threshold_uncertainty_score":0.9999707},"labels":[],"label_agreement":null},{"id":"W1543215536","doi":"10.1007/978-3-642-02611-9_42","title":"Level Set Approaches and Adaptive Asymmetrical SVMs Applied for Nonideal Iris Recognition","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"Concordia University","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Support vector machine; Iris recognition; Wavelet; Segmentation; Feature extraction; Daubechies wavelet; Wavelet transform; Feature (linguistics); Biometrics; Wavelet packet decomposition","score_opus":0.12809400847391386,"score_gpt":0.27001393483039593,"score_spread":0.14191992635648207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543215536","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":[0.00003280805,0.00030909895,0.9950894,0.00068599335,0.000546488,0.0007617227,0.00005147606,0.00009567622,0.0024273295],"genre_scores_gemma":[0.11776478,0.000050785315,0.88019943,0.0011994337,0.0003700481,0.00003403463,0.000059180747,0.000023652074,0.00029865987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658394,0.000029445939,0.0004749673,0.0016414202,0.00076355936,0.0005066878],"domain_scores_gemma":[0.9979699,0.0005772809,0.0002857781,0.0007194097,0.00025165864,0.00019592574],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012273035,0.00041906844,0.00046569706,0.0017931508,0.00030243382,0.0006406808,0.0015587953,0.00042736455,0.0000039681363],"category_scores_gemma":[0.000133265,0.00039954754,0.00011063028,0.0015253654,0.0005043978,0.00030386733,0.0005253138,0.00053312717,0.000019226341],"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":[0.000012467407,0.000030596148,0.0000033504193,0.000019659587,0.000008588876,0.0000033455522,0.00034058964,0.000107561966,0.000017861435,0.020345835,0.00008402315,0.97902614],"study_design_scores_gemma":[0.0010179782,0.00054417626,0.0014892687,0.00013410661,0.000029482702,0.00008853213,0.0000013778306,0.5145583,0.0010482955,0.47201747,0.0076853475,0.0013856549],"about_ca_topic_score_codex":0.0000127097455,"about_ca_topic_score_gemma":0.000024961933,"teacher_disagreement_score":0.97764045,"about_ca_system_score_codex":0.0002051899,"about_ca_system_score_gemma":0.00030455706,"threshold_uncertainty_score":0.9998456},"labels":[],"label_agreement":null},{"id":"W1546049643","doi":"10.1007/978-3-540-74260-9_76","title":"Iris Recognition Based on Zigzag Collarette Region and Asymmetrical Support Vector Machines","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","field":"Computer Science","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":"Concordia University","funders":"","keywords":"Support vector machine; Iris recognition; Computer science; Zigzag; Pattern recognition (psychology); Artificial intelligence; IRIS (biosensor); Segmentation; Feature (linguistics); Matching (statistics); Feature vector; Computer vision; Mathematics; Biometrics","score_opus":0.041335701624962126,"score_gpt":0.2699329665011318,"score_spread":0.22859726487616966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546049643","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":[0.00009742051,0.00015383384,0.9918075,0.0019332672,0.0016133772,0.00037429202,0.00001350918,0.0001547418,0.0038520156],"genre_scores_gemma":[0.385979,0.00017518277,0.5940459,0.017409569,0.0011024798,0.000020824324,0.00011445894,0.000088057575,0.0010645366],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99610907,0.000059291215,0.00052513496,0.0015386723,0.0012569671,0.0005108422],"domain_scores_gemma":[0.99723965,0.0008311112,0.0003084848,0.0010099504,0.00035908565,0.0002516939],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013850519,0.0004377283,0.00042779135,0.0035791586,0.00028146672,0.0006644265,0.0014858427,0.00045757162,0.000030429173],"category_scores_gemma":[0.00033884114,0.000404924,0.00011342093,0.002782276,0.00052214466,0.00035981182,0.0004152689,0.00075527094,0.000058787475],"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":[0.00001570508,0.000057142573,0.00013189671,0.000035512727,0.000005334652,0.00012590256,0.00013493067,0.00016881975,0.000018463146,0.0024668856,0.0003091736,0.99653023],"study_design_scores_gemma":[0.0009014867,0.00075321447,0.0039566318,0.00032731675,0.00002212695,0.00020309414,1.6859859e-7,0.9324321,0.001228177,0.038253415,0.020554459,0.0013677746],"about_ca_topic_score_codex":0.000025817097,"about_ca_topic_score_gemma":0.000026366717,"teacher_disagreement_score":0.9951625,"about_ca_system_score_codex":0.00031307095,"about_ca_system_score_gemma":0.00035379297,"threshold_uncertainty_score":0.99984026},"labels":[],"label_agreement":null},{"id":"W1557826151","doi":"10.1007/11608288_15","title":"Revealing the Secret of FaceHashing","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"University of Waterloo","funders":"","keywords":"Security token; Biometrics; Computer science; Authentication (law); Zero (linguistics); Computer security; Word error rate; Face (sociological concept); Artificial intelligence; Sociology","score_opus":0.02605703618294901,"score_gpt":0.259299749964127,"score_spread":0.233242713781178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557826151","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":[0.00010597221,0.0008232084,0.9926496,0.0022704825,0.0011330993,0.00024071177,0.000005032003,0.00006994274,0.002701942],"genre_scores_gemma":[0.5213906,0.00017723665,0.47380817,0.0025358386,0.0006854349,0.0000065818112,0.000006830726,0.00003183663,0.001357447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971505,0.000042557713,0.00054812717,0.0008688771,0.001015229,0.00037471266],"domain_scores_gemma":[0.9972887,0.00049641775,0.0003999282,0.0014665608,0.0002613092,0.000087108114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017587019,0.00027641622,0.0003435424,0.00089894264,0.0002421642,0.0004374062,0.0042717564,0.0002238707,0.000028906832],"category_scores_gemma":[0.00015259237,0.00020460847,0.00012460815,0.0013785111,0.00063196075,0.0004165736,0.0010315534,0.0006900238,0.000025954716],"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":[0.0000013544887,0.000017934759,0.000022815804,0.000029907169,0.00000845787,0.0000074572936,0.0016336329,0.0052423733,0.00016744237,0.06072472,0.00007390013,0.93207],"study_design_scores_gemma":[0.00030460724,0.00009423532,0.0004892203,0.00045688607,0.00001489457,0.0000769077,8.188065e-7,0.84125966,0.0044140248,0.11613268,0.035882723,0.0008733556],"about_ca_topic_score_codex":0.000030429743,"about_ca_topic_score_gemma":0.0000441747,"teacher_disagreement_score":0.93119663,"about_ca_system_score_codex":0.00015719408,"about_ca_system_score_gemma":0.0003477618,"threshold_uncertainty_score":0.8343691},"labels":[],"label_agreement":null},{"id":"W1560678294","doi":"10.5755/j02.eie.9170","title":"Robust Fingerprint Enhancement by Directional Filtering in Fourier Domain","year":2011,"lang":"en","type":"article","venue":"Elektronika ir Elektrotechnika","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Minutiae; Fingerprint (computing); Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Gabor filter; Fingerprint recognition; Domain (mathematical analysis); Image (mathematics); Mathematics","score_opus":0.03512992400641357,"score_gpt":0.21750876274300396,"score_spread":0.1823788387365904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1560678294","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":[0.03972975,0.0004766617,0.9532741,0.0005130766,0.0005252123,0.0004534781,0.000008690888,0.00039501465,0.004624063],"genre_scores_gemma":[0.64929205,0.00011814121,0.34807256,0.000328608,0.00008768111,0.00034631754,0.00002007293,0.000032255586,0.0017023303],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971701,0.0001257731,0.0005838858,0.00082591374,0.00058573193,0.0007085942],"domain_scores_gemma":[0.9985854,0.00004733358,0.00019089515,0.00092657196,0.00008666633,0.00016314162],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009846456,0.00029241774,0.00028885814,0.000658067,0.00019385984,0.00014627461,0.0012785181,0.00018773788,0.00045235155],"category_scores_gemma":[0.000054317337,0.00030980495,0.0001222817,0.0016336234,0.0000911053,0.000563739,0.00039154006,0.00046770903,0.00018035882],"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":[0.0001969906,0.004769197,0.005134476,0.00018846297,0.00024577044,0.00016818344,0.008584771,0.00009940177,0.21346493,0.2750656,0.053731322,0.4383509],"study_design_scores_gemma":[0.0030057458,0.0005850189,0.025579078,0.00016545913,0.000020708485,0.00010271512,0.0002382577,0.016064519,0.39385596,0.029037034,0.5288342,0.00251131],"about_ca_topic_score_codex":0.0002549504,"about_ca_topic_score_gemma":0.00004371318,"teacher_disagreement_score":0.6095623,"about_ca_system_score_codex":0.00038453282,"about_ca_system_score_gemma":0.00009767603,"threshold_uncertainty_score":0.9999354},"labels":[],"label_agreement":null},{"id":"W1586011873","doi":"10.1007/978-3-642-27733-7_229-2","title":"Multibiometrics and Data Fusion Standardization","year":2014,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Biometrics","topic":"Biometric Identification and Security","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 Calgary","funders":"","keywords":"Standardization; Computer science; Operating system","score_opus":0.03767809695819471,"score_gpt":0.2769305248120929,"score_spread":0.2392524278538982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1586011873","genre_codex":"methods","genre_gemma":"other","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":[0.000017536066,0.0059238495,0.8321222,0.00016687767,0.0014429002,0.00033430822,0.00075968815,0.00012943044,0.1591032],"genre_scores_gemma":[0.009167369,0.14283274,0.31178752,0.0003748402,0.0009502475,0.00000857855,0.004784789,0.00021693912,0.529877],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963452,0.000049130922,0.00087403296,0.0011309296,0.0013321255,0.00026856048],"domain_scores_gemma":[0.99507236,0.0006001166,0.0008981736,0.0026085945,0.00059761346,0.0002231535],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017333446,0.0003728637,0.00061483023,0.010855544,0.000126643,0.00019697397,0.0025299478,0.0005776547,0.00006375786],"category_scores_gemma":[0.0015657763,0.00037062698,0.000093114184,0.0066318796,0.0001865581,0.00039181253,0.0021700598,0.00030726576,0.000058202622],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043187642,0.000048178616,0.00009375007,0.00021540049,0.00005382389,0.000005290522,0.00009059239,5.951124e-7,0.000018043896,0.12208105,0.023530386,0.8538586],"study_design_scores_gemma":[0.0003183442,0.000091305876,0.00035094225,0.000044096865,0.000054231423,0.0000068375325,0.0000026730704,0.004187684,0.0000400846,0.0025359655,0.99195504,0.0004127969],"about_ca_topic_score_codex":0.000019107852,"about_ca_topic_score_gemma":0.0000039128217,"teacher_disagreement_score":0.9684247,"about_ca_system_score_codex":0.00009048363,"about_ca_system_score_gemma":0.00017523681,"threshold_uncertainty_score":0.9998746},"labels":[],"label_agreement":null},{"id":"W1598811766","doi":"10.1007/978-3-642-04474-8_12","title":"Iris Recognition in Nonideal Situations","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"","keywords":"Computer science; Iris recognition; IRIS (biosensor); Artificial intelligence; Motion blur; Biometrics; Feature (linguistics); Process (computing); Computer vision; Set (abstract data type); Pattern recognition (psychology); Gaze; Genetic algorithm; Algorithm; Image (mathematics); Machine learning","score_opus":0.03253299032054413,"score_gpt":0.26267220089363635,"score_spread":0.23013921057309222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1598811766","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":[0.0001157619,0.00022444836,0.9908099,0.0020768298,0.0010322909,0.0003153933,0.000007189525,0.00011820856,0.005299954],"genre_scores_gemma":[0.16629918,0.00024388626,0.82751244,0.004065648,0.0005176185,0.000018603645,0.00006475951,0.0000316517,0.0012462192],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99690014,0.000042916552,0.00055790873,0.0012078879,0.0008331107,0.0004580519],"domain_scores_gemma":[0.9981359,0.00028423045,0.0002337024,0.0009508846,0.00026625773,0.00012897415],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001070474,0.00031431022,0.00034064043,0.0025785083,0.0001783921,0.0005662662,0.002076342,0.00032299026,0.00004062733],"category_scores_gemma":[0.00014179759,0.00032373416,0.00009531006,0.002332785,0.000291254,0.0006637989,0.00040000444,0.0007101353,0.0001855108],"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":[0.0000014675344,0.000039603347,0.00002128177,0.000008069987,0.0000019081967,0.000027748647,0.00043177014,0.0004905519,0.000047852045,0.005006863,0.00004511507,0.99387777],"study_design_scores_gemma":[0.00050509436,0.00014575821,0.004030389,0.0003142397,0.0000071517634,0.00006805191,2.558809e-7,0.29119524,0.0005051165,0.69417846,0.008076488,0.00097376306],"about_ca_topic_score_codex":0.000056780293,"about_ca_topic_score_gemma":0.00028172907,"teacher_disagreement_score":0.992904,"about_ca_system_score_codex":0.00039280887,"about_ca_system_score_gemma":0.00047223322,"threshold_uncertainty_score":0.9999215},"labels":[],"label_agreement":null},{"id":"W1600049415","doi":"10.1007/978-3-642-13775-4_25","title":"Application of Wave Atoms Decomposition and Extreme Learning Machine for Fingerprint Classification","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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 Windsor","funders":"","keywords":"Computer science; Extreme learning machine; Pattern recognition (psychology); Fingerprint (computing); Artificial intelligence; Dimensionality reduction; Principal component analysis; Decomposition; Curse of dimensionality; Feature (linguistics); Fingerprint recognition; Algorithm; Artificial neural network","score_opus":0.03020231767106469,"score_gpt":0.2689959792688525,"score_spread":0.2387936615977878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1600049415","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":[0.00044731324,0.00023554628,0.9974904,0.0005513465,0.00044176032,0.00051163806,0.000006404761,0.00007191147,0.00024369705],"genre_scores_gemma":[0.6099883,0.00003763581,0.38968036,0.000101835976,0.000080452664,0.000018868232,0.000027213131,0.000011491979,0.000053884294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790096,0.000023877059,0.00045634835,0.00094060076,0.00044568544,0.00023251532],"domain_scores_gemma":[0.99802357,0.0003762495,0.00046820054,0.0006963161,0.00034787884,0.00008779866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009838843,0.00023073965,0.00028691723,0.0009243532,0.00023117714,0.00022968861,0.0008729019,0.00028957153,0.0000041552616],"category_scores_gemma":[0.000102554746,0.00022772109,0.00007316906,0.00056237367,0.0003930764,0.00028374908,0.00035803937,0.0005170771,0.0000038804264],"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":[0.0000050087438,0.000025543479,0.00012123989,0.000055911594,0.000004443047,6.117742e-7,0.00032262463,0.00021554879,0.011076537,0.06256616,0.0000011076149,0.92560524],"study_design_scores_gemma":[0.00017371461,0.00006668032,0.0016135629,0.0000508209,0.000006577096,0.000015957672,1.237757e-7,0.902975,0.005955861,0.08686494,0.0020395406,0.00023720565],"about_ca_topic_score_codex":0.000021301177,"about_ca_topic_score_gemma":0.000038832473,"teacher_disagreement_score":0.9253681,"about_ca_system_score_codex":0.00010319312,"about_ca_system_score_gemma":0.00012665056,"threshold_uncertainty_score":0.9286196},"labels":[],"label_agreement":null},{"id":"W1613978410","doi":"10.1109/mwscas.2015.7282214","title":"A novel normalization technique for multimodal biometric systems","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Normalization (sociology); Biometrics; Computer science; Artificial intelligence; Classifier (UML); Word error rate; Pattern recognition (psychology)","score_opus":0.07180104260609219,"score_gpt":0.29581880660181636,"score_spread":0.22401776399572418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1613978410","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":[0.00009828688,0.0000850547,0.9969533,0.00019548382,0.000648597,0.0005988732,0.000008133288,0.00020671789,0.0012055623],"genre_scores_gemma":[0.63205725,0.0000025743695,0.36626342,0.0001013127,0.00005364886,0.00020509616,0.000020894924,0.000006175661,0.0012896415],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991537,0.000019731262,0.00020274305,0.00023267737,0.00024299146,0.00014818959],"domain_scores_gemma":[0.9990075,0.00005246109,0.00007676012,0.00032298066,0.00042623424,0.00011402762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007486584,0.00007020829,0.00009043449,0.0011207281,0.000056150486,0.00022965802,0.00048894016,0.00007657314,0.0000022211918],"category_scores_gemma":[0.00023065376,0.00006107932,0.000034847137,0.004307504,0.00001487265,0.0003805438,0.00007982623,0.00003096948,0.00003319705],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012482627,0.00067226344,0.0006933087,0.000121640536,0.000030153735,0.0000012128785,0.00062161754,0.00020885278,0.03601571,0.9098686,0.03233299,0.019421197],"study_design_scores_gemma":[0.0009622857,0.00009938115,0.0006563034,0.0000065490776,0.0000045958586,0.00003387199,0.000059389648,0.8873897,0.015745152,0.0005390095,0.094248556,0.00025518058],"about_ca_topic_score_codex":0.00022249192,"about_ca_topic_score_gemma":0.000002793784,"teacher_disagreement_score":0.90932953,"about_ca_system_score_codex":0.000068214445,"about_ca_system_score_gemma":0.00007586616,"threshold_uncertainty_score":0.24907425},"labels":[],"label_agreement":null},{"id":"W1729451578","doi":"","title":"A dual-staged classification-selection approach for automated update of biometric templates","year":2012,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Regione Autonoma della Sardegna","keywords":"Computer science; Template; Data mining; Biometrics; Adaptation (eye); Artificial intelligence; Machine learning; Selection (genetic algorithm); Vulnerability (computing); Fingerprint (computing); Dual (grammatical number); Set (abstract data type); Pattern recognition (psychology); Computer security","score_opus":0.04097056034663706,"score_gpt":0.2921281740054009,"score_spread":0.2511576136587639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1729451578","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":[0.0671732,0.00048078925,0.92874676,0.0009119575,0.0006441733,0.00069735694,0.00003126614,0.0008068918,0.0005076276],"genre_scores_gemma":[0.8997898,0.000019182029,0.099302754,0.000057567304,0.000065976914,0.000072543844,0.000066760695,0.000012890995,0.0006124986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984149,0.000116869924,0.00036154364,0.00037516333,0.00035241563,0.00037913397],"domain_scores_gemma":[0.99855775,0.00017576535,0.0003280237,0.0004984438,0.00029476546,0.00014525969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011217993,0.00015511116,0.00021870191,0.0016964704,0.00019157707,0.00014413641,0.00046521184,0.00014405299,0.000021771126],"category_scores_gemma":[0.00030855884,0.00014641377,0.00009795905,0.0075369724,0.00005958617,0.00067967555,0.000083364335,0.00010619062,0.00006885167],"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":[0.0001773498,0.0056468733,0.048885085,0.00094886275,0.00055792613,0.0000014132771,0.009483512,0.0002301416,0.17276177,0.51536924,0.1519798,0.09395805],"study_design_scores_gemma":[0.0013098909,0.0001218306,0.14317915,0.000013256743,0.00004958297,0.000033340915,0.00029542993,0.74793327,0.04556297,0.00025341674,0.060635827,0.0006120613],"about_ca_topic_score_codex":0.00002873673,"about_ca_topic_score_gemma":0.000002401257,"teacher_disagreement_score":0.8326166,"about_ca_system_score_codex":0.000083518426,"about_ca_system_score_gemma":0.00006480891,"threshold_uncertainty_score":0.597058},"labels":[],"label_agreement":null},{"id":"W173890511","doi":"10.1007/978-1-4020-8823-0_39","title":"High Resolution Ultrasonic Method for 3D Fingerprint Representation in Biometrics","year":2008,"lang":"en","type":"book-chapter","venue":"Acoustical imaging","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":24,"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":"Biometrics; Fingerprint (computing); Artificial intelligence; Identification (biology); Ultrasonic sensor; Computer science; Computer vision; Pattern recognition (psychology); Feature (linguistics); Acoustics; Physics","score_opus":0.04273159744232875,"score_gpt":0.3173593112745971,"score_spread":0.27462771383226836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W173890511","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":[0.000004764536,0.00067462964,0.9899591,0.0010965883,0.00081301626,0.00042039243,0.000029404237,0.00015247142,0.0068495944],"genre_scores_gemma":[0.0072664414,0.0006328146,0.9775189,0.00039615933,0.00020314592,0.000038866543,0.00011312702,0.000039774783,0.013790768],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975382,0.000061654675,0.00061897776,0.00088157965,0.00051867514,0.00038088666],"domain_scores_gemma":[0.9973171,0.0013179523,0.00025241225,0.00070034375,0.00029409555,0.00011809778],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008727265,0.00025679154,0.00037336315,0.0021456694,0.0001434076,0.00020154659,0.000659141,0.00023764945,0.00003330177],"category_scores_gemma":[0.00127164,0.00028026346,0.00015263112,0.0011316593,0.000100937665,0.00031853234,0.00022071578,0.00042674254,0.00005862247],"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":[0.000019398565,0.000115280985,0.000044477852,0.00011136816,0.000041056854,0.00007603759,0.00027721503,0.0003220389,0.0007592404,0.24007554,0.013897381,0.74426097],"study_design_scores_gemma":[0.0005444023,0.000028189104,0.00076186383,0.00008426181,0.000045262517,0.00006427836,0.000007094536,0.9004937,0.0002493347,0.027196188,0.0699961,0.0005293171],"about_ca_topic_score_codex":0.00013028117,"about_ca_topic_score_gemma":0.000004857197,"teacher_disagreement_score":0.90017164,"about_ca_system_score_codex":0.00036684665,"about_ca_system_score_gemma":0.0001274253,"threshold_uncertainty_score":0.99996495},"labels":[],"label_agreement":null},{"id":"W181174070","doi":"10.1007/978-3-642-04667-4_40","title":"Nonideal Iris Recognition Using Level Set Approach and Coalitional Game Theory","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"Concordia University","funders":"","keywords":"Computer science; Iris recognition; Boundary (topology); Context (archaeology); Level set (data structures); Segmentation; Image segmentation; Algorithm; Set (abstract data type); Artificial intelligence; Function (biology); Selection (genetic algorithm); Mathematical optimization; Pattern recognition (psychology); Mathematics; Biometrics","score_opus":0.08976174746956248,"score_gpt":0.28253478690842165,"score_spread":0.19277303943885918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W181174070","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":[0.00024511546,0.0003633435,0.9963493,0.00026606218,0.00057332986,0.00027192725,0.00004600981,0.000080532154,0.0018044077],"genre_scores_gemma":[0.10991962,0.00006728333,0.88704973,0.0020902862,0.0004149913,0.000004993011,0.00008322854,0.000021638196,0.00034823953],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99691784,0.00008632116,0.00042624367,0.001298173,0.0008591537,0.00041227532],"domain_scores_gemma":[0.9983174,0.00028932292,0.00025225256,0.00069349847,0.00028581556,0.0001617153],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001644905,0.0003531697,0.0003418734,0.001246639,0.0002628489,0.0007029491,0.0013119922,0.0003230304,0.000015492622],"category_scores_gemma":[0.00009284048,0.00034607205,0.00008551535,0.0009137959,0.00065708614,0.0004767447,0.0005324001,0.00055452547,0.000017336104],"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":[0.000005923492,0.000035557066,0.000013214644,0.000028231465,0.000008267561,0.000012225046,0.00058677455,0.00058375095,0.00006206206,0.02219324,0.000025774867,0.97644496],"study_design_scores_gemma":[0.0003270447,0.000084667794,0.0012869497,0.00015364276,0.000013354119,0.0003322471,5.550528e-7,0.47188434,0.00017620738,0.52357066,0.0014620511,0.0007082687],"about_ca_topic_score_codex":0.0000217151,"about_ca_topic_score_gemma":0.0000084424755,"teacher_disagreement_score":0.97573674,"about_ca_system_score_codex":0.00020546118,"about_ca_system_score_gemma":0.0004096884,"threshold_uncertainty_score":0.99989915},"labels":[],"label_agreement":null},{"id":"W1830545159","doi":"10.1111/j.1541-1338.2011.00537.x","title":"Advances in Biometric Encryption: Taking Privacy by Design from Academic Research to Deployment","year":2012,"lang":"en","type":"article","venue":"Review of Policy Research","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Privacy Analytics (Canada)","funders":"","keywords":"Biometrics; Software deployment; Computer security; Encryption; Computer science; Internet privacy; Information privacy; Key (lock); Context (archaeology); Privacy by Design; Password","score_opus":0.36748250865886833,"score_gpt":0.5532340390646809,"score_spread":0.1857515304058126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1830545159","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045079463,0.8676197,0.10617254,0.016758211,0.00019861136,0.0022561813,0.000022712087,0.000062166386,0.0024019647],"genre_scores_gemma":[0.43009347,0.5515707,0.016921137,0.0005663951,0.0003600495,0.00028991458,0.000010705106,0.000018595068,0.0001690051],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9927114,0.0025653576,0.00069544715,0.0005024366,0.0024481814,0.001077136],"domain_scores_gemma":[0.9950246,0.0027661608,0.00016242749,0.0010123178,0.0005907369,0.00044374552],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.020327412,0.00012924484,0.00034836415,0.005745774,0.00016207554,0.00008776233,0.0025502746,0.00011579751,0.000094401235],"category_scores_gemma":[0.011599346,0.000114654285,0.00006524944,0.03566859,0.00015007434,0.0009002594,0.0010429438,0.00083019986,0.0005233662],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000838275,0.00026872283,0.001530579,0.0014032159,0.000007727441,0.0000013928885,0.0013071523,4.4269316e-7,0.0053778174,0.023082085,0.03178493,0.9352276],"study_design_scores_gemma":[0.00029619015,0.00016154326,0.0071360455,0.0036798273,0.0000032413145,0.000004008138,0.000091364454,0.0002835945,0.010233623,0.0038652741,0.97396964,0.00027565524],"about_ca_topic_score_codex":0.0008914032,"about_ca_topic_score_gemma":0.0000028152017,"teacher_disagreement_score":0.9421847,"about_ca_system_score_codex":0.000573738,"about_ca_system_score_gemma":0.0003989263,"threshold_uncertainty_score":0.9967264},"labels":[],"label_agreement":null},{"id":"W1834407027","doi":"10.1007/978-3-540-69939-2_28","title":"Improving Features Subset Selection Using Genetic Algorithms for Iris Recognition","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"Concordia University","funders":"","keywords":"Iris recognition; Computer science; Feature selection; Pattern recognition (psychology); Selection (genetic algorithm); Artificial intelligence; IRIS (biosensor); Gaussian; Feature (linguistics); Biometrics; Set (abstract data type); Machine learning; Data mining","score_opus":0.045710125798393224,"score_gpt":0.27104437797304654,"score_spread":0.2253342521746533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1834407027","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":[0.00022460587,0.0007005685,0.9959391,0.00016951497,0.0020867817,0.0006133663,0.000022926006,0.00015885233,0.00008429155],"genre_scores_gemma":[0.010024218,0.00010875263,0.9883496,0.00061326247,0.0006446577,0.000016195658,0.000029046341,0.000031790223,0.00018248244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670607,0.000035999692,0.0004902299,0.0014599517,0.0007552092,0.000552538],"domain_scores_gemma":[0.9979472,0.00026924475,0.00039626806,0.0006639828,0.0005849357,0.00013837783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062098046,0.000398482,0.00036800504,0.0017314608,0.00055715,0.0006428058,0.0015415641,0.00041450578,0.000009329281],"category_scores_gemma":[0.00014748958,0.00040465832,0.00015962715,0.0014946059,0.00032216733,0.000563868,0.0003663576,0.00053427264,0.00001388528],"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":[0.0000041530857,0.000023025606,0.00001312144,0.00004182958,0.00000791009,0.000010038771,0.0003342401,0.0021039513,0.00058113,0.00015196651,0.00011805353,0.9966106],"study_design_scores_gemma":[0.00029880015,0.00013032228,0.0003559295,0.0001015658,0.000014995232,0.00032013172,1.5118664e-7,0.97613305,0.0038192738,0.015880939,0.0022965334,0.0006483346],"about_ca_topic_score_codex":0.00011562768,"about_ca_topic_score_gemma":0.00004902383,"teacher_disagreement_score":0.99596226,"about_ca_system_score_codex":0.00045314262,"about_ca_system_score_gemma":0.00055853144,"threshold_uncertainty_score":0.99984056},"labels":[],"label_agreement":null},{"id":"W1837658860","doi":"10.1007/978-3-540-69812-8_89","title":"Optimal Features Subset Selection Using Genetic Algorithms for Iris Recognition","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Iris recognition; Computer science; Biometrics; Feature selection; Pattern recognition (psychology); Artificial intelligence; Selection (genetic algorithm); IRIS (biosensor); Principal component analysis; Feature (linguistics); Gaussian","score_opus":0.051759132888442756,"score_gpt":0.2805869966771369,"score_spread":0.22882786378869413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1837658860","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":[0.00041228635,0.00066991406,0.9958903,0.00022824144,0.0019214082,0.0005899223,0.000028336053,0.00014991952,0.000109624394],"genre_scores_gemma":[0.0076742503,0.00015063018,0.99071634,0.0005666442,0.0005968509,0.00001563736,0.000035345263,0.000029014875,0.00021526728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967037,0.000037543447,0.00047921002,0.0014355056,0.00079499226,0.00054907514],"domain_scores_gemma":[0.9980317,0.00025936353,0.0003277075,0.00065024407,0.0005846116,0.00014637521],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056262413,0.00040517276,0.00038038043,0.0016963745,0.0005172989,0.000589816,0.0016024319,0.00042552137,0.000014245861],"category_scores_gemma":[0.000104319246,0.00041093244,0.00016829705,0.0015332003,0.0003798044,0.00051390956,0.000342089,0.0005272654,0.000019694391],"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":[0.000008462464,0.000043075102,0.00001698366,0.00004051051,0.000015089793,0.000018705454,0.0005832276,0.016798625,0.00029083056,0.0002500369,0.00038538428,0.9815491],"study_design_scores_gemma":[0.0003283078,0.0001627058,0.00045268703,0.00010503405,0.000015571155,0.0004272016,1.5874396e-7,0.97885257,0.0028976856,0.01099836,0.005078103,0.0006816413],"about_ca_topic_score_codex":0.000050894974,"about_ca_topic_score_gemma":0.000024836336,"teacher_disagreement_score":0.98086745,"about_ca_system_score_codex":0.0003994267,"about_ca_system_score_gemma":0.00051752676,"threshold_uncertainty_score":0.99983424},"labels":[],"label_agreement":null},{"id":"W1880564946","doi":"10.1109/iadcc.2015.7154783","title":"Iris detection for gaze tracking using video frames","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Gaze; Computer vision; Computer science; Artificial intelligence; IRIS (biosensor); Tracking (education); Video tracking; Iris recognition; Eye tracking; Biometrics; Video processing; Psychology","score_opus":0.133010642437762,"score_gpt":0.3278201511301814,"score_spread":0.19480950869241942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1880564946","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":[0.019884644,0.000076128665,0.9785141,0.00028548684,0.00061911566,0.000103476435,6.19523e-7,0.00013420782,0.0003822298],"genre_scores_gemma":[0.852895,0.000001381463,0.14660002,0.00018210526,0.000057076082,0.0000050067442,6.349236e-7,0.0000030593262,0.00025567316],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994025,0.00002141378,0.00012219264,0.00018513977,0.00015078193,0.000117972435],"domain_scores_gemma":[0.9994374,0.000049240844,0.000046478715,0.00021162994,0.00018353292,0.00007171353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004041786,0.00004863511,0.000060150534,0.00019322745,0.000081978316,0.0002380858,0.00023971542,0.00005005681,0.0000063883053],"category_scores_gemma":[0.00018327517,0.000044586635,0.00003810985,0.0006861183,0.000012806953,0.00043550774,0.000041304724,0.000041519186,0.000020701658],"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":[0.000012454783,0.00012888899,0.00048140032,0.000029737235,0.000022720666,0.0000015266623,0.0023487569,0.00011286046,0.02100748,0.034001984,0.0068481197,0.93500406],"study_design_scores_gemma":[0.0004366185,0.000050997547,0.0010136289,0.0000055001756,0.0000064797364,0.000013847528,0.00015867017,0.8320405,0.06582398,0.008126564,0.09213545,0.00018777665],"about_ca_topic_score_codex":0.00008949361,"about_ca_topic_score_gemma":0.000014489798,"teacher_disagreement_score":0.9348163,"about_ca_system_score_codex":0.000051984458,"about_ca_system_score_gemma":0.000037818667,"threshold_uncertainty_score":0.22958669},"labels":[],"label_agreement":null},{"id":"W1887546054","doi":"10.1007/11527923_114","title":"Vulnerabilities in Biometric Encryption Systems","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":161,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Computer science; Encryption; Vulnerability (computing); Code (set theory); Computer security; Image (mathematics); Computer vision; Data mining; Artificial intelligence","score_opus":0.024611035481166027,"score_gpt":0.25335752553433877,"score_spread":0.22874649005317274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1887546054","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":[0.00025175948,0.0024028497,0.9898764,0.0005518988,0.0031036786,0.00044322305,0.0000069447733,0.00015466777,0.0032085585],"genre_scores_gemma":[0.82829887,0.00036180665,0.16742066,0.0007265021,0.0008953158,0.000035698544,0.000018093102,0.000041012423,0.00220203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957178,0.00007785117,0.0007983829,0.00152514,0.00126526,0.0006155428],"domain_scores_gemma":[0.9973143,0.0005464897,0.00030849726,0.0013951638,0.00028272814,0.00015283981],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020622795,0.00042336056,0.0005212828,0.008252016,0.00017156418,0.0010033464,0.0030646785,0.00042180115,0.000032267784],"category_scores_gemma":[0.00022275723,0.00040458757,0.00010876785,0.00585195,0.0005220797,0.000868017,0.0007292127,0.000775805,0.00012443338],"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":[0.00000280358,0.00007701079,0.0001979076,0.00008700022,0.000006141429,0.000033387878,0.00093374396,0.008921971,0.000073224364,0.10540815,0.000046719288,0.88421196],"study_design_scores_gemma":[0.00048375665,0.00015643776,0.0016641729,0.0003786475,0.0000057079915,0.000087023785,0.0000010036631,0.9108372,0.00033416145,0.060647253,0.024250902,0.0011537832],"about_ca_topic_score_codex":0.00014832932,"about_ca_topic_score_gemma":0.00011517798,"teacher_disagreement_score":0.9019152,"about_ca_system_score_codex":0.00086325686,"about_ca_system_score_gemma":0.00044762855,"threshold_uncertainty_score":0.9998406},"labels":[],"label_agreement":null},{"id":"W1899716998","doi":"10.1109/cvprw.2015.7301317","title":"Exploratory analysis of an operational iris recognition dataset from a CBSA border-crossing application","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"International Research and Exchanges Board","keywords":"Computer science; Censoring (clinical trials); Iris recognition; Context (archaeology); Hamming distance; Matching (statistics); Truncation (statistics); Artificial intelligence; Pattern recognition (psychology); Biometrics; Algorithm; Machine learning; Statistics; Mathematics; Geography","score_opus":0.08825385058949929,"score_gpt":0.3380363821949452,"score_spread":0.2497825316054459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1899716998","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":[0.13386765,0.00004003255,0.8645417,0.0002533477,0.000074029325,0.00008177928,0.0009831636,0.000057096295,0.00010118952],"genre_scores_gemma":[0.8627765,0.0000035599953,0.11433598,0.00044723632,0.00003856192,0.000023109367,0.02235061,0.0000034521306,0.00002099741],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988861,0.00007701115,0.00027393363,0.00033998035,0.000332725,0.00009022409],"domain_scores_gemma":[0.9988058,0.000035759906,0.000121959565,0.0005938485,0.00032196438,0.00012064871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005412262,0.000066846864,0.000129158,0.00050631084,0.00009628161,0.0003396551,0.00041810263,0.000051268908,0.00008486844],"category_scores_gemma":[0.00005758349,0.00006519671,0.000034941066,0.002749272,0.000051488056,0.0011901727,0.00007680503,0.000045718167,0.00007060089],"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":[0.00006241738,0.002311658,0.009795563,0.000019931795,0.00099554,0.0000048290544,0.011645146,0.00079113565,0.022250805,0.016508864,0.05341085,0.8822033],"study_design_scores_gemma":[0.0005000547,0.00004043227,0.021870444,0.000002767193,0.00014850558,6.7798544e-7,0.0004537143,0.92569363,0.0070959525,0.0026265157,0.041313153,0.00025415703],"about_ca_topic_score_codex":0.0010650374,"about_ca_topic_score_gemma":0.00034797395,"teacher_disagreement_score":0.9249025,"about_ca_system_score_codex":0.000039465056,"about_ca_system_score_gemma":0.00015175875,"threshold_uncertainty_score":0.32753018},"labels":[],"label_agreement":null},{"id":"W1947798063","doi":"10.1109/cscwd.2015.7230995","title":"Vector signature for face recognition","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Facial recognition system; Signature (topology); Face (sociological concept); Bandwidth (computing); Artificial intelligence; Pattern recognition (psychology); Computer vision; Face detection; Signature recognition; Scheme (mathematics); Feature extraction; Mathematics; Computer network","score_opus":0.09515877888085496,"score_gpt":0.2889654981280095,"score_spread":0.19380671924715454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1947798063","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":[0.0011562539,0.00009288961,0.9911475,0.0024943529,0.00054731953,0.00013824855,0.0000074482386,0.00012826144,0.0042877323],"genre_scores_gemma":[0.8154156,0.0000044767426,0.1716299,0.0013593342,0.00011077876,0.000037358735,0.00006020787,0.000005329419,0.01137701],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99957776,0.000014417144,0.000067582405,0.00014776309,0.0001131277,0.00007931938],"domain_scores_gemma":[0.999542,0.000034669967,0.00002368719,0.00016154637,0.00016867006,0.00006941686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022568708,0.000035218127,0.00003926124,0.00008617721,0.000029020035,0.00009693622,0.00021889381,0.000045529403,0.000025623447],"category_scores_gemma":[0.00011963638,0.000029902818,0.000024084984,0.00046513238,0.000007674714,0.0002151267,0.000030679323,0.000034433022,0.0002123377],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009128798,0.00011864659,0.000023259341,0.000014125745,0.000010555772,8.784852e-7,0.000920835,0.0000026874384,0.0015595959,0.06582642,0.47305977,0.45845407],"study_design_scores_gemma":[0.0012361804,0.00015752969,0.000983617,0.0000056360864,0.000006497599,0.000007869191,0.00021910355,0.05064057,0.040923756,0.047477547,0.8579787,0.00036300367],"about_ca_topic_score_codex":0.0000088432,"about_ca_topic_score_gemma":0.0000050974418,"teacher_disagreement_score":0.8195176,"about_ca_system_score_codex":0.000019657698,"about_ca_system_score_gemma":0.000036367066,"threshold_uncertainty_score":0.27292424},"labels":[],"label_agreement":null},{"id":"W1965341102","doi":"10.1109/icci-cc.2012.6311208","title":"Multimodal Cancelable Biometrics","year":2012,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Pattern recognition (psychology); Artificial intelligence; Feature extraction; Signature recognition; Data mining","score_opus":0.031154200633664416,"score_gpt":0.27031097997881764,"score_spread":0.23915677934515323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965341102","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":[0.007508207,0.0005558206,0.9677433,0.00052147434,0.0012484126,0.00006233788,0.000001600002,0.00020356334,0.022155317],"genre_scores_gemma":[0.9047134,0.000017231363,0.090981975,0.0003584922,0.000056622852,0.0000040297987,0.0000016407755,0.0000022653962,0.003864355],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993015,0.000018653524,0.00010467779,0.00012882841,0.00020106287,0.0002452939],"domain_scores_gemma":[0.9994019,0.00004229438,0.000030326066,0.0003312836,0.00005921063,0.00013495998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034221428,0.000050742816,0.00005585937,0.00053856836,0.00006907406,0.00009709932,0.00044621306,0.00003923556,0.00017759722],"category_scores_gemma":[0.000064571366,0.000043172822,0.00002700152,0.0040931376,0.00001646133,0.0006008142,0.00011063994,0.0000469712,0.0007386192],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011146777,0.00046649657,0.031034442,0.000013479485,0.000016634593,0.0000010970964,0.00079889724,0.000001594616,0.0015751236,0.6335045,0.058411263,0.27417538],"study_design_scores_gemma":[0.0003048278,0.00001666854,0.06884306,0.0000013407458,0.0000029609464,0.000010024617,0.00003437207,0.02798188,0.013634412,0.0004974627,0.8884018,0.00027117302],"about_ca_topic_score_codex":0.00019286892,"about_ca_topic_score_gemma":0.0000029286616,"teacher_disagreement_score":0.8972052,"about_ca_system_score_codex":0.0000394592,"about_ca_system_score_gemma":0.000023106759,"threshold_uncertainty_score":0.94937015},"labels":[],"label_agreement":null},{"id":"W1965914109","doi":"10.1109/tsmc.2014.2331920","title":"Decision Fusion for Multimodal Biometrics Using Social Network Analysis","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates - Technology Futures","keywords":"Biometrics; Computer science; Artificial intelligence; Linear discriminant analysis; Dimensionality reduction; Classifier (UML); Machine learning; Pattern recognition (psychology); Word error rate; Feature selection; Data mining","score_opus":0.029794351528835424,"score_gpt":0.2722881500183473,"score_spread":0.24249379848951186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965914109","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":[0.023152322,0.0003605611,0.9716986,0.000033948134,0.0038760158,0.0006103793,0.00004129629,0.00012917367,0.00009773648],"genre_scores_gemma":[0.9920713,0.000038257436,0.007021392,0.000030974687,0.00027047965,0.000053387197,0.000006829722,0.00001928285,0.00048814775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974818,0.00025834487,0.0006748774,0.00062057603,0.00058211025,0.00038228292],"domain_scores_gemma":[0.9982653,0.00044826147,0.00029704903,0.0005236435,0.0002879741,0.00017775905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012992945,0.00024053681,0.00050990103,0.0016489964,0.0007296159,0.00076570304,0.00045143467,0.00025744556,0.000002456045],"category_scores_gemma":[0.000015421869,0.00022803224,0.0002467457,0.0049110237,0.000056339286,0.00017100264,0.0000076949045,0.00014484048,0.000014612101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021518905,0.0013319709,0.0010632974,0.0011965052,0.0023619134,0.000008061036,0.0038274496,0.61215687,0.0026348983,0.07404923,0.0042398483,0.29691476],"study_design_scores_gemma":[0.0006085597,0.00009010996,0.00045712045,0.000042906253,0.00020369349,0.000011757718,0.00009727124,0.98732877,0.00006828696,0.00008262659,0.010728278,0.00028062367],"about_ca_topic_score_codex":0.000483378,"about_ca_topic_score_gemma":0.000038600767,"teacher_disagreement_score":0.9689189,"about_ca_system_score_codex":0.0001168337,"about_ca_system_score_gemma":0.000029378183,"threshold_uncertainty_score":0.9298885},"labels":[],"label_agreement":null},{"id":"W1968172805","doi":"10.1117/12.778910","title":"Optical coherence tomography used for internal biometrics","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Biometrics; Fingerprint (computing); Computer science; Optical coherence tomography; Artificial intelligence; Computer vision; Fingerprint recognition; Identification (biology); Face (sociological concept); Pattern recognition (psychology); Coherence (philosophical gambling strategy); Facial recognition system; Optics; Mathematics; Physics","score_opus":0.01805929052312732,"score_gpt":0.25284077094440205,"score_spread":0.23478148042127472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968172805","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9568323,0.00012172797,0.03835517,0.0019515002,0.000689755,0.0006942972,0.00004394327,0.0001450185,0.0011662701],"genre_scores_gemma":[0.50873566,0.00003446281,0.4904067,0.00015510777,0.0003280418,0.00010196407,0.000008663542,0.000036038433,0.00019339014],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996852,1.5662406e-8,0.0009362173,0.0005796677,0.0010603776,0.0005717397],"domain_scores_gemma":[0.9959408,0.0005191494,0.0004788176,0.00011706465,0.0027121094,0.00023202592],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020384907,0.0003124864,0.00040982643,0.000704663,0.000121182224,0.00035193478,0.0025805554,0.00025493262,0.0000066959724],"category_scores_gemma":[0.0011878274,0.00026983893,0.0008540177,0.0025926179,0.0002913921,0.00081052847,0.0003246532,0.000316357,0.0000023095408],"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":[0.00004845361,0.00020712237,0.000772347,0.00025410551,0.00024012274,1.091751e-7,0.00019570404,0.0000041088715,0.251222,0.74152714,0.0030396357,0.002489147],"study_design_scores_gemma":[0.0038832845,0.001223369,0.010360452,0.00038653464,0.00023270762,0.000057724883,0.0013509073,0.0815648,0.8481337,0.011275883,0.040225983,0.0013046564],"about_ca_topic_score_codex":0.000009344818,"about_ca_topic_score_gemma":2.7445896e-7,"teacher_disagreement_score":0.73025125,"about_ca_system_score_codex":0.00016452697,"about_ca_system_score_gemma":0.000046780046,"threshold_uncertainty_score":0.9999754},"labels":[],"label_agreement":null},{"id":"W1974704230","doi":"10.1111/j.1556-4029.2007.00635.x","title":"Review of: <i>Contrast: An Investigator's Basic Reference Guide to Fingerprint Identification Concepts, 2nd edition</i>","year":2008,"lang":"en","type":"article","venue":"Journal of Forensic Sciences","topic":"Biometric Identification and Security","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":"123 Certification (Canada)","funders":"","keywords":"Commonwealth; George (robot); Library science; Identification (biology); Geography; History; Archaeology; Biology; Ecology; Computer science; Art history","score_opus":0.06170081816490332,"score_gpt":0.33752127388329217,"score_spread":0.27582045571838887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974704230","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":[0.41556835,0.030254597,0.51185465,0.030366125,0.0074170777,0.0008791657,0.00003431565,0.00012360216,0.0035021026],"genre_scores_gemma":[0.8692694,0.0076831775,0.11373831,0.008788343,0.00035298985,0.0000075154953,0.0000039336396,0.0000070346487,0.00014934856],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973169,0.00015661091,0.0010328671,0.00030327297,0.0009888326,0.00020151077],"domain_scores_gemma":[0.99699277,0.00013507868,0.0009788233,0.00042291827,0.0011292028,0.00034119017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003080088,0.000112161804,0.00029728241,0.0005085257,0.0002504889,0.000117325864,0.0015698478,0.00004437694,0.000037912796],"category_scores_gemma":[0.0016840852,0.00008935594,0.00009780348,0.002532316,0.00058324327,0.0013415287,0.00009282345,0.00014407297,0.000018162254],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001896706,0.0005306968,0.003943178,0.0008334087,0.00005898972,0.00005167199,0.004595924,0.00010852077,0.036504798,0.09332531,0.65824324,0.2017853],"study_design_scores_gemma":[0.0016696439,0.002722984,0.1455693,0.01012057,0.00012891683,0.002780168,0.00086286676,0.0048389365,0.26568404,0.021098262,0.54293185,0.0015924587],"about_ca_topic_score_codex":0.00003936824,"about_ca_topic_score_gemma":0.000009947507,"teacher_disagreement_score":0.453701,"about_ca_system_score_codex":0.000055260254,"about_ca_system_score_gemma":0.00074989727,"threshold_uncertainty_score":0.36438295},"labels":[],"label_agreement":null},{"id":"W1974900975","doi":"10.1016/j.patcog.2009.01.018","title":"A survey of palmprint recognition","year":2009,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":541,"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":"Hong Kong Polytechnic University","keywords":"Preprocessor; Biometrics; Computer science; Artificial intelligence; Identification (biology); Pattern recognition (psychology); Computer vision","score_opus":0.09527788621267287,"score_gpt":0.28376420804377916,"score_spread":0.1884863218311063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974900975","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":[0.37337047,0.000051344578,0.624611,0.00037585138,0.00030742772,0.00015874467,0.00007273012,0.000105937695,0.00094647106],"genre_scores_gemma":[0.99635434,0.000042310756,0.0028800708,0.00039830824,0.000023425577,0.000006195089,0.00027813014,0.0000031327988,0.000014079699],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99891365,0.00016854364,0.00029455207,0.00025981799,0.00022594484,0.00013746663],"domain_scores_gemma":[0.9990602,0.000079494566,0.00016748793,0.00027076126,0.00036559138,0.000056477504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067090045,0.000082906576,0.00012057397,0.00029222714,0.00004298429,0.00006843597,0.0002696998,0.00006513843,0.0001044698],"category_scores_gemma":[0.0001488445,0.00008578222,0.000049139104,0.0008912541,0.000017127795,0.0002766304,0.0000311914,0.00008536624,0.0003621797],"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":[0.0000029649061,0.000121242425,0.002164389,0.000008345359,0.000004205422,0.0000010402255,0.00010445167,7.789512e-8,0.0005462479,0.000012337579,0.0001791448,0.99685556],"study_design_scores_gemma":[0.00033789504,0.00010846741,0.9739863,0.0000359396,0.000005708106,0.0000075080343,0.000008024456,0.0039275573,0.015158607,0.0061036297,0.00013993819,0.00018042242],"about_ca_topic_score_codex":0.0002773476,"about_ca_topic_score_gemma":0.00005632114,"teacher_disagreement_score":0.99667513,"about_ca_system_score_codex":0.000022728234,"about_ca_system_score_gemma":0.000021977361,"threshold_uncertainty_score":0.4655208},"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":"W1980629306","doi":"10.1109/allerton.2014.7028603","title":"The privacy/security tradeoff across jointly designed linear authentication systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer security; Authentication (law); Information privacy","score_opus":0.023787243828465133,"score_gpt":0.27622905342761095,"score_spread":0.2524418095991458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980629306","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":[0.02472263,0.00015650447,0.9689639,0.0033044317,0.0010463264,0.00029603604,0.0000021546737,0.00029907378,0.0012088963],"genre_scores_gemma":[0.9939333,0.000019323967,0.004009821,0.00015243934,0.00009653295,0.00002569546,0.0000039446845,0.0000062381455,0.0017526818],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827063,0.00026527836,0.00036471424,0.00036366127,0.0004115621,0.00032412796],"domain_scores_gemma":[0.9981548,0.00030980108,0.00014724981,0.0010940153,0.00017748922,0.00011662166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020917796,0.00011517047,0.00012641608,0.00007777124,0.0005940577,0.00086306076,0.0014253377,0.000085136766,0.000007195286],"category_scores_gemma":[0.00035003154,0.0000768236,0.00007161749,0.0009614319,0.0000966318,0.00032627126,0.00018266495,0.00012439184,0.00030183786],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054052366,0.00014243239,0.000263525,0.000032599524,0.00002316598,5.806949e-7,0.004797017,0.000007091536,0.0015152459,0.96015954,0.0075143324,0.025539069],"study_design_scores_gemma":[0.00038642844,0.00005816777,0.008914353,0.000009175732,0.0000065156923,0.000013705192,0.00022117735,0.6988319,0.0038525925,0.006508682,0.28092933,0.00026793123],"about_ca_topic_score_codex":0.000067555906,"about_ca_topic_score_gemma":0.000015198989,"teacher_disagreement_score":0.9692107,"about_ca_system_score_codex":0.00004119537,"about_ca_system_score_gemma":0.000037650658,"threshold_uncertainty_score":0.8322515},"labels":[],"label_agreement":null},{"id":"W19808541","doi":"10.1007/978-3-540-71078-3_7","title":"Fingerprint Recognition Using a Hierarchical Approach","year":2007,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Biometric Identification and Security","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":"University of Calgary","funders":"","keywords":"Fingerprint (computing); Artificial intelligence; Computer science; Pattern recognition (psychology)","score_opus":0.4060725472006064,"score_gpt":0.4140813184118659,"score_spread":0.00800877121125948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W19808541","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":[0.000032475215,0.0020800256,0.9337023,0.00014465376,0.000812471,0.0002716615,0.000014898758,0.0000847236,0.06285678],"genre_scores_gemma":[0.025359182,0.0011727867,0.95769936,0.0006902438,0.00035009332,0.000027955002,0.00013687815,0.00004727063,0.014516223],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99751997,0.000048399812,0.00078125315,0.0006713905,0.0007008092,0.00027818332],"domain_scores_gemma":[0.9979956,0.0007189317,0.0002874,0.00034301775,0.00057758304,0.00007742457],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009002195,0.00030465017,0.0003972644,0.0011679329,0.0001653916,0.0001069069,0.0007518373,0.000232526,0.000034587894],"category_scores_gemma":[0.0001997423,0.0003208325,0.00013757567,0.00055389054,0.00042218863,0.0001917021,0.0005608976,0.00070370757,0.00013898054],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010143227,0.00009322607,0.000006986355,0.00013410184,0.00011371821,0.000037815655,0.0017994698,0.009286253,6.602117e-7,0.7517938,0.0002649995,0.23645882],"study_design_scores_gemma":[0.0000667137,0.000032105938,0.000039834664,0.0002466929,0.000012891958,0.0000701144,0.000115061135,0.28581515,0.000015364434,0.7082216,0.004890038,0.00047443667],"about_ca_topic_score_codex":0.000015389292,"about_ca_topic_score_gemma":0.00000854441,"teacher_disagreement_score":0.2765289,"about_ca_system_score_codex":0.00040956528,"about_ca_system_score_gemma":0.0001746086,"threshold_uncertainty_score":0.99992436},"labels":[],"label_agreement":null},{"id":"W1982312956","doi":"10.1016/j.sigpro.2012.06.021","title":"A multilevel structural technique for fingerprint representation and matching","year":2012,"lang":"en","type":"article","venue":"Signal Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Fingerprint (computing); Minutiae; Matching (statistics); Pattern recognition (psychology); Benchmark (surveying); Computer science; Fingerprint recognition; Artificial intelligence; Representation (politics); Feature (linguistics); Byte; Mathematics; Statistics","score_opus":0.046674102078890736,"score_gpt":0.3292503450890316,"score_spread":0.2825762430101409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982312956","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":[0.028870532,0.0002602015,0.9703047,0.00014183168,0.000076631564,0.0002027346,0.0000012666617,0.00006790213,0.00007418324],"genre_scores_gemma":[0.7973707,8.577768e-7,0.20245552,0.000055000615,0.00004901599,0.000032205877,0.0000020313835,0.000003505833,0.000031208972],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993879,0.000021170103,0.00013260082,0.0001781054,0.00011677644,0.0001634393],"domain_scores_gemma":[0.9996377,0.000054922766,0.00008260063,0.00009717089,0.00006994489,0.000057655525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003465358,0.000061806306,0.00006663052,0.00012836224,0.00020540698,0.00021224984,0.00015903346,0.00003928203,0.000003899283],"category_scores_gemma":[0.000033973236,0.000056402834,0.000020726618,0.0002628611,0.000022511884,0.00080535637,0.00007566812,0.00006402949,0.0000014625945],"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":[0.0000074657037,0.000026722995,0.0013739252,0.00016116309,0.000005709072,4.6027125e-7,0.0057405457,0.000009176257,0.104511075,0.0133596435,0.000055897803,0.87474823],"study_design_scores_gemma":[0.0008696287,0.000053431748,0.061357975,0.00015537775,0.000022192075,0.0001617036,0.0005324364,0.5049192,0.32095772,0.10859583,0.0016224362,0.0007520917],"about_ca_topic_score_codex":0.00001608705,"about_ca_topic_score_gemma":4.747273e-7,"teacher_disagreement_score":0.87399614,"about_ca_system_score_codex":0.000020361083,"about_ca_system_score_gemma":0.000026368594,"threshold_uncertainty_score":0.23000409},"labels":[],"label_agreement":null},{"id":"W1983192482","doi":"10.4018/jssci.2012070102","title":"A Novel Cross Folding Algorithm for Multimodal Cancelable Biometrics","year":2012,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Pattern recognition (psychology); Artificial intelligence; Data mining; Algorithm","score_opus":0.047497525949918164,"score_gpt":0.35817766473223867,"score_spread":0.3106801387823205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983192482","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":[0.007916183,0.0005801449,0.9880035,0.00035891324,0.0029582463,0.00010020377,0.000033253164,0.00002590136,0.000023675551],"genre_scores_gemma":[0.42838472,0.000039653376,0.5710395,0.00029793705,0.00019016408,0.0000043978994,0.0000026796047,0.000003975561,0.000036961017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997316,0.000014821951,0.0005497288,0.00022666038,0.0015720235,0.00032076106],"domain_scores_gemma":[0.9935187,0.00055816973,0.00043838492,0.00012280235,0.0051108846,0.00025102467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023836975,0.00012187453,0.00015007929,0.0014656084,0.00028245867,0.0007223765,0.001728909,0.00005010524,0.000010662787],"category_scores_gemma":[0.0014017087,0.000111197805,0.000084425075,0.002286637,0.00037177769,0.002855546,0.0002568702,0.0001342602,0.0000073095443],"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":[0.000011691138,0.00028187718,0.0030109072,0.0000086034215,0.00004714868,0.0000027562899,0.00087919453,0.0031790189,0.0005134221,0.04214021,0.00017296264,0.9497522],"study_design_scores_gemma":[0.000910794,0.00023477341,0.030001195,0.00008965953,0.0000186744,0.001168703,0.00036039005,0.9186486,0.011165495,0.022372551,0.014465402,0.0005637728],"about_ca_topic_score_codex":0.000031943906,"about_ca_topic_score_gemma":5.8557265e-7,"teacher_disagreement_score":0.9491884,"about_ca_system_score_codex":0.0002570375,"about_ca_system_score_gemma":0.00054929394,"threshold_uncertainty_score":0.69658935},"labels":[],"label_agreement":null},{"id":"W1985103528","doi":"10.1109/pst.2010.5593246","title":"Security of Error Correcting Code for biometric Encryption","year":2010,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Privacy Analytics (Canada)","funders":"","keywords":"Computer science; Encryption; Biometrics; Theoretical computer science; Generalization; Code (set theory); Cryptography; Hadamard transform; Algorithm; Error detection and correction; Key (lock); Block (permutation group theory); Relation (database); Computer security; Mathematics; Data mining; Programming language","score_opus":0.04606021919436584,"score_gpt":0.3177634592881938,"score_spread":0.27170324009382796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985103528","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":[0.19254422,0.00001912342,0.803977,0.0003017159,0.0015981684,0.00018702232,0.000013705322,0.00010960958,0.0012494291],"genre_scores_gemma":[0.94186354,0.000001970174,0.057752725,0.000058601952,0.000037139358,0.000010320388,0.0000054971524,0.00000309054,0.0002671485],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991749,0.000019030156,0.00023553865,0.00023725891,0.00018985318,0.00014344316],"domain_scores_gemma":[0.9989631,0.0002130959,0.00013674881,0.00036484355,0.0002624086,0.000059797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071971724,0.0000636089,0.000106620675,0.00085086067,0.0000828891,0.00006917235,0.00051549164,0.00007988289,0.000058285248],"category_scores_gemma":[0.00051668915,0.000057875746,0.00006921137,0.0032298863,0.000037644902,0.0002462858,0.00007802955,0.0001023053,0.000019796431],"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":[0.000014955092,0.0006414774,0.0052173925,0.00014616683,0.000032351873,6.997529e-7,0.0018780297,0.0000022870458,0.13195527,0.47998977,0.010171183,0.3699504],"study_design_scores_gemma":[0.0014681125,0.00024269267,0.02780952,0.000011926008,0.0000212551,0.000038666894,0.00028172162,0.40453675,0.43474835,0.028199976,0.102023296,0.0006177081],"about_ca_topic_score_codex":0.000053715165,"about_ca_topic_score_gemma":0.000085751104,"teacher_disagreement_score":0.74931926,"about_ca_system_score_codex":0.000011788307,"about_ca_system_score_gemma":0.000039624083,"threshold_uncertainty_score":0.23601045},"labels":[],"label_agreement":null},{"id":"W1985169127","doi":"10.1007/s00371-013-0907-0","title":"Situation awareness of cancelable biometric system","year":2013,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Biometric Identification and Security","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":"University of Calgary","funders":"High Energy Physics; Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Pattern recognition (psychology); Random projection; Artificial intelligence; Context (archaeology); Transformation (genetics); Feature (linguistics); Face (sociological concept); Data mining","score_opus":0.023442476770035397,"score_gpt":0.27278412701523397,"score_spread":0.24934165024519858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985169127","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":[0.2112048,0.00010793926,0.7871283,0.0002987357,0.00074349745,0.00020271128,0.0000012785098,0.00010726885,0.0002054552],"genre_scores_gemma":[0.9930401,0.000004376163,0.0065895426,0.00010959367,0.000085636755,0.000016991016,0.0000035500211,0.000004311515,0.00014593228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892545,0.000111622314,0.00026183127,0.00021534169,0.00032360488,0.00016217538],"domain_scores_gemma":[0.99893683,0.00012807116,0.00015046752,0.000454266,0.0002817297,0.000048620415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041416654,0.00008595836,0.00014005745,0.00053080084,0.00012682383,0.00018901948,0.00085025665,0.000044682223,0.000025461595],"category_scores_gemma":[0.000012107115,0.000058195135,0.000049541097,0.0037449829,0.000045597364,0.00031967557,0.00022888716,0.00006240378,0.00031464946],"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":[0.000009992276,0.00071550405,0.005455068,0.00052417937,0.00017193907,0.0000042798215,0.0040247054,0.00051574124,0.010146318,0.22515687,0.03689644,0.7163789],"study_design_scores_gemma":[0.0002452984,0.000085681204,0.046002,0.000024135492,0.000007014706,0.000008799373,0.000047668193,0.9448251,0.0059636193,0.00039175875,0.002243922,0.00015497912],"about_ca_topic_score_codex":0.0008849613,"about_ca_topic_score_gemma":0.000003341406,"teacher_disagreement_score":0.94430935,"about_ca_system_score_codex":0.000053034426,"about_ca_system_score_gemma":0.000053298954,"threshold_uncertainty_score":0.40442872},"labels":[],"label_agreement":null},{"id":"W1986102353","doi":"10.4018/ijcini.2013070105","title":"Cancelable Fusion of Face and Ear for Secure Multi-Biometric Template","year":2013,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Pattern recognition (psychology); Artificial intelligence; Face (sociological concept); Data mining","score_opus":0.02576707416317719,"score_gpt":0.3078160101379529,"score_spread":0.2820489359747757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986102353","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":[0.2594418,0.0012497314,0.738212,0.00024829854,0.0005850422,0.00017811739,0.000024645004,0.000005228769,0.0000551396],"genre_scores_gemma":[0.96326315,0.0010575658,0.035365254,0.00017514509,0.00002521104,0.000003292463,0.000004547718,0.000002487581,0.00010337275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885935,0.000015575251,0.00060553284,0.000063956075,0.00034995496,0.00010561668],"domain_scores_gemma":[0.9965188,0.0004102537,0.0006005948,0.000055538057,0.0023396492,0.00007515836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034852145,0.00008964393,0.00015599205,0.0007491375,0.000050956824,0.00020100301,0.00043128507,0.000052350657,0.000014987885],"category_scores_gemma":[0.00046753575,0.00006926489,0.00005561487,0.0005144987,0.00008625484,0.0011330632,0.00013551483,0.00015302766,0.0000045807074],"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":[0.00008622756,0.00016485207,0.0017066704,0.00013349,0.0002571599,0.0000036965175,0.008497312,0.00003071232,0.0016212945,0.009487178,0.0005758742,0.9774355],"study_design_scores_gemma":[0.002608479,0.0009104245,0.038486723,0.00097363675,0.00006641021,0.0006288717,0.008593443,0.83185476,0.09902774,0.010744141,0.005410241,0.00069515465],"about_ca_topic_score_codex":0.000036625064,"about_ca_topic_score_gemma":0.0000023886187,"teacher_disagreement_score":0.97674036,"about_ca_system_score_codex":0.00002337978,"about_ca_system_score_gemma":0.00004548372,"threshold_uncertainty_score":0.28245404},"labels":[],"label_agreement":null},{"id":"W1986897909","doi":"10.1109/ccece.2006.277715","title":"Human Vs. Automatic Measurement of Biometric Sample Quality","year":2006,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Face (sociological concept); Artificial intelligence; Iris recognition; Image quality; Computer science; IRIS (biosensor); Quality (philosophy); Facial recognition system; Pattern recognition (psychology); Quality Score; Identification (biology); Sample (material); Image (mathematics); Gold standard (test); Computer vision; Mathematics; Statistics; Engineering","score_opus":0.08120569486051317,"score_gpt":0.3149023494255307,"score_spread":0.23369665456501754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986897909","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":[0.056809403,0.00011794021,0.93493545,0.0003824818,0.00019689849,0.0001562037,0.000005858263,0.00024235983,0.0071534067],"genre_scores_gemma":[0.9622142,8.367233e-7,0.03748092,0.000055311353,0.000014210492,0.000004420035,0.0000040154187,0.0000024813473,0.00022365422],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.998263,0.000092581875,0.0004718978,0.00023148481,0.000789503,0.00015153454],"domain_scores_gemma":[0.99887276,0.000070620794,0.00017302968,0.00056662085,0.0002723331,0.000044643355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014932237,0.00007570907,0.00015798866,0.0009796377,0.000083175146,0.00008959355,0.000611883,0.00004077583,0.00011364847],"category_scores_gemma":[0.00017653184,0.00006604741,0.0000711882,0.004601288,0.000040630937,0.00015945973,0.00009901018,0.000039179635,0.000041022864],"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":[9.646208e-7,0.0010184569,0.009669746,0.0001567469,0.000029702422,5.014522e-7,0.000121520454,0.0000036084568,0.01857492,0.892452,0.023926582,0.054045223],"study_design_scores_gemma":[0.0005361353,0.00006913606,0.92995816,0.000009420726,0.000008044499,0.0000013456167,0.000016909571,0.009042445,0.028871503,0.018899258,0.012312967,0.00027464842],"about_ca_topic_score_codex":0.0035927433,"about_ca_topic_score_gemma":0.000113298214,"teacher_disagreement_score":0.92028844,"about_ca_system_score_codex":0.00007488385,"about_ca_system_score_gemma":0.00004084911,"threshold_uncertainty_score":0.5431177},"labels":[],"label_agreement":null},{"id":"W1992491299","doi":"10.1109/icassp.2013.6637889","title":"Singular point detection based on orientation filed regularization and poincar&amp;#x00E9; index in fingerprint images","year":2013,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Orientation (vector space); Fingerprint (computing); Regularization (linguistics); Pattern recognition (psychology); Singular point of a curve; Fingerprint recognition; Computer vision; Computer science; Mathematics; Spurious relationship; Helmholtz free energy; Geometry; Physics; Statistics","score_opus":0.009310290878172357,"score_gpt":0.22558103234344504,"score_spread":0.21627074146527267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992491299","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":[0.13677986,0.00000788263,0.86096376,0.0010950293,0.00016171308,0.0002707232,5.9906654e-7,0.00010451807,0.0006159029],"genre_scores_gemma":[0.97523826,0.0000039349043,0.023945142,0.00037363908,0.00001389951,0.000031930682,0.0000143061225,0.0000054100515,0.00037347493],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886423,0.00010699396,0.00024239779,0.00037590467,0.0002635501,0.00014692482],"domain_scores_gemma":[0.9992677,0.0000786554,0.00008943224,0.00036120045,0.00014016246,0.00006283641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037416723,0.00010170131,0.00009551844,0.00068227074,0.00009307803,0.00035533015,0.00016729812,0.00008189374,0.00011280772],"category_scores_gemma":[0.00019480844,0.000096548894,0.00002579136,0.0012524463,0.000029517867,0.0005756451,0.000057581157,0.00010102596,0.00006486608],"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":[0.000052644493,0.000894515,0.01969594,0.00011591726,0.00002592445,0.000006097717,0.0032843177,0.0010846758,0.10155053,0.030734852,0.0015645836,0.84099],"study_design_scores_gemma":[0.00068058324,0.00005252318,0.42065576,0.000018615403,0.0000028320774,0.0000032040377,0.000054507367,0.5475341,0.023831887,0.0064969957,0.00046150075,0.00020748629],"about_ca_topic_score_codex":0.0004100892,"about_ca_topic_score_gemma":0.00006211963,"teacher_disagreement_score":0.8407825,"about_ca_system_score_codex":0.00008730996,"about_ca_system_score_gemma":0.000027293401,"threshold_uncertainty_score":0.39371496},"labels":[],"label_agreement":null},{"id":"W1995909705","doi":"10.1109/icmlc.2009.5212173","title":"An automation for robust design of multimodal biometric systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Biometrics; Computer science; Normalization (sociology); Automation; Scalability; Data mining; Modalities; Artificial intelligence; Set (abstract data type); Matching (statistics); Standard deviation; Machine learning; Robustness (evolution); Pattern recognition (psychology); Database; Engineering; Mathematics","score_opus":0.061491675398633025,"score_gpt":0.292855998788669,"score_spread":0.231364323390036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995909705","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":[0.00081692176,0.000100162215,0.99801785,0.000167994,0.0002584198,0.0003557754,0.0000029478058,0.00017082495,0.00010908708],"genre_scores_gemma":[0.60611594,0.0000038394155,0.3937418,0.00003940548,0.000016512628,0.0000065263707,0.0000050362596,0.000001421435,0.00006954877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999196,0.000057783214,0.00023534217,0.0002004312,0.00019535191,0.00011509551],"domain_scores_gemma":[0.99918264,0.00009290428,0.00010806783,0.00034759485,0.00021238238,0.000056408935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006121531,0.000058561196,0.00010326379,0.0010475611,0.000053882897,0.00014064056,0.0004766345,0.00005609484,0.0000047474564],"category_scores_gemma":[0.00007203445,0.000050993895,0.000032264932,0.0030149973,0.000011358,0.000441124,0.000010381578,0.0000208136,0.000008494554],"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":[0.00001835783,0.0011909588,0.000108179105,0.00007516731,0.000025226687,8.807302e-7,0.00075231533,0.016544111,0.034176994,0.29699796,0.0076553924,0.64245445],"study_design_scores_gemma":[0.00021159074,0.00016323468,0.004635088,0.0000026127839,0.0000017117892,0.0000015214168,0.000013149854,0.9914854,0.0028304332,0.00029243622,0.0002942929,0.000068517555],"about_ca_topic_score_codex":0.00004661131,"about_ca_topic_score_gemma":2.8622384e-7,"teacher_disagreement_score":0.9749413,"about_ca_system_score_codex":0.000026478858,"about_ca_system_score_gemma":0.00003295005,"threshold_uncertainty_score":0.20794706},"labels":[],"label_agreement":null},{"id":"W1997502120","doi":"10.1109/icci-cc.2013.6622228","title":"Novel multimodal template generation algorithm","year":2013,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Random projection; Context (archaeology); Data mining; Algorithm","score_opus":0.03597555625933514,"score_gpt":0.25064113797989873,"score_spread":0.2146655817205636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997502120","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":[0.0029216334,0.000013025688,0.9934768,0.0012370759,0.0004120601,0.00011277664,0.0000012240977,0.00013257163,0.0016928158],"genre_scores_gemma":[0.25238198,0.0000033887052,0.7433163,0.0006451213,0.000084099236,0.000019492383,0.00000937215,0.0000028840375,0.003537372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993836,0.000012287761,0.00012759864,0.00020341344,0.00015794208,0.00011511688],"domain_scores_gemma":[0.9994983,0.000013873996,0.000031241252,0.00027839644,0.00011598924,0.00006216679],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000113934526,0.000053426433,0.00004862833,0.00013894125,0.00008129164,0.00030930637,0.0003233731,0.000040285995,0.00028083444],"category_scores_gemma":[0.000013511152,0.000045085246,0.00002477251,0.0005209308,0.000013147733,0.0005818081,0.000071403956,0.000043380096,0.0011070174],"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":[4.6872128e-8,0.00010117746,0.00004485768,0.0000015084751,0.000006203168,4.0283578e-7,0.00015969398,0.0000061012265,0.042756286,0.021375226,0.023275796,0.9122727],"study_design_scores_gemma":[0.00014739389,0.000007128778,0.0052538845,3.8229274e-7,5.6617534e-7,0.000006036315,0.000005132221,0.9775775,0.007141403,0.00021690792,0.009560924,0.000082746265],"about_ca_topic_score_codex":0.0005413208,"about_ca_topic_score_gemma":0.000007764546,"teacher_disagreement_score":0.97757137,"about_ca_system_score_codex":0.000016458791,"about_ca_system_score_gemma":0.000017121716,"threshold_uncertainty_score":0.99967074},"labels":[],"label_agreement":null},{"id":"W1998131232","doi":"10.1109/roman.2014.6926273","title":"Multimodal biometric identification system for mobile robots combining human metrology to face recognition and speaker identification","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Biometrics; Computer science; Identification (biology); Modality (human–computer interaction); Artificial intelligence; Computer vision; Face (sociological concept); Modalities; Facial recognition system; Pattern recognition (psychology); Robot; Speech recognition","score_opus":0.04209890750620349,"score_gpt":0.2911656486731221,"score_spread":0.24906674116691863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998131232","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":[0.178615,0.000047805097,0.8189881,0.0002847671,0.0006802599,0.0009095008,0.00001752564,0.00029112483,0.00016591471],"genre_scores_gemma":[0.9753628,0.0000041669346,0.023439484,0.00012292403,0.00006269938,0.0003538066,0.00016044515,0.000014765123,0.00047892274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976861,0.00018930997,0.00067847787,0.00082522415,0.00031570898,0.00030520797],"domain_scores_gemma":[0.99813205,0.00026408138,0.00031683824,0.0006708196,0.0004384929,0.00017774712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020712,0.00017591895,0.00025448354,0.00206359,0.0003962709,0.00061007927,0.00060025795,0.00014581012,0.000010620574],"category_scores_gemma":[0.0003181353,0.00018137584,0.000072874056,0.0031483967,0.00004807252,0.0005888757,0.0001493832,0.000085814565,0.00030699608],"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":[0.000032387277,0.00053810474,0.0010546155,0.00043506068,0.000100074554,9.3688163e-7,0.0023167538,0.00021588543,0.31880268,0.08782911,0.0025688945,0.58610547],"study_design_scores_gemma":[0.004400375,0.0010132581,0.14278294,0.00007933694,0.00015503642,0.00006354225,0.0015317464,0.643022,0.1837677,0.0072792517,0.014126881,0.0017779493],"about_ca_topic_score_codex":0.000103823404,"about_ca_topic_score_gemma":0.000017985723,"teacher_disagreement_score":0.7967478,"about_ca_system_score_codex":0.000116819785,"about_ca_system_score_gemma":0.000015590906,"threshold_uncertainty_score":0.7396292},"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":"W1998521490","doi":"10.5220/0005126301460152","title":"Fuzzy Rule Based Quality Measures for Adaptive Multimodal Biometric Fusion at Operation Time","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","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 Calgary","funders":"","keywords":"Biometrics; Computer science; Fuzzy logic; Artificial intelligence; Quality (philosophy); Fuzzy rule; Data mining; Fusion; Machine learning; Fuzzy control system; Pattern recognition (psychology)","score_opus":0.046341301957083095,"score_gpt":0.2929588803871224,"score_spread":0.24661757843003934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998521490","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":[0.015932037,0.000033725883,0.9792421,0.00092565815,0.00028142982,0.0003656554,0.000015748054,0.00019818317,0.0030054948],"genre_scores_gemma":[0.87094146,0.000001722444,0.12650311,0.00048565236,0.00005153211,0.000033970777,0.000049619866,0.0000063756206,0.0019265772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983401,0.0002270771,0.0003073949,0.00046297078,0.0004515688,0.00021090865],"domain_scores_gemma":[0.9985504,0.00038234468,0.00011314737,0.00049884,0.00034208412,0.00011318101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017960333,0.00012259526,0.00016690018,0.0007792896,0.0003043801,0.00019056656,0.00050927186,0.00010173993,0.00008007918],"category_scores_gemma":[0.0004787047,0.00010332488,0.00009956693,0.0019281968,0.000036844747,0.00031801363,0.0001275003,0.000050346494,0.0005600175],"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":[0.00018903177,0.0010924243,0.0013088299,0.0000757478,0.0000541065,6.0918234e-7,0.0007075614,0.00031890938,0.15907386,0.15513854,0.023689698,0.6583507],"study_design_scores_gemma":[0.0011196922,0.00013354942,0.01896425,0.000003452573,0.0000060878847,7.207709e-7,0.0000079354,0.92659163,0.03375633,0.0014031869,0.01774298,0.00027016344],"about_ca_topic_score_codex":0.00015611887,"about_ca_topic_score_gemma":0.000030999672,"teacher_disagreement_score":0.92627275,"about_ca_system_score_codex":0.00011839128,"about_ca_system_score_gemma":0.000051481315,"threshold_uncertainty_score":0.71980786},"labels":[],"label_agreement":null},{"id":"W2000690241","doi":"10.1007/s11760-011-0226-8","title":"Markov chain model for multimodal biometric rank fusion","year":2011,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Hamming distance; Rank (graph theory); Artificial intelligence; Markov chain; Pattern recognition (psychology); Feature (linguistics); Data mining; Machine learning; Mathematics; Algorithm","score_opus":0.044668846646684815,"score_gpt":0.27880677901681816,"score_spread":0.23413793237013336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000690241","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":[0.005492526,0.00085370266,0.9925733,0.00016495114,0.000070802715,0.0002047316,0.0000062457275,0.000105496176,0.0005282296],"genre_scores_gemma":[0.73635024,0.000024784087,0.26305467,0.0002022447,0.000031353517,0.000020927228,0.000004394217,0.000007931833,0.00030343005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998845,0.00003378218,0.0002392015,0.0004216651,0.00019818972,0.00026218372],"domain_scores_gemma":[0.999296,0.00006257517,0.00011651829,0.00018759024,0.0002232334,0.00011407575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007342574,0.00013082338,0.0001461094,0.0007644086,0.00031383062,0.00033511923,0.00037799164,0.000076248034,0.000017421495],"category_scores_gemma":[0.00009439826,0.00011582379,0.000055616107,0.0016177485,0.00007299081,0.000989374,0.00013855055,0.00008529251,0.0000075008716],"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":[0.00003357982,0.000131329,0.00009378819,0.00015682053,0.0000072487283,0.000003630724,0.0026041113,0.0000015969559,0.027396917,0.0007989062,0.0004504866,0.96832156],"study_design_scores_gemma":[0.0005101723,0.000037097067,0.0005891826,0.00002233371,0.0000095597825,0.000007212733,0.000041144533,0.9818718,0.012882268,0.0035956588,0.00024997714,0.00018358075],"about_ca_topic_score_codex":0.000027481803,"about_ca_topic_score_gemma":0.0000015380423,"teacher_disagreement_score":0.98187023,"about_ca_system_score_codex":0.000018215092,"about_ca_system_score_gemma":0.00007254758,"threshold_uncertainty_score":0.4723157},"labels":[],"label_agreement":null},{"id":"W2002055230","doi":"10.1117/12.976344","title":"High-speed biometrics ultrasonic system for 3D fingerprint imaging","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Biometrics; Fingerprint (computing); Ultrasonic sensor; Computer science; Artificial intelligence; Computer vision; Identification (biology); Fingerprint recognition; Image resolution; Materials science; Acoustics","score_opus":0.014880523927238752,"score_gpt":0.23379842510385906,"score_spread":0.2189179011766203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002055230","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9799175,0.00033264537,0.014861768,0.0020454961,0.0013016183,0.0007137792,0.00006004012,0.00022504693,0.00054209225],"genre_scores_gemma":[0.6075735,0.00004078347,0.39165425,0.00007486256,0.00042356705,0.00010020616,0.000008324332,0.000034595858,0.000089895715],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997243,2.4887749e-8,0.00080842484,0.00046698385,0.0008598196,0.0006217598],"domain_scores_gemma":[0.99661523,0.000334416,0.00055201515,0.0001218428,0.0021663506,0.00021012365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016842441,0.0003113018,0.00042275354,0.00048841943,0.00015444956,0.0003321773,0.0019456613,0.00017043791,0.0000044460144],"category_scores_gemma":[0.0010311333,0.0002699328,0.00063460716,0.0017860095,0.00014921928,0.0012179053,0.00029293343,0.00024715642,0.0000042623233],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019042607,0.00015586258,0.0005417672,0.00060263224,0.00019901054,2.8032783e-8,0.00025723377,0.000014655769,0.1957506,0.7982398,0.0022281446,0.001991187],"study_design_scores_gemma":[0.003780159,0.00038638583,0.007352996,0.00066157436,0.0003904898,0.00009432593,0.0026656995,0.38999295,0.5526921,0.0021381844,0.038241267,0.0016038853],"about_ca_topic_score_codex":0.000015535927,"about_ca_topic_score_gemma":3.364714e-8,"teacher_disagreement_score":0.7961017,"about_ca_system_score_codex":0.00036589842,"about_ca_system_score_gemma":0.000042625375,"threshold_uncertainty_score":0.99997526},"labels":[],"label_agreement":null},{"id":"W2006709587","doi":"10.1109/pst.2010.5593251","title":"You are the key: Generating cryptographic keys from voice biometrics","year":2010,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Biometrics; Cryptography; Entropy (arrow of time); Population; Theoretical computer science; Speech recognition; Computer security","score_opus":0.025174659259522073,"score_gpt":0.244051794557293,"score_spread":0.21887713529777092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006709587","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":[0.33356547,0.00027124138,0.6556933,0.00590969,0.002304784,0.00013480075,0.000012127639,0.0002862222,0.0018223621],"genre_scores_gemma":[0.89090544,0.000027100812,0.10635015,0.0020398765,0.00024419936,0.000009634364,0.000008097553,0.000007058788,0.00040846108],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986582,0.000068771136,0.00023854764,0.0003955592,0.00041733866,0.00022157391],"domain_scores_gemma":[0.99823797,0.00026156974,0.00014137763,0.0009921262,0.00026266195,0.0001042992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006148612,0.00011529399,0.000106677886,0.00075728964,0.00036931405,0.0007815913,0.0015236889,0.00010847387,0.00012263245],"category_scores_gemma":[0.00045862337,0.00007647498,0.00008382112,0.007667968,0.000084170904,0.00029667132,0.00026121593,0.00031742413,0.0002806028],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045134193,0.0007138275,0.051967464,0.000025724969,0.00015046862,0.000037683993,0.003874274,0.000012317023,0.17688157,0.3466392,0.046093527,0.37359944],"study_design_scores_gemma":[0.00070142513,0.0000415364,0.18206292,0.000008323341,0.000028128565,0.000026991225,0.00056661514,0.2269566,0.021022864,0.010527469,0.5571936,0.0008635594],"about_ca_topic_score_codex":0.00043824868,"about_ca_topic_score_gemma":0.00024502949,"teacher_disagreement_score":0.55733997,"about_ca_system_score_codex":0.000008847369,"about_ca_system_score_gemma":0.000034391807,"threshold_uncertainty_score":0.7536903},"labels":[],"label_agreement":null},{"id":"W2013415492","doi":"10.1155/2009/260148","title":"Sorted Index Numbers for Privacy Preserving Face Recognition","year":2009,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Facial recognition system; Face (sociological concept); Index (typography); Set (abstract data type); Pattern recognition (psychology); Transformation (genetics); Artificial intelligence; Projection (relational algebra); Feature (linguistics); Matching (statistics); Random projection; Data mining; Algorithm; Mathematics","score_opus":0.03888488282297544,"score_gpt":0.3285459080691336,"score_spread":0.28966102524615817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013415492","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":[0.014651976,0.0021432373,0.97980547,0.00127519,0.0002827941,0.00021981282,0.0000017720953,0.00008229774,0.0015374427],"genre_scores_gemma":[0.9669112,0.00020974716,0.03187718,0.0007307041,0.00014913845,0.0000067878655,0.0000035680184,0.000008768743,0.00010290528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825734,0.00009573269,0.000496163,0.0003487576,0.00046121026,0.00034077503],"domain_scores_gemma":[0.99889535,0.00014437248,0.0004187742,0.00016333966,0.00025493724,0.00012323042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083074404,0.00015726656,0.0001833114,0.00054060586,0.00033228396,0.0006108848,0.00080598705,0.000070922164,0.000019018944],"category_scores_gemma":[0.00025046256,0.00014522368,0.00007397493,0.001413747,0.000029505794,0.0031313286,0.000039010654,0.000432232,0.000009871947],"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":[0.00005167392,0.0001462363,0.00038727035,0.00003257506,0.0000029407654,0.00001249237,0.000569152,0.0006551686,0.00047904422,0.0002834489,0.0001367033,0.9972433],"study_design_scores_gemma":[0.0061093993,0.0010866765,0.028128311,0.0018672483,0.000025051875,0.0005154543,0.00074484316,0.54847705,0.009974891,0.28539765,0.11601841,0.0016549797],"about_ca_topic_score_codex":7.640394e-7,"about_ca_topic_score_gemma":0.0000015444729,"teacher_disagreement_score":0.9955883,"about_ca_system_score_codex":0.00012127415,"about_ca_system_score_gemma":0.000106164276,"threshold_uncertainty_score":0.5922049},"labels":[],"label_agreement":null},{"id":"W2015362166","doi":"10.1155/2008/743103","title":"Optimal Features Subset Selection and Classification for Iris Recognition","year":2008,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Institute of Standards and Technology","keywords":"Biometrics; Iris recognition; Pattern recognition (psychology); Artificial intelligence; Selection (genetic algorithm); Computer science; IRIS (biosensor); Feature selection","score_opus":0.056798878833319376,"score_gpt":0.30251417082216925,"score_spread":0.24571529198884987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015362166","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":[0.27715668,0.0008719116,0.7191074,0.0023605418,0.00015211184,0.00013348466,0.0000037162677,0.000063942774,0.00015021721],"genre_scores_gemma":[0.89541155,0.00059857423,0.10314467,0.00044662884,0.0001963321,0.000009437603,0.000008898535,0.000009336678,0.00017458983],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99912137,0.000056844212,0.00021978663,0.00025828948,0.00018632151,0.00015736642],"domain_scores_gemma":[0.9992298,0.00006994224,0.00020613018,0.000070979244,0.00031977874,0.00010339132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044610765,0.00010197631,0.0001070043,0.00032462587,0.0008467987,0.0007104143,0.00012128864,0.00006154674,0.0000036781103],"category_scores_gemma":[0.00016870478,0.000089302266,0.000033753462,0.00046819722,0.000050439332,0.0012625093,0.000018800605,0.0002142026,0.0000047445856],"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":[0.00010277171,0.0001320867,0.0010871907,0.000098662735,0.00001753652,0.000015040272,0.001890482,0.0000030568647,0.026383646,0.000288317,0.012595952,0.95738524],"study_design_scores_gemma":[0.005825953,0.0016366012,0.63925034,0.00053991715,0.00012552983,0.015964778,0.0007996592,0.19404158,0.06562321,0.008466843,0.06602529,0.0017002973],"about_ca_topic_score_codex":0.0000016679797,"about_ca_topic_score_gemma":7.914982e-7,"teacher_disagreement_score":0.95568496,"about_ca_system_score_codex":0.000030878953,"about_ca_system_score_gemma":0.000061754174,"threshold_uncertainty_score":0.6850542},"labels":[],"label_agreement":null},{"id":"W2016441392","doi":"10.1109/waina.2014.133","title":"Intent Biometrics: An Enhanced Form of Multimodal Biometric Systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Biometrics; Authentication (law); Computer science; Modal; Computer security; Field (mathematics); Iris recognition; Factor (programming language); Mathematics","score_opus":0.024402641874114995,"score_gpt":0.2652139927013856,"score_spread":0.24081135082727062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016441392","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":[0.045846656,0.0001664227,0.9507064,0.00008661072,0.0010710824,0.00017590745,0.0000040362197,0.00016738554,0.0017754792],"genre_scores_gemma":[0.96469617,0.000029233917,0.034819573,0.00007700532,0.000043737447,0.000010643937,0.00000827737,0.000005963488,0.00030941245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982484,0.00008993865,0.0004732934,0.00041003627,0.0005250258,0.00025331738],"domain_scores_gemma":[0.99813616,0.00017306472,0.0002186221,0.00085634657,0.00044040862,0.00017539978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010591774,0.0001255816,0.00023960634,0.0052522435,0.00006612037,0.0002010968,0.0012228811,0.00009762752,0.00002748549],"category_scores_gemma":[0.0003916094,0.0001032123,0.000078948244,0.016084483,0.000058133453,0.00057407346,0.00018708459,0.000071860086,0.00009641331],"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":[0.0000055433925,0.0008341112,0.0006996135,0.00010940089,0.000043420772,6.8241206e-7,0.0010320809,0.000011249245,0.037097037,0.22630359,0.00080047955,0.7330628],"study_design_scores_gemma":[0.0011136428,0.0006238611,0.01331068,0.000021487669,0.000009112254,0.000009159777,0.00028997107,0.8572665,0.104252905,0.0012928565,0.021274049,0.0005357961],"about_ca_topic_score_codex":0.0006470312,"about_ca_topic_score_gemma":0.0000069887897,"teacher_disagreement_score":0.91884947,"about_ca_system_score_codex":0.00005813278,"about_ca_system_score_gemma":0.00003085115,"threshold_uncertainty_score":0.77280605},"labels":[],"label_agreement":null},{"id":"W2018196039","doi":"10.1007/s11390-005-0008-2","title":"Online Palmprint Identification System for Civil Applications","year":2005,"lang":"en","type":"article","venue":"Journal of Computer Science and Technology","topic":"Biometric Identification and Security","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":"University of Waterloo","funders":"","keywords":"Computer science; Preprocessor; Biometrics; Artificial intelligence; Identification (biology); Process (computing); Computer vision; Feature extraction; Feature (linguistics); Pattern recognition (psychology); Matching (statistics); False positive rate; Interface (matter); Identity (music)","score_opus":0.0144270102444696,"score_gpt":0.268303194913213,"score_spread":0.2538761846687434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018196039","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":[0.017147249,0.00027889822,0.9733231,0.008627145,0.0003566127,0.00016880807,0.000002010654,0.00007717984,0.000018968998],"genre_scores_gemma":[0.7950646,0.000031569365,0.20463273,0.000098342876,0.00014731252,0.000009963508,4.0567141e-7,0.0000020532084,0.000012988514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877715,0.000011847731,0.00045019857,0.00025036224,0.00033804224,0.00017241309],"domain_scores_gemma":[0.9977566,0.00004425043,0.00039342846,0.00038964165,0.001334431,0.00008164115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012517009,0.000069815294,0.00014675503,0.0014569104,0.00023692266,0.00023101342,0.0014340398,0.00006741495,5.312396e-7],"category_scores_gemma":[0.000053815973,0.000059419846,0.00003594512,0.0027452281,0.00026850484,0.00063775014,0.0002118312,0.000119900025,0.0000051730726],"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":[0.0000010707573,0.00012069171,0.00009394437,0.000017828854,0.000006631586,8.1176427e-7,0.000078605786,0.000014271842,0.0045218663,0.215392,0.00031847414,0.7794338],"study_design_scores_gemma":[0.001459946,0.0004670108,0.009005557,0.000076225515,0.00003411181,0.0015265035,0.000219034,0.5708498,0.047306437,0.020985257,0.34762272,0.0004474377],"about_ca_topic_score_codex":6.3297705e-7,"about_ca_topic_score_gemma":0.0000025132865,"teacher_disagreement_score":0.7789864,"about_ca_system_score_codex":0.00010126917,"about_ca_system_score_gemma":0.00020312775,"threshold_uncertainty_score":0.26648265},"labels":[],"label_agreement":null},{"id":"W2019409703","doi":"10.1109/tim.2011.2179330","title":"Chaos-Based Security Solution for Fingerprint Data During Communication and Transmission","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Encryption; Robustness (evolution); Chaotic; Computer science; Wavelet transform; Singular value decomposition; Algorithm; Chaotic map; Fingerprint (computing); Wavelet packet decomposition; Artificial intelligence; Data mining; Computer vision; Wavelet; Computer security","score_opus":0.10200268985223201,"score_gpt":0.29942546146697185,"score_spread":0.19742277161473984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019409703","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":[0.030888276,0.00027780345,0.96638995,0.0015472302,0.00029915807,0.0004602058,0.000024471497,0.000079476245,0.000033455166],"genre_scores_gemma":[0.9763818,0.00023974181,0.023092873,0.00015169104,0.000013675749,0.0000785328,0.000022261127,0.0000062915196,0.000013181913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883205,0.00009512926,0.00024166245,0.0002892792,0.00035773873,0.00018412592],"domain_scores_gemma":[0.9991418,0.000038074977,0.00008784022,0.0004885467,0.00010367808,0.00014004622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009485177,0.000114823946,0.00009726356,0.00022443512,0.0005087873,0.00011834886,0.00024581805,0.000059161757,0.000011718814],"category_scores_gemma":[0.000007923667,0.00011702184,0.000029926094,0.00024356123,0.000044060802,0.00076281396,0.0000059838612,0.00010383597,0.0000027021313],"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":[0.00013853532,0.0009734072,0.00022702738,0.00028472993,0.00007323622,1.1825721e-7,0.0040281583,0.000070075905,0.03477714,0.0021811116,0.00018459672,0.9570619],"study_design_scores_gemma":[0.007944514,0.00028777565,0.022817815,0.0002601309,0.00018507583,0.00002269311,0.00051688333,0.524864,0.41924763,0.0008005505,0.022126764,0.0009261548],"about_ca_topic_score_codex":0.00004177224,"about_ca_topic_score_gemma":0.000026306381,"teacher_disagreement_score":0.9561357,"about_ca_system_score_codex":0.00011802011,"about_ca_system_score_gemma":0.000040436338,"threshold_uncertainty_score":0.4772012},"labels":[],"label_agreement":null},{"id":"W2021296267","doi":"10.1086/670029","title":"Enhanced Kin Recognition through Population Estimation","year":2013,"lang":"en","type":"article","venue":"The American Naturalist","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Kin recognition; Population; Estimation; Biology; Evolutionary biology; Demography; Economics; Sociology","score_opus":0.021383015317803372,"score_gpt":0.2779939126350559,"score_spread":0.2566108973172525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021296267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5132299,0.00005417156,0.4747637,0.009613848,0.00043881134,0.0003512689,0.0000043406194,0.00025521615,0.0012887659],"genre_scores_gemma":[0.9700103,0.000019275883,0.028014274,0.0015985238,0.000041397027,0.000030303572,0.00006531327,0.0000043969467,0.00021626127],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991032,0.00012187282,0.0001783749,0.00022144431,0.00023093306,0.00014420673],"domain_scores_gemma":[0.999066,0.00009939892,0.0002407949,0.00043372347,0.00012954089,0.0000305689],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016688005,0.00008285984,0.000103183265,0.000090113725,0.00018146809,0.00026878508,0.0004967745,0.00002267883,0.00007127157],"category_scores_gemma":[0.00012595618,0.000058432448,0.00004297812,0.0015453424,0.00010331917,0.00083260954,0.000062590305,0.00012121739,0.000874355],"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":[0.000005296364,0.00004158414,0.000069127316,0.000006135663,0.000012762537,3.2069062e-7,0.00093453703,0.000025965477,0.0033408632,0.014798713,0.0077868314,0.9729779],"study_design_scores_gemma":[0.0005406562,0.00011088162,0.59703994,0.00003191714,0.000028689476,0.000030245285,0.00035532343,0.24389783,0.018632552,0.13375258,0.0047699963,0.00080937694],"about_ca_topic_score_codex":0.005337467,"about_ca_topic_score_gemma":0.000025132278,"teacher_disagreement_score":0.9721685,"about_ca_system_score_codex":0.00005775752,"about_ca_system_score_gemma":0.0000101442,"threshold_uncertainty_score":0.99990356},"labels":[],"label_agreement":null},{"id":"W2021304433","doi":"10.1109/icsmc.2007.4413633","title":"Application of Multi-objective Genetic Algorithm and asymmetrical Support Vector Machine to improve the reliability of an iris recognition system","year":2007,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Iris recognition; Computer science; Artificial intelligence; Pattern recognition (psychology); IRIS (biosensor); Reliability (semiconductor); Feature (linguistics); Matching (statistics); Genetic algorithm; Feature extraction; Algorithm; Machine learning; Biometrics; Mathematics","score_opus":0.01329949598345583,"score_gpt":0.26362250386875863,"score_spread":0.2503230078853028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021304433","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":[0.07363282,0.000028877881,0.92538714,0.0000963801,0.00011413599,0.0005442744,0.000033742126,0.000044819288,0.00011777872],"genre_scores_gemma":[0.757506,0.000002503494,0.24239455,0.000036696943,0.000015786869,0.000015867592,0.00000749401,0.000003059145,0.000018008812],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99872977,0.00009255348,0.00041319832,0.0003632938,0.00027310709,0.00012810175],"domain_scores_gemma":[0.9985998,0.00018378747,0.00017255895,0.0005235911,0.00041637893,0.000103844905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014834612,0.00008097869,0.00014901074,0.00032367374,0.00005634794,0.000028575052,0.00033414454,0.00006912222,0.0000035682929],"category_scores_gemma":[0.00015604278,0.000058290694,0.000037820835,0.0018047761,0.000061005067,0.00013360087,0.000103700055,0.00007548172,0.000008643607],"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":[0.000011686489,0.00023989525,0.0008269833,0.000045168144,0.000007742374,4.4719195e-7,0.00046129906,0.000002141023,0.00293097,0.00048210885,0.000017982158,0.9949736],"study_design_scores_gemma":[0.00047751615,0.0004019351,0.4978784,0.000005009942,0.000018891553,0.000015363477,0.00028737317,0.42311987,0.077152565,0.0002011175,0.00028479323,0.00015716514],"about_ca_topic_score_codex":0.0009840819,"about_ca_topic_score_gemma":0.000027503907,"teacher_disagreement_score":0.9948164,"about_ca_system_score_codex":0.00006480049,"about_ca_system_score_gemma":0.000028748578,"threshold_uncertainty_score":0.23770256},"labels":[],"label_agreement":null},{"id":"W2022565920","doi":"10.1504/ijbm.2009.024277","title":"Fusing multiple matcher's outputs for secure human identification","year":2009,"lang":"en","type":"article","venue":"International Journal of Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Voting; Rank (graph theory); Artificial intelligence; Majority rule; Key (lock); Machine learning; Modalities; Data mining; Authentication (law); Process (computing); Sensor fusion; Modality (human–computer interaction); Pattern recognition (psychology); Computer security; Mathematics","score_opus":0.03718520374411133,"score_gpt":0.3314449995893372,"score_spread":0.2942597958452259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022565920","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":[0.02557225,0.0003970839,0.9673604,0.003716426,0.0026682543,0.000114552684,0.000016290736,0.000035985337,0.00011877772],"genre_scores_gemma":[0.9542006,0.000051946394,0.044775933,0.00037772118,0.0003662037,0.0000014250995,0.000018116361,0.000005688397,0.00020235885],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979825,0.00003716722,0.0007327643,0.00020172521,0.0008868778,0.00015894062],"domain_scores_gemma":[0.9967528,0.00016907655,0.00082221697,0.00022956754,0.001921642,0.00010467042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011050438,0.00010703691,0.00016280514,0.0036538118,0.000106233274,0.0006031739,0.0016072697,0.00009290558,0.000007805732],"category_scores_gemma":[0.00074288226,0.00010240591,0.00019216364,0.0028959438,0.000025986817,0.00079077116,0.000062095256,0.0001385396,0.000010444752],"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":[0.00006263445,0.0013018546,0.0017922381,0.000026889415,0.0002621439,0.00006190686,0.0014344254,0.000043854954,0.1269318,0.13167867,0.016472114,0.7199315],"study_design_scores_gemma":[0.00877448,0.0012634603,0.24012408,0.00019580542,0.000119667166,0.0007697793,0.0003710667,0.035475895,0.16639996,0.11784324,0.42725575,0.0014068065],"about_ca_topic_score_codex":0.000003900237,"about_ca_topic_score_gemma":0.0000011444704,"teacher_disagreement_score":0.9286284,"about_ca_system_score_codex":0.00018779234,"about_ca_system_score_gemma":0.00007002964,"threshold_uncertainty_score":0.581642},"labels":[],"label_agreement":null},{"id":"W2023434790","doi":"10.1117/12.921601","title":"Multimodal biometric approach for cancelable face template generation","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Pattern recognition (psychology); Computer science; Artificial intelligence; Feature (linguistics); Random projection; Feature vector; Classifier (UML); Projection (relational algebra); Algorithm","score_opus":0.027830158743575514,"score_gpt":0.24964831558907832,"score_spread":0.22181815684550282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023434790","genre_codex":"empirical","genre_gemma":"methods","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":[0.9495633,0.00029725677,0.04664931,0.001080702,0.00060162897,0.00080693344,0.000060312243,0.00010825449,0.0008322497],"genre_scores_gemma":[0.4423402,0.00005176431,0.55647767,0.00007948488,0.000460873,0.00024827503,0.00002272794,0.000027139258,0.00029187714],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977872,2.0180618e-8,0.00061069167,0.00042374848,0.0006840958,0.00049426715],"domain_scores_gemma":[0.9976307,0.000120205026,0.00039742913,0.00009148204,0.001593328,0.00016685302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011905568,0.0002547528,0.00031783368,0.0003687448,0.00015171454,0.0002439219,0.0014107802,0.00020022073,0.000004598582],"category_scores_gemma":[0.00054838153,0.00021866737,0.0004663065,0.0015981595,0.00011447373,0.0013183688,0.00019769043,0.00019303113,0.0000019428192],"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":[0.000019920966,0.00027612902,0.00042288643,0.00034537085,0.00022466762,8.077929e-9,0.00039881837,0.00011909716,0.39043367,0.5976545,0.008650377,0.0014545047],"study_design_scores_gemma":[0.0013862232,0.0001982016,0.0013351165,0.000046623896,0.00009681168,0.000014043224,0.0005548027,0.7274438,0.25020626,0.00046088867,0.017730784,0.00052641093],"about_ca_topic_score_codex":0.000019331932,"about_ca_topic_score_gemma":6.091555e-8,"teacher_disagreement_score":0.7273247,"about_ca_system_score_codex":0.0002007609,"about_ca_system_score_gemma":0.000038494487,"threshold_uncertainty_score":0.8916997},"labels":[],"label_agreement":null},{"id":"W2023874736","doi":"10.1007/s00371-012-0741-9","title":"A multi-modal approach for high-dimensional feature recognition","year":2012,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":8,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Modal; Metaverse; Facial recognition system; Focus (optics); Computer security; Feature (linguistics); Avatar; Computer graphics; Human–computer interaction; Event (particle physics); Domain (mathematical analysis); Data science; Artificial intelligence; Pattern recognition (psychology); Virtual reality","score_opus":0.05831572908919798,"score_gpt":0.3019619590431422,"score_spread":0.24364622995394425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023874736","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":[0.028429672,0.00010398927,0.969141,0.00080565206,0.0010344728,0.0003260191,0.0000104477695,0.00012049913,0.000028245644],"genre_scores_gemma":[0.5365825,9.922331e-7,0.46165013,0.0009345789,0.0005041061,0.000032411965,0.00007148426,0.0000067135543,0.00021706731],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990022,0.00010534621,0.00013547798,0.00024720066,0.00022762308,0.00028214374],"domain_scores_gemma":[0.99931127,0.00011629572,0.000072610535,0.00028676243,0.00012455211,0.000088513945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062029186,0.00011542336,0.00011160548,0.00013563482,0.00023079627,0.0001445381,0.00048114927,0.00008345549,0.000008642543],"category_scores_gemma":[0.000015891559,0.00007798957,0.0000758621,0.0005270349,0.000041978175,0.00033516457,0.00020488238,0.00013818982,0.00009313422],"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":[0.00011929503,0.0042230766,0.00050565857,0.00013467655,0.00022519391,0.0000018122482,0.004641725,0.00014332491,0.004391915,0.055226646,0.15810335,0.7722833],"study_design_scores_gemma":[0.0008806196,0.0001023679,0.019006718,0.000006283365,0.000015872209,0.000030820895,0.000010148027,0.97004616,0.0014875738,0.00066649733,0.0074755023,0.00027144927],"about_ca_topic_score_codex":0.000011313705,"about_ca_topic_score_gemma":4.165741e-7,"teacher_disagreement_score":0.9699028,"about_ca_system_score_codex":0.00002660085,"about_ca_system_score_gemma":0.000019688616,"threshold_uncertainty_score":0.31803223},"labels":[],"label_agreement":null},{"id":"W2023883184","doi":"10.1049/iet-bmt.2014.0064","title":"Multibiometric system using fuzzy level set, and genetic and evolutionary feature extraction","year":2015,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Biometrics; Feature extraction; Segmentation; Face (sociological concept); Feature (linguistics); Modality (human–computer interaction); Iris recognition; IRIS (biosensor); Computer vision; Level set (data structures); Set (abstract data type)","score_opus":0.12134947672945452,"score_gpt":0.3084924593791735,"score_spread":0.18714298264971901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023883184","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":[0.33871514,0.018710466,0.6395025,0.00035865032,0.0018149082,0.00035214977,0.00007498515,0.00023936806,0.0002318376],"genre_scores_gemma":[0.8395241,0.00017512245,0.15995093,0.00004510262,0.000091317896,0.0000033619235,0.000012126296,0.000010749432,0.00018719041],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9981647,0.0001092828,0.00026942574,0.0005584379,0.00062088476,0.0002772668],"domain_scores_gemma":[0.99853486,0.0001394859,0.00018278105,0.0004309541,0.0003771801,0.00033473072],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00061680155,0.00017990687,0.0001983914,0.0068690823,0.00020168244,0.00038246065,0.00035643595,0.00022310115,7.720636e-7],"category_scores_gemma":[0.00042263512,0.00017614644,0.000039939077,0.023210902,0.00008849471,0.0004483495,0.0002792114,0.000155135,0.000017240929],"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":[0.000099190525,0.0009173726,0.11425096,0.0011619037,0.00031229475,0.0003074078,0.0026943132,0.00017846971,0.017557567,0.013053669,0.044290766,0.8051761],"study_design_scores_gemma":[0.0020721296,0.00033150538,0.61666954,0.000053050087,0.00008294836,0.002405121,0.00066640065,0.3302124,0.0008318744,0.00057647005,0.04511342,0.000985105],"about_ca_topic_score_codex":0.00015919343,"about_ca_topic_score_gemma":0.0000024760468,"teacher_disagreement_score":0.804191,"about_ca_system_score_codex":0.0002738798,"about_ca_system_score_gemma":0.00011178674,"threshold_uncertainty_score":0.9975512},"labels":[],"label_agreement":null},{"id":"W2025774247","doi":"10.1016/j.cag.2010.05.012","title":"A multiresolution approach to iris synthesis","year":2010,"lang":"en","type":"article","venue":"Computers & Graphics","topic":"Biometric Identification and Security","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"IRIS (biosensor); Computer science; Artificial intelligence; Computer vision; Biometrics","score_opus":0.024011561678525504,"score_gpt":0.24260317152783525,"score_spread":0.21859160984930975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025774247","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":[0.020005152,0.00003008479,0.97585356,0.0012353448,0.0015270798,0.00019624324,0.0000039672354,0.00031225322,0.00083628704],"genre_scores_gemma":[0.66843575,0.000007395025,0.33063605,0.00077058206,0.000079171085,0.000026690248,0.0000039450897,0.0000072664334,0.00003315625],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985722,0.000065418106,0.00022219135,0.00051337143,0.00034420574,0.00028261993],"domain_scores_gemma":[0.9984996,0.00015783052,0.00007157956,0.0008938871,0.0001465903,0.00023052694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005070955,0.0001414686,0.00015061238,0.0007656429,0.00021616374,0.0003088998,0.0013720985,0.00012788738,0.0000034758884],"category_scores_gemma":[0.00014289422,0.00014114895,0.00011020032,0.00272153,0.00007844017,0.0002494901,0.00030107918,0.00028533075,0.00008351717],"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":[0.0000068062595,0.0006680281,0.0011754655,0.000041609972,0.00004684481,0.000005557847,0.0019802495,0.000049237777,0.00354853,0.71547747,0.03980793,0.2371923],"study_design_scores_gemma":[0.000295167,0.00004030191,0.04684663,0.000014879323,0.000014829181,0.00004025618,0.00002231414,0.72168416,0.0013006562,0.0025189014,0.2266294,0.00059250166],"about_ca_topic_score_codex":0.00006372207,"about_ca_topic_score_gemma":0.0000097649945,"teacher_disagreement_score":0.7216349,"about_ca_system_score_codex":0.000017901577,"about_ca_system_score_gemma":0.000038186132,"threshold_uncertainty_score":0.5755887},"labels":[],"label_agreement":null},{"id":"W2026324054","doi":"10.1142/s0219467814500132","title":"Multibiometric System Using Level Set, Modified LBP and Random Forest","year":2014,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Biometrics; Computer science; Local binary patterns; Artificial intelligence; Pattern recognition (psychology); Face (sociological concept); Iris recognition; Feature (linguistics); IRIS (biosensor); Set (abstract data type); Boundary (topology); Feature extraction; Random forest; Process (computing); Feature vector; Feature selection; Computer vision; Histogram; Image (mathematics); Mathematics","score_opus":0.0491726033402247,"score_gpt":0.2956917217472233,"score_spread":0.2465191184069986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026324054","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":[0.23617579,0.00034029724,0.76246786,0.00032932695,0.00056205643,0.00003551856,0.000006658068,0.0000087959625,0.00007366384],"genre_scores_gemma":[0.978177,0.00015462644,0.021389686,0.00013168098,0.0001232568,3.2350178e-7,0.0000015369294,0.0000038420117,0.000018061335],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891824,0.00007368662,0.00034641768,0.00013551032,0.00043505238,0.00009108902],"domain_scores_gemma":[0.99864674,0.00016771322,0.00030504182,0.00011276375,0.00067223964,0.0000954921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095801806,0.00008105576,0.00014984923,0.0014616665,0.00007755021,0.00037995764,0.00044403042,0.000048499034,7.224832e-7],"category_scores_gemma":[0.00024805692,0.00006737909,0.00006936325,0.000691311,0.00007176686,0.00044645107,0.00010880765,0.000121904886,7.791533e-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":[0.0005133202,0.00060570677,0.059218377,0.00036113863,0.0013214812,0.00036363045,0.003394019,0.00029409904,0.02258727,0.6392623,0.0025728408,0.26950583],"study_design_scores_gemma":[0.0066129994,0.00014850833,0.093221985,0.00016394796,0.00006016484,0.0022120227,0.00016285614,0.88543564,0.0013254042,0.005576523,0.0047295596,0.00035035933],"about_ca_topic_score_codex":0.000049299775,"about_ca_topic_score_gemma":0.00000593619,"teacher_disagreement_score":0.88514155,"about_ca_system_score_codex":0.000023375766,"about_ca_system_score_gemma":0.000030047522,"threshold_uncertainty_score":0.366394},"labels":[],"label_agreement":null},{"id":"W2027382049","doi":"10.1007/s11760-010-0193-5","title":"Iris segmentation using game theory","year":2010,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Segmentation; IRIS (biosensor); Artificial intelligence; Iris recognition; Computer science; Computer vision; Boundary (topology); Pupil; Sclera; Novelty; Image segmentation; Pattern recognition (psychology); Mathematics; Biometrics; Optics; Physics","score_opus":0.020674566971648097,"score_gpt":0.2950688709081115,"score_spread":0.2743943039364634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027382049","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":[0.10718497,0.00026764485,0.89174175,0.0001596259,0.000117073825,0.000060217786,9.2444213e-7,0.00007260265,0.00039520496],"genre_scores_gemma":[0.8888345,0.000005908881,0.11069706,0.00028054471,0.000065347114,0.0000027375104,0.000002056334,0.0000051924326,0.00010665344],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922615,0.000053227657,0.00015168522,0.00025634182,0.00016499714,0.00014761125],"domain_scores_gemma":[0.9995241,0.000053289034,0.0000907448,0.0001420641,0.00011995989,0.0000698323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064120896,0.000082099905,0.00007772057,0.0001823357,0.00021316274,0.000733001,0.00021342828,0.00004917352,0.00003744494],"category_scores_gemma":[0.000053741776,0.0000744026,0.000022827006,0.0005336575,0.00008913589,0.0012923726,0.00007877806,0.00015371098,0.000012291562],"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":[0.000003059024,0.000024049,0.00017424626,0.000032013162,0.0000030787912,0.0000037215098,0.0010124575,6.213389e-7,0.51556784,0.002040381,0.000056534664,0.481082],"study_design_scores_gemma":[0.0011150821,0.000066448214,0.006568684,0.0000974637,0.00005630573,0.0002250536,0.00075687433,0.5351764,0.39261428,0.056399778,0.006038683,0.00088497234],"about_ca_topic_score_codex":0.00001305946,"about_ca_topic_score_gemma":0.000001453924,"teacher_disagreement_score":0.78164953,"about_ca_system_score_codex":0.000009888849,"about_ca_system_score_gemma":0.000062600935,"threshold_uncertainty_score":0.70683455},"labels":[],"label_agreement":null},{"id":"W2029711940","doi":"10.1109/cw.2014.45","title":"Multimodal Biometrics Using Cancelable Feature Fusion","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Feature (linguistics); Template; Fusion; Pattern recognition (psychology); Authentication (law); Artificial intelligence; Feature extraction; Computer security","score_opus":0.025919097319513102,"score_gpt":0.2663342569666038,"score_spread":0.24041515964709068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029711940","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":[0.019646972,0.000105548024,0.97456753,0.0007241897,0.00075047003,0.00006826453,0.0000014143407,0.00015393025,0.003981704],"genre_scores_gemma":[0.7102091,0.000013537517,0.2862681,0.0005081914,0.000065900014,0.0000014830953,0.0000036030044,0.0000045884003,0.0029255166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990863,0.000043634973,0.00011283828,0.0002847191,0.00028144984,0.00019101913],"domain_scores_gemma":[0.9992266,0.000056767076,0.00005724462,0.0004418884,0.00012503845,0.00009242981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038580838,0.00007832662,0.00009196284,0.0007392021,0.00015583093,0.0002131197,0.00054907263,0.000086051055,0.000052642554],"category_scores_gemma":[0.00011821167,0.00006618096,0.000038339433,0.0048201596,0.00002185087,0.00029078184,0.00016808423,0.000089037625,0.00008953982],"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":[0.0000062409904,0.00037345846,0.0058630644,0.00006165681,0.00002560073,0.0000055346904,0.0006453265,0.00017450133,0.04670704,0.21946932,0.049620047,0.6770482],"study_design_scores_gemma":[0.00023189673,0.000017328293,0.0039970754,0.0000034163452,0.0000023793164,0.000006280151,0.000008345917,0.83303773,0.0058561615,0.0004748289,0.15621518,0.00014935377],"about_ca_topic_score_codex":0.00040426146,"about_ca_topic_score_gemma":0.00001587487,"teacher_disagreement_score":0.8328633,"about_ca_system_score_codex":0.000057619774,"about_ca_system_score_gemma":0.000038295282,"threshold_uncertainty_score":0.26987812},"labels":[],"label_agreement":null},{"id":"W2030526293","doi":"10.1109/bsc.2008.4563282","title":"Improved identification of iris and eyelash features","year":2008,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Eyelash; Iris recognition; Artificial intelligence; IRIS (biosensor); Segmentation; Computer science; Computer vision; Hough transform; Image segmentation; Pattern recognition (psychology); Image (mathematics); Biometrics","score_opus":0.015849439161913413,"score_gpt":0.2424863789035106,"score_spread":0.22663693974159718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030526293","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":[0.24313506,0.00046249846,0.7529036,0.0013368576,0.0003014788,0.00015004074,0.0000034577135,0.00012740126,0.001579629],"genre_scores_gemma":[0.9845542,0.000096550946,0.011814539,0.000073354,0.000009065232,0.0000025613847,0.0000017938646,0.0000015931425,0.0034463457],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99944454,0.000019195511,0.0001705711,0.00017389205,0.00012382095,0.00006799968],"domain_scores_gemma":[0.99946785,0.00002840294,0.00007942803,0.00029291943,0.00009433463,0.000037094964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015979781,0.000042588646,0.00006582485,0.0001729191,0.00007014749,0.000040821815,0.0002579815,0.000037359685,0.000010226647],"category_scores_gemma":[0.00004868699,0.000036974303,0.000022105689,0.00057982554,0.00006338941,0.0002241284,0.00007032809,0.000038450922,0.000009574329],"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":[0.0000120550485,0.00042759167,0.009744591,0.00009424851,0.000055996217,0.0000055803534,0.00578136,0.0000015838958,0.4664385,0.2903685,0.044468947,0.18260106],"study_design_scores_gemma":[0.00044493863,0.000046433088,0.7832782,0.0000032259832,0.000005914998,0.00006448861,0.000062218394,0.02097587,0.18279266,0.001999527,0.010110763,0.00021575391],"about_ca_topic_score_codex":0.000054734726,"about_ca_topic_score_gemma":0.000003912882,"teacher_disagreement_score":0.7735336,"about_ca_system_score_codex":0.0000064034593,"about_ca_system_score_gemma":0.000019055491,"threshold_uncertainty_score":0.15077683},"labels":[],"label_agreement":null},{"id":"W2031996436","doi":"10.1145/1101389.1101411","title":"Iris synthesis","year":2005,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Iris recognition; IRIS (biosensor); Computer science; Biometrics; Heuristics; Artificial intelligence; Subdivision; Computer vision; Identification (biology); Image (mathematics); Pattern recognition (psychology); Set (abstract data type); Engineering","score_opus":0.020455810663294258,"score_gpt":0.24480466796896652,"score_spread":0.22434885730567228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031996436","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":[0.0020428074,0.000057483496,0.93611854,0.010181581,0.00011064456,0.000026842266,3.8120217e-7,0.00020083389,0.051260885],"genre_scores_gemma":[0.85046715,0.000009456096,0.14378227,0.00078853493,0.000035616304,0.0000037615632,1.5875665e-7,0.0000011725715,0.0049118684],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99962026,0.00001304593,0.00006899771,0.00011763857,0.00010543162,0.000074642994],"domain_scores_gemma":[0.99961364,0.00004400823,0.00001399471,0.00026561657,0.000025015785,0.000037715738],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013251072,0.00002776265,0.000033216165,0.00011596031,0.000038251826,0.00008907023,0.00038106577,0.00001879848,0.00035958286],"category_scores_gemma":[0.000044735232,0.00002314244,0.00002190389,0.0005247351,0.000009177183,0.0002164359,0.00005189857,0.000022483657,0.0014063775],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.8450007e-7,0.000045301287,0.000116643656,0.0000011794094,0.000002972426,4.595397e-7,0.00010362166,9.461609e-7,0.00025361488,0.20518136,0.047425933,0.7468678],"study_design_scores_gemma":[0.000036761616,0.000002628718,0.0045188027,7.416482e-7,9.991543e-7,0.000003484294,0.0000064295773,0.025446346,0.012557768,0.00049079634,0.956861,0.000074225885],"about_ca_topic_score_codex":0.000012745732,"about_ca_topic_score_gemma":0.0000041435987,"teacher_disagreement_score":0.9094351,"about_ca_system_score_codex":0.000013451214,"about_ca_system_score_gemma":0.0000104486035,"threshold_uncertainty_score":0.9993712},"labels":[],"label_agreement":null},{"id":"W2033998900","doi":"10.1016/j.engappai.2010.06.014","title":"Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs","year":2010,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Support vector machine; Iris recognition; Segmentation; Boundary (topology); Feature selection; Algorithm; Biometrics; Mathematics","score_opus":0.07143496385051744,"score_gpt":0.31392271155487944,"score_spread":0.242487747704362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033998900","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":[0.0032665683,0.000030382651,0.9956492,0.00029608246,0.00020296851,0.0003021693,0.000053228017,0.00010127983,0.00009812399],"genre_scores_gemma":[0.4199978,0.000008114248,0.57978916,0.000032965847,0.000055250035,0.000089224086,0.000010293897,0.0000078616695,0.000009283943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868673,0.000033716035,0.0003924293,0.00039104748,0.00030645586,0.00018961991],"domain_scores_gemma":[0.99864835,0.00032327167,0.00011057217,0.000538863,0.0002474932,0.00013142586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005594277,0.00014592992,0.00015775514,0.0006708272,0.00009732656,0.000096998025,0.00053302763,0.00012213315,0.00001737382],"category_scores_gemma":[0.00025848768,0.00015733307,0.000058691636,0.0020843537,0.00007118423,0.000091888294,0.000080306636,0.00029010721,0.00005342604],"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":[0.000004934247,0.00011093935,0.000010206748,0.0000124717435,0.000008129841,4.691752e-7,0.00012422864,0.0021735982,0.0049708006,0.031997167,0.000031649906,0.96055543],"study_design_scores_gemma":[0.00002804493,0.000080008365,0.0027718388,0.000008126469,0.000008745381,0.0000061342525,0.000030732113,0.9373429,0.05381288,0.004136857,0.0015766256,0.00019712301],"about_ca_topic_score_codex":0.00011614576,"about_ca_topic_score_gemma":0.0000095886935,"teacher_disagreement_score":0.96035826,"about_ca_system_score_codex":0.000025917692,"about_ca_system_score_gemma":0.000066584114,"threshold_uncertainty_score":0.64158565},"labels":[],"label_agreement":null},{"id":"W2034759048","doi":"10.1016/j.patcog.2007.09.002","title":"Three measures for secure palmprint identification","year":2007,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Biometrics; Replay attack; Feature (linguistics); Computer security; Authentication (law); Digital watermarking; Identification (biology); Gabor filter; Filter (signal processing); Artificial intelligence; Pattern recognition (psychology); Feature extraction; Data mining; Computer vision; Image (mathematics)","score_opus":0.06557127377274317,"score_gpt":0.2886709161847236,"score_spread":0.22309964241198044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034759048","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":[0.03229254,0.00006126659,0.9654522,0.00057366374,0.00082067493,0.00038475994,0.00002519368,0.00016964108,0.00022003973],"genre_scores_gemma":[0.9867006,0.000013768596,0.012575754,0.00031578314,0.00016162633,0.00006041213,0.00012963272,0.000009078483,0.000033347485],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987587,0.000023426026,0.0003327999,0.00036850927,0.00029033027,0.00022625677],"domain_scores_gemma":[0.9989472,0.00010783495,0.00016043012,0.0003487857,0.00035833582,0.00007738052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013638174,0.00009941545,0.000090541806,0.0003200861,0.00014593048,0.00021048369,0.00037928266,0.00009000416,0.000028486704],"category_scores_gemma":[0.000119259115,0.000102262275,0.00008273014,0.00054099393,0.000021253654,0.00032470888,0.00004656258,0.00008647444,0.00026814788],"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":[0.0000046661994,0.00006330606,0.0010669688,0.000023881808,0.000009810933,9.2471197e-7,0.00020662,1.7214388e-7,0.0025057066,0.0006971069,0.0006473162,0.9947735],"study_design_scores_gemma":[0.002127751,0.00017585511,0.56178033,0.00008905517,0.000065524546,0.000050533734,0.00019776002,0.0337916,0.20416337,0.1525719,0.04379621,0.0011900854],"about_ca_topic_score_codex":0.000040290102,"about_ca_topic_score_gemma":0.00024011599,"teacher_disagreement_score":0.99358344,"about_ca_system_score_codex":0.00005386408,"about_ca_system_score_gemma":0.000018368977,"threshold_uncertainty_score":0.41701347},"labels":[],"label_agreement":null},{"id":"W2035311144","doi":"10.1109/csci.2014.30","title":"Subspace State Estimator for Facial Biometric Verification","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Science and Technology Council; National Science Council","keywords":"Biometrics; Subspace topology; Computer science; Estimator; Face (sociological concept); Noise (video); Image (mathematics); State (computer science); Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Nonlinear system; Computational complexity theory; State estimator; Algorithm; Mathematics; Statistics","score_opus":0.020238414360448,"score_gpt":0.2635720581999885,"score_spread":0.24333364383954054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035311144","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":[0.0056257914,0.000024212619,0.99110293,0.0012974046,0.000481177,0.00019036855,0.000005921652,0.00020323438,0.0010689292],"genre_scores_gemma":[0.82728904,0.0000068469026,0.17020471,0.00020924733,0.000038902916,0.000031876407,0.0000148686195,0.0000056226145,0.002198871],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913555,0.00003206381,0.00016700105,0.00029648535,0.00019505934,0.000173848],"domain_scores_gemma":[0.99914056,0.0001259776,0.0000758187,0.0004201658,0.00015253028,0.00008493728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051607966,0.00007350602,0.00008923198,0.0007061223,0.00013235278,0.00023354236,0.0005180548,0.000040555908,0.000017545895],"category_scores_gemma":[0.0002985344,0.00006690695,0.000046516914,0.0027485245,0.000025138675,0.00027841673,0.000048023023,0.000034065175,0.00020448331],"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":[0.000006347227,0.000117264186,0.00047192766,0.000028196026,0.0000105826875,1.5003974e-7,0.000256768,0.000008192964,0.003044039,0.37497056,0.014992225,0.60609376],"study_design_scores_gemma":[0.0006003736,0.00008619571,0.02063756,0.0000019793686,0.0000045142456,0.0000021772578,0.000011840559,0.41798583,0.015914917,0.00848371,0.53600186,0.00026905857],"about_ca_topic_score_codex":0.000025588952,"about_ca_topic_score_gemma":0.0000042723427,"teacher_disagreement_score":0.82166326,"about_ca_system_score_codex":0.00002873644,"about_ca_system_score_gemma":0.000031038824,"threshold_uncertainty_score":0.27283862},"labels":[],"label_agreement":null},{"id":"W2035587080","doi":"10.1155/2012/124176","title":"Chaotic Neural Network for Biometric Pattern Recognition","year":2012,"lang":"en","type":"article","venue":"Advances in Artificial Intelligence","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Biometrics; Artificial intelligence; Authentication (law); Subspace topology; Feature (linguistics); Set (abstract data type); Curse of dimensionality; Artificial neural network; Feature vector; Cluster analysis; Chaotic; Machine learning; Identity (music); Data mining; Pattern recognition (psychology); Computer security","score_opus":0.09626537499652227,"score_gpt":0.3439022344579791,"score_spread":0.2476368594614568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035587080","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":[0.011843565,0.0038773185,0.9801215,0.00032210237,0.0032395844,0.00032630426,0.0000088419865,0.00009238659,0.00016839668],"genre_scores_gemma":[0.9703004,0.00038219197,0.028486017,0.0003097275,0.00039490324,0.00007670839,0.000018215462,0.000008573295,0.00002331123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983927,0.000074729454,0.00046501425,0.00029420282,0.00021948402,0.0005538521],"domain_scores_gemma":[0.99890846,0.00039943555,0.00014565245,0.00032549605,0.00011113407,0.000109806155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083784526,0.0001330206,0.00015670237,0.0006194637,0.00012963377,0.000117219744,0.00059266645,0.000070737005,0.0000357133],"category_scores_gemma":[0.00031652595,0.00013433647,0.00006902283,0.0043561673,0.00006835023,0.0014547537,0.00008912403,0.00012404588,0.00021363542],"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":[0.000005559553,0.00011977535,0.0015351439,0.000016950637,0.00000206035,5.2548484e-7,0.00030584243,0.00025578344,0.000037699152,0.013670216,0.00005730811,0.9839931],"study_design_scores_gemma":[0.0001761362,0.00033872912,0.008105006,0.000104784005,0.000022269911,0.000029276785,0.0006343627,0.5910087,0.024424488,0.30966353,0.06408965,0.0014030635],"about_ca_topic_score_codex":0.000023682218,"about_ca_topic_score_gemma":0.00005509973,"teacher_disagreement_score":0.9825901,"about_ca_system_score_codex":0.000059359718,"about_ca_system_score_gemma":0.000015357198,"threshold_uncertainty_score":0.54780823},"labels":[],"label_agreement":null},{"id":"W2036107618","doi":"10.1109/dt.2014.6868715","title":"Application of biometric technologies in biomedical systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Biometric data; Access control; Human–computer interaction; Health care; Computer security","score_opus":0.012723876648874251,"score_gpt":0.24510108711723097,"score_spread":0.23237721046835672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036107618","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":[0.0042733112,0.00017080651,0.99331814,0.00064480765,0.0001573301,0.000110567154,6.9004903e-7,0.00022895227,0.0010953952],"genre_scores_gemma":[0.98880553,0.000014207413,0.011085382,0.000011000311,0.0000057608254,0.000017309185,0.000002405739,0.0000014775097,0.000056934005],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991688,0.000034128305,0.00025686366,0.00020194126,0.00023228524,0.00010594071],"domain_scores_gemma":[0.99928653,0.0000947352,0.00008346043,0.00045486377,0.00005791896,0.000022496113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061024394,0.000046672685,0.00011131382,0.0023401973,0.000015787444,0.00003621619,0.00070870697,0.00009016084,0.000002008372],"category_scores_gemma":[0.00021214351,0.000038074963,0.0000188935,0.009028927,0.00007013354,0.00010765994,0.00011977129,0.000051750558,0.000041221047],"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":[5.121091e-7,0.00014510784,0.0038263144,0.00003756118,0.0000026505995,1.9182129e-7,0.000040362494,0.0000025458185,0.0032637864,0.59955037,0.00053240417,0.39259818],"study_design_scores_gemma":[0.00044788385,0.0000864899,0.023919925,0.00001273873,0.0000020396478,0.000006705008,0.0001774183,0.8423863,0.007598237,0.005942411,0.119219914,0.00019991634],"about_ca_topic_score_codex":0.00016528586,"about_ca_topic_score_gemma":0.000002729277,"teacher_disagreement_score":0.98453224,"about_ca_system_score_codex":0.000022899996,"about_ca_system_score_gemma":0.0000135911205,"threshold_uncertainty_score":0.43381006},"labels":[],"label_agreement":null},{"id":"W2036122878","doi":"10.1109/icdsp.2009.5201106","title":"Random translational transformation for changeable face verification","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Biometrics; Transformation (genetics); Face (sociological concept); Template; Artificial intelligence; Data mining; Feature extraction; Invertible matrix; Software deployment; Index (typography); Pattern recognition (psychology); Mathematics; Software engineering","score_opus":0.034069444810420624,"score_gpt":0.2720054981638157,"score_spread":0.23793605335339507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036122878","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":[0.00046990032,0.00006881756,0.9821004,0.012695701,0.00013696495,0.00039614603,0.0000062218846,0.00013861388,0.003987223],"genre_scores_gemma":[0.93069404,0.000019316774,0.06775042,0.0006535742,0.000034611145,0.00003202188,0.00007684293,0.0000020105401,0.0007371382],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935615,0.000018524777,0.00017539137,0.0001653562,0.00016961679,0.0001149655],"domain_scores_gemma":[0.9996098,0.00004228402,0.000038882215,0.0001770345,0.000092217444,0.000039737854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033782207,0.000057513473,0.00006975765,0.00017135662,0.00011752935,0.00011830282,0.00025648807,0.000045995286,0.000029400599],"category_scores_gemma":[0.000015044146,0.000053223877,0.00005546636,0.000564112,0.000007806137,0.00065861695,0.0000021145463,0.000030059658,0.000032481876],"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":[0.000029274162,0.00009922418,0.0000042808433,0.000010914863,0.000005296879,5.231721e-8,0.0022217743,0.00006571396,0.0035419918,0.6769871,0.0025689593,0.31446537],"study_design_scores_gemma":[0.0036960344,0.000113367576,0.007110138,0.0000051474217,0.000009809393,0.0000060373463,0.000077898076,0.71031004,0.02007415,0.031075789,0.22722861,0.00029296053],"about_ca_topic_score_codex":0.0000034922857,"about_ca_topic_score_gemma":0.0000021193216,"teacher_disagreement_score":0.9302242,"about_ca_system_score_codex":0.000014016138,"about_ca_system_score_gemma":0.000024702882,"threshold_uncertainty_score":0.21704067},"labels":[],"label_agreement":null},{"id":"W2038594258","doi":"10.1109/icci-cc.2014.6921445","title":"Rank level fusion of multimodal cancelable biometrics","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Random projection; Computer science; Rank (graph theory); Pattern recognition (psychology); Artificial intelligence; Projection (relational algebra); Authentication (law); Face (sociological concept); Feature (linguistics); Random forest; Data mining; Mathematics; Algorithm","score_opus":0.04108140343147133,"score_gpt":0.2622822795912208,"score_spread":0.22120087615974948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038594258","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":[0.009158823,0.000088360786,0.9838283,0.00049622654,0.0004651985,0.00006272515,0.0000038629582,0.000067596055,0.00582892],"genre_scores_gemma":[0.8724871,0.000034234527,0.12556745,0.00020051845,0.000021392263,0.000001986988,0.0000026768207,0.000002511382,0.0016820985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991156,0.00004055382,0.00020029276,0.00021139857,0.00029604565,0.00013609853],"domain_scores_gemma":[0.9991386,0.00010375273,0.00008398006,0.00043410924,0.00017512361,0.00006444074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005327147,0.000060652837,0.00011099779,0.0007590773,0.00005760821,0.000052257612,0.0005945015,0.00005435508,0.00009435436],"category_scores_gemma":[0.00017907271,0.000051301085,0.000041387466,0.0040115966,0.000033345146,0.00018520385,0.00014208467,0.000047566868,0.00007945911],"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":[0.0000047721173,0.00026965342,0.0017272606,0.000036581314,0.000012265026,5.449737e-7,0.0003794081,0.000013520646,0.021210449,0.21155548,0.013886339,0.7509037],"study_design_scores_gemma":[0.0014473104,0.0001227589,0.057612676,0.000012469937,0.0000051605725,0.0000044879234,0.000026384461,0.58417106,0.11383444,0.003265055,0.2391158,0.0003823974],"about_ca_topic_score_codex":0.00090422394,"about_ca_topic_score_gemma":0.000022696551,"teacher_disagreement_score":0.86332834,"about_ca_system_score_codex":0.000021214002,"about_ca_system_score_gemma":0.000035464705,"threshold_uncertainty_score":0.20919976},"labels":[],"label_agreement":null},{"id":"W2040322660","doi":"10.1109/icip.2006.312773","title":"IRIS Segmentation: Detecting Pupil, Limbus and Eyelids","year":2006,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Boundary (topology); Active contour model; IRIS (biosensor); Segmentation; Pupil; Computer vision; Computer science; Artificial intelligence; Eyelid; Geometry; Mathematics; Image segmentation; Physics; Optics; Mathematical analysis; Biometrics","score_opus":0.013173384555931856,"score_gpt":0.23919372915526163,"score_spread":0.22602034459932976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040322660","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":[0.101079844,0.00022411633,0.88954693,0.0011231581,0.00021255726,0.00007243286,5.331495e-7,0.00018128986,0.0075591602],"genre_scores_gemma":[0.9199397,0.000010020381,0.077941604,0.00024075562,0.000046449033,0.0000037712423,0.0000017385136,0.000002154088,0.0018138078],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994306,0.0000207704,0.00012377268,0.00018910234,0.00013490811,0.00010086171],"domain_scores_gemma":[0.999677,0.000040289262,0.00003574323,0.00017052659,0.000043692704,0.000032742537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017374253,0.00004858945,0.00004713592,0.0001470893,0.00012138287,0.00026592132,0.00016636914,0.000027522712,0.00003567772],"category_scores_gemma":[0.000014378679,0.00004447108,0.000015757885,0.0006742455,0.000019771347,0.00026770929,0.00007162761,0.0000399397,0.000045245208],"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":[0.0000034831328,0.00018485807,0.018754495,0.000040399314,0.000017912662,0.000014373109,0.0009920989,0.00001309884,0.02338668,0.16213416,0.020609517,0.77384895],"study_design_scores_gemma":[0.0024030248,0.00023178091,0.43675673,0.000021567766,0.000026513622,0.00019851339,0.0007398188,0.16255449,0.18529941,0.027252637,0.18314376,0.0013717422],"about_ca_topic_score_codex":0.0001790689,"about_ca_topic_score_gemma":0.000025942105,"teacher_disagreement_score":0.8188599,"about_ca_system_score_codex":0.000015709336,"about_ca_system_score_gemma":0.000010182615,"threshold_uncertainty_score":0.25642854},"labels":[],"label_agreement":null},{"id":"W2045255514","doi":"10.1109/ultsym.2013.0184","title":"Resonance based analysis of acoustic waves for 3D deep-layer fingerprint reconstruction","year":2013,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Superposition principle; Fingerprint (computing); Computer science; Acoustics; Fingerprint recognition; Artificial intelligence; Nonlinear system; SIGNAL (programming language); Computer vision; Iterative reconstruction; Pattern recognition (psychology); Physics","score_opus":0.024169533379499005,"score_gpt":0.25419795137857415,"score_spread":0.23002841799907514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045255514","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":[0.028625004,0.000058717786,0.97019154,0.0003592256,0.00017067544,0.00015938547,0.0000032356966,0.000048002406,0.0003842367],"genre_scores_gemma":[0.7219086,0.0000046075465,0.27761444,0.00012242643,0.0000075812386,0.000025478555,0.0000033165445,0.0000017540507,0.0003117713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922794,0.000026757109,0.00024217517,0.00024380014,0.00014250129,0.00011680127],"domain_scores_gemma":[0.9990162,0.00016591983,0.00010925486,0.00039666312,0.00026969786,0.000042217158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023684128,0.0000590384,0.00014353603,0.0006169338,0.000053957225,0.00008024178,0.0002983055,0.000045792138,0.00027522963],"category_scores_gemma":[0.00012035185,0.000051158535,0.00010689012,0.0021900302,0.000034771256,0.00022160904,0.000035172226,0.00003293212,0.000018566663],"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":[0.0000047649364,0.00011500537,0.0029749996,0.0000365235,0.00014461389,1.5791969e-7,0.00022539032,0.0007729557,0.007970386,0.012458976,0.0009517109,0.9743445],"study_design_scores_gemma":[0.000100737474,0.000012894541,0.045737684,0.0000026562834,0.00004280917,3.7868088e-7,0.000015204855,0.94870543,0.004043113,0.00047014517,0.0008024614,0.000066466746],"about_ca_topic_score_codex":0.00012312322,"about_ca_topic_score_gemma":0.00005759322,"teacher_disagreement_score":0.97427803,"about_ca_system_score_codex":0.00002415911,"about_ca_system_score_gemma":0.000029393836,"threshold_uncertainty_score":0.30135712},"labels":[],"label_agreement":null},{"id":"W2047545178","doi":"10.1109/bcc.2007.4430549","title":"Fuzzy Vault for Face Based Cryptographic Key Generation","year":2007,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Key generation; Key (lock); Cryptography; Quantization (signal processing); Fuzzy logic; Artificial intelligence; Pattern recognition (psychology); Scheme (mathematics); Data mining; Theoretical computer science; Algorithm; Mathematics; Computer security","score_opus":0.04394088988569127,"score_gpt":0.28468198395765826,"score_spread":0.24074109407196698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047545178","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":[0.005999176,0.000045061348,0.9885955,0.0012374194,0.00044400417,0.00019197498,0.00000199517,0.00014626916,0.0033386033],"genre_scores_gemma":[0.76028377,0.000002684081,0.23807356,0.0010153005,0.00006704803,0.00000964036,0.000014622032,0.0000031049306,0.000530247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992073,0.000016574868,0.00017189466,0.00024362191,0.00019001006,0.00017055673],"domain_scores_gemma":[0.9993385,0.00007468732,0.000046935435,0.00031266204,0.00015525296,0.00007195438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071807747,0.00006307385,0.000059033777,0.00038689733,0.00012422822,0.00016202066,0.00034009764,0.00005986928,0.000017979408],"category_scores_gemma":[0.00005201793,0.00005692257,0.00006190609,0.0012843991,0.000017539554,0.00020083117,0.000023461153,0.000039128085,0.000034430806],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006494404,0.00018260728,0.00048387403,0.000016981052,0.0000114692275,0.0000011627139,0.000317539,0.000040487117,0.02073516,0.8446862,0.02504317,0.10847487],"study_design_scores_gemma":[0.0013372131,0.00012567695,0.020257628,0.000003934919,0.000009517228,0.000003317713,0.000059482973,0.5976771,0.103165686,0.008806682,0.26806077,0.0004929867],"about_ca_topic_score_codex":0.000019808345,"about_ca_topic_score_gemma":0.00005936321,"teacher_disagreement_score":0.8358795,"about_ca_system_score_codex":0.000017221904,"about_ca_system_score_gemma":0.000030161476,"threshold_uncertainty_score":0.23212351},"labels":[],"label_agreement":null},{"id":"W2048114889","doi":"10.1155/2008/529879","title":"Biometric Methods for Secure Communications in Body Sensor Networks: Resource-Efficient Key Management and Signal-Level Data Scrambling","year":2007,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Encryption; Secure communication; Cryptography; Key (lock); Biometrics; Scrambling; Key management; Context (archaeology); Computer security; Computer network; Distributed computing; Algorithm","score_opus":0.11870241365886368,"score_gpt":0.4260407637518721,"score_spread":0.3073383500930084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048114889","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":[0.0010421869,0.030711483,0.9665883,0.0005500422,0.00017317441,0.00035269742,0.0000067780215,0.00004242423,0.0005328865],"genre_scores_gemma":[0.35058284,0.00147928,0.647418,0.000332758,0.00008627573,0.000008670148,0.000012928064,0.000018044753,0.000061193976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970229,0.0003709502,0.0009409478,0.0006375628,0.00047504227,0.0005525773],"domain_scores_gemma":[0.9971279,0.0011759029,0.0005437798,0.0007795351,0.00018778683,0.00018511264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009154157,0.00022931208,0.00030556254,0.003064715,0.0005422894,0.00068510714,0.0023400038,0.00009714917,0.0000044344947],"category_scores_gemma":[0.00017346494,0.00021240371,0.00005041868,0.006937165,0.0001365647,0.0010946271,0.000709955,0.0006955762,0.0000019350148],"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":[0.000058603047,0.00028433863,0.0003791158,0.000082317914,0.000011349652,0.000027158221,0.00046761316,0.004743552,0.00011383767,0.0015770152,0.000059829923,0.99219525],"study_design_scores_gemma":[0.0010632715,0.00009281808,0.0024944588,0.00040626086,0.000014943097,0.000088419125,0.00074384833,0.92050445,0.00022100216,0.0014074452,0.072625436,0.00033761747],"about_ca_topic_score_codex":0.0000015783022,"about_ca_topic_score_gemma":0.0000059152158,"teacher_disagreement_score":0.99185765,"about_ca_system_score_codex":0.00018773593,"about_ca_system_score_gemma":0.000057425696,"threshold_uncertainty_score":0.8661572},"labels":[],"label_agreement":null},{"id":"W2048766821","doi":"10.1109/icdsp.2009.5201257","title":"Face recognition with biometric encryption for privacy-enhancing self-exclusion","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Encryption; Computer science; Facial recognition system; Internet privacy; Computer security; Face (sociological concept); Information privacy; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.02564510453854688,"score_gpt":0.2576696620329867,"score_spread":0.23202455749443981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048766821","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":[0.030266255,0.000051592913,0.966354,0.001470946,0.00015842983,0.00038835956,0.0000021437338,0.00043234232,0.0008759377],"genre_scores_gemma":[0.7027171,0.000031163163,0.29658115,0.00036047614,0.000035278314,0.000012281165,0.000022614715,0.0000035128103,0.00023640331],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989019,0.00003150335,0.00020780525,0.00036955293,0.00028423386,0.00020500876],"domain_scores_gemma":[0.99916613,0.0000810167,0.00010699503,0.00033631173,0.00023073355,0.00007880568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046848712,0.00010204649,0.00010323486,0.0010360461,0.00020151911,0.00021923501,0.000399969,0.00007088067,0.0000197557],"category_scores_gemma":[0.00009650166,0.000081840415,0.000043830107,0.0044282246,0.000009122479,0.00065160147,0.000050284394,0.00006211743,0.000084452746],"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":[0.000021878212,0.0004538431,0.000054243417,0.00003602529,0.00001537535,0.0000015837655,0.0012318143,0.0000044988865,0.015230284,0.00945787,0.0023640608,0.9711285],"study_design_scores_gemma":[0.00590353,0.0036808918,0.05995639,0.00014419376,0.00009403785,0.0001231689,0.00043689788,0.41319132,0.37163,0.04633715,0.096278876,0.0022235438],"about_ca_topic_score_codex":0.0000094289135,"about_ca_topic_score_gemma":0.0000033875017,"teacher_disagreement_score":0.968905,"about_ca_system_score_codex":0.000080830025,"about_ca_system_score_gemma":0.00004448599,"threshold_uncertainty_score":0.33373553},"labels":[],"label_agreement":null},{"id":"W2049505818","doi":"10.1109/bcc.2007.4430530","title":"Face Based Biometric Authentication with Changeable and Privacy Preservable Templates","year":2007,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Authentication (law); Face (sociological concept); Key (lock); Orthonormal basis; Information privacy; Transformation (genetics); Invertible matrix; Data mining; Theoretical computer science; Artificial intelligence; Computer security; Mathematics","score_opus":0.02740355720544886,"score_gpt":0.25455955674486275,"score_spread":0.2271559995394139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049505818","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":[0.06714163,0.00018425175,0.92938036,0.0013192871,0.00006494798,0.00020686057,0.0000012436733,0.00016553739,0.0015358783],"genre_scores_gemma":[0.91032565,0.000008407121,0.087230094,0.00017348083,0.0000106041825,0.000006744344,0.000006075215,0.000004581145,0.002234391],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900746,0.00001938687,0.0001507721,0.00031580823,0.0002769534,0.00022961009],"domain_scores_gemma":[0.99910724,0.00013215623,0.00006907558,0.00046336665,0.000114983806,0.00011314853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007141422,0.00008591837,0.000086029795,0.0010383641,0.00012654626,0.00025704116,0.0004411278,0.000049790677,0.00004544549],"category_scores_gemma":[0.000070131304,0.00006552509,0.000014402167,0.0048204344,0.00004103289,0.00048002633,0.00010379708,0.000052232648,0.000041239564],"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":[0.00015158218,0.002595735,0.12820734,0.0006275215,0.00020096045,0.000045538494,0.008559948,0.000049760743,0.024212701,0.27585042,0.019637175,0.5398613],"study_design_scores_gemma":[0.0017757488,0.00033614744,0.35872182,0.000030617615,0.000023419881,0.000029645305,0.00017340234,0.40532506,0.09355703,0.0016538285,0.13765997,0.00071331265],"about_ca_topic_score_codex":0.00012261316,"about_ca_topic_score_gemma":0.00001541409,"teacher_disagreement_score":0.843184,"about_ca_system_score_codex":0.000025950667,"about_ca_system_score_gemma":0.000031989377,"threshold_uncertainty_score":0.26720357},"labels":[],"label_agreement":null},{"id":"W2051943574","doi":"10.1109/ccece.2014.6901024","title":"Robust identity verification based on human acoustic signature with BioHashing","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Biometrics; Spoofing attack; Robustness (evolution); Random projection; Speech recognition; Modalities; Pattern recognition (psychology); Artificial intelligence; Computer security","score_opus":0.039518343717165984,"score_gpt":0.26324718239043216,"score_spread":0.22372883867326618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051943574","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":[0.009002706,0.000005823738,0.9850937,0.0007670383,0.00016734414,0.00010709064,9.4755524e-7,0.00023656245,0.0046187947],"genre_scores_gemma":[0.97665846,5.1308257e-7,0.022099268,0.00080221443,0.00003895774,0.0000066215207,0.000013816455,0.0000056352683,0.00037450856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988367,0.000079268735,0.00014102386,0.00037819342,0.00041247165,0.00015232233],"domain_scores_gemma":[0.9989706,0.00007280462,0.00008499247,0.00067614147,0.000120309174,0.000075123586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005285752,0.00009961963,0.00008917028,0.00031577187,0.00023843662,0.00045697213,0.00069449254,0.000076984135,0.000059561018],"category_scores_gemma":[0.000074804564,0.00007831019,0.000030893407,0.0012507897,0.000035772562,0.0005598288,0.00003906979,0.00015174605,0.00009246902],"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":[0.000021671285,0.0007913265,0.002073493,0.0001004358,0.000026079353,0.0000070233586,0.00045448696,0.016245209,0.03181443,0.9158583,0.009109158,0.023498373],"study_design_scores_gemma":[0.0005445222,0.00016188386,0.037558585,0.000023206027,0.000009949178,0.0000020988625,0.000019989015,0.95282286,0.0040325425,0.0011274596,0.003404717,0.000292183],"about_ca_topic_score_codex":0.000042033225,"about_ca_topic_score_gemma":0.000066019515,"teacher_disagreement_score":0.9676558,"about_ca_system_score_codex":0.000044292017,"about_ca_system_score_gemma":0.000027585233,"threshold_uncertainty_score":0.44065928},"labels":[],"label_agreement":null},{"id":"W2053271428","doi":"10.1080/17470210802372987","title":"On the Preliminary Psychophysics of Fingerprint Identification","year":2008,"lang":"en","type":"article","venue":"Quarterly Journal of Experimental Psychology","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"National Human Genome Research Institute","keywords":"Matching (statistics); Identification (biology); Fingerprint (computing); Artificial intelligence; Similarity (geometry); Psychophysics; Pattern recognition (psychology); Computer science; Biometrics; Psychology; Image (mathematics); Perception; Mathematics; Statistics","score_opus":0.038132908398208626,"score_gpt":0.322401901005968,"score_spread":0.2842689926077594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053271428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8910466,0.00048624375,0.1039732,0.0019343585,0.0015304567,0.00011079527,0.0000013550965,0.000012700726,0.0009042815],"genre_scores_gemma":[0.9977949,0.000022359927,0.001585893,0.00047801086,0.000059439986,0.0000044795347,5.904317e-7,0.0000050341587,0.000049268485],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986075,0.00015900937,0.0005933783,0.00018409587,0.00033175797,0.0001242621],"domain_scores_gemma":[0.99849546,0.00012760173,0.0006206367,0.0005543512,0.0001464462,0.000055502693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040206374,0.00009601143,0.0001671022,0.00026542542,0.000102978105,0.000027154167,0.0009731367,0.000057117802,0.00004640239],"category_scores_gemma":[0.000019125879,0.000069687165,0.00014153935,0.00049071247,0.00016878177,0.00024200483,0.000019803965,0.0001866377,0.000050273473],"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":[0.00051429425,0.0048176497,0.00024566587,0.000010844403,0.00015111746,0.000071721835,0.028026462,0.000008562891,0.6696653,0.19902264,0.046636768,0.050828975],"study_design_scores_gemma":[0.006634724,0.031386346,0.19593623,0.00015363385,0.00004454536,0.0042934986,0.0042687114,0.0027476146,0.6598194,0.08583916,0.0077811047,0.0010950327],"about_ca_topic_score_codex":0.0000025580919,"about_ca_topic_score_gemma":6.837903e-8,"teacher_disagreement_score":0.19569057,"about_ca_system_score_codex":0.000026679549,"about_ca_system_score_gemma":0.000029448167,"threshold_uncertainty_score":0.28417602},"labels":[],"label_agreement":null},{"id":"W2054090438","doi":"10.1002/sec.6","title":"Biometric‐based user authentication in mobile <i>ad hoc</i> networks","year":2008,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Defence Research and Development Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Defence Research and Development Canada","keywords":"Computer science; Biometrics; Authentication (law); Computer security; Authentication protocol; Session (web analytics); Computer network; Vulnerability (computing); Exploit; Wireless ad hoc network; Mobile ad hoc network; Challenge–response authentication; Multi-factor authentication; Telecommunications; World Wide Web; Wireless","score_opus":0.017296571375948925,"score_gpt":0.24073885097243017,"score_spread":0.22344227959648125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054090438","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":[0.15914312,0.12270376,0.71276265,0.0023414816,0.0005170016,0.001066892,0.0000075608027,0.0004956683,0.00096186704],"genre_scores_gemma":[0.96649975,0.02741346,0.005125499,0.00064314547,0.00002528471,0.00011031811,0.00009250028,0.000011377595,0.00007868804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800885,0.0004094098,0.0005009021,0.00045333555,0.00028489166,0.00034259164],"domain_scores_gemma":[0.9975206,0.00036833546,0.00022045993,0.0015782338,0.00016505965,0.00014726697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009540472,0.00018837432,0.00024019992,0.000720918,0.0004652612,0.00019604363,0.0013127131,0.00024850824,0.000021886244],"category_scores_gemma":[0.000052792424,0.00020238507,0.00007397743,0.004692683,0.00024349385,0.00056795107,0.0003862149,0.00048994744,0.000015369791],"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":[0.00020833549,0.0058161295,0.04579847,0.00016408175,0.00015777009,0.000043842465,0.023488095,0.017433431,0.00013008538,0.14160256,0.021298595,0.74385864],"study_design_scores_gemma":[0.0008628262,0.00006282334,0.024335807,0.000035372024,0.000008294651,0.000017218628,0.000092338596,0.86943614,0.00003688275,0.0014011869,0.103352405,0.000358679],"about_ca_topic_score_codex":0.000030331512,"about_ca_topic_score_gemma":0.00008182474,"teacher_disagreement_score":0.85200274,"about_ca_system_score_codex":0.00007356663,"about_ca_system_score_gemma":0.00005807648,"threshold_uncertainty_score":0.82530236},"labels":[],"label_agreement":null},{"id":"W2057832679","doi":"10.1109/i2mtc.2012.6229585","title":"Contourlet based distance measurement to improve fingerprint identification accuracy","year":2012,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Contourlet; Artificial intelligence; Pattern recognition (psychology); Kullback–Leibler divergence; Fingerprint (computing); Computer science; Gaussian; Classifier (UML); Distance measures; Measure (data warehouse); Entropy (arrow of time); Mathematics; Computer vision; Wavelet transform; Data mining","score_opus":0.04296580032674252,"score_gpt":0.2787297695327293,"score_spread":0.2357639692059868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057832679","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":[0.0029752585,0.0001139133,0.99025273,0.0035793288,0.001105973,0.0003192089,0.000004075025,0.00017573993,0.0014737638],"genre_scores_gemma":[0.9769992,0.0000020713478,0.021160606,0.0011881619,0.00007099012,0.00006214876,0.000003239094,0.0000050183576,0.000508542],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99833727,0.00006444191,0.00031305008,0.00033300734,0.0006425674,0.0003096558],"domain_scores_gemma":[0.9983977,0.000069120266,0.0001195568,0.0008417729,0.00033231845,0.00023953656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015718366,0.00010750297,0.00010316966,0.00021472128,0.000118271215,0.00031600567,0.00074068963,0.000041937772,0.00008511813],"category_scores_gemma":[0.0005668655,0.00009799817,0.00005557407,0.00094586797,0.000016532449,0.00065182045,0.00011537349,0.00007079753,0.0005838149],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020962525,0.0010639994,0.003147099,0.000072092356,0.00003693919,0.0000011926358,0.0019326363,0.000022972901,0.15979381,0.40897927,0.036105867,0.38882315],"study_design_scores_gemma":[0.0007386202,0.000049910668,0.15895571,0.000023867262,0.000014603634,0.0000019829533,0.00008075471,0.042707868,0.27384725,0.00086607336,0.5219918,0.00072156196],"about_ca_topic_score_codex":0.00005575545,"about_ca_topic_score_gemma":0.00002002625,"teacher_disagreement_score":0.97402394,"about_ca_system_score_codex":0.00019491428,"about_ca_system_score_gemma":0.00006257567,"threshold_uncertainty_score":0.7503954},"labels":[],"label_agreement":null},{"id":"W2059067639","doi":"10.1109/mci.2007.353415","title":"Technology review - Biometrics-Technology, Application, Challenge, and Computational Intelligence Solutions","year":2007,"lang":"en","type":"article","venue":"IEEE Computational Intelligence Magazine","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Biometrics; Computer science; Fingerprint (computing); Fingerprint recognition; Face (sociological concept); Facial recognition system; Artificial intelligence; Computer security; Human–computer interaction; Data science; Pattern recognition (psychology)","score_opus":0.045127048154836,"score_gpt":0.3244289430034975,"score_spread":0.2793018948486615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059067639","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":[0.00022023721,0.019788751,0.96054894,0.017249435,0.0004381216,0.0005911105,0.000021723814,0.00056068884,0.0005810124],"genre_scores_gemma":[0.7269171,0.004930165,0.2663793,0.001299439,0.00008512007,0.000088799105,0.00009325515,0.000024325422,0.00018249423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99649084,0.00005911355,0.0011157282,0.00100294,0.00073898415,0.00059240765],"domain_scores_gemma":[0.9965415,0.0006104673,0.0004090877,0.00068374415,0.0015300813,0.00022512263],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016786902,0.00033033485,0.00039222452,0.00410167,0.00045211884,0.00014293902,0.0015227047,0.00027684524,0.000047684505],"category_scores_gemma":[0.00046320606,0.00035330822,0.000093457165,0.01489219,0.0007159608,0.00044328193,0.0004241802,0.0004536942,0.0008004333],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033044885,0.0002615552,0.00020037142,0.0001366737,0.00003351984,0.000010541289,0.00006437878,0.004751122,0.00004184787,0.5654093,0.0016457412,0.42744163],"study_design_scores_gemma":[0.00014404413,0.00016650665,0.0018714285,0.0002419055,0.000030241838,0.0003500224,0.00007785828,0.34535697,0.000772335,0.60849637,0.04178858,0.0007037515],"about_ca_topic_score_codex":0.0000077212835,"about_ca_topic_score_gemma":0.000013115356,"teacher_disagreement_score":0.72669685,"about_ca_system_score_codex":0.00016050343,"about_ca_system_score_gemma":0.00018174962,"threshold_uncertainty_score":0.9999776},"labels":[],"label_agreement":null},{"id":"W2061899713","doi":"10.1109/est.2014.10","title":"Multispectral Hand Biometrics","year":2014,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RGB color model; Biometrics; Computer science; Artificial intelligence; Multispectral image; Computer vision; Palm print; Palm; Pattern recognition (psychology)","score_opus":0.015455981295924622,"score_gpt":0.2371469425885965,"score_spread":0.22169096129267188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061899713","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":[0.0051442035,0.000035282414,0.9794047,0.000788792,0.0003937534,0.000035130695,4.1147993e-7,0.00013298765,0.014064735],"genre_scores_gemma":[0.91781837,0.000004899245,0.07901998,0.00037017884,0.000032587195,0.0000015215912,0.0000012786373,0.0000019295026,0.002749232],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993608,0.000027276039,0.00010396883,0.00019358793,0.00018053007,0.0001338574],"domain_scores_gemma":[0.9994254,0.00006730579,0.000027312037,0.00034972923,0.000056276807,0.00007395218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030175425,0.000047870646,0.000058099373,0.00060987985,0.00009105396,0.00031147213,0.0005031383,0.000034224293,0.000058311245],"category_scores_gemma":[0.00013972924,0.00004015344,0.000030433901,0.0030513208,0.000033093507,0.00018811734,0.000078980454,0.00004262795,0.000399106],"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":[4.4632634e-7,0.00007631224,0.00082788226,0.0000043013083,0.0000044859944,7.4742786e-7,0.00014690134,6.885209e-7,0.0015448281,0.73348826,0.011042386,0.25286275],"study_design_scores_gemma":[0.00047680503,0.000057069847,0.046803985,0.000001571073,0.0000021913022,0.000009455452,0.00000778545,0.2177683,0.022641134,0.0066879466,0.70530057,0.00024317534],"about_ca_topic_score_codex":0.000040081715,"about_ca_topic_score_gemma":0.0000063384,"teacher_disagreement_score":0.9126742,"about_ca_system_score_codex":0.000013927963,"about_ca_system_score_gemma":0.000010966936,"threshold_uncertainty_score":0.5129833},"labels":[],"label_agreement":null},{"id":"W2063112528","doi":"10.1145/2492517.2492598","title":"Cancelable fusion using social network analysis","year":2013,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Centrality; Computer science; Transformation (genetics); Artificial intelligence; Biometrics; Social network analysis; Fusion; Domain (mathematical analysis); Pattern recognition (psychology); Sensor fusion; Data mining; Mathematics; Social media","score_opus":0.029375549891005083,"score_gpt":0.26661044098310077,"score_spread":0.2372348910920957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063112528","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":[0.03673794,0.000049533344,0.9583539,0.00065655494,0.00022076801,0.00006065434,3.103001e-7,0.00008209031,0.0038382725],"genre_scores_gemma":[0.9169868,0.000004967775,0.08066189,0.0004700804,0.000102717466,0.0000039162323,0.0000027526294,0.0000018890789,0.0017649892],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933946,0.000032757278,0.00011985871,0.00017905688,0.00016352322,0.00016532809],"domain_scores_gemma":[0.99959344,0.000014793817,0.000048203823,0.0002015695,0.0000980422,0.000043968106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016513398,0.00004557518,0.00009166991,0.0002086324,0.00022949823,0.0002505646,0.00032203202,0.000039200844,0.00074464415],"category_scores_gemma":[0.000005462351,0.000039705636,0.00006592365,0.0047765626,0.000014784907,0.00029902722,0.000112100744,0.000040662082,0.00015900897],"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":[0.0000024551634,0.00024920242,0.03702667,0.000019441703,0.00060673204,0.0000038080927,0.002151885,0.0026514987,0.0042423066,0.43377304,0.26382336,0.2554496],"study_design_scores_gemma":[0.00007327098,0.0000035891926,0.045789395,6.793337e-7,0.000031973454,5.2505715e-7,0.000021730719,0.938732,0.00015091572,0.0037970562,0.011277573,0.00012124664],"about_ca_topic_score_codex":0.002543302,"about_ca_topic_score_gemma":0.00009535297,"teacher_disagreement_score":0.9360806,"about_ca_system_score_codex":0.000037096383,"about_ca_system_score_gemma":0.000026908021,"threshold_uncertainty_score":0.81533307},"labels":[],"label_agreement":null},{"id":"W2064553809","doi":"10.1007/s11265-011-0630-x","title":"A Filter Bank Based Approach for Rotation Invariant Fingerprint Recognition","year":2011,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Biometric Identification and Security","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":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Principal component analysis; Thresholding; Gabor filter; Computer science; Fingerprint (computing); Linear discriminant analysis; Dimensionality reduction; Filter bank; Filter (signal processing); Computer vision; Invariant (physics); Curse of dimensionality; Feature extraction; Mathematics; Image (mathematics)","score_opus":0.10456319697824266,"score_gpt":0.2586738773583835,"score_spread":0.1541106803801408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064553809","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":[0.0018121614,0.00028223166,0.9968415,0.00007119282,0.00028991545,0.00021978028,0.000002876482,0.000026317275,0.00045405194],"genre_scores_gemma":[0.8304447,0.0000014570719,0.16927928,0.00007445398,0.0001369835,0.000019607704,0.0000040816085,0.0000064271358,0.0000330183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986147,0.00010499922,0.00060293014,0.00017709404,0.00035517945,0.00014505265],"domain_scores_gemma":[0.99802816,0.00006581304,0.0008522645,0.000120145,0.00084988814,0.00008373732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016982517,0.00009505094,0.00019071008,0.0004615992,0.00012575646,0.0003580131,0.00042014185,0.00007561976,0.000009831136],"category_scores_gemma":[0.000078125406,0.00007724588,0.00009498209,0.0005711812,0.000021864746,0.00076323433,0.000018523793,0.00013116798,0.000004582344],"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":[0.0006773935,0.0028991222,0.0010524828,0.0050126887,0.00025817862,0.00006184195,0.019627314,0.0021778697,0.030485392,0.0069423234,0.0063049807,0.9245004],"study_design_scores_gemma":[0.00094945705,0.00026911584,0.0007086438,0.00033741668,0.000034532357,0.00015763905,0.0002782388,0.9888946,0.005878283,0.0014844692,0.00078475574,0.00022288362],"about_ca_topic_score_codex":0.000015029245,"about_ca_topic_score_gemma":2.1220109e-7,"teacher_disagreement_score":0.9867167,"about_ca_system_score_codex":0.0000628088,"about_ca_system_score_gemma":0.00021842825,"threshold_uncertainty_score":0.34523284},"labels":[],"label_agreement":null},{"id":"W2066964254","doi":"10.1142/s0218001408006302","title":"MULTIMODAL BIOMETRICS BY FACE AND HAND IMAGES TAKEN BY A CELL PHONE CAMERA","year":2008,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Royal Society of Canada","keywords":"Artificial intelligence; Computer science; Biometrics; Hand geometry; Computer vision; Face (sociological concept); Support vector machine; Feature (linguistics); Gabor filter; Pattern recognition (psychology); Facial recognition system; Feature extraction; Feature vector; Key (lock); Phone","score_opus":0.06142910906458087,"score_gpt":0.28766703394891296,"score_spread":0.2262379248843321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066964254","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":[0.3561725,0.0008367488,0.64087516,0.0013038174,0.00057227653,0.000054633372,0.00013489157,0.000012342772,0.000037658283],"genre_scores_gemma":[0.99437314,0.002092594,0.0027406893,0.0005121851,0.000101147154,0.0000022946367,0.000031327985,0.0000069705748,0.00013963234],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984266,0.00007265628,0.000578887,0.00022999977,0.0005360243,0.00015581088],"domain_scores_gemma":[0.99853736,0.00016676287,0.00040345386,0.00009524934,0.00063087995,0.00016631307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034964582,0.00013900346,0.00017146417,0.00065090024,0.00015149837,0.0004297992,0.00048537605,0.00007396373,0.000091631504],"category_scores_gemma":[0.00013807262,0.00013106725,0.0000637067,0.00053260487,0.0002177215,0.000635125,0.00010347951,0.00020757495,0.00006108001],"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":[0.000028095606,0.0003917322,0.00086655986,0.000009986106,0.000045448534,0.000053102227,0.0016408043,0.000002010177,0.05182967,0.00002295377,0.005810529,0.9392991],"study_design_scores_gemma":[0.00060802605,0.0003235832,0.0018853671,0.00008532009,0.000026368549,0.0010532263,0.0010784876,0.0155749535,0.9654262,0.0031986807,0.010119569,0.00062021485],"about_ca_topic_score_codex":0.00012410237,"about_ca_topic_score_gemma":0.0000038809185,"teacher_disagreement_score":0.9386789,"about_ca_system_score_codex":0.000036403617,"about_ca_system_score_gemma":0.000038238057,"threshold_uncertainty_score":0.5344767},"labels":[],"label_agreement":null},{"id":"W2076707216","doi":"10.1142/s0219467814500211","title":"Fingerprint Liveness Detection Using Multiple Static Features and Random Forests","year":2014,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; University of Calgary","keywords":"Liveness; Fingerprint (computing); Computer science; Artificial intelligence; Pattern recognition (psychology); Spoofing attack; Random forest; Classifier (UML); Fingerprint recognition; Biometrics; Fingerprint Verification Competition; Noise (video); Image (mathematics)","score_opus":0.013567145675621898,"score_gpt":0.2714141691401842,"score_spread":0.2578470234645623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076707216","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":[0.4134751,0.0001550783,0.5856944,0.00031599705,0.0003253391,0.000020693049,0.0000010309086,0.000004572006,0.00000779917],"genre_scores_gemma":[0.98832786,0.00015817648,0.011278648,0.00015045094,0.00007571807,4.2594164e-7,5.1583294e-7,0.0000025715292,0.000005654414],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993031,0.00006222034,0.00020610669,0.00009657234,0.00026840443,0.00006362229],"domain_scores_gemma":[0.99903744,0.00020963,0.00020357472,0.00006996672,0.00042277176,0.000056610177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052370754,0.000057810714,0.00009406218,0.00042969504,0.00006744914,0.00029748204,0.00023704952,0.000032190965,0.0000012134499],"category_scores_gemma":[0.00032675796,0.00004841502,0.000044740293,0.00018134771,0.000055639433,0.00044456546,0.000068594956,0.00011909305,3.2200091e-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":[0.00046749998,0.00037696114,0.0689332,0.000119517616,0.00047916832,0.00017597538,0.004341271,0.00022669641,0.037963457,0.028343968,0.00031294965,0.8582593],"study_design_scores_gemma":[0.0041897204,0.00016556424,0.48325774,0.00012729126,0.000039377665,0.0021082216,0.00009020849,0.4649121,0.011615553,0.030258797,0.0029578283,0.0002776067],"about_ca_topic_score_codex":0.000044321718,"about_ca_topic_score_gemma":0.000045047283,"teacher_disagreement_score":0.85798174,"about_ca_system_score_codex":0.000013011736,"about_ca_system_score_gemma":0.000018344444,"threshold_uncertainty_score":0.2868626},"labels":[],"label_agreement":null},{"id":"W2077389880","doi":"10.1109/btas.2009.5339014","title":"Biometric authentication using augmented face and random projection","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Random projection; Subspace topology; Face (sociological concept); Authentication (law); Artificial intelligence; Projection (relational algebra); Computer vision; Similarity (geometry); Facial recognition system; Domain (mathematical analysis); Pattern recognition (psychology); Image (mathematics); Computer security; Mathematics; Algorithm","score_opus":0.03300636142813996,"score_gpt":0.2876591288554343,"score_spread":0.25465276742729437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077389880","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":[0.09116186,0.00012951164,0.90682006,0.0009317327,0.00015179737,0.00018391243,4.2839204e-7,0.00013965517,0.00048106618],"genre_scores_gemma":[0.9733218,0.00002234513,0.026013413,0.00013770211,0.000012854167,0.0000017263002,0.0000025870545,0.0000014789348,0.0004860864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992921,0.000040129293,0.00014930309,0.00023424767,0.00017519477,0.00010903882],"domain_scores_gemma":[0.9995829,0.000025940964,0.00006112675,0.00021077284,0.00006993906,0.00004932824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000309197,0.000060595892,0.00007149186,0.0009794089,0.00011568457,0.00022240366,0.00017068705,0.0000401958,0.000010329934],"category_scores_gemma":[0.00006179182,0.00005269746,0.000021484644,0.0038884871,0.000017177836,0.0003875701,0.0000315686,0.000039092745,0.000015003371],"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":[0.000024136169,0.00043860896,0.0007476242,0.00002087361,0.000026350877,0.0000017165582,0.0016866148,0.0000068703234,0.08044,0.07287105,0.0010381262,0.84269804],"study_design_scores_gemma":[0.0014716514,0.000081467806,0.058657043,0.0000070427063,0.0000138297,0.000030273308,0.000076400655,0.9208895,0.012731336,0.002440694,0.0033756145,0.0002251566],"about_ca_topic_score_codex":0.000034159144,"about_ca_topic_score_gemma":7.595476e-7,"teacher_disagreement_score":0.92088264,"about_ca_system_score_codex":0.000037779067,"about_ca_system_score_gemma":0.000022792987,"threshold_uncertainty_score":0.21489401},"labels":[],"label_agreement":null},{"id":"W2078249719","doi":"10.1117/12.703511","title":"Iris identification using contourlet transform","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Contourlet; Computer science; Biometrics; Iris recognition; Artificial intelligence; Wavelet transform; Authentication (law); Computer vision; Pattern recognition (psychology); IRIS (biosensor); Wavelet; Matching (statistics); Identification (biology); Feature extraction; Mathematics; Computer security","score_opus":0.018750410021937874,"score_gpt":0.2544253936831847,"score_spread":0.2356749836612468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078249719","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9679842,0.0001033015,0.027025279,0.0023636834,0.00048143166,0.00048315802,0.000023537857,0.000118951575,0.0014164742],"genre_scores_gemma":[0.7178604,0.00006373081,0.28126672,0.00014234234,0.00031673603,0.000038730657,0.00000808304,0.00003474245,0.00026855076],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973228,1.2450257e-8,0.00091871846,0.0004473815,0.0008974929,0.00041362288],"domain_scores_gemma":[0.99707955,0.00014040244,0.00048226942,0.00010046623,0.0020525148,0.00014477473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018850997,0.0002454539,0.00031128476,0.0002878225,0.00013495293,0.0002851685,0.0017529352,0.00019982766,0.000007508361],"category_scores_gemma":[0.0004059576,0.00021899694,0.0005434571,0.0011075025,0.00018422848,0.0011215564,0.00014143085,0.00026042704,0.0000026491177],"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":[0.000020759833,0.00009646064,0.00012870449,0.00015130133,0.00012875344,6.376838e-8,0.00031403548,0.000013792831,0.45647624,0.5397564,0.0011918496,0.0017216459],"study_design_scores_gemma":[0.0021109704,0.0002651651,0.006865475,0.0002562725,0.00020346802,0.000057315352,0.001992022,0.21276394,0.73967546,0.00846586,0.02643655,0.0009074843],"about_ca_topic_score_codex":0.000021483069,"about_ca_topic_score_gemma":3.1420996e-7,"teacher_disagreement_score":0.53129053,"about_ca_system_score_codex":0.00022329141,"about_ca_system_score_gemma":0.000042612275,"threshold_uncertainty_score":0.8930436},"labels":[],"label_agreement":null},{"id":"W2079716275","doi":"10.1109/cw.2011.48","title":"A Novel Multi-modal Biometric Architecture for High-Dimensional Features","year":2011,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Linear subspace; Cluster analysis; Artificial intelligence; Modal; Dimensionality reduction; Pattern recognition (psychology); Feature vector; Subspace topology; Facial recognition system; Curse of dimensionality; Feature (linguistics); Face (sociological concept); Set (abstract data type); Data set; Data mining; Machine learning; Mathematics","score_opus":0.05642416682214457,"score_gpt":0.26542694638805275,"score_spread":0.20900277956590818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079716275","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":[0.0036671236,0.00010434617,0.99402773,0.0007822616,0.00063374464,0.00024450407,0.000024043822,0.00018016009,0.00033609985],"genre_scores_gemma":[0.37548503,0.0000010029747,0.6222783,0.0005096274,0.0000309962,0.00001973226,0.00001072245,0.0000053249923,0.0016592699],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9989095,0.000016908702,0.0001742963,0.00040738913,0.00025015965,0.00024177141],"domain_scores_gemma":[0.9991482,0.00009315545,0.00006624589,0.00041895194,0.00015994055,0.00011350923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024713215,0.00012092901,0.0001233434,0.0012146839,0.00012628606,0.00008950465,0.00072344387,0.000096828815,0.000059119106],"category_scores_gemma":[0.00011873676,0.000093089635,0.00008569614,0.0027936387,0.000046026664,0.00015548828,0.00016404015,0.000099039666,0.000047005564],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005383617,0.0019779832,0.00024218377,0.000050480834,0.00010589383,0.000005766861,0.0016688552,0.000009888754,0.030149724,0.6980254,0.024617057,0.2430929],"study_design_scores_gemma":[0.009860579,0.0008098932,0.6447863,0.000028145168,0.00006162221,0.00030054263,0.00007282768,0.08325717,0.16149385,0.035218388,0.06186827,0.002242442],"about_ca_topic_score_codex":0.00031559207,"about_ca_topic_score_gemma":0.00003998047,"teacher_disagreement_score":0.66280705,"about_ca_system_score_codex":0.000021447075,"about_ca_system_score_gemma":0.000048030262,"threshold_uncertainty_score":0.3796085},"labels":[],"label_agreement":null},{"id":"W2081158785","doi":"10.1117/12.850669","title":"A multi-algorithm-based automatic person identification system","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Biometrics; Majority rule; Artificial intelligence; Voting; Machine learning; Identification (biology); Rank (graph theory); Decision tree; Face (sociological concept); Pattern recognition (psychology); Facial recognition system; Markov chain; Data mining; Mathematics","score_opus":0.014645260594417146,"score_gpt":0.23440749857735413,"score_spread":0.21976223798293698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081158785","genre_codex":"empirical","genre_gemma":"methods","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":[0.97975457,0.000039021634,0.015915489,0.0021156701,0.0009017669,0.00060234056,0.000038097103,0.00030463774,0.0003284376],"genre_scores_gemma":[0.43814674,0.000005358501,0.56129473,0.000056115634,0.00017701447,0.00014326924,0.000008369407,0.000027729622,0.00014065603],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974838,2.5561688e-8,0.0007358563,0.00052048784,0.0009086083,0.00035123806],"domain_scores_gemma":[0.9968163,0.0001360234,0.00058198447,0.00014757649,0.002167945,0.0001501796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012615302,0.00027327862,0.00033623906,0.0002933905,0.00014968317,0.00042268506,0.0021524776,0.00023307628,0.0000075918647],"category_scores_gemma":[0.00059411983,0.00023734543,0.000561423,0.0010122127,0.00019056331,0.0008194568,0.00013902089,0.00039518476,0.0000076288084],"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":[0.0000057203133,0.00017500223,0.000116229865,0.00053108385,0.00012388888,7.637216e-8,0.00026671647,0.00001119496,0.55507004,0.43971264,0.0011504965,0.0028368942],"study_design_scores_gemma":[0.0007881853,0.00007112751,0.0030108695,0.00012211793,0.000060781917,0.000015366608,0.000707757,0.8950228,0.09845478,0.0001487213,0.0013185621,0.00027891892],"about_ca_topic_score_codex":0.000015619193,"about_ca_topic_score_gemma":2.2729047e-7,"teacher_disagreement_score":0.8950116,"about_ca_system_score_codex":0.00016800645,"about_ca_system_score_gemma":0.00006622587,"threshold_uncertainty_score":0.9678666},"labels":[],"label_agreement":null},{"id":"W2084833344","doi":"10.1109/icsmc.2006.384497","title":"An Iris Recognition Method Based On Zigzag Collarette Area and Asymmetrical Support Vector Machines","year":2006,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Artificial intelligence; Iris recognition; Pattern recognition (psychology); Computer science; Hamming distance; Zigzag; Mahalanobis distance; Segmentation; Backpropagation; Artificial neural network; Feature extraction; k-nearest neighbors algorithm; Feature vector; Biometrics; Mathematics; Algorithm","score_opus":0.02648827730265249,"score_gpt":0.2860598783262367,"score_spread":0.25957160102358423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084833344","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":[0.013011681,0.00001726239,0.9758421,0.0022823184,0.0002314815,0.00016271358,0.000028664634,0.0002282926,0.008195485],"genre_scores_gemma":[0.7387702,0.00000325105,0.25876847,0.0017607682,0.00006555389,0.000012194137,0.00010992101,0.0000077581135,0.0005018704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862117,0.000176899,0.0002243732,0.0004504353,0.00034793682,0.00017916995],"domain_scores_gemma":[0.999041,0.00024687249,0.00007146316,0.00038761017,0.00013556467,0.000117496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006737546,0.00012100568,0.0001409445,0.00071897934,0.0001228426,0.0003201573,0.00029668293,0.00009291934,0.00018087671],"category_scores_gemma":[0.00010514999,0.000103575105,0.000044484565,0.0021221165,0.00002717289,0.0002786818,0.00003589881,0.00009902722,0.00005149931],"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":[0.00006726226,0.0018876723,0.014363993,0.000052010284,0.000026585658,0.000057859754,0.00017032618,0.00007400928,0.0041851625,0.026718602,0.097501785,0.85489476],"study_design_scores_gemma":[0.00070842466,0.00031891,0.11609952,0.0000048167353,0.000013492475,0.000018259283,0.000007953943,0.854901,0.007245309,0.0029479018,0.017393975,0.00034039887],"about_ca_topic_score_codex":0.00025356704,"about_ca_topic_score_gemma":0.000027742104,"teacher_disagreement_score":0.85482705,"about_ca_system_score_codex":0.00003768537,"about_ca_system_score_gemma":0.000044318316,"threshold_uncertainty_score":0.42236704},"labels":[],"label_agreement":null},{"id":"W2085249604","doi":"10.1117/12.883533","title":"C-BET evaluation of voice biometrics","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Defence Research and Development Canada","keywords":"Biometrics; Computer science; Ranking (information retrieval); Artificial intelligence; Data mining; Speech recognition; Pattern recognition (psychology)","score_opus":0.04229836341432109,"score_gpt":0.26343670479075015,"score_spread":0.22113834137642907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085249604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99266183,0.00017243775,0.0013345699,0.0006975271,0.0004308329,0.00047783452,0.000028316717,0.00007313633,0.0041234945],"genre_scores_gemma":[0.7586521,0.00007235672,0.24089174,0.00005816881,0.00012371229,0.00007931934,0.000005759562,0.000023808958,0.000093030205],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969944,3.3940715e-8,0.00078474724,0.00040573507,0.0015214065,0.0002937019],"domain_scores_gemma":[0.9938101,0.00013494577,0.0006238065,0.00011521703,0.005204942,0.00011095792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002458877,0.00022013379,0.00032804348,0.0005208382,0.00006583671,0.000101138095,0.0019953179,0.00018184858,0.000019291741],"category_scores_gemma":[0.0014110182,0.00019009077,0.00048157506,0.0023639253,0.00019910774,0.00085343706,0.00027696905,0.00020167932,0.0000035051996],"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":[0.00002172111,0.00026924087,0.0005612141,0.00026919623,0.00024068754,2.4716098e-8,0.0006936473,0.000009256878,0.19503632,0.797795,0.0022116774,0.002891985],"study_design_scores_gemma":[0.0026141638,0.0006193113,0.017831331,0.00027419036,0.00044537667,0.000021389764,0.0014930604,0.2518168,0.7006932,0.016261298,0.007134781,0.0007951346],"about_ca_topic_score_codex":0.000021689488,"about_ca_topic_score_gemma":1.2072513e-7,"teacher_disagreement_score":0.7815337,"about_ca_system_score_codex":0.00015423971,"about_ca_system_score_gemma":0.00007177401,"threshold_uncertainty_score":0.77516764},"labels":[],"label_agreement":null},{"id":"W2088199355","doi":"10.1109/tsmca.2011.2147307","title":"A New Fractional Random Wavelet Transform for Fingerprint Security","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Wavelet transform; Harmonic wavelet transform; Second-generation wavelet transform; Stationary wavelet transform; Discrete wavelet transform; Constant Q transform; Fractional Fourier transform; S transform; Wavelet packet decomposition; Computer science; Wavelet; Lifting scheme; Pattern recognition (psychology); Artificial intelligence; Top-hat transform; Algorithm; Mathematics; Image processing; Image (mathematics); Fourier transform; Digital image processing; Mathematical analysis; Fourier analysis","score_opus":0.041430383964073385,"score_gpt":0.24452421745208955,"score_spread":0.20309383348801616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088199355","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":[0.004535723,0.00078848813,0.98879886,0.00013328904,0.0024849083,0.0010976862,0.000097182696,0.00013297421,0.001930921],"genre_scores_gemma":[0.9933142,0.00027538225,0.0008969226,0.00005301976,0.00016612522,0.0002299615,0.0000060333955,0.000019846137,0.0050384854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998072,0.000092987284,0.00059389445,0.00057967816,0.00033585672,0.00032555943],"domain_scores_gemma":[0.99885595,0.00012809459,0.0001647694,0.0004212658,0.00013221444,0.00029768597],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054995454,0.00026706516,0.00041037635,0.00033881847,0.00047079133,0.000470905,0.00028322518,0.00018713364,0.000038901337],"category_scores_gemma":[0.0000034011177,0.00024722115,0.00014005753,0.0002689267,0.00007589465,0.00027318083,0.0000039156503,0.00021589115,0.000019659012],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086228485,0.0024473947,0.00015455727,0.003183232,0.0014749976,0.000045778994,0.073676996,0.00048890896,0.0005183083,0.78325886,0.029861914,0.104026765],"study_design_scores_gemma":[0.011829993,0.0013108243,0.0013195898,0.0006498063,0.00034115952,0.0004719256,0.0024800317,0.13903233,0.0018437264,0.0064713582,0.83216184,0.0020873996],"about_ca_topic_score_codex":0.0013800893,"about_ca_topic_score_gemma":0.00025682282,"teacher_disagreement_score":0.9887785,"about_ca_system_score_codex":0.000046501194,"about_ca_system_score_gemma":0.00006879863,"threshold_uncertainty_score":0.99999803},"labels":[],"label_agreement":null},{"id":"W2094505522","doi":"10.1007/s11265-014-0911-2","title":"Iris Recognition using Robust Localization and Nonsubsampled Contourlet Based Features","year":2014,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Contourlet; Artificial intelligence; Pattern recognition (psychology); Iris recognition; Computer science; Computer vision; Support vector machine; Feature extraction; Feature selection; Feature vector; Biometrics; Wavelet transform","score_opus":0.048431143471582686,"score_gpt":0.2589170760828741,"score_spread":0.21048593261129145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094505522","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":[0.009515581,0.0011638717,0.9886997,0.0001646873,0.0003016146,0.000076997014,0.0000016720799,0.000025386711,0.000050491086],"genre_scores_gemma":[0.9704611,0.0000064884816,0.029149352,0.00013589645,0.00022140819,9.752033e-7,0.0000035082069,0.0000071291342,0.000014196471],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985986,0.00021372053,0.00047834907,0.00015976188,0.00041941775,0.00013017558],"domain_scores_gemma":[0.99814075,0.00011209956,0.0007694144,0.000097709584,0.0007767065,0.00010329093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014661249,0.00009963215,0.00022017334,0.00045342496,0.00020485076,0.00074688933,0.00023920117,0.000086691616,0.0000048831116],"category_scores_gemma":[0.00013084592,0.000082882536,0.000043575885,0.00068453443,0.000041627085,0.00069637236,0.000019443001,0.00014428822,0.0000025122467],"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":[0.00023253403,0.0006865758,0.0066432334,0.0030203871,0.0001672267,0.000057802717,0.0038094593,0.057756837,0.03686164,0.0019162822,0.008456403,0.8803916],"study_design_scores_gemma":[0.0005730176,0.00007329556,0.00048315455,0.00039357177,0.000024010897,0.00022386521,0.00008456285,0.99484605,0.00053594424,0.00023945508,0.0023956893,0.00012738671],"about_ca_topic_score_codex":0.000040923787,"about_ca_topic_score_gemma":0.000001872792,"teacher_disagreement_score":0.9609455,"about_ca_system_score_codex":0.000057236193,"about_ca_system_score_gemma":0.00012710586,"threshold_uncertainty_score":0.7202271},"labels":[],"label_agreement":null},{"id":"W2094563755","doi":"10.1109/icassp.2010.5495348","title":"Singular point detection using Discrete Hodge Helmholtz Decomposition in fingerprint images","year":2010,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fingerprint (computing); Singular point of a curve; Singular value decomposition; Artificial intelligence; Mathematics; Helmholtz equation; Point (geometry); Fingerprint recognition; Matching (statistics); Ridge; Pattern recognition (psychology); Singular spectrum analysis; Computer vision; Computer science; Algorithm; Mathematical analysis; Geometry; Geography","score_opus":0.012979124520110815,"score_gpt":0.286553733945507,"score_spread":0.2735746094253962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094563755","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":[0.40777466,0.0000098256005,0.59093076,0.00030208932,0.00042716225,0.00007147188,4.6364516e-7,0.00007664271,0.00040696238],"genre_scores_gemma":[0.9078257,0.0000024816475,0.091985926,0.00010058901,0.000030464544,0.00000361089,0.0000018004648,0.000004264375,0.000045152905],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906707,0.000053966793,0.0002167366,0.00030988886,0.00017628659,0.00017608052],"domain_scores_gemma":[0.9993877,0.000035800287,0.00006775931,0.00037989186,0.00006675971,0.00006211379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004945884,0.00008739911,0.000090976064,0.00046882898,0.00011446219,0.0002970367,0.00031359316,0.00007451643,0.00003218811],"category_scores_gemma":[0.00006154263,0.00008373295,0.000047505426,0.0009556555,0.000036665522,0.00056206185,0.00013366024,0.00020826833,0.00003178487],"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":[0.0000037858792,0.00008764938,0.0009589769,0.000009766622,0.0000038115545,0.000007693565,0.0003228953,0.000019036175,0.91344553,0.00714941,0.000025355856,0.077966094],"study_design_scores_gemma":[0.00039855894,0.00003334355,0.1043554,0.000015239736,0.000004774752,0.000070688766,0.00003744377,0.32819542,0.558036,0.0076130084,0.0009023355,0.00033773365],"about_ca_topic_score_codex":0.00040709137,"about_ca_topic_score_gemma":0.00021975009,"teacher_disagreement_score":0.5000511,"about_ca_system_score_codex":0.00005600554,"about_ca_system_score_gemma":0.000024447161,"threshold_uncertainty_score":0.34145308},"labels":[],"label_agreement":null},{"id":"W2096655583","doi":"10.5539/cis.v5n1p77","title":"Robustness of Multi Biometric Authentication Systems against Spoofing","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Spoofing attack; Biometrics; Computer science; Robustness (evolution); Vulnerability (computing); Computer security; Trait; Authentication (law); Data mining; Artificial intelligence","score_opus":0.051277159753765754,"score_gpt":0.2502041331524494,"score_spread":0.19892697339868362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096655583","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":[0.047366574,0.00006348768,0.95064867,0.00002187568,0.0009241756,0.00013260602,0.0000020997334,0.00006262127,0.00077789044],"genre_scores_gemma":[0.911531,0.000035915822,0.08834097,0.00006262892,0.000013091694,0.000003836029,0.0000029075884,0.0000011118058,0.000008541132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998838,0.000022355924,0.00040380962,0.0001752096,0.00040006265,0.00016058313],"domain_scores_gemma":[0.9987563,0.000027942984,0.0002490257,0.00036460967,0.0005041485,0.000097991964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010518638,0.0000747885,0.00010493588,0.0019939188,0.00018198117,0.00035578682,0.00084569835,0.000035024354,0.0000022654463],"category_scores_gemma":[0.00006263013,0.000065842054,0.000022307724,0.00556301,0.00022823148,0.00722107,0.00026349744,0.000047333608,0.000015717607],"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":[0.000004707143,0.0001916193,0.0020625065,0.00022663565,0.000013100028,5.114806e-7,0.014518366,0.0013420836,0.0010170771,0.26519468,0.0002488925,0.7151798],"study_design_scores_gemma":[0.00015451938,0.000019536987,0.04521499,0.000013154916,0.000001384669,0.0000059473723,0.00005736967,0.9525894,0.00094462157,0.000013407411,0.0008989783,0.00008668749],"about_ca_topic_score_codex":0.000017344157,"about_ca_topic_score_gemma":9.281289e-8,"teacher_disagreement_score":0.95124733,"about_ca_system_score_codex":0.00002744963,"about_ca_system_score_gemma":0.00007786642,"threshold_uncertainty_score":0.52351016},"labels":[],"label_agreement":null},{"id":"W2097029236","doi":"10.1109/tip.2005.864161","title":"Fingerprint registration by maximization of mutual information","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Fingerprint (computing); Artificial intelligence; Computer science; Orientation (vector space); Pattern recognition (psychology); Maximization; Image registration; Mutual information; Matching (statistics); Computer vision; Feature (linguistics); Feature extraction; Fingerprint recognition; Mathematics; Image (mathematics); Statistics","score_opus":0.007991599894368791,"score_gpt":0.2233873522798351,"score_spread":0.21539575238546632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097029236","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":[0.0022489429,0.000035075882,0.99580234,0.00034692083,0.0001553976,0.00010408503,0.000011985258,0.00012857188,0.0011666766],"genre_scores_gemma":[0.95196193,0.000007979868,0.047668353,0.0000610815,0.000010282123,0.000012187164,0.000018022658,0.000003954008,0.0002562208],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904954,0.000027106897,0.00036643032,0.00015404762,0.0002909675,0.00011190182],"domain_scores_gemma":[0.99928266,0.000021448384,0.00022693329,0.00020262285,0.00023914358,0.000027218968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018990162,0.00008551667,0.00008387548,0.0003320001,0.00017475957,0.0002869706,0.00021696357,0.000060433627,0.0000145143285],"category_scores_gemma":[0.0000088277275,0.000090529466,0.00004024555,0.000997703,0.000049217117,0.0022116306,0.0000012766518,0.00009903992,0.000025332933],"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":[0.00002839695,0.0005371552,0.000017176426,0.00022324194,0.000010483458,7.680658e-7,0.0013335863,0.003597418,0.059123807,0.0021549903,0.0033138117,0.9296592],"study_design_scores_gemma":[0.0005609852,0.000060044003,0.0003932393,0.00005007143,0.000015357684,0.000010840946,0.00007575755,0.44422126,0.5495173,0.0012554254,0.003568381,0.0002713576],"about_ca_topic_score_codex":0.000079355406,"about_ca_topic_score_gemma":0.000006649797,"teacher_disagreement_score":0.949713,"about_ca_system_score_codex":0.000051300496,"about_ca_system_score_gemma":0.00006972338,"threshold_uncertainty_score":0.36916846},"labels":[],"label_agreement":null},{"id":"W2100199956","doi":"10.1016/j.ins.2013.09.004","title":"A bio-cryptographic system based on offline signature images","year":2013,"lang":"en","type":"article","venue":"Information Sciences","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":30,"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 du Québec à Montréal; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Pattern recognition (psychology); Population; Feature vector; Representation (politics); Feature extraction; Artificial intelligence; Cryptography; Fingerprint (computing); Password; Data mining; Iris recognition; Digital signature; Feature selection; Algorithm; Hash function; Computer security","score_opus":0.012041585393201076,"score_gpt":0.2319202389507387,"score_spread":0.21987865355753763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100199956","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":[0.0072761755,0.000046776902,0.95816416,0.0055287844,0.00086039194,0.00040583877,0.000009899049,0.0004714406,0.027236557],"genre_scores_gemma":[0.98517615,0.0000021095752,0.013233791,0.0014997266,0.000017406815,0.000025786629,0.000005926368,0.00000102626,0.00003807059],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986354,0.0000458041,0.00030122936,0.00016442916,0.0006674257,0.0001856728],"domain_scores_gemma":[0.99906635,0.00009143853,0.00018082152,0.00031483485,0.00026556695,0.00008099881],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007483536,0.00009048927,0.0000847033,0.0011406739,0.00032107392,0.0012417375,0.0009815219,0.000059100646,0.00005035156],"category_scores_gemma":[0.00009304957,0.00006589959,0.000050076713,0.0039157746,0.00014665081,0.0037387582,0.00005544594,0.00009008344,0.00093194266],"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":[0.000007853218,0.00021155155,0.0025700938,0.00025635527,0.000017145372,0.0000019343238,0.0027795203,0.0023503685,0.0012387987,0.46899042,0.09256179,0.42901418],"study_design_scores_gemma":[0.00022350004,0.0000826356,0.016010517,0.000027315666,0.0000015205376,0.0000039576307,0.00038105279,0.96143377,0.001422582,0.0005265386,0.01970336,0.00018325112],"about_ca_topic_score_codex":0.00006309062,"about_ca_topic_score_gemma":7.603208e-7,"teacher_disagreement_score":0.97789997,"about_ca_system_score_codex":0.000024308918,"about_ca_system_score_gemma":0.00009289303,"threshold_uncertainty_score":0.9998459},"labels":[],"label_agreement":null},{"id":"W2101251249","doi":"10.1109/tsmcb.2010.2098439","title":"On Random Transformations for Changeable Face Verification","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Biometrics; Random projection; Computer science; Template; Transformation (genetics); Domain (mathematical analysis); Multiplicative function; Face (sociological concept); Variety (cybernetics); Software deployment; Feature (linguistics); Theoretical computer science; Data mining; Artificial intelligence; Pattern recognition (psychology); Mathematics; Software engineering","score_opus":0.05089145960254462,"score_gpt":0.24380020693612783,"score_spread":0.1929087473335832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101251249","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":[0.005042998,0.00027470078,0.98580104,0.00029503807,0.0018018788,0.0013441037,0.00012198324,0.00022528591,0.005092944],"genre_scores_gemma":[0.99086976,0.00046554374,0.0023344546,0.00019365289,0.00005948187,0.0004643697,0.000018541814,0.00003079847,0.005563397],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780124,0.0001416162,0.00061214896,0.00061004795,0.0004217337,0.00041320827],"domain_scores_gemma":[0.99836797,0.00021136436,0.00019095115,0.0007848438,0.00019973512,0.00024514386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005274579,0.0003056576,0.00033388878,0.00048937683,0.00041701112,0.00030826402,0.0005371315,0.00021487105,0.000052462503],"category_scores_gemma":[0.000011286448,0.0003021672,0.00015512477,0.0006647154,0.0001081738,0.0002799947,0.000003585513,0.00022743363,0.000202561],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009871273,0.005336098,0.00003884324,0.0011484325,0.00081306545,0.00001685556,0.05334364,0.006695778,0.0025251198,0.72524714,0.028276434,0.17557149],"study_design_scores_gemma":[0.014931624,0.003016033,0.0013674207,0.00056219916,0.000515507,0.00019852421,0.0021451123,0.51860857,0.049986217,0.009150237,0.39625585,0.003262742],"about_ca_topic_score_codex":0.00012711545,"about_ca_topic_score_gemma":0.000049102637,"teacher_disagreement_score":0.9858268,"about_ca_system_score_codex":0.00006962167,"about_ca_system_score_gemma":0.000045421402,"threshold_uncertainty_score":0.999943},"labels":[],"label_agreement":null},{"id":"W2103596938","doi":"10.1109/icpr.2010.314","title":"Cancelable Face Recognition Using Random Multiplicative Transform","year":2010,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Facial recognition system; Multiplicative function; Computer science; Face (sociological concept); Artificial intelligence; Pattern recognition (psychology); Computer vision; Speech recognition; Mathematics","score_opus":0.040451785112247486,"score_gpt":0.27994306713604694,"score_spread":0.23949128202379946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103596938","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":[0.052141063,0.000017309947,0.9392833,0.00071533053,0.00042857483,0.00021544297,0.000006233362,0.00013135717,0.007061363],"genre_scores_gemma":[0.8770812,0.000011602712,0.12197721,0.00019258037,0.000029307634,0.000014916998,0.0000063930015,0.0000036151703,0.00068319106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931103,0.000020434338,0.00014387254,0.00023854201,0.00014340991,0.00014270992],"domain_scores_gemma":[0.9994234,0.000056651985,0.000046849,0.0002810387,0.00012220518,0.00006984742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002784943,0.000066798835,0.00008236856,0.00014270523,0.00012974824,0.00012388188,0.00034592254,0.000062598665,0.00015961351],"category_scores_gemma":[0.00004125139,0.000058357833,0.000040877865,0.0008130322,0.000035476733,0.0004497371,0.000026740985,0.00014339865,0.00013276412],"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":[0.000024457499,0.00022251396,0.00017568575,0.000019267509,0.0000261588,0.000001931728,0.003322808,0.000011379911,0.12978354,0.010718223,0.0014137686,0.8542803],"study_design_scores_gemma":[0.0027673237,0.000025641508,0.0014532643,0.00000781223,0.000011564682,0.000026154148,0.00018579344,0.6703319,0.26783627,0.007467199,0.04945366,0.00043339006],"about_ca_topic_score_codex":0.00040462177,"about_ca_topic_score_gemma":0.00020121734,"teacher_disagreement_score":0.8538469,"about_ca_system_score_codex":0.000021857164,"about_ca_system_score_gemma":0.000050077306,"threshold_uncertainty_score":0.23797633},"labels":[],"label_agreement":null},{"id":"W2105675371","doi":"10.1109/robio.2009.4913115","title":"A statistical approach towards performance analysis of multimodal biometric systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Normalization (sociology); Computer science; Biometrics; Artificial intelligence; Fusion; Sensor fusion; Machine learning; Statistical analysis; Data mining; Simple (philosophy); Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.026141409191145295,"score_gpt":0.27138054509870774,"score_spread":0.24523913590756244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105675371","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":[0.022982504,0.00015604371,0.97164196,0.00007499749,0.00011223874,0.000097548604,0.00001444316,0.00007824166,0.004842003],"genre_scores_gemma":[0.89606863,0.000025695652,0.10361178,0.000053852385,0.00000986551,0.0000030834883,0.000021982265,0.0000013695146,0.0002037128],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986439,0.000054265285,0.0003406937,0.00030695423,0.00047624257,0.00017796132],"domain_scores_gemma":[0.9990985,0.000053438966,0.00010111735,0.00048324253,0.00016958926,0.000094146795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049836125,0.0000879204,0.00027091443,0.0031432468,0.00004897897,0.00012427184,0.0006264532,0.00005874505,0.000026665153],"category_scores_gemma":[0.000064663975,0.00007030955,0.000081285674,0.018670602,0.000038116694,0.0002187144,0.000052235388,0.000062579595,0.000015057218],"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":[0.00001128507,0.0012421331,0.0053961882,0.000075678,0.00051631965,0.0000028032475,0.0007097948,0.0010560541,0.0004871408,0.3706636,0.0016651885,0.6181738],"study_design_scores_gemma":[0.000096011805,0.00004424434,0.24532989,8.729003e-7,0.00003521594,0.0000013967956,0.000013423031,0.75383234,0.00013250872,0.000017831455,0.0004218834,0.00007439986],"about_ca_topic_score_codex":0.00021250469,"about_ca_topic_score_gemma":5.325393e-7,"teacher_disagreement_score":0.87308615,"about_ca_system_score_codex":0.000035607,"about_ca_system_score_gemma":0.000042751253,"threshold_uncertainty_score":0.8970606},"labels":[],"label_agreement":null},{"id":"W2106188494","doi":"10.1109/icassp.2012.6288261","title":"A novel eye region based privacy protection scheme","year":2012,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; JPEG; Facial recognition system; Information privacy; Probabilistic logic; Encoder; Identification (biology); Computational complexity theory; Scheme (mathematics); Pattern recognition (psychology); Image (mathematics); Computer security","score_opus":0.07826590849861004,"score_gpt":0.2784176848048433,"score_spread":0.20015177630623326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106188494","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":[0.0061122733,0.000024268034,0.9887371,0.00282233,0.0003344533,0.00016989293,1.8573779e-7,0.00021621003,0.0015832743],"genre_scores_gemma":[0.8646684,8.317787e-7,0.13363375,0.00038612273,0.00005849078,0.000020963247,0.0000012822155,0.0000028360269,0.0012272868],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993498,0.000022602037,0.00011171103,0.0001631629,0.00018238352,0.00017035306],"domain_scores_gemma":[0.9993792,0.000011496586,0.000052187683,0.0004060084,0.000067723566,0.00008336549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002817613,0.000057149988,0.00005002711,0.00021851891,0.00008188146,0.00009626899,0.00032381053,0.00005038679,0.00003082741],"category_scores_gemma":[0.00007591989,0.000049453236,0.000034514156,0.0011194695,0.000014870225,0.0005922605,0.0000708603,0.00006931087,0.00016984323],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019559713,0.0024757725,0.0134243015,0.00010365989,0.000029616196,0.0000017221893,0.0018009014,0.0000033852646,0.12685692,0.63022363,0.014747372,0.21031316],"study_design_scores_gemma":[0.0010933584,0.000062158055,0.12788974,0.000015452682,0.0000051684683,0.000025027015,0.000025196574,0.30651996,0.060952492,0.00053714117,0.5023882,0.00048611633],"about_ca_topic_score_codex":0.000049497092,"about_ca_topic_score_gemma":8.562051e-7,"teacher_disagreement_score":0.85855615,"about_ca_system_score_codex":0.0000385965,"about_ca_system_score_gemma":0.000028211945,"threshold_uncertainty_score":0.21830478},"labels":[],"label_agreement":null},{"id":"W2106300688","doi":"10.1109/coginf.2011.6016128","title":"A novel fuzzy multimodal information fusion technology for human biometric traits identification","year":2011,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Iris recognition; Fuzzy logic; Artificial intelligence; Identification (biology); Sensor fusion; Information security; Data mining; Machine learning; Fuzzy control system; Face (sociological concept); Computer security","score_opus":0.04698600808818974,"score_gpt":0.27172755100304846,"score_spread":0.2247415429148587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106300688","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":[0.019450827,0.000019113168,0.9768,0.00036270134,0.00032616017,0.00048607023,0.000014090137,0.00041049373,0.0021305394],"genre_scores_gemma":[0.8562094,0.000003592211,0.14325973,0.000106523185,0.000015193814,0.000081415186,0.00003933626,0.0000041431126,0.0002806597],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9987825,0.000012421866,0.00046948003,0.00027966045,0.0002346329,0.00022129578],"domain_scores_gemma":[0.9988521,0.00002890781,0.00022837517,0.00042369246,0.00040124587,0.00006568396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049076497,0.000111567264,0.00011645606,0.0038344255,0.00025106125,0.0001747418,0.0008808646,0.00017364787,0.00003190653],"category_scores_gemma":[0.00016717268,0.00010507179,0.00006598063,0.0060655805,0.0000534898,0.0013986463,0.0001339646,0.00007701226,0.00015754247],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004861305,0.00028540293,0.00012486627,0.000028208511,0.000011700667,1.2493638e-7,0.0010624323,3.680588e-7,0.06284772,0.5995508,0.0006271203,0.33545643],"study_design_scores_gemma":[0.0064170044,0.00070533913,0.40231907,0.00003088783,0.000058468777,0.000073591924,0.0009342292,0.10706822,0.31340855,0.10223235,0.06501891,0.0017333813],"about_ca_topic_score_codex":0.00009509411,"about_ca_topic_score_gemma":0.000012564064,"teacher_disagreement_score":0.83675855,"about_ca_system_score_codex":0.000050772345,"about_ca_system_score_gemma":0.000033719607,"threshold_uncertainty_score":0.4284703},"labels":[],"label_agreement":null},{"id":"W2107971353","doi":"10.1155/2012/282589","title":"Measuring Biometric Sample Quality in terms of Biometric Feature Information in Iris Images","year":2012,"lang":"en","type":"article","venue":"Journal of Electrical and Computer Engineering","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Biometrics; Iris recognition; Pattern recognition (psychology); Principal component analysis; Artificial intelligence; Feature (linguistics); Feature extraction; Computer science; Entropy (arrow of time); IRIS (biosensor); Independent component analysis; Context (archaeology); Measure (data warehouse); Mutual information; Mathematics; Data mining","score_opus":0.021455948841585575,"score_gpt":0.2401567457012068,"score_spread":0.2187007968596212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107971353","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":[0.20093238,0.0012885135,0.7973336,0.00010045738,0.00026453406,0.00005269593,0.0000014743441,0.000012490688,0.000013832246],"genre_scores_gemma":[0.9432407,0.00012635991,0.056535333,0.000026008682,0.00006591293,8.7456675e-7,8.6558344e-7,0.0000027025633,0.0000012613629],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99863636,0.00005223793,0.00061839214,0.00008632036,0.00035258822,0.0002540783],"domain_scores_gemma":[0.9990819,0.00032860087,0.00026243753,0.00012022189,0.00009245329,0.00011439303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011900328,0.000102771155,0.00028326327,0.0061760256,0.00001643987,0.000089380264,0.00033203576,0.00007925268,9.0151286e-7],"category_scores_gemma":[0.0003489279,0.000087660475,0.00006579672,0.010164529,0.00001024275,0.0012758435,0.000088221226,0.00028220617,8.1703917e-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":[0.000033102482,0.0006667599,0.21631487,0.00032202815,0.00006509694,0.000007415551,0.0013172423,0.001480112,0.006062497,0.0065419385,0.00053538586,0.76665354],"study_design_scores_gemma":[0.00065130414,0.000106967374,0.89823276,0.000042749052,0.0000042576257,0.000058611524,0.0000037132872,0.09597824,0.0027407603,0.00015075582,0.0018643334,0.00016554243],"about_ca_topic_score_codex":0.000032026237,"about_ca_topic_score_gemma":3.340334e-7,"teacher_disagreement_score":0.766488,"about_ca_system_score_codex":0.00009576241,"about_ca_system_score_gemma":0.000018213275,"threshold_uncertainty_score":0.55108035},"labels":[],"label_agreement":null},{"id":"W2110343412","doi":"","title":"An Optimal Score Fusion Strategy For a Multimodal Biometric Authentication System for Mobile Device","year":2010,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Biometrics; Computer science; Normalization (sociology); Authentication (law); Mobile device; Reliability (semiconductor); Artificial intelligence; Access control; Modal; Data mining; Machine learning; Computer security","score_opus":0.036210431510435855,"score_gpt":0.2749491212342444,"score_spread":0.23873868972380857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110343412","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.761344,0.000049498034,0.23667033,0.00011110761,0.00046309098,0.0009807472,0.00016719142,0.00016009252,0.000053906024],"genre_scores_gemma":[0.91959333,0.0000032169355,0.07982361,0.00001655309,0.0000626402,0.000011432938,0.00016668376,0.000015820733,0.00030671692],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800825,0.00010545702,0.0002847545,0.0007509488,0.00045099037,0.000399596],"domain_scores_gemma":[0.99738353,0.00021697699,0.00036974673,0.000957992,0.0007815438,0.00029019092],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014177186,0.00020639708,0.00029222673,0.0014314166,0.00080199126,0.00020547744,0.0018218529,0.00034666585,0.000053726137],"category_scores_gemma":[0.00013122598,0.00024685301,0.00023273563,0.002528715,0.00013648323,0.0016769017,0.00021866239,0.0002672551,0.00004420964],"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":[0.0008917423,0.001949788,0.016748432,0.0012394215,0.00020425176,0.000012208249,0.0066677323,0.0002865342,0.79627305,0.034550674,0.00040205076,0.14077413],"study_design_scores_gemma":[0.011257491,0.002409989,0.41803622,0.00016146578,0.0003748849,0.00007824375,0.0063065942,0.44329414,0.09076623,0.00094918796,0.02429205,0.0020734938],"about_ca_topic_score_codex":0.00006395633,"about_ca_topic_score_gemma":0.0000922249,"teacher_disagreement_score":0.7055068,"about_ca_system_score_codex":0.00012724528,"about_ca_system_score_gemma":0.0001672585,"threshold_uncertainty_score":0.9999984},"labels":[],"label_agreement":null},{"id":"W2110372543","doi":"10.1109/ccece.2011.6030421","title":"Using cyclic redundancy check to eliminate key storage for revocable iris templates","year":2011,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Computer science; Data deduplication; Redundancy (engineering); Shuffling; Template; Iris recognition; Computer security; Cyclic redundancy check; Database; Key (lock); Data mining; Information retrieval; Operating system","score_opus":0.17727815524138998,"score_gpt":0.3216733195100358,"score_spread":0.14439516426864582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110372543","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":[0.043402553,0.00009235413,0.9500394,0.00047046182,0.0005127952,0.00033975783,0.000004460527,0.00017222892,0.004966007],"genre_scores_gemma":[0.49829188,0.000007673766,0.49657345,0.00040965073,0.00003366129,0.000021038346,0.0000022662987,0.000009187066,0.0046511735],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988971,0.000027403172,0.00024220208,0.0003910194,0.00016212589,0.00028015525],"domain_scores_gemma":[0.99903184,0.000038485672,0.00007677992,0.0005559031,0.00015749347,0.00013949811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045398017,0.00010650461,0.00013687646,0.00034925944,0.0001836663,0.0001504939,0.0006892474,0.00006427614,0.00009441889],"category_scores_gemma":[0.00009903389,0.000098189244,0.00006502789,0.0011926203,0.000022100394,0.0004026621,0.00016362834,0.000056752895,0.00015462162],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009771019,0.0014773955,0.0024368877,0.0004318326,0.000175094,0.00003667591,0.036068603,0.000066242435,0.12371243,0.52993244,0.14938228,0.15618242],"study_design_scores_gemma":[0.0015326513,0.00053050113,0.029175641,0.00010735701,0.000056762554,0.000060715083,0.00065714907,0.19751915,0.27805686,0.02712266,0.4633844,0.0017961463],"about_ca_topic_score_codex":0.0003347092,"about_ca_topic_score_gemma":0.00001360407,"teacher_disagreement_score":0.50280976,"about_ca_system_score_codex":0.000054155505,"about_ca_system_score_gemma":0.000044166954,"threshold_uncertainty_score":0.40040413},"labels":[],"label_agreement":null},{"id":"W2113238712","doi":"10.1109/smcsia.2003.1232417","title":"Accuracy performance analysis of multimodal biometrics","year":2004,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Biometrics; Spoofing attack; Computer science; Word error rate; Artificial intelligence; Computer security","score_opus":0.025560966447584352,"score_gpt":0.2782069892779223,"score_spread":0.25264602283033794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113238712","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":[0.22856028,0.00010763315,0.76978475,0.00030670295,0.00012454911,0.000044248587,0.0000029540151,0.000065634114,0.0010032562],"genre_scores_gemma":[0.92930114,0.000112321046,0.07036233,0.000116755604,0.0000058370333,0.0000014991393,0.0000054945244,0.0000013968062,0.00009323544],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991353,0.000012274116,0.00022938424,0.00020339394,0.000297363,0.00012225156],"domain_scores_gemma":[0.99911517,0.00007382149,0.00010880831,0.00048587835,0.00016018728,0.000056135617],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00026560054,0.00005898544,0.00014067581,0.003093766,0.000048884347,0.00006213266,0.0006720767,0.00004044739,0.00005271154],"category_scores_gemma":[0.00011778176,0.000050281247,0.00010117058,0.025253039,0.00003445956,0.00038823026,0.000108585795,0.000044969034,0.000044632838],"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":[0.0000061484893,0.0009081482,0.023694493,0.00003740337,0.0007292611,0.0000030670162,0.0016951993,0.0028397301,0.0043450817,0.18193536,0.00036242246,0.7834437],"study_design_scores_gemma":[0.0006524038,0.00006691543,0.5258706,0.0000040489676,0.000093712355,0.0000019242732,0.000028869013,0.42519647,0.04075285,0.0003961538,0.0066719633,0.00026408295],"about_ca_topic_score_codex":0.00023038876,"about_ca_topic_score_gemma":0.000009002235,"teacher_disagreement_score":0.7831796,"about_ca_system_score_codex":0.000036738566,"about_ca_system_score_gemma":0.000049778784,"threshold_uncertainty_score":0.9954657},"labels":[],"label_agreement":null},{"id":"W2114143739","doi":"10.1093/bioinformatics/18.3.484","title":"Assembly of fingerprint contigs: parallelized FPC","year":2002,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency","funders":"National Human Genome Research Institute; BC Cancer Agency","keywords":"Contig; Fingerprint (computing); Computer science; File Transfer Protocol; Artificial intelligence; Operating system; Biology; Genome; Genetics; The Internet","score_opus":0.039415668603740885,"score_gpt":0.24664642156911182,"score_spread":0.20723075296537094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114143739","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":[0.014819912,0.0001614375,0.9645546,0.0006613333,0.00043747417,0.0001817333,0.00001005548,0.0001505767,0.019022893],"genre_scores_gemma":[0.8092661,0.00028737928,0.18858883,0.0003464096,0.000026340462,0.000007612212,0.000008338612,0.0000045644347,0.0014644231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991484,0.000017025342,0.00039609484,0.00007900167,0.00021881195,0.00014061708],"domain_scores_gemma":[0.9991358,0.00005142945,0.00019133932,0.0004610398,0.0000992139,0.00006117529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001824661,0.00007540896,0.0001435086,0.00020310108,0.000045634166,0.0000940281,0.000520036,0.00005512792,0.00008141887],"category_scores_gemma":[0.00007735789,0.00006418308,0.00006845955,0.0006086896,0.00003402512,0.00036231743,0.00008421667,0.000045016088,0.00029723835],"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":[0.000004891725,0.0004389565,0.00040551118,0.00019228828,0.00006724302,0.00000318934,0.012827962,0.000015774089,0.0018746541,0.15809827,0.020965485,0.8051058],"study_design_scores_gemma":[0.00051175186,0.00006289815,0.0015792134,0.000012057159,0.0000044572416,0.0000068437807,0.00006610989,0.77158093,0.0036437295,0.00017905273,0.22219308,0.00015986327],"about_ca_topic_score_codex":0.0000091617585,"about_ca_topic_score_gemma":0.0000013691013,"teacher_disagreement_score":0.80494595,"about_ca_system_score_codex":0.000023049555,"about_ca_system_score_gemma":0.000018224198,"threshold_uncertainty_score":0.38204968},"labels":[],"label_agreement":null},{"id":"W2115969720","doi":"10.1109/tsmcb.2008.2009071","title":"Multimodal Biometric System Using Rank-Level Fusion Approach","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":199,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Rank (graph theory); Linear discriminant analysis; Artificial intelligence; Authentication (law); Pattern recognition (psychology); Data mining; Machine learning; Mathematics","score_opus":0.05091966737059919,"score_gpt":0.25750198462378876,"score_spread":0.20658231725318957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115969720","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":[0.037563793,0.0008707578,0.95527613,0.000117065545,0.0022432406,0.00083607784,0.000071765855,0.0004039965,0.002617188],"genre_scores_gemma":[0.98593587,0.00021690714,0.011301689,0.00010980184,0.00014907437,0.00003329188,0.000010612722,0.000034048477,0.0022086988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99599075,0.00031469698,0.0009481018,0.0010905341,0.0009908141,0.0006650737],"domain_scores_gemma":[0.99765605,0.0001314778,0.00032834764,0.001143036,0.0002822562,0.0004588146],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007752312,0.0005038523,0.00059187,0.0017561123,0.0005484851,0.00079501147,0.00086448377,0.00038654124,0.000012779221],"category_scores_gemma":[0.000010801283,0.0004923557,0.00020829888,0.0033252907,0.00014041341,0.00030264937,0.00001458242,0.00043157558,0.00012036985],"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":[0.00044269938,0.011861945,0.000544706,0.0030960008,0.0012869528,0.00027431274,0.013963307,0.0688662,0.028975174,0.18418135,0.008222341,0.678285],"study_design_scores_gemma":[0.002108306,0.00039120036,0.0019552468,0.00024531301,0.00014482741,0.00038549208,0.0005258605,0.9799272,0.0034817501,0.000089356414,0.009633046,0.0011124036],"about_ca_topic_score_codex":0.000394249,"about_ca_topic_score_gemma":0.000010112346,"teacher_disagreement_score":0.94837207,"about_ca_system_score_codex":0.00024550554,"about_ca_system_score_gemma":0.00008235185,"threshold_uncertainty_score":0.9997528},"labels":[],"label_agreement":null},{"id":"W2118407210","doi":"10.1109/tifs.2009.2033227","title":"Multibiometric Cryptosystem: Model Structure and Performance Analysis","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Biometrics; Cryptosystem; Computer science; Cryptography; Data mining; Security analysis; Pattern recognition (psychology); Artificial intelligence; Computer security; Theoretical computer science","score_opus":0.009882225496413516,"score_gpt":0.2182776658833833,"score_spread":0.20839544038696978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118407210","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":[0.2187177,0.000030083562,0.78050303,0.00020027213,0.00012740586,0.00012848589,0.000065304266,0.00008050241,0.00014725016],"genre_scores_gemma":[0.9905062,0.00015451475,0.00888391,0.00040920044,0.0000063266875,0.0000029290982,0.000017395298,0.000002034249,0.000017510063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890447,0.00002325757,0.0003631096,0.00020900824,0.00032222463,0.0001779395],"domain_scores_gemma":[0.9991794,0.000031841886,0.00013518818,0.00032864886,0.00019015258,0.00013472736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002110288,0.00015403227,0.00020294145,0.001665794,0.00034763743,0.0003694315,0.00019432706,0.00012355161,0.0000067588453],"category_scores_gemma":[0.0000061736796,0.00014048793,0.00007626074,0.0034071126,0.000048349226,0.0018950037,0.0000034876568,0.00019725588,0.00000598712],"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":[0.000058127323,0.00015746738,0.00036617546,0.0001699897,0.00030098963,8.713232e-7,0.010244178,0.022775901,0.000106624735,0.056089774,0.0003241141,0.90940577],"study_design_scores_gemma":[0.0003232619,0.000083714156,0.00524589,0.000004822841,0.00006396787,0.000008169312,0.00003849787,0.9902377,0.0011003703,0.0023902846,0.00032567966,0.00017763105],"about_ca_topic_score_codex":0.000016795666,"about_ca_topic_score_gemma":0.000009745016,"teacher_disagreement_score":0.9674618,"about_ca_system_score_codex":0.00003723614,"about_ca_system_score_gemma":0.00002761782,"threshold_uncertainty_score":0.57289314},"labels":[],"label_agreement":null},{"id":"W2119225965","doi":"10.5539/mas.v3n5p127","title":"The Network Identity Authentication System Based on Iris Feature Identification","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Font; Computer science; Identity (music); Face (sociological concept); Artificial intelligence; Linguistics; Art","score_opus":0.012130817692029788,"score_gpt":0.24639178445393806,"score_spread":0.23426096676190827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119225965","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":[0.0014737435,0.000053084786,0.9894703,0.0039396477,0.00070162304,0.00040100826,0.000002133853,0.00031534795,0.003643115],"genre_scores_gemma":[0.99224615,0.000004721818,0.0066809733,0.0006326076,0.00007792995,0.00003409815,0.000005399811,0.000004680786,0.0003134297],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99698055,0.00006396344,0.00031132516,0.0007743643,0.0014023877,0.00046742166],"domain_scores_gemma":[0.99764663,0.00010516107,0.00025407423,0.0016154099,0.00023470644,0.00014404125],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003275681,0.00015232473,0.0001200299,0.00022211326,0.0019649314,0.0020640462,0.0030012184,0.00007745961,9.999432e-7],"category_scores_gemma":[0.000088974826,0.000116220384,0.000059139566,0.004391159,0.00026835813,0.00073538086,0.00011404806,0.0002020197,0.00023945956],"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":[0.0000107873975,0.00008444714,0.000029079478,0.000008429815,0.0000024490807,8.28466e-7,0.00035480803,0.00092340464,0.044058893,0.88216984,0.002540772,0.06981627],"study_design_scores_gemma":[0.00013220888,0.000016776918,0.027592866,0.00001031194,0.00000497176,0.0000019063658,0.0000269146,0.94946057,0.00166133,0.017711677,0.003225006,0.0001554893],"about_ca_topic_score_codex":0.0000039271154,"about_ca_topic_score_gemma":0.0000033985552,"teacher_disagreement_score":0.9907724,"about_ca_system_score_codex":0.00025982305,"about_ca_system_score_gemma":0.00017113214,"threshold_uncertainty_score":0.9993344},"labels":[],"label_agreement":null},{"id":"W2119537429","doi":"10.1109/tsmcb.2009.2037131","title":"An Analysis of Random Projection for Changeable and Privacy-Preserving Biometric Verification","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Random projection; Computer science; Independent and identically distributed random variables; Data mining; Curse of dimensionality; Software deployment; Projection (relational algebra); Feature (linguistics); Random variable; Pattern recognition (psychology); Similarity (geometry); Feature vector; Information privacy; Gaussian; Domain (mathematical analysis); Artificial intelligence; Algorithm; Image (mathematics); Mathematics; Computer security; Statistics","score_opus":0.0293628581158245,"score_gpt":0.2740160634878878,"score_spread":0.2446532053720633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119537429","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":[0.24483177,0.00022958545,0.752664,0.00011666061,0.0010315509,0.0008070324,0.000074457916,0.00009540808,0.00014951079],"genre_scores_gemma":[0.9943696,0.00032018733,0.004282241,0.000024981076,0.00006149893,0.0001614414,0.000023717115,0.000018856856,0.00073743827],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792796,0.00013981207,0.0005855162,0.0006678883,0.0003966222,0.00028216882],"domain_scores_gemma":[0.99794286,0.00024162584,0.00030814734,0.00096917496,0.00033442798,0.0002037657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091375475,0.00022479397,0.00043340446,0.0022305998,0.00025838858,0.00040142352,0.00052371935,0.00022168313,0.000017793795],"category_scores_gemma":[0.000035934692,0.00022410326,0.0001276879,0.0034490793,0.00012742293,0.00030842703,0.000010275616,0.00020333436,0.00000455353],"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":[0.0010732539,0.008811518,0.009775443,0.0032910507,0.0063133193,0.000009859095,0.03234928,0.01281172,0.2971145,0.09526623,0.0031925405,0.52999127],"study_design_scores_gemma":[0.0017490622,0.00042173712,0.00953365,0.000038074202,0.0006741139,0.000017094151,0.00028011927,0.95702434,0.014983851,0.00019644175,0.0146033345,0.0004781861],"about_ca_topic_score_codex":0.0005281199,"about_ca_topic_score_gemma":0.00025954033,"teacher_disagreement_score":0.9442126,"about_ca_system_score_codex":0.000031555828,"about_ca_system_score_gemma":0.00003776554,"threshold_uncertainty_score":0.9138666},"labels":[],"label_agreement":null},{"id":"W2121368353","doi":"10.1109/indcon.2009.5409481","title":"Secure Group Authentication Using a Non-Perfect Secret Sharing Scheme Based on Controlled Mixing","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Secret sharing; Shamir's Secret Sharing; Information leakage; Computer science; Computer security; Homomorphic secret sharing; Biometrics; Authentication (law); Verifiable secret sharing; Scheme (mathematics); Secure multi-party computation; Leakage (economics); Cryptography; Theoretical computer science; Mathematics","score_opus":0.023086793837507277,"score_gpt":0.27455373951068884,"score_spread":0.25146694567318156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121368353","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":[0.11650841,0.000024696663,0.8794853,0.00086815696,0.0002169249,0.00034701527,8.91141e-7,0.00017445635,0.0023741627],"genre_scores_gemma":[0.95762014,0.0000012113264,0.04108709,0.001056781,0.000048685768,0.000008133083,0.000009433065,0.00000589688,0.00016259501],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984828,0.00005733875,0.00030571688,0.00050612615,0.00038453366,0.00026347578],"domain_scores_gemma":[0.9989133,0.00008686179,0.0001331319,0.000657007,0.00010334223,0.00010636683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007389915,0.00015247325,0.00022090612,0.00053229055,0.00021450857,0.00042932603,0.0006497707,0.00008954056,0.000059024384],"category_scores_gemma":[0.000106698666,0.00013379284,0.00012392664,0.001355532,0.000017180728,0.00037097654,0.000054657536,0.00014865736,0.000045305515],"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":[0.00036475094,0.0021028942,0.00447609,0.00011256449,0.00012340398,0.000036541795,0.0032967126,0.0011077808,0.373909,0.58223236,0.0011025898,0.031135319],"study_design_scores_gemma":[0.0018042845,0.00006797161,0.004733072,0.000026509304,0.0000082757715,0.0000025578147,0.000012065887,0.98972225,0.002506731,0.00076309923,0.00018461616,0.00016853625],"about_ca_topic_score_codex":0.000025431775,"about_ca_topic_score_gemma":0.0000025741763,"teacher_disagreement_score":0.9886145,"about_ca_system_score_codex":0.00009527252,"about_ca_system_score_gemma":0.000044761826,"threshold_uncertainty_score":0.54559135},"labels":[],"label_agreement":null},{"id":"W2121756138","doi":"10.1109/tvt.2010.2103098","title":"Distributed Combined Authentication and Intrusion Detection With Data Fusion in High-Security Mobile Ad Hoc Networks","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Carleton University","funders":"","keywords":"Intrusion detection system; Computer science; Authentication (law); Biometrics; Sensor fusion; Mobile ad hoc network; Wireless ad hoc network; Mobile device; Computer network; Computer security; Artificial intelligence; Wireless; Telecommunications","score_opus":0.015165333983381433,"score_gpt":0.2194709862193239,"score_spread":0.20430565223594246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121756138","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":[0.3547861,0.00020961688,0.644049,0.00018627047,0.00019088168,0.00029942684,0.00001873442,0.0002575369,0.00000244502],"genre_scores_gemma":[0.9950977,0.00057092507,0.004139024,0.000027101569,0.0000043243263,0.000097895856,0.000046002227,0.000009978404,0.0000070617207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985452,0.000088668516,0.00028216196,0.00065654726,0.00018885917,0.00023851059],"domain_scores_gemma":[0.9984378,0.000034823766,0.00011553134,0.0012493043,0.000098936354,0.00006361067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031374273,0.00016304613,0.00018876357,0.00089223776,0.0002155514,0.00005225262,0.00074729574,0.00033625608,0.000015494901],"category_scores_gemma":[0.000010264488,0.00014993473,0.000022694756,0.002695712,0.00017251068,0.00040024708,0.000030987896,0.00045103475,0.000011641666],"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":[0.00032480207,0.0021686393,0.00040886964,0.00004722896,0.00008527758,0.000047981117,0.0010834764,0.0005331729,0.008188591,0.0038146563,0.000035921657,0.9832614],"study_design_scores_gemma":[0.0037272733,0.0018849885,0.013507141,0.000108595676,0.00010618094,0.00015137615,0.00036931597,0.8763217,0.09269692,0.0068641785,0.0034097054,0.0008526279],"about_ca_topic_score_codex":0.000057286492,"about_ca_topic_score_gemma":0.0004419128,"teacher_disagreement_score":0.98240876,"about_ca_system_score_codex":0.00007585434,"about_ca_system_score_gemma":0.000025461144,"threshold_uncertainty_score":0.61141604},"labels":[],"label_agreement":null},{"id":"W2121860927","doi":"10.1109/icpr.2006.234","title":"An Anatomy of IrisCode for Precise Phase Representation","year":2006,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hamming distance; Hamming code; Bitwise operation; Pattern recognition (psychology); Mathematics; Representation (politics); Binomial distribution; Ellipse; Artificial intelligence; Computer science; Algorithm; Statistics; Decoding methods; Block code","score_opus":0.03308967488447189,"score_gpt":0.3912184013458387,"score_spread":0.3581287264613668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121860927","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":[0.062100396,0.000021338941,0.9357134,0.00025077222,0.0000965022,0.00016563677,0.000007010268,0.000057147616,0.0015877967],"genre_scores_gemma":[0.9071975,0.0000015330372,0.092100315,0.000025275225,0.000022700575,0.000012094124,0.000025559235,0.0000018223977,0.0006132063],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994181,0.000022090007,0.00017630946,0.00018205114,0.00012899582,0.00007248142],"domain_scores_gemma":[0.9993722,0.000041445517,0.00007103963,0.00035997725,0.00012747003,0.00002788731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018417115,0.00003574404,0.00006152042,0.00018647518,0.0000342481,0.00006279719,0.00030599113,0.00002453136,0.000020537736],"category_scores_gemma":[0.00002132663,0.00003255011,0.000033790606,0.0006637981,0.000017628594,0.0003866508,0.00001811683,0.000015122702,0.00000450041],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027083823,0.0019232485,0.0021364286,0.000034840094,0.00001183638,0.0000011983188,0.00037583773,0.000066833294,0.04306164,0.6794839,0.03668673,0.23619042],"study_design_scores_gemma":[0.0023641095,0.00019256781,0.01154186,0.0000034378077,0.000008778199,0.0000028073175,0.000057024212,0.52496,0.40755063,0.028687658,0.02443787,0.00019326454],"about_ca_topic_score_codex":0.00015459699,"about_ca_topic_score_gemma":0.000020603919,"teacher_disagreement_score":0.8450971,"about_ca_system_score_codex":0.000008342451,"about_ca_system_score_gemma":0.000018896626,"threshold_uncertainty_score":0.13273549},"labels":[],"label_agreement":null},{"id":"W2122132806","doi":"10.1109/ccece.2004.1347697","title":"Automatic fingerprint recognition algorithm","year":2004,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Minutiae; Computer science; Preprocessor; Fingerprint (computing); Artificial intelligence; Fingerprint recognition; Pattern recognition (psychology); Algorithm; Simple (philosophy); Feature extraction; Computer vision","score_opus":0.026256594713140805,"score_gpt":0.24593415130530838,"score_spread":0.2196775565921676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122132806","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":[0.007419687,0.000017890896,0.98727477,0.0014004585,0.00027400217,0.00006238654,8.361794e-7,0.00051737524,0.0030325835],"genre_scores_gemma":[0.30635804,0.0000075431494,0.69289637,0.0005367297,0.000021694961,0.000007726795,0.0000044474828,0.0000022346321,0.00016520174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999451,0.000013911172,0.00012066641,0.00016460895,0.00014986162,0.00009997376],"domain_scores_gemma":[0.9996077,0.000016268594,0.00003165338,0.00024188981,0.00005361338,0.000048914833],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017254827,0.000044769746,0.000048281127,0.00016906265,0.000058219684,0.00013505109,0.000281589,0.000029935158,0.00011650685],"category_scores_gemma":[0.00002981869,0.00004028636,0.000029146997,0.000737265,0.000013175531,0.00024442314,0.000061597784,0.000045765435,0.0011640367],"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":[4.4775835e-8,0.000047066158,0.0000026586324,0.000002747164,0.0000022994345,0.0000018963201,0.00015490133,8.711118e-7,0.00005796594,0.01415701,0.00035014751,0.9852224],"study_design_scores_gemma":[0.0014721267,0.000108043016,0.018667366,0.00004464964,0.000009812883,0.00010574483,0.00009084247,0.6514466,0.036897313,0.2716536,0.018806763,0.0006971497],"about_ca_topic_score_codex":0.000056821573,"about_ca_topic_score_gemma":0.0000034456,"teacher_disagreement_score":0.98452526,"about_ca_system_score_codex":0.00004435164,"about_ca_system_score_gemma":0.000037189642,"threshold_uncertainty_score":0.99961364},"labels":[],"label_agreement":null},{"id":"W2124452153","doi":"10.1007/978-1-4899-7488-4_62","title":"Security and Liveness, Overview","year":2015,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Liveness; Computer security; Computer science; Programming language","score_opus":0.053743511242862974,"score_gpt":0.276198227823812,"score_spread":0.222454716580949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124452153","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006394893,0.120449275,0.011851944,0.00029179215,0.0019717843,0.00042093478,0.00021598948,0.00016460688,0.8645697],"genre_scores_gemma":[0.007262296,0.34157112,0.022807645,0.00035840296,0.0007307223,0.000022638951,0.0002855244,0.00014536844,0.6268163],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974681,0.000038480463,0.0006228111,0.00065396033,0.000976977,0.00023963966],"domain_scores_gemma":[0.9973712,0.00024206443,0.00056715263,0.00096471334,0.0005808541,0.00027403736],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00095033273,0.00033391936,0.00059589837,0.003775984,0.00006123782,0.00010675038,0.0011278649,0.0004970746,0.00011535334],"category_scores_gemma":[0.00034836595,0.00033324436,0.00014536365,0.0027916813,0.00018354399,0.0002953724,0.0007449647,0.00032021495,0.00012042641],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003914846,0.00010053969,0.00012075573,0.0004988871,0.000075521675,0.000024141375,0.000429888,1.3701377e-7,0.0000025761556,0.70983064,0.037645247,0.25126776],"study_design_scores_gemma":[0.00019188612,0.00006990763,0.000220317,0.00005687656,0.000033423657,0.000011971474,0.0000049860105,0.00012867218,0.000009990423,0.05764681,0.94127476,0.00035038733],"about_ca_topic_score_codex":0.000037235808,"about_ca_topic_score_gemma":0.00000471328,"teacher_disagreement_score":0.90362954,"about_ca_system_score_codex":0.00009548638,"about_ca_system_score_gemma":0.00028537298,"threshold_uncertainty_score":0.99991196},"labels":[],"label_agreement":null},{"id":"W2126375223","doi":"10.1142/s0218001410008421","title":"IMPROVEMENT OF IRIS RECOGNITION PERFORMANCE USING REGION-BASED ACTIVE CONTOURS, GENETIC ALGORITHMS AND SVMs","year":2010,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Biometric Identification and Security","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":"Concordia University","funders":"","keywords":"Iris recognition; Computer science; Artificial intelligence; IRIS (biosensor); Computer vision; Support vector machine; Pattern recognition (psychology); Matching (statistics); Motion blur; Process (computing); Genetic algorithm; Algorithm; Biometrics; Image (mathematics); Mathematics; Machine learning","score_opus":0.09164895539831534,"score_gpt":0.3088363613109581,"score_spread":0.21718740591264274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126375223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5620135,0.000024097011,0.43672058,0.00034767788,0.0007957394,0.00006210237,0.000020493644,0.0000060049756,0.000009798583],"genre_scores_gemma":[0.98572004,0.00018284547,0.013643426,0.00023698689,0.00019479379,0.0000026546013,0.000009723398,0.0000056364133,0.0000039043834],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986268,0.00004339169,0.000627171,0.00018355742,0.0004005729,0.00011849401],"domain_scores_gemma":[0.9978845,0.000119198114,0.0006484837,0.00010602967,0.0011413955,0.00010035495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003650714,0.00011335569,0.00015561687,0.0004897683,0.00007106101,0.00016678029,0.00033404413,0.00007518499,0.00005480465],"category_scores_gemma":[0.00011265056,0.00010782811,0.000065601096,0.00021763932,0.00014316296,0.00046720743,0.000058986843,0.00023847056,0.000007238073],"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":[0.000036131845,0.000119265314,0.00075457955,0.000012757242,0.00003095851,0.000010204277,0.00032518865,0.000008205669,0.019125229,0.000029896722,0.000006021439,0.97954154],"study_design_scores_gemma":[0.00052243634,0.0006405655,0.014454517,0.00036227162,0.000063049396,0.0005722175,0.0007349467,0.26971874,0.69761425,0.014564003,0.00027925015,0.00047376865],"about_ca_topic_score_codex":0.00008512828,"about_ca_topic_score_gemma":0.000019735564,"teacher_disagreement_score":0.9790678,"about_ca_system_score_codex":0.00003109102,"about_ca_system_score_gemma":0.00007610513,"threshold_uncertainty_score":0.4397103},"labels":[],"label_agreement":null},{"id":"W2127047437","doi":"10.1109/iccitechn.2007.4579426","title":"Multi-class SVM based iris recognition","year":2007,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Iris recognition; Pattern recognition (psychology); Computer science; Support vector machine; Hamming distance; Feature extraction; Mahalanobis distance; Segmentation; Backpropagation; Artificial neural network; Feature vector; Biometrics; Algorithm","score_opus":0.06322394410514093,"score_gpt":0.2884440128882235,"score_spread":0.2252200687830826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127047437","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":[0.0050742356,0.000016956712,0.9878507,0.00087821257,0.0003513225,0.000071062444,0.000001608055,0.00020881633,0.0055470546],"genre_scores_gemma":[0.6200581,0.0000026394148,0.3766042,0.001688398,0.000029279057,0.0000026466314,0.000012023125,0.000003159814,0.0015995515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927187,0.000024186083,0.00015481807,0.00021346692,0.00018156643,0.00015409941],"domain_scores_gemma":[0.99940014,0.00007645767,0.000041962136,0.0002838043,0.000117692114,0.0000799384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006576339,0.000053800562,0.000051316958,0.0003185516,0.000068797046,0.00010483228,0.0003133102,0.000053760297,0.00017542647],"category_scores_gemma":[0.000070725204,0.000049185695,0.000038072016,0.0011446007,0.000019455796,0.00020731789,0.00003746186,0.00006146988,0.00074744015],"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":[0.0000085010915,0.0006252538,0.0010505858,0.000019285482,0.000011530139,0.00001800455,0.00031945505,0.00000241305,0.010359829,0.007612253,0.022456411,0.9575165],"study_design_scores_gemma":[0.0016705518,0.000065983164,0.084456936,0.000011178675,0.000007077068,0.000010530516,0.00007948627,0.5442891,0.089099295,0.0011662256,0.27861798,0.00052564376],"about_ca_topic_score_codex":0.0000523951,"about_ca_topic_score_gemma":0.000038065093,"teacher_disagreement_score":0.95699084,"about_ca_system_score_codex":0.000030977306,"about_ca_system_score_gemma":0.000025230464,"threshold_uncertainty_score":0.9607081},"labels":[],"label_agreement":null},{"id":"W2129774602","doi":"10.1109/icassp.2004.1327088","title":"A thinning process for an implementation in a pixel array circuit","year":2004,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pixel; Computer science; Thinning; Process (computing); Diode-or circuit; Computer hardware; Logic gate; Electronic engineering; Circuit extraction; Artificial intelligence; Equivalent circuit; Electrical engineering; Algorithm; Engineering; Voltage","score_opus":0.07584882732991508,"score_gpt":0.3732714438498373,"score_spread":0.2974226165199222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129774602","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":[0.08109097,0.0000096133435,0.91734153,0.0005548543,0.00008110383,0.00024101062,0.0000011977784,0.00007703739,0.000602655],"genre_scores_gemma":[0.97998255,8.0653865e-7,0.01951071,0.00039366743,0.000014973258,0.000052915646,0.000009293125,0.000002566454,0.000032488286],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99935544,0.000011733714,0.00015352106,0.00021634491,0.00012934452,0.00013359122],"domain_scores_gemma":[0.9996712,0.000012945609,0.000042758278,0.00016627685,0.000067805886,0.000038980794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003367098,0.000045651926,0.000052709267,0.00025119534,0.000060675957,0.00013719266,0.0003198056,0.000024387053,0.000010855406],"category_scores_gemma":[0.000018089138,0.00004348005,0.000017403028,0.00092022755,0.000007873177,0.00064540666,0.000012228476,0.00003513187,0.000007066056],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053583694,0.00039677526,0.0024488869,0.00006378355,0.00000840355,9.876527e-7,0.03079178,0.00008951935,0.02469829,0.8295169,0.00015057987,0.11182872],"study_design_scores_gemma":[0.007452542,0.000557417,0.050556157,0.000038051243,0.000009993001,0.000023452103,0.007844387,0.02077334,0.16169232,0.7419407,0.008153803,0.0009578582],"about_ca_topic_score_codex":0.00012413088,"about_ca_topic_score_gemma":0.00032678834,"teacher_disagreement_score":0.8988916,"about_ca_system_score_codex":0.000049641327,"about_ca_system_score_gemma":0.00008557586,"threshold_uncertainty_score":0.17730649},"labels":[],"label_agreement":null},{"id":"W2131282907","doi":"10.1109/tip.2009.2033427","title":"An Analysis of IrisCode","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hamming distance; Iris recognition; Bitwise operation; Pattern recognition (psychology); Biometrics; Artificial intelligence; Coding (social sciences); Computer science; Mathematics; Cluster analysis; Gabor filter; Hamming code; Algorithm; Feature extraction; Decoding methods; Statistics","score_opus":0.019225955053716135,"score_gpt":0.3032656252028255,"score_spread":0.28403967014910936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131282907","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":[0.010923215,0.00003910609,0.9881871,0.0003085242,0.000072750554,0.0000431578,0.0000063979783,0.00012214447,0.00029764682],"genre_scores_gemma":[0.9360331,0.00000777396,0.063721746,0.00015914533,0.0000058835744,0.0000023191167,0.0000016303179,0.0000027058045,0.00006563862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990818,0.000038456576,0.00022821348,0.0002756219,0.00024653823,0.00012937923],"domain_scores_gemma":[0.999254,0.000018864343,0.000095689684,0.00040358762,0.00015900453,0.00006887108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019903574,0.00008134627,0.00015448146,0.0011227095,0.00015919232,0.0002117127,0.00043845573,0.000042784155,0.000028295257],"category_scores_gemma":[0.0000033345582,0.00007944167,0.000103320024,0.004391575,0.000039967283,0.0009186176,4.5418224e-7,0.00010347792,0.000008275947],"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":[0.000007964573,0.00075339363,0.000020181576,0.00001677068,0.000066821754,0.0000026210403,0.0011931093,0.003213553,0.0474408,0.000277234,0.000020740526,0.9469868],"study_design_scores_gemma":[0.00015900093,0.00008211469,0.0046769776,0.000010843863,0.0001730042,0.0000023831792,0.00006476604,0.8618365,0.13248113,0.0002859197,0.000071199174,0.00015616325],"about_ca_topic_score_codex":0.000014436371,"about_ca_topic_score_gemma":0.0000077372915,"teacher_disagreement_score":0.94683063,"about_ca_system_score_codex":0.000026384198,"about_ca_system_score_gemma":0.00004953581,"threshold_uncertainty_score":0.32395372},"labels":[],"label_agreement":null},{"id":"W2133076120","doi":"10.1504/ijbm.2011.042818","title":"Artificial finger detection by spectrum analysis","year":2011,"lang":"en","type":"article","venue":"International Journal of Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Algonquin College; Defence Research and Development Canada; National Research Council Canada","funders":"University of Waterloo","keywords":"Computer science; Spectrum (functional analysis); Artificial intelligence; Pattern recognition (psychology)","score_opus":0.03843300447347347,"score_gpt":0.2654433037624804,"score_spread":0.2270102992890069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133076120","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":[0.039663907,0.00023469602,0.9565136,0.0005507195,0.00251957,0.000025690935,0.000011334037,0.000022722152,0.00045779103],"genre_scores_gemma":[0.9902729,0.000071337025,0.009278387,0.0001293813,0.00014902867,5.4847936e-7,0.000003762751,0.0000037496852,0.000090935275],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99819386,0.00004977369,0.00056446536,0.00016650117,0.00089334865,0.00013205495],"domain_scores_gemma":[0.9983991,0.00008153041,0.00056629564,0.0001906314,0.0006585999,0.00010384238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007861743,0.00008474382,0.00015908113,0.008048042,0.000047133988,0.0002234222,0.0012984339,0.00007143728,0.00014835819],"category_scores_gemma":[0.0003050891,0.000077318226,0.0002442396,0.011457777,0.000035208126,0.000545134,0.000097262775,0.00015108161,0.000041411724],"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":[0.00010349974,0.0016113416,0.011539548,0.0000058859896,0.0031702293,0.00014873597,0.0015256573,0.000017637965,0.018759051,0.013974546,0.0054878746,0.94365597],"study_design_scores_gemma":[0.001148603,0.0008210842,0.15029223,0.000016601993,0.00062214944,0.00043149275,0.000172693,0.042602286,0.66460854,0.025350315,0.11295414,0.0009798676],"about_ca_topic_score_codex":0.00006471763,"about_ca_topic_score_gemma":0.000009093974,"teacher_disagreement_score":0.95060897,"about_ca_system_score_codex":0.00013549875,"about_ca_system_score_gemma":0.000038434864,"threshold_uncertainty_score":0.7181184},"labels":[],"label_agreement":null},{"id":"W2133555949","doi":"10.1109/ccece.2003.1226104","title":"Sample images can be independently restored from face recognition templates","year":2004,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Computer science; Template; Artificial intelligence; Sample (material); Context (archaeology); Facial recognition system; Encryption; Computer vision; Face (sociological concept); Image (mathematics); Pattern recognition (psychology); Computer security","score_opus":0.04991123238049745,"score_gpt":0.26291488477475033,"score_spread":0.2130036523942529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133555949","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":[0.15364902,0.00007770209,0.8371278,0.0069612344,0.0004256552,0.00015316095,0.0001876723,0.00033596007,0.0010818205],"genre_scores_gemma":[0.917477,0.000024211004,0.08107279,0.00091052515,0.00003775489,0.0000083918285,0.00015899749,0.0000053113886,0.00030497837],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894506,0.000037137877,0.00018779497,0.00036227406,0.0002971733,0.00017055855],"domain_scores_gemma":[0.9992229,0.0001095686,0.00006889354,0.00040100387,0.000101339254,0.00009629076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000168988,0.00009401723,0.000095961375,0.00022287788,0.00012915688,0.00029966453,0.00052398245,0.00007609909,0.00014032508],"category_scores_gemma":[0.00013418266,0.00008809209,0.00004170827,0.0007634103,0.000033866272,0.00040602926,0.00011187258,0.00010343747,0.00015660374],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000700463,0.0022136674,0.014880268,0.00007297556,0.00033866082,0.00013652186,0.023889037,0.000114556045,0.076296814,0.1554164,0.088598914,0.6379721],"study_design_scores_gemma":[0.0033903122,0.00016575318,0.15021129,0.000045230456,0.000030437226,0.000029565086,0.0011466667,0.0024040458,0.45939454,0.3386379,0.043241408,0.0013028571],"about_ca_topic_score_codex":0.016173821,"about_ca_topic_score_gemma":0.0009813606,"teacher_disagreement_score":0.76382804,"about_ca_system_score_codex":0.00008127559,"about_ca_system_score_gemma":0.00007136769,"threshold_uncertainty_score":0.99037755},"labels":[],"label_agreement":null},{"id":"W2133942072","doi":"10.1109/imtc.2011.5944015","title":"Combining cryptography and watermarking to secure revocable iris templates","year":2011,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Digital watermarking; Cryptosystem; Biometrics; Key (lock); Iris recognition; Cryptography; Fingerprint (computing); Discrete wavelet transform; Shuffling; Algorithm; Theoretical computer science; Pattern recognition (psychology); Artificial intelligence; Computer security; Wavelet transform; Image (mathematics); Wavelet","score_opus":0.03902994298956725,"score_gpt":0.23069435178139291,"score_spread":0.19166440879182567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133942072","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":[0.18241422,0.00019794038,0.786484,0.0009220456,0.00046402996,0.00021816981,0.0000015795998,0.00036374963,0.028934283],"genre_scores_gemma":[0.8510822,0.000016192103,0.14807558,0.0005760647,0.000009035616,0.0000059015456,9.95477e-7,0.0000036092397,0.00023039036],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923086,0.000028210245,0.0001489512,0.0002810286,0.00012784464,0.00018311958],"domain_scores_gemma":[0.9994509,0.000027550073,0.00003381785,0.00030420098,0.00005959227,0.00012390738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002905306,0.00007916417,0.000093685754,0.0003892845,0.00013944313,0.00018157643,0.00039036255,0.000042570682,0.00007624293],"category_scores_gemma":[0.000017234835,0.000066490335,0.000027776912,0.0010896181,0.000025435433,0.00031203014,0.00022219423,0.000064104956,0.00006127391],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018726936,0.00028859518,0.081959814,0.00009537525,0.00008488587,0.000028472112,0.046449278,8.582993e-7,0.009336294,0.7270819,0.038470563,0.096185274],"study_design_scores_gemma":[0.0018426677,0.00055980927,0.32177648,0.0001592472,0.000038215836,0.00015010801,0.0015700177,0.020238182,0.10813561,0.091919936,0.45142063,0.0021890937],"about_ca_topic_score_codex":0.00018403327,"about_ca_topic_score_gemma":0.0000085273205,"teacher_disagreement_score":0.66866803,"about_ca_system_score_codex":0.000006028163,"about_ca_system_score_gemma":0.000008150065,"threshold_uncertainty_score":0.2711397},"labels":[],"label_agreement":null},{"id":"W2135460074","doi":"10.1109/icpr.2006.236","title":"An efficient algorithm for fingerprint matching","year":2006,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Delaunay triangulation; Fingerprint (computing); Matching (statistics); Computer science; Interpolation (computer graphics); Blossom algorithm; Triangulation; Fingerprint recognition; Algorithm; Artificial intelligence; Pattern recognition (psychology); Computer vision; Mathematics; Image (mathematics)","score_opus":0.012362850334427432,"score_gpt":0.26083979621019154,"score_spread":0.2484769458757641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135460074","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":[0.008563432,0.00002001362,0.989341,0.00031982263,0.0002827359,0.000115885254,0.0000025442835,0.00018343734,0.0011711305],"genre_scores_gemma":[0.42462364,3.2245518e-7,0.5746765,0.00013555575,0.00004930369,0.000010317829,0.000005504804,0.0000025494294,0.00049630256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934816,0.000014755938,0.00012969355,0.00023065785,0.00013746743,0.000139272],"domain_scores_gemma":[0.9995158,0.00003621297,0.000032681142,0.0003124419,0.00006250228,0.000040408762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029281378,0.000051289884,0.000054500684,0.00014487683,0.00010880805,0.00023172068,0.00041493852,0.000027539421,0.0000147552255],"category_scores_gemma":[0.000004508149,0.000045204455,0.00003729295,0.00039893916,0.000010671125,0.000109553774,0.00004575222,0.000030502913,0.000033325552],"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":[4.7810096e-7,0.00026110464,0.000021603224,0.0000051425427,0.0000025103805,9.667924e-7,0.00020639924,0.00059650623,0.0010510564,0.48952696,0.0015546222,0.50677264],"study_design_scores_gemma":[0.00012637576,0.000015824273,0.0023206042,0.0000010871513,9.606184e-7,0.000002268964,0.000014273085,0.9768335,0.003308371,0.007561061,0.009725808,0.00008981576],"about_ca_topic_score_codex":0.00019252309,"about_ca_topic_score_gemma":0.000008603707,"teacher_disagreement_score":0.97623706,"about_ca_system_score_codex":0.000022576118,"about_ca_system_score_gemma":0.00001764357,"threshold_uncertainty_score":0.22344878},"labels":[],"label_agreement":null},{"id":"W2135898004","doi":"10.1109/syscon.2011.5929061","title":"A fuzzy vault implementation for securing revocable iris templates","year":2011,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Computer science; Template; Shuffling; Iris recognition; Artificial intelligence; IRIS (biosensor); Set (abstract data type); Data mining; Fingerprint (computing); Cryptosystem; Cryptography; Computer security","score_opus":0.07879637702743476,"score_gpt":0.30983034062380954,"score_spread":0.2310339635963748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135898004","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":[0.014793268,0.000059305683,0.97112113,0.00040355133,0.0003552858,0.00036907318,0.0000064674073,0.00021956464,0.012672367],"genre_scores_gemma":[0.7805933,0.000012201659,0.21788351,0.00025474583,0.000027821405,0.00005507882,0.00001093016,0.0000048101547,0.0011575962],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99926543,0.000016550453,0.00018978845,0.00023659949,0.00011708095,0.00017454753],"domain_scores_gemma":[0.99946463,0.000035605222,0.00006579435,0.00027203036,0.00010936647,0.00005254802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032599247,0.00006373748,0.00007028873,0.00019068536,0.00012333257,0.00010147934,0.00037440544,0.000032727872,0.00014440737],"category_scores_gemma":[0.00002012385,0.000057709476,0.00004453253,0.0005526413,0.000010922414,0.0004451254,0.00007464749,0.000031451465,0.00007441279],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000102226695,0.000173892,0.0033989341,0.00007162987,0.00004264382,0.0000018732475,0.008128198,5.347821e-7,0.0025098477,0.7970798,0.05607302,0.13250938],"study_design_scores_gemma":[0.0035600918,0.00050212396,0.053647894,0.000028215496,0.00004722125,0.00003864036,0.0033079656,0.020983838,0.27918875,0.28546566,0.35189468,0.0013349351],"about_ca_topic_score_codex":0.00033318612,"about_ca_topic_score_gemma":0.000047618647,"teacher_disagreement_score":0.76580006,"about_ca_system_score_codex":0.000023816465,"about_ca_system_score_gemma":0.000025696623,"threshold_uncertainty_score":0.23533241},"labels":[],"label_agreement":null},{"id":"W2136572375","doi":"10.1109/cibim.2011.5949215","title":"Signature based Fuzzy Vaults with Boosted Feature Selection","year":2011,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Password; Computer science; Cryptography; Authentication (law); Signature (topology); Computer security; Feature selection; Data mining; Pattern recognition (psychology); Artificial intelligence; Mathematics","score_opus":0.020782202171126942,"score_gpt":0.2107335324529499,"score_spread":0.18995133028182296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136572375","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":[0.0050000628,0.000071319126,0.95076007,0.0015110219,0.0002938688,0.00022917926,0.0000021215374,0.00060318696,0.04152919],"genre_scores_gemma":[0.88482875,0.000001171963,0.10982455,0.0008570664,0.000020120267,0.0000065857907,0.0000068008217,0.0000048009338,0.004450145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.999212,0.00004039265,0.000078665726,0.0002913302,0.00022652287,0.00015103897],"domain_scores_gemma":[0.99938023,0.00001866445,0.00005166501,0.0002957662,0.00017773778,0.00007592728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013272226,0.000090785026,0.000073181494,0.00025156623,0.00009583678,0.00009502404,0.00038389734,0.00011411569,0.000120769946],"category_scores_gemma":[0.000014693438,0.00006433395,0.000028627483,0.002014988,0.000020387894,0.000291602,0.000028487777,0.0001667111,0.00006284152],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003119644,0.0018523793,0.012583201,0.00012371322,0.00018213344,0.000077817145,0.005593946,0.000029598565,0.013479323,0.49901396,0.3281495,0.13860247],"study_design_scores_gemma":[0.0042865607,0.0012642153,0.35888922,0.00006213785,0.000060560873,0.00014690917,0.00015862938,0.18914543,0.22463843,0.0049323305,0.2145747,0.0018408926],"about_ca_topic_score_codex":0.000054226268,"about_ca_topic_score_gemma":0.000066146604,"teacher_disagreement_score":0.8798287,"about_ca_system_score_codex":0.00002412208,"about_ca_system_score_gemma":0.00006242698,"threshold_uncertainty_score":0.26234624},"labels":[],"label_agreement":null},{"id":"W2137762873","doi":"10.1109/isspa.2010.5605564","title":"Combining DWT and LSB watermarking to secure revocable iris templates","year":2010,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Shuffling; Computer science; Digital watermarking; Hamming distance; Least significant bit; Robustness (evolution); Hamming code; Artificial intelligence; Watermark; Code (set theory); Discrete wavelet transform; IRIS (biosensor); Iris recognition; Computer vision; Template; Pattern recognition (psychology); Algorithm; Image (mathematics); Wavelet transform; Wavelet; Biometrics; Block code; Decoding methods","score_opus":0.01148102394150199,"score_gpt":0.24301711953842542,"score_spread":0.23153609559692342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137762873","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75427896,0.000065735425,0.22492772,0.006939925,0.0013365517,0.00022842393,0.0000022176425,0.00035299567,0.011867494],"genre_scores_gemma":[0.9373467,0.0000058399646,0.0601588,0.00072751933,0.000028992552,0.0000048572433,0.0000018251919,0.0000039548477,0.0017215089],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99920934,0.000019542935,0.0001462292,0.0002894153,0.00015011978,0.00018532493],"domain_scores_gemma":[0.9993472,0.000057831086,0.000032666685,0.0003622539,0.000066650086,0.0001333945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039773167,0.000077132056,0.000093710485,0.0002262692,0.00016966519,0.00040283948,0.00042567248,0.0000610261,0.00007312489],"category_scores_gemma":[0.00005764325,0.000064383355,0.000019023088,0.0006729096,0.000024357465,0.00030935093,0.00026933948,0.00014959511,0.000108451866],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010759142,0.0002215368,0.049463257,0.00010439141,0.000047721118,0.000029952016,0.013415148,0.0000042532765,0.21902923,0.4508317,0.09009265,0.1767494],"study_design_scores_gemma":[0.0007332418,0.00009868655,0.05688097,0.00003431895,0.000009290563,0.00012320743,0.00023495387,0.04259515,0.06581866,0.009437858,0.8232629,0.0007708118],"about_ca_topic_score_codex":0.00011724066,"about_ca_topic_score_gemma":0.00005940909,"teacher_disagreement_score":0.7331702,"about_ca_system_score_codex":0.0000066065495,"about_ca_system_score_gemma":0.000014707647,"threshold_uncertainty_score":0.38845903},"labels":[],"label_agreement":null},{"id":"W2138735546","doi":"10.1109/ccece.2009.5090086","title":"Face verification with changeable templates","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Template; Orthonormal basis; Face (sociological concept); Set (abstract data type); Similarity (geometry); Feature (linguistics); Biometrics; Feature extraction; Pattern recognition (psychology); Artificial intelligence; Translation (biology); Data mining; Image (mathematics)","score_opus":0.019028532234236728,"score_gpt":0.2311053315679269,"score_spread":0.21207679933369017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138735546","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":[0.0060167853,0.00006837175,0.9760928,0.0051825666,0.00006264345,0.000091048125,4.392958e-7,0.0002216895,0.012263638],"genre_scores_gemma":[0.968509,0.000010044983,0.027933564,0.00056236907,0.000011633884,0.0000029283547,0.0000035051726,0.0000012376026,0.0029656978],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995015,0.000011046429,0.00006912593,0.0001790271,0.0001385434,0.000100755635],"domain_scores_gemma":[0.9995397,0.000010235453,0.000030115365,0.00032597783,0.000053045274,0.000040940762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011039847,0.000046240242,0.000045997556,0.00012288243,0.00006751054,0.0001406767,0.00032215085,0.000024381996,0.000032017426],"category_scores_gemma":[0.0000073900264,0.000034247947,0.000010626142,0.00093789,0.000010182108,0.00031897795,0.000013041982,0.000033078202,0.0001610134],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008091693,0.00026036814,0.00048502532,0.0000075122443,0.000010772536,0.0000043194987,0.0014936987,0.000021388623,0.0046292134,0.70471406,0.017982546,0.27038303],"study_design_scores_gemma":[0.0013448941,0.0007120184,0.24417444,0.000022958546,0.0000121871735,0.0000756678,0.0003493611,0.15609975,0.12099089,0.012943686,0.46226445,0.0010097207],"about_ca_topic_score_codex":0.000017443557,"about_ca_topic_score_gemma":0.0000034406228,"teacher_disagreement_score":0.9624922,"about_ca_system_score_codex":0.000013397109,"about_ca_system_score_gemma":0.000015205182,"threshold_uncertainty_score":0.20695554},"labels":[],"label_agreement":null},{"id":"W2138736414","doi":"10.1109/iccitechn.2007.4579428","title":"Iris Recognition: A Java based implementation","year":2007,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Iris recognition; Computer vision; Artificial intelligence; IRIS (biosensor); Biometrics; Edge detection; Thresholding; Hamming distance; Blob detection; Canny edge detector; Image (mathematics); Image processing; Algorithm","score_opus":0.04862114701705869,"score_gpt":0.32716463285875086,"score_spread":0.2785434858416922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138736414","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":[0.008988462,0.00001308398,0.9822379,0.0013139452,0.0001900577,0.00008264023,0.0000020594773,0.00014008542,0.007031771],"genre_scores_gemma":[0.842409,0.0000023177245,0.15517482,0.0019472861,0.0000388276,0.000004327,0.000039178463,0.0000023457162,0.00038184636],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99935156,0.000018854742,0.0001587771,0.00016393367,0.0001805268,0.00012636889],"domain_scores_gemma":[0.9995535,0.000046383822,0.000042270018,0.00020069096,0.0000997432,0.000057402045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006257308,0.000042009935,0.000037590595,0.00026611466,0.00006815709,0.000103455204,0.00021268072,0.00002565073,0.0008181985],"category_scores_gemma":[0.00001576139,0.000039419025,0.000027885544,0.0010788289,0.000010533916,0.00023251412,0.000026747379,0.00003344434,0.00036866814],"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":[0.0000028918075,0.000076587734,0.0015275386,0.000006349862,0.00000500458,0.0000045952097,0.000227486,1.061267e-7,0.0012489243,0.014787187,0.026496481,0.95561683],"study_design_scores_gemma":[0.0026894107,0.00018330853,0.24741802,0.000008831705,0.000014816126,0.000024487897,0.00066572014,0.02498596,0.17105493,0.012138801,0.5400293,0.00078646117],"about_ca_topic_score_codex":0.00006069983,"about_ca_topic_score_gemma":0.000072417206,"teacher_disagreement_score":0.9548304,"about_ca_system_score_codex":0.00002854569,"about_ca_system_score_gemma":0.000028811291,"threshold_uncertainty_score":0.89587},"labels":[],"label_agreement":null},{"id":"W2140877861","doi":"10.1109/robio.2010.5723349","title":"User authentication on mobile devices with dynamical selection of biometric techniques for optimal performance","year":2010,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Biometrics; Computer science; Mobile device; Normalization (sociology); Authentication (law); Identification (biology); Access control; Human–computer interaction; Selection (genetic algorithm); Mobile computing; Computer security; Artificial intelligence; Computer network; World Wide Web","score_opus":0.009735579677962743,"score_gpt":0.26334617305563873,"score_spread":0.253610593377676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140877861","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.513829,0.0000022224156,0.48548305,0.00006949815,0.000069758324,0.00025001878,0.000001647761,0.00010408137,0.00019076343],"genre_scores_gemma":[0.8014108,0.0000027552785,0.19820294,0.00002915505,0.000014534915,0.00007209593,0.0000057923417,0.0000037852715,0.00025812347],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926555,0.000011558586,0.00016692327,0.00023422754,0.00020462487,0.00011709301],"domain_scores_gemma":[0.99927413,0.00007130798,0.000103847386,0.00025653327,0.000256036,0.00003815607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002974461,0.00007214942,0.000085916305,0.0008609403,0.00007647094,0.00007215235,0.0003529875,0.00007254366,0.000025046684],"category_scores_gemma":[0.000027920403,0.00005363328,0.000029222603,0.0026067833,0.000045861387,0.00031604656,0.000029265897,0.000086742286,0.000008599599],"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":[0.00013228004,0.0017841691,0.029932985,0.00027182166,0.00006590926,2.3383039e-7,0.0005115731,0.00003782559,0.15778527,0.22041196,0.00095519563,0.5881108],"study_design_scores_gemma":[0.0002931384,0.0010716333,0.073528,0.0000122486135,0.000012286934,0.000009176142,0.000017777846,0.4148095,0.49630585,0.00007673961,0.013644373,0.00021928796],"about_ca_topic_score_codex":0.0000122087995,"about_ca_topic_score_gemma":0.000012769372,"teacher_disagreement_score":0.58789146,"about_ca_system_score_codex":0.000019405798,"about_ca_system_score_gemma":0.000035803634,"threshold_uncertainty_score":0.21871017},"labels":[],"label_agreement":null},{"id":"W2142297467","doi":"10.1109/ias.2007.44","title":"Modeling Security Protocols as Games","year":2007,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Notation; Cryptographic protocol; Protocol (science); Programming language; Semantics (computer science); Theoretical computer science; Game semantics; Simple (philosophy); Computer security model; Tree (set theory); Operational semantics; Computer security; Cryptography; Denotational semantics; Mathematics","score_opus":0.03869929730434387,"score_gpt":0.3341285578682582,"score_spread":0.29542926056391433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142297467","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":[0.010881575,0.000016526958,0.95976967,0.00044986748,0.00010271029,0.0019704334,2.8131694e-7,0.00025480005,0.026554167],"genre_scores_gemma":[0.97115165,0.0000016805983,0.026964815,0.0005910218,0.00003999736,0.0002452634,9.4849685e-7,0.0000032355888,0.0010013704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902385,0.000019445732,0.00020501405,0.00025890197,0.00028477912,0.00020801697],"domain_scores_gemma":[0.99928606,0.00002879507,0.000029336656,0.00043727897,0.00011587157,0.000102691294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008923789,0.00006724355,0.00007372992,0.00022905548,0.00008111531,0.00019739842,0.0006067813,0.00005511046,0.000084835],"category_scores_gemma":[0.00006650751,0.000058643553,0.000042114185,0.00097109436,0.000016382928,0.00033552572,0.00012770998,0.000089903486,0.00028801293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006967573,0.00021185614,0.00035359836,0.000022112245,0.00000857354,0.000012101356,0.0014156278,0.000036809895,0.00048724544,0.9252948,0.002113213,0.07003707],"study_design_scores_gemma":[0.00035054924,0.000046481076,0.0006172851,0.000010715602,0.0000014925348,0.000021803786,0.0001097552,0.86603403,0.012147689,0.0550422,0.065344535,0.00027346236],"about_ca_topic_score_codex":0.000112922906,"about_ca_topic_score_gemma":0.000026111084,"teacher_disagreement_score":0.9602701,"about_ca_system_score_codex":0.000026441834,"about_ca_system_score_gemma":0.00004351775,"threshold_uncertainty_score":0.37019196},"labels":[],"label_agreement":null},{"id":"W2146111502","doi":"10.1109/pst.2011.5971968","title":"Transparent non-intrusive multimodal biometric system for video conference using the fusion of face and ear recognition","year":2011,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Computer science; Modal; Facial recognition system; Artificial intelligence; Optimal distinctiveness theory; Speech recognition; Face (sociological concept); Computer vision; Pattern recognition (psychology)","score_opus":0.14939722149715773,"score_gpt":0.286511190623789,"score_spread":0.13711396912663126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146111502","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":[0.22550987,0.000047184338,0.7735798,0.000043267824,0.00020910935,0.00036007905,0.000018152738,0.000029387376,0.00020316815],"genre_scores_gemma":[0.9482016,0.000014194542,0.051714744,0.00002517236,0.00000961055,0.000012805312,0.0000033098556,0.000003124201,0.00001545451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915975,0.0000491165,0.0002477414,0.00024650793,0.00017101807,0.00012588513],"domain_scores_gemma":[0.9991759,0.000104837665,0.00014638933,0.0002412801,0.00028073776,0.000050851722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039225063,0.000081795435,0.00013070066,0.00044201294,0.0001273622,0.00006329051,0.0003310864,0.0000492522,0.00001306604],"category_scores_gemma":[0.00004034568,0.000056214467,0.000045712426,0.001372731,0.00006396026,0.00020767577,0.00006206125,0.000043502725,0.0000041396706],"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":[0.00019225273,0.00077319914,0.0029133416,0.0013521977,0.00017192125,0.000005353777,0.036906652,0.000015946916,0.11968053,0.041499734,0.0002530129,0.79623586],"study_design_scores_gemma":[0.000973861,0.00017315336,0.014865232,0.000086022905,0.00004147949,0.000020083564,0.0028777516,0.79584825,0.18413891,0.0006064543,0.00012243957,0.00024638363],"about_ca_topic_score_codex":0.00053693313,"about_ca_topic_score_gemma":0.00001540679,"teacher_disagreement_score":0.79598945,"about_ca_system_score_codex":0.000025257357,"about_ca_system_score_gemma":0.00003481313,"threshold_uncertainty_score":0.22923593},"labels":[],"label_agreement":null},{"id":"W2148882677","doi":"10.5430/air.v5n1p78","title":"Singular-minutiae points relationship-based approach to fingerprint matching","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Minutiae; Fingerprint (computing); Pattern recognition (psychology); Matching (statistics); Artificial intelligence; Computer science; Orientation (vector space); Fingerprint recognition; Feature (linguistics); Point (geometry); Mathematics; Statistics","score_opus":0.43513860947884847,"score_gpt":0.44013394823791774,"score_spread":0.004995338759069268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148882677","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":[0.0196256,0.000057590223,0.9680619,0.006650407,0.0003363403,0.0004003086,0.0000018544671,0.00015599972,0.004709952],"genre_scores_gemma":[0.8769808,0.0000012530069,0.122468196,0.0002035835,0.000079803176,0.00004812433,0.0000068290296,0.000011369464,0.000200028],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99605006,0.0005957233,0.0004797808,0.0007191104,0.0015009496,0.00065435644],"domain_scores_gemma":[0.9967883,0.00062526955,0.000060923314,0.0010393441,0.0009374462,0.0005487018],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007569611,0.00013806434,0.00015849117,0.0013179886,0.0004055252,0.00087544305,0.001675631,0.00012124352,0.000024076777],"category_scores_gemma":[0.0029991514,0.0001393723,0.00007066518,0.005558794,0.00016208219,0.00038385417,0.0004992848,0.00056300964,0.0024637606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029124418,0.0005368511,0.0004129885,0.00002132304,0.00000766712,0.000010363298,0.007557908,0.002522651,0.0008238027,0.8728948,0.0015628425,0.113619685],"study_design_scores_gemma":[0.00006766784,0.00017488617,0.0012271041,0.000044439344,0.0000036656836,0.000010940354,0.002930005,0.40323332,0.045668337,0.5373392,0.008796001,0.0005044211],"about_ca_topic_score_codex":0.0004395635,"about_ca_topic_score_gemma":0.00003331501,"teacher_disagreement_score":0.85735524,"about_ca_system_score_codex":0.00027829097,"about_ca_system_score_gemma":0.00049084553,"threshold_uncertainty_score":0.99831295},"labels":[],"label_agreement":null},{"id":"W2149467905","doi":"10.1504/ijbm.2012.047645","title":"Fusion of biometric systems using Boolean combination: an application to iris-based authentication","year":2012,"lang":"en","type":"article","venue":"International Journal of Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Authentication (law); Interpolation (computer graphics); Iris recognition; Artificial intelligence; Pattern recognition (psychology); Data mining; Machine learning; Image (mathematics); Computer security","score_opus":0.04831245475587677,"score_gpt":0.33778982876268326,"score_spread":0.2894773740068065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149467905","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":[0.19615415,0.00056481326,0.7999433,0.0004326625,0.0026746588,0.00015960072,0.000014969663,0.0000222247,0.000033590888],"genre_scores_gemma":[0.95216453,0.000025939993,0.0474213,0.00009603628,0.00024223849,0.0000032784728,0.00002092052,0.0000096085605,0.000016149437],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968896,0.00013745943,0.00094417203,0.00019607265,0.0016301883,0.00020250783],"domain_scores_gemma":[0.9951673,0.00019701965,0.0012259546,0.0003966324,0.0027329505,0.00028011866],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0022654117,0.00012853318,0.00022716381,0.013561988,0.00006815641,0.00022856679,0.0015807867,0.00010694092,0.000010672682],"category_scores_gemma":[0.00060964184,0.00012328748,0.000116500465,0.014222243,0.00004151447,0.001197806,0.00012894123,0.00011651547,0.000020738315],"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":[0.00017746807,0.009125767,0.06775149,0.0001816042,0.00042770064,0.000012362664,0.0030381223,0.0023366169,0.2779296,0.11279696,0.0019858775,0.52423644],"study_design_scores_gemma":[0.0030637088,0.0010264012,0.21535254,0.00017802538,0.00013597588,0.00028637538,0.00047540557,0.6611183,0.052885722,0.00087783777,0.06372733,0.0008723776],"about_ca_topic_score_codex":0.00006661838,"about_ca_topic_score_gemma":3.3295817e-7,"teacher_disagreement_score":0.75601035,"about_ca_system_score_codex":0.00037036662,"about_ca_system_score_gemma":0.00013174703,"threshold_uncertainty_score":0.99761844},"labels":[],"label_agreement":null},{"id":"W2155628890","doi":"10.1109/ccece.2006.277447","title":"Towards a Measure of Biometric Information","year":2006,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Entropy (arrow of time); Pattern recognition (psychology); Computer science; Artificial intelligence; Population; Feature (linguistics); Measure (data warehouse); Kullback–Leibler divergence; Facial recognition system; Gaussian; Information theory; Face (sociological concept); Mathematics; Data mining; Statistics","score_opus":0.012667419537149874,"score_gpt":0.21747940846222044,"score_spread":0.20481198892507058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155628890","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":[0.0037158725,0.00006574279,0.9489145,0.00040996022,0.00015159129,0.000054851113,0.0000021870635,0.000080289734,0.046605006],"genre_scores_gemma":[0.97132474,0.0000026321727,0.028358787,0.00007572278,0.000009014734,0.0000017489784,0.0000057532948,6.79687e-7,0.00022091865],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9993515,0.000013765832,0.00020168818,0.00006520384,0.00029509887,0.0000727195],"domain_scores_gemma":[0.999468,0.000013125648,0.00007719531,0.00022211179,0.00019870007,0.00002086827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026772195,0.000036867204,0.00005777067,0.0011497067,0.000024246516,0.000078245765,0.00033824393,0.00003407411,0.000032268097],"category_scores_gemma":[0.000052220727,0.00003063995,0.00003246856,0.0054413746,0.000015640522,0.00068896764,0.00005028409,0.000024665196,0.00008358776],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010754302,0.0000783331,0.00063438,0.000018830471,0.0000046687687,1.8198372e-7,0.00012863659,0.0000031001318,0.0006097932,0.63700247,0.012268169,0.34925035],"study_design_scores_gemma":[0.0012454386,0.00011288792,0.43389776,0.000011752831,0.0000105711515,0.000016647195,0.00007268776,0.05484919,0.08985761,0.021013813,0.39841852,0.0004931389],"about_ca_topic_score_codex":0.00038910072,"about_ca_topic_score_gemma":0.00000414172,"teacher_disagreement_score":0.96760887,"about_ca_system_score_codex":0.000016747992,"about_ca_system_score_gemma":0.000039211427,"threshold_uncertainty_score":0.26144},"labels":[],"label_agreement":null},{"id":"W2156342517","doi":"10.1109/ccece.2009.5090128","title":"Pipelined minutiae extraction from fingerprint images","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Minutiae; Fingerprint (computing); Computer science; Fingerprint recognition; Artificial intelligence; Pattern recognition (psychology); Extraction (chemistry); Feature extraction; Computer vision","score_opus":0.016605236842096926,"score_gpt":0.27144123923676805,"score_spread":0.25483600239467114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156342517","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":[0.010072351,0.00010511921,0.9752779,0.007981549,0.0003504175,0.00005101726,0.0000017730058,0.00023451084,0.005925332],"genre_scores_gemma":[0.9087107,0.000017184582,0.088808194,0.0008218888,0.000057644556,0.0000011565473,0.0000057025395,0.0000012555278,0.0015762629],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993074,0.000023924102,0.00014653974,0.0002502861,0.0001653806,0.000106503794],"domain_scores_gemma":[0.9994087,0.00004542185,0.000046473786,0.00038350615,0.000060777817,0.000055074775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014587957,0.000059070193,0.00006349775,0.00014510864,0.000052478408,0.00020160314,0.000378501,0.000041714793,0.00018848757],"category_scores_gemma":[0.00004880264,0.000052763327,0.000038654023,0.00051883794,0.000010026027,0.00035188458,0.000035969293,0.000067152396,0.00033382868],"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":[0.00000386653,0.00022706632,0.00015832162,0.0000012739783,0.000006027138,0.000007448498,0.00040363875,0.0000030420272,0.053342342,0.026946016,0.031041369,0.8878596],"study_design_scores_gemma":[0.00068041054,0.00007780171,0.50091934,0.000010136246,0.00001012681,0.0000134686425,0.00009079291,0.077685215,0.2512301,0.045852937,0.12289022,0.00053945323],"about_ca_topic_score_codex":0.000138697,"about_ca_topic_score_gemma":0.000005831385,"teacher_disagreement_score":0.89863837,"about_ca_system_score_codex":0.000022807599,"about_ca_system_score_gemma":0.000018519257,"threshold_uncertainty_score":0.42908037},"labels":[],"label_agreement":null},{"id":"W2157448368","doi":"10.1109/icsmc.2008.4811504","title":"A small scale fingerprint matching scheme using Digital Curvelet Transform","year":2008,"lang":"en","type":"article","venue":"Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Curvelet; Artificial intelligence; Pattern recognition (psychology); Computer science; Fingerprint (computing); Wavelet transform; Classifier (UML); Matching (statistics); Wavelet; Feature extraction; Multiresolution analysis; k-nearest neighbors algorithm; Computer vision; Discrete wavelet transform; Mathematics","score_opus":0.11951173194598863,"score_gpt":0.2847817978431467,"score_spread":0.1652700658971581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157448368","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84638953,0.00014210973,0.105727136,0.0016019809,0.0015878071,0.0011169683,0.00010107488,0.00061090075,0.04272249],"genre_scores_gemma":[0.9877949,0.00085429905,0.007982442,0.00022471668,0.0003428538,0.00013864599,0.000029541909,0.000060728453,0.0025719078],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939012,0.000024984694,0.0014625929,0.0018979615,0.0016428767,0.0010703935],"domain_scores_gemma":[0.9940198,0.000083262865,0.000938126,0.00038179997,0.0039427574,0.0006342433],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007794553,0.00095219834,0.0009269269,0.0010868849,0.00072108087,0.0052762255,0.0029275736,0.0005086766,0.000101816855],"category_scores_gemma":[0.00017330136,0.00095627375,0.00021042679,0.0010363435,0.00059011544,0.0027439636,0.0005047233,0.0010007159,0.00012564403],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015820636,0.0006970557,0.018262006,0.0007194775,0.00026327075,0.00003846783,0.021110982,0.000008508598,0.023583554,0.922464,0.00088391476,0.011810534],"study_design_scores_gemma":[0.0037142916,0.00091365736,0.00789161,0.003138731,0.00012292506,0.0022619476,0.014045212,0.89615005,0.007810594,0.04263132,0.016572103,0.004747541],"about_ca_topic_score_codex":0.0002465281,"about_ca_topic_score_gemma":0.000019048475,"teacher_disagreement_score":0.8961415,"about_ca_system_score_codex":0.00031283862,"about_ca_system_score_gemma":0.00060862716,"threshold_uncertainty_score":0.9992888},"labels":[],"label_agreement":null},{"id":"W2158635403","doi":"10.1109/itng.2008.254","title":"FES: A System for Combining Face, Ear and Signature Biometrics Using Rank Level Fusion","year":2008,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Linear discriminant analysis; Computer science; Principal component analysis; Pattern recognition (psychology); Rank (graph theory); Artificial intelligence; Signature (topology); Face (sociological concept); Identity (music); Identification (biology); Modalities; Logistic regression; Sensor fusion; Data mining; Machine learning; Speech recognition; Mathematics","score_opus":0.11467076481137646,"score_gpt":0.2842814238254874,"score_spread":0.16961065901411093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158635403","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":[0.088537805,0.00047252953,0.9097533,0.00013791859,0.00045198543,0.00026301717,0.000011540527,0.00015324449,0.00021863458],"genre_scores_gemma":[0.8984808,0.000030580628,0.10087529,0.00009869553,0.000022642133,0.000004649768,0.000004795256,0.00000593583,0.00047666306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894416,0.00003777895,0.00021639162,0.0003341594,0.0002719518,0.00019558347],"domain_scores_gemma":[0.99921066,0.00015698402,0.000093846604,0.00027423026,0.00017021026,0.000094078045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004075068,0.0001045054,0.00016132917,0.001000783,0.00041113875,0.00015807684,0.00035304826,0.00012277122,0.0000027652652],"category_scores_gemma":[0.000092473165,0.000090371854,0.00004990141,0.003771926,0.00004359101,0.00028156603,0.00015359063,0.000085659696,0.0000069567177],"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":[0.000119109776,0.0011280858,0.009313945,0.0021407108,0.00028545628,0.00012588165,0.020882022,0.00012297502,0.13042329,0.41381592,0.030211953,0.39143065],"study_design_scores_gemma":[0.0030273022,0.00016208961,0.0127104465,0.00008860729,0.000024075305,0.00032530315,0.0009959033,0.94498384,0.019062584,0.0002882941,0.017615905,0.00071563607],"about_ca_topic_score_codex":0.000053405834,"about_ca_topic_score_gemma":0.0000014320295,"teacher_disagreement_score":0.9448609,"about_ca_system_score_codex":0.00005270105,"about_ca_system_score_gemma":0.000051499548,"threshold_uncertainty_score":0.3685257},"labels":[],"label_agreement":null},{"id":"W2160359476","doi":"10.1109/cib.2009.4925691","title":"Enhancing security through a hybrid multibiometric system","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Machine learning; Rank (graph theory); Authentication (law); Data mining; Majority rule; Sensor fusion; Face (sociological concept); Pattern recognition (psychology); Computer security","score_opus":0.016453243872842284,"score_gpt":0.25613766659889065,"score_spread":0.23968442272604837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160359476","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":[0.014297913,0.00031090493,0.96881765,0.00065934134,0.0005336018,0.00016375059,0.000002517551,0.0007349784,0.014479354],"genre_scores_gemma":[0.9430439,0.000011803872,0.05594273,0.00058955624,0.000049798913,0.0000025818777,0.000003219839,0.0000031171644,0.00035327842],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984631,0.00006711001,0.00032541688,0.0004558415,0.0003943719,0.00029414956],"domain_scores_gemma":[0.99890107,0.000070775146,0.0000990319,0.0006869954,0.00013917469,0.00010294505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048197556,0.00012852739,0.00017652943,0.00066407124,0.00016020787,0.00030671357,0.00085387053,0.000051802715,0.00003374993],"category_scores_gemma":[0.00009094461,0.00011344257,0.00008826714,0.0048904996,0.000018724906,0.0006995671,0.00009950008,0.00011462428,0.00037159285],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032398148,0.00041959895,0.00013665728,0.00008486457,0.000026117732,0.000056831497,0.0018512406,0.0000022218223,0.004985676,0.85590446,0.0077366373,0.12879246],"study_design_scores_gemma":[0.0024888907,0.00045452648,0.011080526,0.00012025713,0.00003870683,0.0004147065,0.0011763104,0.22366421,0.5721128,0.021201689,0.1652335,0.0020138575],"about_ca_topic_score_codex":0.00016478557,"about_ca_topic_score_gemma":0.000005533614,"teacher_disagreement_score":0.928746,"about_ca_system_score_codex":0.00011644337,"about_ca_system_score_gemma":0.00003926453,"threshold_uncertainty_score":0.47761983},"labels":[],"label_agreement":null},{"id":"W2160763845","doi":"10.1109/icitst.2009.5402631","title":"Secure biometric system for accessing home appliances via Internet","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; The Internet; Authentication (law); Iris recognition; Computer security; Computer network; Biometrics; Operating system","score_opus":0.028485951939391827,"score_gpt":0.2791674819050135,"score_spread":0.25068152996562165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160763845","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":[0.0035372349,0.0003585028,0.9918943,0.00069222983,0.00070676435,0.00022627752,0.0000028217164,0.0003569459,0.002224939],"genre_scores_gemma":[0.94431865,0.000004721157,0.054392863,0.00028160226,0.00007214562,0.000013472977,0.0000059508643,0.000003397355,0.00090721686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889165,0.000022489568,0.00025108515,0.0003738461,0.00023422683,0.00022670071],"domain_scores_gemma":[0.99926645,0.00006260784,0.000111247515,0.00035381745,0.00011891995,0.00008696033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035312996,0.00010667311,0.00015137209,0.0009857139,0.00009626779,0.00059928396,0.0010494278,0.00007383784,0.000014132571],"category_scores_gemma":[0.000020052314,0.000088017594,0.00007366751,0.004503104,0.000018489412,0.00052597444,0.000071428294,0.000057705853,0.00007968664],"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":[0.0000064764763,0.00012734639,0.00023351457,0.00011951749,0.000017988594,0.0000031843763,0.0003611261,0.0000014730186,0.0016196022,0.19695614,0.010422404,0.7901312],"study_design_scores_gemma":[0.0020111182,0.00045637603,0.022434695,0.00011530388,0.00003260967,0.00009670381,0.00033559525,0.74416304,0.04968076,0.013006524,0.16629758,0.0013696994],"about_ca_topic_score_codex":0.000018332083,"about_ca_topic_score_gemma":0.0000016854447,"teacher_disagreement_score":0.9407814,"about_ca_system_score_codex":0.000067996705,"about_ca_system_score_gemma":0.000022798728,"threshold_uncertainty_score":0.5778909},"labels":[],"label_agreement":null},{"id":"W2161421314","doi":"10.1111/j.1467-8659.2006.00955.x","title":"A Predictive Light Transport Model for the Human Iris","year":2006,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Rendering (computer graphics); Computer graphics; Predictability; Artificial intelligence; Graphics; Procedural modeling; Computer vision; Computer graphics (images)","score_opus":0.020559448173719297,"score_gpt":0.24411933764943292,"score_spread":0.22355988947571362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161421314","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":[0.0019250547,0.00021528063,0.9919573,0.004535404,0.0005271446,0.00043765933,0.000027686667,0.0001736268,0.00020082344],"genre_scores_gemma":[0.97649807,0.000012939247,0.021833321,0.00096552435,0.0001611435,0.00008233573,0.000027036132,0.000012555326,0.00040706375],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861974,0.000022124677,0.0003004669,0.0004215253,0.0002973276,0.00033881515],"domain_scores_gemma":[0.99886566,0.0000892648,0.000102933256,0.0006885123,0.00019340425,0.000060209688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003416604,0.00015329095,0.00014577099,0.00031495545,0.0005718128,0.00017085731,0.0012561383,0.00009848144,0.0000015060602],"category_scores_gemma":[0.0000028322318,0.000115901996,0.00022574882,0.0010631158,0.00008848056,0.00021026368,0.00011336478,0.0001520402,0.000004448768],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032975827,0.00013256431,0.001216014,0.0000124589205,0.00003185136,0.0000013544953,0.0003885108,0.00066278817,0.0000768415,0.95606476,0.039041717,0.002367843],"study_design_scores_gemma":[0.00029765253,0.0000435424,0.007673196,0.0000048506895,0.000014332201,0.0000032847056,0.000004607696,0.88820094,0.00012628248,0.06319454,0.040294718,0.0001420522],"about_ca_topic_score_codex":0.000049104743,"about_ca_topic_score_gemma":0.0000691963,"teacher_disagreement_score":0.974573,"about_ca_system_score_codex":0.000020516534,"about_ca_system_score_gemma":0.0000421387,"threshold_uncertainty_score":0.4726346},"labels":[],"label_agreement":null},{"id":"W2164021584","doi":"10.1109/cjece.2015.2416200","title":"BioHashing for Human Acoustic Signature Based on Random Projection","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Computer science; Random projection; Biometrics; Spoofing attack; Pattern recognition (psychology); Speech recognition; Artificial intelligence; Modalities; Robustness (evolution); Binary number; Mathematics","score_opus":0.01804421270967992,"score_gpt":0.21688103828971497,"score_spread":0.19883682558003504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164021584","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":[0.0073977835,0.00025782053,0.9913794,0.0003262965,0.0005142536,0.00008979801,0.0000010354526,0.00001806155,0.000015564086],"genre_scores_gemma":[0.9839107,0.0000010018254,0.015623692,0.00019782303,0.0002429389,0.0000022103404,0.0000012256183,0.000005327375,0.000015118732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936384,0.000019152392,0.00018085966,0.00011834864,0.00012719401,0.00019063076],"domain_scores_gemma":[0.9991473,0.00011795649,0.00006317569,0.00008324937,0.0001722398,0.00041608608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034436246,0.000081117854,0.00013234087,0.00071017304,0.00008143096,0.00021191989,0.00023668828,0.00006175466,6.805426e-7],"category_scores_gemma":[0.0001267876,0.00007086916,0.000053176635,0.00061692804,0.0000082168035,0.00014028547,0.000006671096,0.00019815264,4.0748654e-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":[0.00020092091,0.00035543033,0.0017234971,0.0002999284,0.00028648903,0.00046623984,0.002755792,0.37635133,0.005992227,0.11745945,0.046128586,0.4479801],"study_design_scores_gemma":[0.00089004426,0.00034996172,0.0005893859,0.000022671362,0.0000069216526,0.000048511884,0.000001402223,0.9899447,0.00017553415,0.0002624105,0.0076101758,0.000098295415],"about_ca_topic_score_codex":0.000069867485,"about_ca_topic_score_gemma":0.00004528539,"teacher_disagreement_score":0.97651285,"about_ca_system_score_codex":0.00010687683,"about_ca_system_score_gemma":0.0002774191,"threshold_uncertainty_score":0.28899604},"labels":[],"label_agreement":null},{"id":"W2165249306","doi":"10.1109/mwscas.2013.6674895","title":"Enhancement of low-quality fingerprint images by a three-stage filtering scheme","year":2013,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Fingerprint (computing); Artificial intelligence; Computer vision; Ridge; Computer science; Filter (signal processing); Fingerprint recognition; Orientation (vector space); Compensation (psychology); Pattern recognition (psychology); Image quality; Stage (stratigraphy); Image (mathematics); Mathematics; Geology","score_opus":0.02678706904824827,"score_gpt":0.2814084847744591,"score_spread":0.25462141572621083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165249306","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":[0.1642916,0.00007571762,0.8320394,0.0006438153,0.000112527196,0.00014902411,0.0000042865217,0.000065425746,0.002618176],"genre_scores_gemma":[0.92016,0.000016343809,0.076524094,0.00020077243,0.000008028481,0.000026392843,0.0000035158537,0.0000030494352,0.0030578065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998917,0.00003081949,0.00032786338,0.00027534057,0.0002766737,0.00017233507],"domain_scores_gemma":[0.9990676,0.0000483532,0.0001255992,0.00055986666,0.00013066782,0.000067910085],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00037048748,0.000083917395,0.00012824232,0.000107760425,0.0000465953,0.00014769223,0.0006050165,0.000034991866,0.0011838151],"category_scores_gemma":[0.000051411036,0.000073349365,0.00005118875,0.0004740218,0.000044426408,0.00036314235,0.0003071279,0.00006257421,0.00021759307],"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":[0.0000015436572,0.00029474907,0.0009927488,0.0001202903,0.000020499081,4.5091096e-7,0.00030007225,7.1870534e-7,0.9148617,0.026818957,0.011786069,0.044802245],"study_design_scores_gemma":[0.00020404335,0.000025576643,0.012686556,0.000013421451,8.949115e-7,3.4116124e-7,0.000031493364,0.0136310095,0.9679461,0.0008546915,0.0044202935,0.00018556048],"about_ca_topic_score_codex":0.0006297175,"about_ca_topic_score_gemma":0.000007949142,"teacher_disagreement_score":0.7558684,"about_ca_system_score_codex":0.000026930595,"about_ca_system_score_gemma":0.000021084177,"threshold_uncertainty_score":0.9997292},"labels":[],"label_agreement":null},{"id":"W2167839456","doi":"10.1109/cib.2009.4925682","title":"Secure and simplified access to home appliances using Iris recognition","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Home automation; Access control; Authentication (law); Biometrics; Computer network; Computer security; Multi-factor authentication; Authentication protocol; Operating system","score_opus":0.09589123111397532,"score_gpt":0.33386842799156496,"score_spread":0.23797719687758964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167839456","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":[0.16291563,0.000064067266,0.83235806,0.0028849624,0.0001486744,0.00015721098,0.0000029289695,0.00011695269,0.0013515035],"genre_scores_gemma":[0.9491344,0.000020195263,0.047991585,0.002705014,0.00003920794,0.0000028059703,0.0000031961438,0.0000018621021,0.000101702506],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993192,0.000018132063,0.00012114841,0.00027108873,0.00014139857,0.00012901005],"domain_scores_gemma":[0.99958444,0.00002044883,0.00003724452,0.0001907947,0.00007158569,0.000095517375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014953563,0.00006495691,0.0000781549,0.0002341203,0.00009932799,0.0005632534,0.00036726432,0.000042018783,0.000029529187],"category_scores_gemma":[0.000017502662,0.000058763475,0.000016281565,0.0012512555,0.000011591137,0.00062663574,0.000084826184,0.000046466925,0.000041413026],"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":[0.0000067008486,0.00007928954,0.0004998182,0.000014999019,0.000006264118,0.0000032309595,0.00059453107,0.000009191676,0.0043005827,0.0077037243,0.0076698507,0.9791118],"study_design_scores_gemma":[0.0022005683,0.00048645952,0.42341933,0.00011728778,0.000045760436,0.00013639918,0.00044121718,0.214922,0.04807952,0.17587501,0.13164167,0.0026347747],"about_ca_topic_score_codex":0.000030062543,"about_ca_topic_score_gemma":0.0000059629424,"teacher_disagreement_score":0.976477,"about_ca_system_score_codex":0.00001621801,"about_ca_system_score_gemma":0.000014754399,"threshold_uncertainty_score":0.54314655},"labels":[],"label_agreement":null},{"id":"W2168474660","doi":"10.1109/icsmc.1990.142122","title":"A combined statistical and structural approach for fingerprint image postprocessing","year":2002,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Minutiae; Fingerprint (computing); Artificial intelligence; Computer science; Pattern recognition (psychology); Fingerprint recognition; Ridge; Image (mathematics); Window (computing); Computer vision; Data mining; Geography; Cartography","score_opus":0.028126982523848647,"score_gpt":0.2628072350893005,"score_spread":0.23468025256545183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168474660","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":[0.0049645086,0.000050107286,0.99244493,0.00055588305,0.000055353346,0.00012410797,0.000005654348,0.00007790981,0.0017215323],"genre_scores_gemma":[0.54400176,0.0000016470723,0.4556436,0.00011503671,0.0000071497643,0.0000052634246,0.0000040637115,0.0000018055733,0.00021967564],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938667,0.000014864107,0.000120354816,0.00024134216,0.000101984966,0.00013479385],"domain_scores_gemma":[0.99960864,0.00007246565,0.000029844383,0.00015603038,0.00006528939,0.000067730885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011531058,0.000060628445,0.000078484016,0.00009188484,0.00012346823,0.00034620144,0.00020936868,0.000027698423,0.00004343801],"category_scores_gemma":[0.000077974815,0.000050040417,0.000015430574,0.0002772522,0.000052629443,0.00021658883,0.0000789884,0.000045944154,0.0000051261572],"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":[0.000011785289,0.0001443985,0.00048353674,0.00016149336,0.000018025463,0.0000027486276,0.001678477,0.0000017632797,0.0013987632,0.6954853,0.005939936,0.2946738],"study_design_scores_gemma":[0.00031564347,0.00003363307,0.004468754,0.0000010949017,0.0000023875234,0.000007907558,0.000024495854,0.9899709,0.00029468222,0.0044992156,0.00028708467,0.000094201256],"about_ca_topic_score_codex":0.000008703528,"about_ca_topic_score_gemma":3.6830977e-7,"teacher_disagreement_score":0.98996913,"about_ca_system_score_codex":0.000010690567,"about_ca_system_score_gemma":0.000006592019,"threshold_uncertainty_score":0.33384284},"labels":[],"label_agreement":null},{"id":"W2170519129","doi":"10.1109/icassp.2009.4959726","title":"Face recognition with enhanced privacy protection","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Facial recognition system; Computer science; Biometrics; Face (sociological concept); Artificial intelligence; Pattern recognition (psychology); Similarity (geometry); Identification (biology); Set (abstract data type); Transformation (genetics); Index (typography); Three-dimensional face recognition; Similarity measure; Data mining; Computer vision; Face detection; Image (mathematics)","score_opus":0.035096529359084014,"score_gpt":0.24526867741563857,"score_spread":0.21017214805655454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170519129","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":[0.022960255,0.0000069124762,0.96839523,0.0020513094,0.00006179175,0.00019807245,2.4036817e-7,0.00023030084,0.006095895],"genre_scores_gemma":[0.9594317,0.0000037190755,0.0393475,0.0003519223,0.000014435929,0.000009341409,0.0000025724244,0.0000012221107,0.0008376081],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999459,0.000020966316,0.0000831948,0.0001967861,0.00014927669,0.00009073687],"domain_scores_gemma":[0.99960244,0.000005625149,0.000041619718,0.00022908101,0.000085508385,0.000035755562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010381375,0.000048591617,0.000043868135,0.00013691236,0.000065879714,0.00012413776,0.00020618291,0.000029634191,0.00003002657],"category_scores_gemma":[0.000021251928,0.000037379254,0.000013980325,0.0009831548,0.000008306429,0.00041103867,0.00001405571,0.00005546967,0.00017203679],"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":[0.000006363161,0.000083424944,0.0000025063148,0.0000026732212,0.0000024846076,5.8008584e-7,0.00037463452,0.0000018250552,0.010496588,0.004361862,0.00029645103,0.9843706],"study_design_scores_gemma":[0.0012900786,0.00091629406,0.031177258,0.000032838558,0.000007718825,0.000044323562,0.00009694548,0.03237037,0.8845334,0.025413966,0.023486776,0.00063004944],"about_ca_topic_score_codex":0.000011237333,"about_ca_topic_score_gemma":0.0000024989881,"teacher_disagreement_score":0.98374057,"about_ca_system_score_codex":0.000020063657,"about_ca_system_score_gemma":0.000018237944,"threshold_uncertainty_score":0.22112423},"labels":[],"label_agreement":null},{"id":"W2171279519","doi":"10.1109/icc.2009.59","title":"Using Duality and Hopfield Neural Network for Delaunay Triangulation Based Fingerprint Matching","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Delaunay triangulation; Minutiae; Fingerprint (computing); Artificial neural network; Constrained Delaunay triangulation; Artificial intelligence; Computer science; Matching (statistics); Triangulation; Subspace topology; Set (abstract data type); Pattern recognition (psychology); Mathematics; Algorithm; Computer vision; Fingerprint recognition; Geometry","score_opus":0.07616531440848781,"score_gpt":0.324157308138618,"score_spread":0.24799199373013017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171279519","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":[0.1002639,0.00004602413,0.897367,0.0017633353,0.00020099517,0.00015762636,7.810583e-7,0.000078194724,0.0001221263],"genre_scores_gemma":[0.74435526,6.7449474e-7,0.25436884,0.0011814401,0.000050909966,0.0000015357782,0.0000026757382,0.000001452818,0.000037202946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993025,0.000046155874,0.0001780804,0.00021717465,0.00010952921,0.00014656465],"domain_scores_gemma":[0.999488,0.00013793686,0.00006594731,0.00020637861,0.00005046018,0.000051260628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056085316,0.00006357395,0.00008746127,0.00009214598,0.0001630744,0.00021957544,0.00015142326,0.000049076112,0.000007726045],"category_scores_gemma":[0.000049219412,0.000058562848,0.0000413671,0.00042541817,0.000009128955,0.00022912357,0.0000280922,0.000050023107,7.4930415e-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":[0.000058314006,0.00016901348,0.0026226009,0.00004578768,0.000016834327,0.0000031497757,0.00092889636,0.015962908,0.0041670334,0.34259546,0.0021153162,0.6313147],"study_design_scores_gemma":[0.0002755384,0.000022114078,0.023442406,0.000003973729,0.0000036282088,0.0000021214287,0.000006131337,0.9594171,0.0004589802,0.015803438,0.00047499532,0.00008956327],"about_ca_topic_score_codex":0.000053923886,"about_ca_topic_score_gemma":0.00000965535,"teacher_disagreement_score":0.9434542,"about_ca_system_score_codex":0.000017585831,"about_ca_system_score_gemma":0.000019986192,"threshold_uncertainty_score":0.23881236},"labels":[],"label_agreement":null},{"id":"W2177425930","doi":"10.1007/978-3-319-23192-1_18","title":"Confidence Based Rank Level Fusion for Multimodal Biometric Systems","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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 Calgary","funders":"","keywords":"Biometrics; Computer science; Rank (graph theory); Identification (biology); Artificial intelligence; Fingerprint (computing); Variance (accounting); Word error rate; Selection (genetic algorithm); Pattern recognition (psychology); Mathematics","score_opus":0.08379366309774533,"score_gpt":0.2964665696396887,"score_spread":0.21267290654194337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2177425930","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":[0.000014147078,0.0017128946,0.99025226,0.0006237365,0.005464143,0.0011460031,0.00007728654,0.00019896669,0.0005105873],"genre_scores_gemma":[0.32993022,0.00004694521,0.66647387,0.0010325747,0.00070325815,0.00007343197,0.00006581279,0.000060548933,0.0016133696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99469674,0.00006480383,0.0007906611,0.0018964993,0.0018256797,0.00072563067],"domain_scores_gemma":[0.99485457,0.0011310785,0.00051660184,0.0017444583,0.0014065481,0.00034676102],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00307534,0.0005500696,0.0006753382,0.0051014135,0.00034194684,0.0011693313,0.0044142404,0.0005264232,0.000016439899],"category_scores_gemma":[0.00064700324,0.0005031936,0.0001928873,0.004174532,0.0006070278,0.0006009022,0.0007698034,0.00053060043,0.00007243675],"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":[0.00003384392,0.00015828239,0.000034458437,0.00032889182,0.000024734834,0.000047915626,0.00053916225,0.020932622,0.0005683992,0.122991495,0.0010481114,0.8532921],"study_design_scores_gemma":[0.00078798423,0.00016509446,0.00008143943,0.00021166442,0.000009718107,0.000026712209,2.279772e-7,0.95591223,0.0005577374,0.026316997,0.015263169,0.0006670359],"about_ca_topic_score_codex":0.00013815248,"about_ca_topic_score_gemma":0.000028191831,"teacher_disagreement_score":0.9349796,"about_ca_system_score_codex":0.0005999331,"about_ca_system_score_gemma":0.0013920637,"threshold_uncertainty_score":0.99986756},"labels":[],"label_agreement":null},{"id":"W2179756850","doi":"10.4018/ijssci.2015010101","title":"Feature and Rank Level Fusion for Privacy Preserved Multi-Biometric System","year":2015,"lang":"en","type":"article","venue":"International Journal of Software Science and Computational Intelligence","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Biometrics; Random projection; Feature (linguistics); Authentication (law); Artificial intelligence; Face (sociological concept); Projection (relational algebra); Pattern recognition (psychology); Facial recognition system; Template; Data mining; Computer security; Algorithm","score_opus":0.12280318711744213,"score_gpt":0.3503421812595449,"score_spread":0.22753899414210277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179756850","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":[0.02049442,0.000798821,0.97487634,0.0022685442,0.0013350325,0.00015692993,0.000022198823,0.000033280234,0.000014437489],"genre_scores_gemma":[0.664355,0.000043205302,0.33523875,0.00017945596,0.000081929225,0.000004091783,0.000003837337,0.0000039001407,0.000089852896],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975015,0.00004023305,0.00041100234,0.00029709845,0.0015693121,0.000180846],"domain_scores_gemma":[0.99253744,0.0003903407,0.00040658956,0.00014157797,0.006244141,0.0002799154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022145754,0.00012457928,0.00017092079,0.0014463868,0.00019468044,0.0006969751,0.0016595196,0.000058199348,0.0000014303165],"category_scores_gemma":[0.0023366425,0.00010476553,0.000057280227,0.0017944896,0.00029791414,0.0014972131,0.00039908776,0.00013897326,0.0000041164194],"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":[0.00025000828,0.00055029587,0.009339373,0.00014497351,0.00017811834,0.000064217,0.005401383,0.006904576,0.0007733342,0.15942976,0.0053330343,0.81163096],"study_design_scores_gemma":[0.0023118681,0.0005951029,0.055290103,0.0003077892,0.000031580315,0.0018494641,0.0015972371,0.88933486,0.0032832862,0.028592976,0.016171196,0.00063454354],"about_ca_topic_score_codex":0.000015466587,"about_ca_topic_score_gemma":0.0000012157155,"teacher_disagreement_score":0.88243026,"about_ca_system_score_codex":0.00020773115,"about_ca_system_score_gemma":0.00065666315,"threshold_uncertainty_score":0.6720947},"labels":[],"label_agreement":null},{"id":"W2233638795","doi":"10.1007/978-3-642-20505-7_22","title":"Strict Authentication of Multimodal Biometric Images Using Near Sets","year":2011,"lang":"en","type":"book-chapter","venue":"Advances in intelligent and soft computing","topic":"Biometric Identification and Security","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":"University of Manitoba","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Encryption; Authentication (law); Feature (linguistics); Computer vision; Watermark; Pattern recognition (psychology); Digital watermarking; Embedding; Fingerprint (computing); Scheme (mathematics); Image (mathematics); Mathematics; Computer security","score_opus":0.042240744815563495,"score_gpt":0.30137956819068357,"score_spread":0.25913882337512006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2233638795","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":[0.0015937127,0.0196907,0.9712822,0.000018809964,0.00082316686,0.00026511634,0.000011409093,0.00007093756,0.006243982],"genre_scores_gemma":[0.7405275,0.008149359,0.2489177,0.000050271246,0.00011509043,0.0000026301923,0.000036621404,0.000050356844,0.0021504262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980959,0.00004525895,0.0007266928,0.0005610465,0.00033030566,0.00024078066],"domain_scores_gemma":[0.9983003,0.00038376407,0.0006031734,0.00041846416,0.00021926482,0.00007504398],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061622425,0.00025915404,0.00039058228,0.0015222753,0.00012824043,0.000118742864,0.0006397501,0.0001887673,0.000026501897],"category_scores_gemma":[0.00014607693,0.00026610726,0.00010150218,0.00083464856,0.00021027464,0.00038557907,0.00038782554,0.00026928936,0.000013653431],"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":[0.0000067985293,0.000059275153,0.0004911059,0.00012758003,0.000025235246,0.000005271894,0.0012405422,0.000092712005,0.00003633421,0.04832041,0.000011794783,0.94958293],"study_design_scores_gemma":[0.00058596884,0.00016703003,0.0024112263,0.0010157138,0.000067954556,0.00004251202,0.00007379155,0.83726966,0.002360181,0.10144206,0.053037185,0.001526725],"about_ca_topic_score_codex":0.00007452767,"about_ca_topic_score_gemma":0.00000389005,"teacher_disagreement_score":0.9480562,"about_ca_system_score_codex":0.00007489362,"about_ca_system_score_gemma":0.00006130784,"threshold_uncertainty_score":0.99997914},"labels":[],"label_agreement":null},{"id":"W2277867702","doi":"10.1007/978-1-4471-5230-9_9","title":"Biometric Encryption: Creating a Privacy-Preserving ‘Watch-List’ Facial Recognition System","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Biometric Identification and Security","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; Privacy Analytics (Canada)","funders":"","keywords":"Biometrics; Encryption; Computer science; Computer security; Internet privacy","score_opus":0.05434956454480095,"score_gpt":0.24212731943289537,"score_spread":0.18777775488809442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2277867702","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013814129,0.00036880423,0.19188084,0.00043372816,0.001431971,0.0006654652,0.000041746785,0.0008791614,0.8041602],"genre_scores_gemma":[0.031495646,0.0002306824,0.064916365,0.00023909764,0.0008899494,0.000095584204,0.0003554832,0.0000765755,0.9017006],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99670523,0.00006748817,0.00086460664,0.0010149867,0.00095913676,0.00038852612],"domain_scores_gemma":[0.9968837,0.0002059906,0.0006360485,0.0013678208,0.000673455,0.0002329955],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00065510155,0.00043524688,0.0004900818,0.002318389,0.0003665758,0.0011736893,0.002049674,0.00059887755,0.002926259],"category_scores_gemma":[0.00024075725,0.00041786028,0.00026790725,0.0015555912,0.00006393741,0.00089261204,0.0010422019,0.00042465978,0.00540911],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004918402,0.0000643413,0.000019764231,0.0006501407,0.00014107178,0.00002929029,0.0006159363,4.0794754e-7,0.00022391397,0.4560826,0.036234144,0.50593346],"study_design_scores_gemma":[0.0008279639,0.00014611083,0.00029820352,0.0008786306,0.00011264029,0.00014848901,0.00012361663,0.023369769,0.0008450691,0.032073237,0.93901414,0.0021621527],"about_ca_topic_score_codex":0.0003720633,"about_ca_topic_score_gemma":0.0000100695715,"teacher_disagreement_score":0.90278,"about_ca_system_score_codex":0.00038011407,"about_ca_system_score_gemma":0.00011753473,"threshold_uncertainty_score":0.9998632},"labels":[],"label_agreement":null},{"id":"W2287063714","doi":"10.1109/cw.2015.30","title":"A Novel Index-Based Rank Fusion Method for Occluded Ear Recognition","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Headphones; Biometrics; Computer science; Pattern recognition (psychology); Feature extraction; Occlusion; Identification (biology); Fusion; Computer vision; Feature (linguistics); Speech recognition; Engineering; Medicine","score_opus":0.12889208061751883,"score_gpt":0.33816958045987683,"score_spread":0.209277499842358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2287063714","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":[0.0016715955,0.000014268084,0.99439967,0.0017793153,0.00043998045,0.00028751325,0.000009483527,0.00017206153,0.0012261264],"genre_scores_gemma":[0.10539207,9.022098e-7,0.8925268,0.0013928994,0.000044562054,0.000036057663,0.00003385677,0.0000052436985,0.00056762283],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990493,0.000058657843,0.00018529718,0.0002921764,0.0002652029,0.00014935227],"domain_scores_gemma":[0.99895346,0.00015092568,0.000073839015,0.00031273594,0.0003821805,0.00012685584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001252137,0.00007631195,0.00010146349,0.00033356197,0.0000729494,0.00014593243,0.0003652939,0.000081555045,0.000023242217],"category_scores_gemma":[0.00028977086,0.00006707951,0.00006186116,0.0010420331,0.000013530803,0.0002411872,0.00005882243,0.00005712647,0.00007504823],"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":[0.0001449493,0.00084859005,0.0004183892,0.0000564419,0.00002701471,0.0000017547684,0.000981366,0.0000443634,0.018876553,0.026351862,0.03993399,0.9123147],"study_design_scores_gemma":[0.0034379556,0.000117535914,0.0019612252,0.0000086797745,0.000008737533,0.000007148293,0.000077079465,0.9209195,0.024278004,0.007914371,0.041013286,0.0002565162],"about_ca_topic_score_codex":0.00015401965,"about_ca_topic_score_gemma":0.000027703618,"teacher_disagreement_score":0.9208751,"about_ca_system_score_codex":0.000042279262,"about_ca_system_score_gemma":0.00012344943,"threshold_uncertainty_score":0.2735423},"labels":[],"label_agreement":null},{"id":"W2289706180","doi":"10.1109/icitst.2015.7412065","title":"Fingerprint security for protecting EMV payment cards","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Computer security; Payment; Identity theft; Computer science; Fingerprint (computing); Biometrics; Authentication (law); Eavesdropping; Counterfeit; Internet privacy; Credit card; Payment card; Issuing bank; Countermeasure; Phishing; The Internet; World Wide Web; Engineering","score_opus":0.06382096003522034,"score_gpt":0.30010665679778836,"score_spread":0.23628569676256803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2289706180","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":[0.01051414,0.000069398004,0.9820538,0.0021340502,0.00059348735,0.00048718543,0.0000023580358,0.0002193459,0.003926209],"genre_scores_gemma":[0.9412026,0.0000011947808,0.05770158,0.00024990676,0.000060328093,0.00009292924,0.000001980923,0.0000036191072,0.00068590126],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999087,0.000046201676,0.0001653617,0.00027320453,0.00024932975,0.00017890683],"domain_scores_gemma":[0.9991719,0.000049837137,0.000056303532,0.0003734795,0.00022136889,0.00012712479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012092815,0.0000693668,0.000088191424,0.00012413917,0.00010846984,0.00019181852,0.00039340637,0.00004378022,0.000008747919],"category_scores_gemma":[0.00029831126,0.000059273247,0.000053261432,0.00052389305,0.000014074886,0.00018319898,0.0001739236,0.00008407775,0.000039333925],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024359511,0.000550149,0.0010623244,0.00013533141,0.00006538887,0.0000056563654,0.019192424,0.000034478475,0.0008268123,0.60337293,0.101468824,0.2732613],"study_design_scores_gemma":[0.001587432,0.00025712056,0.0017372848,0.00001627037,0.000010116389,0.00001945022,0.0008609185,0.26500368,0.037120055,0.07863083,0.6141201,0.0006367406],"about_ca_topic_score_codex":0.000119666656,"about_ca_topic_score_gemma":0.0000133635385,"teacher_disagreement_score":0.93068844,"about_ca_system_score_codex":0.00008145207,"about_ca_system_score_gemma":0.00009555225,"threshold_uncertainty_score":0.24170929},"labels":[],"label_agreement":null},{"id":"W2294925427","doi":"10.1109/est.2015.11","title":"Depth Assisted Palm Region Extraction Using the Kinect v2 Sensor","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"USable; Computer science; Computer vision; RGB color model; Palm; Artificial intelligence; Multispectral image; Feature extraction; Sensor fusion; Window (computing); Sliding window protocol","score_opus":0.20771879328845205,"score_gpt":0.3446196723106979,"score_spread":0.13690087902224585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294925427","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":[0.02484831,0.00005775988,0.9673653,0.0021091143,0.0007006149,0.00009341319,1.7368296e-7,0.00012593927,0.0046993587],"genre_scores_gemma":[0.9749807,0.0000052744003,0.023059702,0.00034375172,0.000068886424,0.0000020301252,0.0000014598024,0.0000034565612,0.0015347076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991698,0.000111949295,0.00013690538,0.00019894558,0.00026293725,0.000119463],"domain_scores_gemma":[0.9991873,0.00005758855,0.0000819902,0.00044092594,0.00015991702,0.00007228198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041588594,0.00006150059,0.00005955666,0.00014586213,0.00011611283,0.00022947972,0.0003347612,0.000048345544,0.000007317296],"category_scores_gemma":[0.000111259404,0.000040061946,0.000037545553,0.0011783141,0.000025806588,0.00031031485,0.000062173014,0.00008417595,0.000054033873],"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":[0.000057056906,0.0008878244,0.019805696,0.000041517604,0.00011802788,0.00013043238,0.0053341473,0.00034597315,0.035483945,0.15341434,0.15915179,0.62522924],"study_design_scores_gemma":[0.0007794706,0.00006205084,0.12901308,0.000012045138,0.000023633014,0.0007358018,0.00061561196,0.66452557,0.01112167,0.0019322244,0.19075534,0.0004235088],"about_ca_topic_score_codex":0.00026362858,"about_ca_topic_score_gemma":0.000041570183,"teacher_disagreement_score":0.9501324,"about_ca_system_score_codex":0.000074548894,"about_ca_system_score_gemma":0.000060206243,"threshold_uncertainty_score":0.22128782},"labels":[],"label_agreement":null},{"id":"W2295254979","doi":"10.5430/air.v5n1p160","title":"Effect of parameter values on fingerprint filtering","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Normalization (sociology); Artificial intelligence; Fingerprint (computing); Pattern recognition (psychology); Biometrics; Computer science; Palm print; Gabor filter; Consistency (knowledge bases); Segmentation; Noise (video); Filter (signal processing); Feature extraction; Computer vision; Mathematics; Image (mathematics)","score_opus":0.19135039188560393,"score_gpt":0.44676516596504073,"score_spread":0.2554147740794368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295254979","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":[0.4060638,0.000042975076,0.59062386,0.00153584,0.0003659495,0.0002761605,0.0000030633148,0.00007103202,0.0010173516],"genre_scores_gemma":[0.99791795,0.00004207993,0.0017168742,0.0000170551,0.000047746056,0.00002555756,3.2467395e-7,0.000005896919,0.00022650688],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997424,0.0006191733,0.00033663097,0.00042105076,0.00079693133,0.0004022006],"domain_scores_gemma":[0.99513084,0.0036873647,0.00005745005,0.0007619891,0.00025659736,0.00010575671],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004538995,0.000096599404,0.0001548222,0.0006648921,0.00013913248,0.00013759838,0.0010922949,0.000068193774,0.00013245437],"category_scores_gemma":[0.0026005046,0.000060850893,0.000083230036,0.00147611,0.00026570464,0.00020889475,0.0003243239,0.0001807749,0.0009967361],"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":[0.000037649446,0.000064609834,0.00008159786,0.000018741654,0.0000066389016,0.0000038035198,0.00024391607,0.000004455404,0.046120476,0.084350765,0.00012707216,0.8689403],"study_design_scores_gemma":[0.000019197945,0.00059339456,0.0001607375,0.00005799358,9.4780853e-7,0.0000012302874,0.00001620384,0.0052105333,0.95833206,0.03489535,0.00063076,0.00008157396],"about_ca_topic_score_codex":0.00006272866,"about_ca_topic_score_gemma":0.000003985347,"teacher_disagreement_score":0.9122116,"about_ca_system_score_codex":0.00006883712,"about_ca_system_score_gemma":0.00004520925,"threshold_uncertainty_score":0.99978113},"labels":[],"label_agreement":null},{"id":"W2300716503","doi":"10.1017/s0069005800010869","title":"Distinguishing Friend from Foe: Law and Policy in the Age of Battlefield Biometrics","year":2013,"lang":"en","type":"article","venue":"Canadian Yearbook of international Law/Annuaire canadien de droit international","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Queen's University; University of Ottawa; Global Affairs Canada","funders":"","keywords":"Biometrics; Context (archaeology); Computer security; Internet privacy; National security; Law; Political science; International law; Computer science; Geography","score_opus":0.010999265429911509,"score_gpt":0.23353822656431134,"score_spread":0.22253896113439983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300716503","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3558227,0.0003596056,0.007947301,0.027876208,0.0028811137,0.00066649413,0.0013505714,0.000051649247,0.60304433],"genre_scores_gemma":[0.9937535,0.000014561179,0.0026221194,0.0028520566,0.00013703934,0.00001899994,0.00011202535,0.000008524353,0.0004811726],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984953,0.000048834325,0.00041579144,0.0002909792,0.00047837992,0.00027073748],"domain_scores_gemma":[0.9986422,0.0002882862,0.00017172027,0.00031779113,0.00036724095,0.00021277029],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038962177,0.00011972722,0.00015849959,0.0012240098,0.00006856365,0.0002600561,0.0017512166,0.000103727114,0.00016790285],"category_scores_gemma":[0.0006568172,0.00012198289,0.000064223765,0.0007574683,0.00021761093,0.0004374004,0.00011375118,0.0001912832,0.000010730947],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017643194,0.00002439196,0.004819724,0.000005574822,0.000041532254,0.000038675393,0.0012931459,0.0000047767476,0.00008567087,0.9900481,0.0013167671,0.0023198705],"study_design_scores_gemma":[0.0012303937,0.00010505814,0.29911867,0.00022300128,0.000020904197,0.00013627528,0.0010016175,0.015794788,0.0009703252,0.21337882,0.46730208,0.0007180601],"about_ca_topic_score_codex":0.7870303,"about_ca_topic_score_gemma":0.5436008,"teacher_disagreement_score":0.77666926,"about_ca_system_score_codex":0.0006563896,"about_ca_system_score_gemma":0.00037608386,"threshold_uncertainty_score":0.49743178},"labels":[],"label_agreement":null},{"id":"W2331484623","doi":"10.2299/jsp.17.1","title":"Construction and Performance of Authentication Systems for Fingerprint with Neural Networks","year":2013,"lang":"en","type":"article","venue":"Journal of Signal Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tembec","funders":"","keywords":"Authentication (law); Fingerprint (computing); Computer science; Artificial neural network; Backpropagation; Fingerprint recognition; Artificial intelligence; Pattern recognition (psychology); Data mining; Computer security","score_opus":0.01470726648146666,"score_gpt":0.2234681009276895,"score_spread":0.20876083444622284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331484623","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":[0.39760786,0.00047451523,0.60168064,0.00009547001,0.00006608925,0.00006472911,9.1117066e-8,0.000003936422,0.000006677568],"genre_scores_gemma":[0.97909576,0.000019733523,0.020811483,0.000011175149,0.00004810727,0.0000028680263,1.9391302e-7,0.0000022575825,0.000008415247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940395,0.000015828055,0.0002806427,0.00007198618,0.0001548555,0.000072748204],"domain_scores_gemma":[0.9988,0.00003626347,0.0005252686,0.000051888608,0.0005467632,0.000039816863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030694026,0.00004481035,0.000104447725,0.00015236679,0.00007490477,0.00019350162,0.00013824685,0.000026756587,0.0000014230187],"category_scores_gemma":[0.000010097341,0.000032165863,0.000017640854,0.0002800448,0.000052566495,0.0007730225,0.000014719971,0.0000721528,1.5017397e-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":[0.00006336676,0.00006540102,0.008208793,0.0005365034,0.0000287541,5.9845087e-7,0.0009438982,0.0047549563,0.0047575887,0.0015733759,0.00007067703,0.9789961],"study_design_scores_gemma":[0.00019554707,0.00016126552,0.007894491,0.00009908231,0.000009184714,0.00012715464,0.000082347025,0.99064463,0.00061008125,0.00009979529,0.000034120378,0.000042291875],"about_ca_topic_score_codex":0.0000035478981,"about_ca_topic_score_gemma":6.1345354e-8,"teacher_disagreement_score":0.9858897,"about_ca_system_score_codex":0.00001413697,"about_ca_system_score_gemma":0.000041729134,"threshold_uncertainty_score":0.18659405},"labels":[],"label_agreement":null},{"id":"W2344170986","doi":"10.1109/tsmc.2015.2501279","title":"A Study on Performance Improvement Due to Linear Fusion in Biometric Authentication Tasks","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; National Natural Science Foundation of China","keywords":"Fusion; Context (archaeology); Vagueness; Biometrics; Ambiguity; Computer science; Authentication (law); Fusion mechanism; Construct (python library); Fusion rules; Term (time); Pattern recognition (psychology); Artificial intelligence; Mathematics; Data mining; Machine learning; Image fusion; Image (mathematics)","score_opus":0.04791265917104725,"score_gpt":0.27332092865711716,"score_spread":0.2254082694860699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344170986","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6202476,0.00011893798,0.37488064,0.00010676558,0.002583607,0.0016307131,0.000012420753,0.000104777224,0.00031455283],"genre_scores_gemma":[0.99797416,0.000019284544,0.00013113092,0.000043758664,0.000054383283,0.00027654343,0.0000021595592,0.00001594771,0.0014826297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974334,0.00022121474,0.0006547485,0.0006172139,0.00077973685,0.00029364132],"domain_scores_gemma":[0.99850863,0.000068199886,0.00016145692,0.0007625886,0.00020312889,0.00029599923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012438704,0.0002282108,0.00032265493,0.0021170336,0.00014696868,0.00037212574,0.00046262157,0.000116509356,0.0000012254105],"category_scores_gemma":[0.000012175332,0.00020725252,0.00004112785,0.0032250239,0.000024886465,0.00017960278,0.00001065156,0.00019769052,0.00021279215],"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":[0.0010923337,0.04144148,0.011887467,0.003528889,0.0010781329,0.00045896418,0.21057214,0.10976723,0.020160783,0.03032947,0.006452861,0.5632303],"study_design_scores_gemma":[0.0068241716,0.009628418,0.032515276,0.00078385015,0.00009480185,0.00019343042,0.015528322,0.91385275,0.00409001,0.00003076675,0.014653172,0.001805052],"about_ca_topic_score_codex":0.0007523453,"about_ca_topic_score_gemma":0.000039485523,"teacher_disagreement_score":0.8040855,"about_ca_system_score_codex":0.0002577532,"about_ca_system_score_gemma":0.00005684379,"threshold_uncertainty_score":0.84515125},"labels":[],"label_agreement":null},{"id":"W2344513859","doi":"","title":"Architecture and Assessment: Privacy Preserving Biometrically Secured Electronic Documents","year":2015,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Biometrics; Authorization; Computer security; Internet privacy; Architecture; Electronic surveillance; Key (lock); Information privacy; Citizenship; Computer science; Business; Political science; Law; Geography","score_opus":0.028423357649046722,"score_gpt":0.30577164957475234,"score_spread":0.2773482919257056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344513859","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":[0.025415434,0.00079187326,0.9532053,0.0043865675,0.00023348109,0.00025567878,0.0000016213038,0.00026422107,0.015445827],"genre_scores_gemma":[0.91154695,0.000055346423,0.086272866,0.00035751943,0.0000296617,0.000010726034,0.000006321188,0.0000063525654,0.0017142365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845517,0.00009603213,0.00020193435,0.00041049087,0.00049163855,0.00034472515],"domain_scores_gemma":[0.9989364,0.000069421716,0.00007419594,0.00053984945,0.00013358939,0.00024653142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006392369,0.00011729954,0.00012741648,0.00080633356,0.00008456985,0.00052477047,0.0009598489,0.00006769804,0.000035375986],"category_scores_gemma":[0.00017752672,0.00009714659,0.00003178794,0.0033529687,0.000031398074,0.0005001885,0.0006341426,0.00021193345,0.000025676096],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016194117,0.0005205449,0.00884794,0.00006425737,0.00014058237,0.000014507489,0.0028890145,0.000011197731,0.0015915312,0.5669545,0.029046763,0.38990295],"study_design_scores_gemma":[0.0020714123,0.00033821233,0.035887513,0.0000117673135,0.000016464957,0.00006332396,0.000071747,0.05497596,0.0010607804,0.11393211,0.7909546,0.0006160962],"about_ca_topic_score_codex":0.00007819142,"about_ca_topic_score_gemma":0.000008676966,"teacher_disagreement_score":0.8861315,"about_ca_system_score_codex":0.00010235804,"about_ca_system_score_gemma":0.00020753064,"threshold_uncertainty_score":0.50603735},"labels":[],"label_agreement":null},{"id":"W2345603840","doi":"10.5539/cis.v9n2p140","title":"Human Identification Based on Geometric Feature Extraction Using a Number of Biometric Systems Available: Review","year":2016,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Feature extraction; Hand geometry; Feature (linguistics); Fingerprint (computing); Palm print; Identification (biology); Pattern recognition (psychology); Keystroke dynamics; Computer vision; Password; Computer security","score_opus":0.036443034763002466,"score_gpt":0.31351331477440664,"score_spread":0.27707028001140416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345603840","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":[0.0060594263,0.00039187912,0.9911979,0.00032074208,0.00078175653,0.00029532975,0.000009814909,0.00005783761,0.00088533125],"genre_scores_gemma":[0.9870394,0.00069535343,0.011581767,0.0005271307,0.000036674115,0.00001044365,0.000007082627,0.0000031977759,0.000098978926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998033,0.000058040652,0.0005474693,0.00029294516,0.00087519124,0.00019335485],"domain_scores_gemma":[0.9978628,0.000099995435,0.0005497845,0.00058996666,0.00078708923,0.000110356166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020893074,0.0001153684,0.00017511666,0.003053274,0.00029376982,0.0004814397,0.0007147219,0.000057304173,0.000025777443],"category_scores_gemma":[0.00021791125,0.000082109174,0.000040969382,0.013812326,0.00017315194,0.007253454,0.00012261921,0.00006847901,0.00014355424],"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":[0.000011302106,0.0003357401,0.0061111543,0.0029813314,0.000025725627,0.0000017871084,0.00036485447,0.00027426853,0.023820218,0.11672704,0.038164712,0.81118184],"study_design_scores_gemma":[0.0010037239,0.00014219047,0.0956473,0.0015290619,0.00002326422,0.00009894825,0.000012478873,0.8442789,0.0060245744,0.000111093614,0.05055819,0.0005702963],"about_ca_topic_score_codex":0.00001440273,"about_ca_topic_score_gemma":4.570442e-8,"teacher_disagreement_score":0.9809799,"about_ca_system_score_codex":0.00011985368,"about_ca_system_score_gemma":0.00013111965,"threshold_uncertainty_score":0.66363645},"labels":[],"label_agreement":null},{"id":"W2347737370","doi":"","title":"Study on Fingerprint Examiner's Stability of Feature Selection","year":2015,"lang":"en","type":"article","venue":"Xingshi jishu","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"","keywords":"Minutiae; Fingerprint (computing); Fingerprint recognition; Computer science; Pattern recognition (psychology); Identification (biology); Artificial intelligence; Selection (genetic algorithm); Stability (learning theory); Feature selection; Data mining; Quality (philosophy); Machine learning; Biology","score_opus":0.09118403537136523,"score_gpt":0.2962402244164011,"score_spread":0.20505618904503586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2347737370","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9759532,0.000028567434,0.02024837,0.0005049893,0.00044315628,0.00025061786,0.0000022376732,0.00012780065,0.002441048],"genre_scores_gemma":[0.99716187,5.5589425e-7,0.0025316204,0.00006645448,0.000032861462,0.000008695431,0.0000017745573,0.0000037255018,0.0001924606],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99883467,0.00011951764,0.00017439233,0.00032466205,0.0004167535,0.0001300188],"domain_scores_gemma":[0.9990155,0.00006059572,0.000110765046,0.00046594886,0.0002562218,0.00009097998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009277895,0.00008476329,0.00012934569,0.00020478698,0.000048269572,0.000089494024,0.0004559,0.000057707177,0.000009582671],"category_scores_gemma":[0.0003603296,0.00007652389,0.000035961744,0.0013109854,0.00002399117,0.00019360305,0.00012128555,0.00015029969,0.000029753744],"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":[0.0001630402,0.011485912,0.6582348,0.00013490728,0.00019579567,0.000032655324,0.08894869,0.00009783548,0.014741394,0.04518192,0.062402796,0.11838029],"study_design_scores_gemma":[0.0012903853,0.0011682963,0.93081754,0.000019041121,0.0000150013675,0.000010060808,0.0018885676,0.00483014,0.045053627,0.001466894,0.013052234,0.00038823325],"about_ca_topic_score_codex":0.00008450824,"about_ca_topic_score_gemma":0.000028621278,"teacher_disagreement_score":0.27258277,"about_ca_system_score_codex":0.000087895954,"about_ca_system_score_gemma":0.00006925378,"threshold_uncertainty_score":0.31205535},"labels":[],"label_agreement":null},{"id":"W2348959401","doi":"","title":"A Fingerprint Matching Algorithm Based on Sector-Sampling","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Minutiae; Fingerprint (computing); Computer science; Matching (statistics); Blossom algorithm; Fingerprint recognition; Artificial intelligence; Point (geometry); Pattern recognition (psychology); Sampling (signal processing); Construct (python library); Feature (linguistics); Fingerprint Verification Competition; Algorithm; Computer vision; Mathematics; Statistics","score_opus":0.02054434519300001,"score_gpt":0.26732549704509595,"score_spread":0.24678115185209593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2348959401","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":[0.00036855735,0.000040001738,0.9955872,0.0023675407,0.000045135985,0.00041001244,0.0000075382954,0.00042352316,0.0007504841],"genre_scores_gemma":[0.086390875,0.000003064784,0.9095306,0.0037478227,0.0001360584,0.00008824756,0.000023736764,0.000007773328,0.00007183248],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986366,0.000035729452,0.00027401253,0.00056311087,0.0002293268,0.0002612408],"domain_scores_gemma":[0.99886054,0.00009924807,0.00009730793,0.00073455024,0.000093957955,0.000114401555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023597649,0.0001588888,0.00013846996,0.0004076926,0.00029355305,0.00039306385,0.0010252323,0.00007107072,0.00001538783],"category_scores_gemma":[8.8743917e-7,0.00016429511,0.00009649135,0.001271369,0.000019436544,0.00015428272,0.00009120209,0.00019170415,0.0002561395],"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":[7.524564e-7,0.00024521135,0.0000074586515,0.0000052210785,0.000004418811,9.703696e-7,0.0001970635,0.00048212838,0.0025490113,0.028729565,0.0007617412,0.96701646],"study_design_scores_gemma":[0.0004662889,0.000064465305,0.006806331,0.000027496475,0.000006777041,0.000015271065,0.0000083858995,0.37483558,0.0042846375,0.01904958,0.5939468,0.0004883401],"about_ca_topic_score_codex":0.000011167724,"about_ca_topic_score_gemma":6.373828e-7,"teacher_disagreement_score":0.9665281,"about_ca_system_score_codex":0.00008286667,"about_ca_system_score_gemma":0.000055034463,"threshold_uncertainty_score":0.66997606},"labels":[],"label_agreement":null},{"id":"W2373911727","doi":"","title":"A New Fast Iris Localization Approach Based on Mathematics Morphology","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Bottleneck; Dilation (metric space); IRIS (biosensor); Mathematical morphology; Artificial intelligence; Iris recognition; Computer vision; Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Process (computing); Image (mathematics); Image processing; Biometrics; Mathematics; Embedded system; Geometry","score_opus":0.01565679943426505,"score_gpt":0.2521072541915493,"score_spread":0.23645045475728424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2373911727","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":[0.00008064128,0.000030122246,0.9959266,0.0007142897,0.000040311028,0.00057747005,0.0000048280144,0.0002955682,0.0023301712],"genre_scores_gemma":[0.018615562,0.0000023723312,0.97851837,0.0021933585,0.00012372817,0.00006894425,0.00007084986,0.00001404549,0.0003927965],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985258,0.000025661358,0.00039222298,0.00052227324,0.00025039,0.0002836717],"domain_scores_gemma":[0.99861413,0.00010139086,0.00014573339,0.00079972117,0.00016900593,0.00017002586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041578256,0.00016177537,0.00015479517,0.000547472,0.00019884348,0.00018204223,0.0009933211,0.00012417544,0.000022063356],"category_scores_gemma":[0.0000025222448,0.00016292103,0.00007681734,0.0021014411,0.000040832452,0.00012127184,0.00012487115,0.00014217915,0.0003410013],"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":[0.0000053978615,0.0013979927,0.00015387726,0.00007211722,0.000025237518,0.000003349519,0.0017235205,0.0012548188,0.0033403905,0.1664758,0.057649303,0.7678982],"study_design_scores_gemma":[0.0004736083,0.000032599757,0.0010368545,0.0000072589723,0.00000918732,0.000028024606,0.000027882847,0.447419,0.003101677,0.0024678023,0.54511154,0.00028461817],"about_ca_topic_score_codex":0.00002125958,"about_ca_topic_score_gemma":0.000001934656,"teacher_disagreement_score":0.7676136,"about_ca_system_score_codex":0.00008039753,"about_ca_system_score_gemma":0.00008266622,"threshold_uncertainty_score":0.6643727},"labels":[],"label_agreement":null},{"id":"W2376980315","doi":"","title":"Iris Segmentation from Human Eye Image","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Iris recognition; Hough transform; Computer science; Thresholding; IRIS (biosensor); Artificial intelligence; Computer vision; Segmentation; Human eye; Image segmentation; Boundary (topology); Edge detection; Image (mathematics); Gray (unit); Pattern recognition (psychology); Biometrics; Image processing; Mathematics","score_opus":0.012584293111615968,"score_gpt":0.29357451792780814,"score_spread":0.2809902248161922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2376980315","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":[0.010968243,0.000102101694,0.9864593,0.0005453876,0.000047654845,0.00043747536,0.000014438698,0.0003171935,0.001108192],"genre_scores_gemma":[0.07922167,0.0000071059862,0.9192044,0.0006903283,0.00021551212,0.00009372265,0.00019707425,0.000010941682,0.0003592058],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986586,0.000027224001,0.00037021984,0.00049863325,0.00020413887,0.0002411763],"domain_scores_gemma":[0.99892735,0.00006374705,0.0001348507,0.0006174049,0.00013910123,0.000117549585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032207923,0.00012911121,0.00011225725,0.00031654927,0.00032259434,0.00032451574,0.0009581376,0.00007325795,0.00005076948],"category_scores_gemma":[6.085512e-7,0.0001406307,0.00007054302,0.001189443,0.00005345212,0.00036299345,0.0002122737,0.00012042535,0.0006544836],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012927993,0.0003075598,0.0010597147,0.000008272295,0.000028702265,0.0000027931578,0.0009896309,0.0000012731155,0.4264792,0.031125106,0.013002605,0.5269939],"study_design_scores_gemma":[0.00043228827,0.000013753299,0.047308393,0.00000448408,0.000010704556,0.000003791118,0.00004446846,0.0009042296,0.08301577,0.007229398,0.8607185,0.00031422055],"about_ca_topic_score_codex":0.00012375657,"about_ca_topic_score_gemma":0.000020083291,"teacher_disagreement_score":0.8477159,"about_ca_system_score_codex":0.00008179386,"about_ca_system_score_gemma":0.00002185347,"threshold_uncertainty_score":0.8412281},"labels":[],"label_agreement":null},{"id":"W2380637966","doi":"","title":"Research and improvement of the chromosome image procession","year":2004,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Thinning; Image (mathematics); Ideal (ethics); Chromosome; Procession; Noise (video); Computer vision; Artificial intelligence; Image processing; Skeleton (computer programming); Algorithm","score_opus":0.021396538278787798,"score_gpt":0.305102852603995,"score_spread":0.2837063143252072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2380637966","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":[0.036620557,0.00014854102,0.9582187,0.0039517474,0.000020919671,0.00071170746,0.0000053151102,0.000047957154,0.00027458376],"genre_scores_gemma":[0.7775179,0.00003035508,0.22173034,0.00019674408,0.000051863513,0.00025607436,0.0000037882485,0.0000062988047,0.00020662787],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908876,0.000018481362,0.00019229914,0.00029978453,0.00024714338,0.000153505],"domain_scores_gemma":[0.99905705,0.000030669104,0.000071656,0.0005175222,0.00027295997,0.000050119936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031491966,0.00006276393,0.00007114079,0.0001752806,0.0002812752,0.00012416826,0.00092766114,0.00003888587,0.0000025259585],"category_scores_gemma":[0.0000011312504,0.00004564553,0.000027330694,0.0017098921,0.00017244997,0.00015786303,0.0005894023,0.00013396828,0.000023587969],"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":[0.0000013090746,0.00057221786,0.00010608868,0.00011313572,0.000012054741,3.12753e-7,0.0011644278,0.00000493527,0.58929825,0.16218387,0.0014255845,0.24511781],"study_design_scores_gemma":[0.0008234566,0.00010191026,0.01432145,0.000052475672,0.0000052509276,0.000023523075,0.0000777308,0.0006077367,0.6718848,0.08343588,0.22843648,0.00022932504],"about_ca_topic_score_codex":0.000033682736,"about_ca_topic_score_gemma":0.0000035521612,"teacher_disagreement_score":0.74089736,"about_ca_system_score_codex":0.000048853602,"about_ca_system_score_gemma":0.00008981874,"threshold_uncertainty_score":0.21633698},"labels":[],"label_agreement":null},{"id":"W2387338163","doi":"","title":"Design and Implementation of Iris Recognition System Based on DSP","year":2011,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; BIOS; Iris recognition; Digital signal processing; IRIS (biosensor); Scheduling (production processes); Software; Embedded system; Kernel (algebra); Computer hardware; Biometrics; Artificial intelligence; Operating system","score_opus":0.04528359672955168,"score_gpt":0.26269007460916344,"score_spread":0.21740647787961176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2387338163","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":[0.0019816356,0.00002377565,0.9968446,0.00009352767,0.000019614212,0.0006798353,0.00001104284,0.00011236928,0.00023360025],"genre_scores_gemma":[0.3220738,0.0000041198723,0.6775581,0.00012240386,0.000013380008,0.00019898941,0.000019960424,0.000004465754,0.0000047588655],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916226,0.00006878523,0.00025332323,0.00029425343,0.0001151827,0.00010618046],"domain_scores_gemma":[0.9993015,0.0000612,0.00014276154,0.0003135863,0.00012885038,0.000052131596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026776546,0.000084144944,0.00009488179,0.00031659487,0.00009900612,0.00005081341,0.00031264895,0.00004238169,0.000015826186],"category_scores_gemma":[3.1276113e-7,0.00008610936,0.000030005041,0.0006578281,0.000029236257,0.00012989376,0.000052174462,0.000046871046,0.00004427761],"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":[0.0000062417666,0.00021519285,0.00028311688,0.000097727985,0.000018059736,5.7269085e-7,0.0012319988,0.000011143523,0.0050641107,0.014713363,0.0011015763,0.9772569],"study_design_scores_gemma":[0.0035605635,0.0006521047,0.071440615,0.0001390203,0.000109954744,0.000060199873,0.00059963716,0.29671183,0.5341312,0.0054686507,0.08585156,0.0012746287],"about_ca_topic_score_codex":0.00006010606,"about_ca_topic_score_gemma":0.0000010902769,"teacher_disagreement_score":0.97598225,"about_ca_system_score_codex":0.000033062282,"about_ca_system_score_gemma":0.00003414588,"threshold_uncertainty_score":0.35114375},"labels":[],"label_agreement":null},{"id":"W2387383588","doi":"","title":"A Matching Algorithm of Flowing Trend Pattern of Fingerprint Ridges","year":2007,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Minutiae; Ridge; Fingerprint (computing); Computer science; Matching (statistics); Sampling (signal processing); Algorithm; Feature (linguistics); Blossom algorithm; Stability (learning theory); Pattern recognition (psychology); Artificial intelligence; Computer vision; Fingerprint recognition; Mathematics; Geology; Statistics; Paleontology","score_opus":0.01632976366645645,"score_gpt":0.2630548901703259,"score_spread":0.24672512650386946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2387383588","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":[0.12386478,0.00004267181,0.874621,0.00008778332,0.00018346334,0.000030162475,0.0000022356817,0.00003374171,0.0011342152],"genre_scores_gemma":[0.9011566,0.000004100675,0.098676376,0.00005150759,0.000014037562,4.2246884e-7,0.0000011662523,0.000002037579,0.00009374666],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919796,0.00001650107,0.00028928,0.00015064602,0.00022611482,0.00011949814],"domain_scores_gemma":[0.99947906,0.00008778219,0.00006456525,0.0002814413,0.000048129663,0.000039048467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006506663,0.000052471827,0.00011150955,0.00033111416,0.000025668747,0.000025280719,0.00038875494,0.000032190008,0.000028087115],"category_scores_gemma":[0.0000108571085,0.00004642604,0.000058225185,0.0007125046,0.000021895306,0.00013359592,0.00012738045,0.00004997676,0.0000057210505],"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":[5.747535e-7,0.000074309355,0.00046413695,0.000019925608,0.000010642718,0.0000021364765,0.002426342,0.0000027858505,0.0053959615,0.0050188024,0.00006175395,0.9865226],"study_design_scores_gemma":[0.0008083295,0.00009914851,0.14390942,0.00008655288,0.0000146655175,0.000021318303,0.0011680882,0.08549477,0.76080084,0.0034402674,0.003697718,0.00045889764],"about_ca_topic_score_codex":0.00042859,"about_ca_topic_score_gemma":0.00005498784,"teacher_disagreement_score":0.9860637,"about_ca_system_score_codex":0.000011891399,"about_ca_system_score_gemma":0.0000125974775,"threshold_uncertainty_score":0.1893199},"labels":[],"label_agreement":null},{"id":"W2387768832","doi":"","title":"An Approach to Palm-dorsal Vein Recognition Based on Local Gabor Phase Feature","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Pattern recognition (psychology); Gabor filter; Preprocessor; Histogram; Feature (linguistics); Computer vision; Feature extraction; Matching (statistics); Palm print; Image (mathematics); Mathematics; Statistics","score_opus":0.015414207377225445,"score_gpt":0.27879778631998003,"score_spread":0.2633835789427546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2387768832","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":[0.0054039294,0.000008545375,0.99046636,0.0017550152,0.00010328851,0.00089522416,0.000072550014,0.00030147072,0.0009936303],"genre_scores_gemma":[0.3467638,5.815497e-7,0.6494731,0.0026337556,0.00019172208,0.00052004436,0.00033193166,0.000015555308,0.000069540736],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99828976,0.000066024,0.0002413411,0.0008364368,0.00027260833,0.0002938359],"domain_scores_gemma":[0.9982387,0.000058677342,0.00008553136,0.0011158177,0.00022904812,0.00027225597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003137428,0.00020081936,0.00015455348,0.0005443926,0.00028426023,0.0004348794,0.0013182679,0.0001730212,0.000020638483],"category_scores_gemma":[0.0000023236335,0.0002019954,0.00007879623,0.0019890072,0.000063857646,0.00019776472,0.00009134926,0.0004099163,0.0005006708],"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":[0.000010874998,0.0024713345,0.000016476977,0.000014745173,0.0000070922997,8.725733e-7,0.00026514844,0.000096377255,0.032285497,0.00856204,0.008395754,0.9478738],"study_design_scores_gemma":[0.00093353866,0.00012182014,0.0016130477,0.000005699283,0.000008986152,0.000016051416,0.000015949385,0.2905223,0.013431063,0.000667546,0.6922591,0.00040490934],"about_ca_topic_score_codex":0.000015472982,"about_ca_topic_score_gemma":0.0000057258208,"teacher_disagreement_score":0.9474689,"about_ca_system_score_codex":0.000049492,"about_ca_system_score_gemma":0.00008582997,"threshold_uncertainty_score":0.8237133},"labels":[],"label_agreement":null},{"id":"W2388946189","doi":"","title":"Research on Watermarking in Fingerprint Recognition","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Digital watermarking; Fingerprint (computing); Artificial intelligence; Computer vision; Pattern recognition (psychology); Computer security; Image (mathematics)","score_opus":0.06388982754206157,"score_gpt":0.3313605719735808,"score_spread":0.26747074443151925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2388946189","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":[0.027587445,0.00004917432,0.9646372,0.0015657191,0.00003517231,0.0005360294,0.000004582661,0.00015318034,0.005431471],"genre_scores_gemma":[0.8094116,0.000011057513,0.18924314,0.0003032799,0.00017664183,0.0005039465,0.00006166908,0.000009545331,0.00027916033],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986125,0.000110013476,0.00027374766,0.00047318285,0.00024549462,0.00028506017],"domain_scores_gemma":[0.9991806,0.00013820831,0.000046026846,0.00045175292,0.00014217739,0.00004119304],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00072264625,0.00008653297,0.0000869432,0.0009839099,0.00019912906,0.0002766142,0.0007339648,0.000067832814,0.000011882819],"category_scores_gemma":[7.8126743e-7,0.00008959768,0.00003758287,0.0023054683,0.000041952473,0.0001570575,0.00020065287,0.00025556172,0.00078469294],"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":[0.0000028229051,0.0005944984,0.00042753757,0.00001947856,0.000003588746,0.000004390346,0.00024627833,0.00008601761,0.006320195,0.08477265,0.0072531374,0.9002694],"study_design_scores_gemma":[0.00043986453,0.000027057495,0.04984723,0.00003648012,0.0000013969398,0.000013624565,0.000020231679,0.009627894,0.019909078,0.050864495,0.86890805,0.00030459106],"about_ca_topic_score_codex":0.00018598457,"about_ca_topic_score_gemma":0.000035303423,"teacher_disagreement_score":0.8999648,"about_ca_system_score_codex":0.0001267783,"about_ca_system_score_gemma":0.00003236221,"threshold_uncertainty_score":0.9999933},"labels":[],"label_agreement":null},{"id":"W2396140704","doi":"10.1109/wacv.2016.7477580","title":"Score reliability based weighting technique for score-level fusion in multi-biometric systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","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":"Concordia University","funders":"","keywords":"Weighting; Biometrics; Word error rate; Computer science; Pattern recognition (psychology); Matching (statistics); Artificial intelligence; Reliability (semiconductor); A priori and a posteriori; Data mining; A-weighting; Statistics; Mathematics","score_opus":0.1044973534938128,"score_gpt":0.2973635612907555,"score_spread":0.1928662077969427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396140704","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":[0.014053326,0.00006805321,0.9832145,0.0006176164,0.00049625855,0.0011710746,0.000022068201,0.00020267941,0.00015443523],"genre_scores_gemma":[0.82231337,0.000006989745,0.17668328,0.00007314134,0.000020440853,0.00022577296,0.000004186514,0.000007905058,0.00066491246],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980538,0.00012095558,0.0005146493,0.0006484599,0.0003313355,0.0003308213],"domain_scores_gemma":[0.9980675,0.0005362867,0.00016128221,0.0008082257,0.0003198037,0.00010693244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023064162,0.00014764047,0.00021250805,0.0019734825,0.00011530522,0.00014298773,0.00086395704,0.00016211138,0.00001774073],"category_scores_gemma":[0.0007972187,0.0000967009,0.00008759541,0.0054000644,0.00005266946,0.00040095169,0.00016979141,0.00008107568,0.000031873573],"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":[0.000079568534,0.002733058,0.17040502,0.00121361,0.000023633154,0.0000164808,0.0003444247,0.00003183724,0.39347497,0.11898573,0.005901877,0.3067898],"study_design_scores_gemma":[0.005408127,0.00028075126,0.19639981,0.0006243302,0.0000103172415,0.00001253814,0.000044657314,0.51568145,0.2641,0.002275185,0.01392294,0.0012398801],"about_ca_topic_score_codex":0.0002490684,"about_ca_topic_score_gemma":0.000030063384,"teacher_disagreement_score":0.8082601,"about_ca_system_score_codex":0.0002353032,"about_ca_system_score_gemma":0.00011704749,"threshold_uncertainty_score":0.39433482},"labels":[],"label_agreement":null},{"id":"W2397770359","doi":"10.1109/isdfs.2016.7473538","title":"Enhancing of biometric authentication with pass strings and cryptographic checksums","year":2016,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University of Edmonton","funders":"","keywords":"Biometrics; Computer science; Password; Hash function; Authentication (law); Checksum; Cryptography; Computer security","score_opus":0.009064209690263192,"score_gpt":0.20819357957716914,"score_spread":0.19912936988690594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397770359","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":[0.28157678,0.00006452942,0.7174516,0.0005297791,0.00004046042,0.00006236628,8.9481165e-7,0.000050022227,0.00022360848],"genre_scores_gemma":[0.97710526,0.000049838483,0.022561299,0.000024945988,0.0000056172184,0.0000037975567,3.5958152e-7,0.0000026006417,0.00024629268],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9992411,0.000020042558,0.00017334131,0.0002313082,0.0002198599,0.000114332164],"domain_scores_gemma":[0.9993067,0.00009305524,0.000104549705,0.0003077434,0.00012836611,0.00005959344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030534444,0.000061445855,0.00008771086,0.0011607335,0.000041795047,0.00005809226,0.00026091246,0.000034655564,0.00002192524],"category_scores_gemma":[0.000060145012,0.00003515722,0.000019860228,0.003909836,0.00008019585,0.0003124577,0.00006924278,0.00002332729,0.0000088825],"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":[0.000008333595,0.00017346762,0.024807606,0.00006829556,0.000047121375,9.4288436e-7,0.00066664215,4.2734495e-8,0.3548651,0.36185926,0.00010493251,0.25739825],"study_design_scores_gemma":[0.0010302427,0.00019514696,0.5981236,0.00006935411,0.00001763575,0.000012846638,0.000073054274,0.00087045465,0.39180472,0.0041434006,0.0033464795,0.0003130637],"about_ca_topic_score_codex":0.00003370589,"about_ca_topic_score_gemma":0.000008044955,"teacher_disagreement_score":0.69552845,"about_ca_system_score_codex":0.000011239747,"about_ca_system_score_gemma":0.000022807668,"threshold_uncertainty_score":0.18785466},"labels":[],"label_agreement":null},{"id":"W2399784191","doi":"","title":"Towards automated transactions based on the offline handwritten signatures","year":2013,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Digital signature; Signature (topology); Key (lock); Authentication (law); Public-key cryptography; Handwriting recognition; The Internet; Computer security; Artificial intelligence; World Wide Web; Feature extraction; Hash function; Encryption","score_opus":0.016754036421830012,"score_gpt":0.23931171022897665,"score_spread":0.22255767380714664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399784191","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":[0.0011543725,0.00001980172,0.9483528,0.04113074,0.00024624524,0.0002293072,0.0000048397874,0.0008505655,0.008011339],"genre_scores_gemma":[0.985563,0.0000021640674,0.008529721,0.0044489563,0.000012123511,0.000035032193,0.000004507545,0.0000025572408,0.0014019691],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923456,0.00006498232,0.00012580008,0.00018961956,0.00025328156,0.00013176666],"domain_scores_gemma":[0.9992317,0.00011857865,0.00002891594,0.00044828487,0.00011525935,0.000057289533],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020793907,0.00007426428,0.00006359387,0.00018322514,0.00016458893,0.00029339356,0.0005870116,0.000056660083,0.0016865876],"category_scores_gemma":[0.00003377318,0.00004250088,0.000056091594,0.0010870863,0.00003433661,0.00017324353,0.000011990888,0.00011898387,0.0005452809],"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":[0.000007689217,0.0009555148,0.00003175752,0.000022283486,0.00006279096,0.0000042065803,0.0007948086,0.0012815846,0.0065203044,0.12586285,0.6301487,0.23430751],"study_design_scores_gemma":[0.00012789178,0.000022526572,0.004428313,0.0000022550632,0.0000020857544,6.1667583e-7,0.000014551893,0.97969,0.0043573347,0.0004341733,0.010844113,0.00007614563],"about_ca_topic_score_codex":0.00018989728,"about_ca_topic_score_gemma":0.000009018585,"teacher_disagreement_score":0.9844086,"about_ca_system_score_codex":0.000016911637,"about_ca_system_score_gemma":0.000044749293,"threshold_uncertainty_score":0.99922603},"labels":[],"label_agreement":null},{"id":"W2503531746","doi":"10.4018/978-1-4666-3646-0","title":"Multimodal Biometrics and Intelligent Image Processing for Security Systems","year":2013,"lang":"en","type":"book","venue":"IGI Global eBooks","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Image processing; Computer security; Image (mathematics); Artificial intelligence","score_opus":0.02497941683288024,"score_gpt":0.2753041876213943,"score_spread":0.25032477078851406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2503531746","genre_codex":"methods","genre_gemma":"other","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":[0.00009371953,0.0059066634,0.6946392,0.00010052039,0.0021567955,0.0022052235,0.0003858073,0.00042255627,0.2940895],"genre_scores_gemma":[0.37931672,0.00022534275,0.17339845,0.0014470998,0.0026763272,0.0013437236,0.00023765289,0.00027956435,0.44107512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973874,0.000050393348,0.00060626183,0.00092013367,0.0005731162,0.00046271342],"domain_scores_gemma":[0.99776477,0.00011477797,0.00045446088,0.0006863044,0.0006875939,0.00029210307],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00047720905,0.00041323208,0.00051708485,0.00071725564,0.00022774129,0.0017715279,0.0011391267,0.0005184515,0.000002713591],"category_scores_gemma":[0.00013381764,0.0003963378,0.00016041787,0.00056930893,0.00016403208,0.00027105337,0.00045984623,0.00025527136,0.000080271166],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065161016,0.000059561728,0.0000079135825,0.0010645491,0.000059362264,0.000008853099,0.00035117546,4.2737128e-7,0.000021030572,0.854329,0.04628305,0.09780855],"study_design_scores_gemma":[0.0010738664,0.00023087172,0.000082051796,0.00045618252,0.00012098965,0.0001431621,0.00008180188,0.14945467,0.00024414746,0.27198914,0.57432,0.0018031112],"about_ca_topic_score_codex":0.0001523892,"about_ca_topic_score_gemma":0.000007564895,"teacher_disagreement_score":0.5823399,"about_ca_system_score_codex":0.0005132866,"about_ca_system_score_gemma":0.00057682616,"threshold_uncertainty_score":0.99984884},"labels":[],"label_agreement":null},{"id":"W2511019053","doi":"10.1109/iscas.2016.7527178","title":"A new anchored normalization technique for score-level fusion in multimodal biometrie systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","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":"Concordia University","funders":"","keywords":"Normalization (sociology); Biometrics; Word error rate; Computer science; Artificial intelligence; Pattern recognition (psychology); A priori and a posteriori","score_opus":0.050655638716280635,"score_gpt":0.27564824777996,"score_spread":0.22499260906367935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511019053","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":[0.0011797475,0.00007028825,0.99647623,0.00062353717,0.00046168757,0.00082914607,0.0000140836,0.00013060424,0.00021468474],"genre_scores_gemma":[0.85045755,0.000028584749,0.14685558,0.00006565458,0.000058923146,0.000121424004,0.000010901876,0.00000770102,0.0023936715],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988509,0.000043017,0.00031827972,0.0003477099,0.00022903475,0.00021108026],"domain_scores_gemma":[0.9991433,0.000114200804,0.00009814608,0.0003824875,0.00016458303,0.00009728602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005965031,0.00009666691,0.00012838905,0.0013603512,0.00005628698,0.00013685117,0.0005233502,0.0001164283,0.000021876773],"category_scores_gemma":[0.00020241854,0.000066211236,0.000042105316,0.0034034052,0.00001665258,0.00051908795,0.00010424458,0.00003089021,0.000028452228],"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":[0.0000372948,0.0002666628,0.008378275,0.00010795965,0.000014279555,0.0000033729025,0.00036898113,0.000008461503,0.25712088,0.26870635,0.020100862,0.44488662],"study_design_scores_gemma":[0.010134762,0.0005332572,0.13739125,0.0005198792,0.000015102172,0.00004050209,0.00008900357,0.22460093,0.48702377,0.011076721,0.12679026,0.0017845802],"about_ca_topic_score_codex":0.00053362316,"about_ca_topic_score_gemma":0.000057660385,"teacher_disagreement_score":0.84962064,"about_ca_system_score_codex":0.00010677549,"about_ca_system_score_gemma":0.00010307171,"threshold_uncertainty_score":0.2700016},"labels":[],"label_agreement":null},{"id":"W2532506597","doi":"10.1109/aipr.2008.4906455","title":"Integrating monomodal biometric matchers through logistic regression rank aggregation approach","year":2008,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Logistic regression; Rank (graph theory); Computer science; Artificial intelligence; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.09804354388511081,"score_gpt":0.29765492491347034,"score_spread":0.1996113810283595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2532506597","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":[0.015682809,0.000326332,0.9695661,0.00039659435,0.0003634572,0.00016924908,0.0000013048286,0.00030260262,0.013191537],"genre_scores_gemma":[0.7447784,0.000086119464,0.25370562,0.00015677576,0.000038475522,0.000020010246,0.0000153958,0.000005965662,0.0011932314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843854,0.00009866208,0.0003329873,0.00047810146,0.00041101186,0.00024070438],"domain_scores_gemma":[0.9989531,0.00011263671,0.00016628676,0.00054881646,0.00013976361,0.00007939741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037228965,0.00015260279,0.0001742538,0.00076319283,0.00030481466,0.00015819678,0.00071795046,0.00011874696,0.000045043405],"category_scores_gemma":[0.0002737754,0.000114039,0.00008087664,0.0061333016,0.000107668726,0.000749433,0.00014683025,0.00016446815,0.00012064842],"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":[0.000032711025,0.0013952405,0.006815149,0.00016209781,0.0000953714,0.00005910256,0.014529082,0.00016379016,0.0029759898,0.41818383,0.04790804,0.5076796],"study_design_scores_gemma":[0.0018520147,0.00016631043,0.016568998,0.000058062767,0.000020857757,0.0003393231,0.0010103587,0.93095505,0.015899928,0.013741371,0.018223893,0.001163837],"about_ca_topic_score_codex":0.0002763188,"about_ca_topic_score_gemma":0.0000016610894,"teacher_disagreement_score":0.93079126,"about_ca_system_score_codex":0.000096197386,"about_ca_system_score_gemma":0.000069061185,"threshold_uncertainty_score":0.46503752},"labels":[],"label_agreement":null},{"id":"W2541439781","doi":"10.1109/bcc.2006.4341618","title":"Measuring Biometric Sample Quality in Terms of Biometric Information","year":2006,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Computer science; Sample (material); Quality (philosophy); Artificial intelligence; Pattern recognition (psychology)","score_opus":0.05464484584472577,"score_gpt":0.2706097510212528,"score_spread":0.215964905176527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2541439781","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":[0.086194955,0.00006783918,0.9012295,0.00015759253,0.00024159046,0.00016012807,0.000014098544,0.00010775077,0.011826526],"genre_scores_gemma":[0.9714569,0.000008905982,0.02839837,0.00004720428,0.000010149131,0.0000049006744,0.000020464358,0.0000018330866,0.000051302424],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99835575,0.00006921568,0.00069447374,0.00017507118,0.00051408366,0.00019142873],"domain_scores_gemma":[0.9989312,0.00024517495,0.00023394669,0.00042593485,0.00012212466,0.000041581323],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0012682539,0.000084750674,0.00016552911,0.009217315,0.00004010786,0.00014328277,0.0006029416,0.00006927747,0.000024506684],"category_scores_gemma":[0.0005437046,0.000077263736,0.00005764899,0.031066665,0.00003314227,0.0012238666,0.00012987296,0.00006368696,0.000052869298],"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":[0.0000075467424,0.00059714785,0.21963064,0.00017617542,0.000012856392,6.085604e-7,0.00038132907,0.000039893566,0.0033718988,0.38499635,0.001530657,0.38925487],"study_design_scores_gemma":[0.00052108406,0.000022822935,0.96304864,0.0000059467548,0.0000015280194,0.0000015258884,0.000018731349,0.0073824963,0.013225144,0.00762613,0.007960947,0.0001850035],"about_ca_topic_score_codex":0.004958378,"about_ca_topic_score_gemma":0.00008096177,"teacher_disagreement_score":0.8852619,"about_ca_system_score_codex":0.00008793061,"about_ca_system_score_gemma":0.000026904007,"threshold_uncertainty_score":0.9895285},"labels":[],"label_agreement":null},{"id":"W2545047192","doi":"10.1109/bliss.2009.27","title":"Fundamentals of Biometric System Design: New Course for Electrical, Computer, and Software Engineering Students","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Software; Fingerprint (computing); Software engineering; Software design; Multimedia; Artificial intelligence; Software development; Operating system","score_opus":0.029587481636403706,"score_gpt":0.28425088659906433,"score_spread":0.2546634049626606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545047192","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":[0.010498795,0.0007562701,0.9878908,0.000085164844,0.0002081431,0.00037184413,0.000002473093,0.00018142654,0.0000051224974],"genre_scores_gemma":[0.56140643,0.000019506107,0.43834996,0.00007961175,0.000029593519,0.0000039834863,0.0000019985403,0.0000036631006,0.00010525569],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989179,0.000022098875,0.0002756292,0.0002747694,0.00031442815,0.00019518608],"domain_scores_gemma":[0.99920964,0.00021224658,0.00009497227,0.00025324515,0.0000903717,0.00013952005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037714408,0.000105660285,0.00019808422,0.0007712598,0.00005487195,0.00021364416,0.00057737535,0.00005500637,0.0000021002036],"category_scores_gemma":[0.000052212545,0.00009720012,0.000047880476,0.002797166,0.000010208945,0.00018534416,0.00007183803,0.000040290797,0.0000040560467],"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":[0.000029059189,0.0012145233,0.0047162483,0.00028149842,0.00018399012,0.000007947649,0.00063525356,0.00020745564,0.0036055804,0.07190991,0.028631384,0.88857716],"study_design_scores_gemma":[0.00390485,0.0019762125,0.12145403,0.00011005461,0.00007164293,0.00006892906,0.00004593128,0.8491413,0.016825432,0.00041294866,0.0051750806,0.00081359764],"about_ca_topic_score_codex":0.000013882381,"about_ca_topic_score_gemma":1.4331498e-7,"teacher_disagreement_score":0.88776356,"about_ca_system_score_codex":0.000060556027,"about_ca_system_score_gemma":0.000051935207,"threshold_uncertainty_score":0.3963706},"labels":[],"label_agreement":null},{"id":"W2547998279","doi":"10.1109/iciea.2016.7603637","title":"Cancellable multi-modal biometrie authentication for cloud based mobilityfirst like environment","year":2016,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cloud computing; Computer science; Biometrics; Server; Authentication (law); Computer security; Key (lock); The Internet; Computer network; World Wide Web; Operating system","score_opus":0.033629970104867,"score_gpt":0.25523779335076585,"score_spread":0.22160782324589884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547998279","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":[0.004218536,0.000074451236,0.9913021,0.0030963141,0.00059818407,0.00044930415,0.00003758522,0.00012962191,0.000093915536],"genre_scores_gemma":[0.8554251,0.000024786645,0.13731624,0.00031078153,0.00004408164,0.0001295292,0.0000134241445,0.000008213145,0.0067278254],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99866074,0.00004057485,0.000264775,0.0005109706,0.0002623783,0.0002605632],"domain_scores_gemma":[0.9986751,0.00023051232,0.00009410719,0.0007926395,0.0000824177,0.00012520984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059988565,0.00011156086,0.000113908245,0.00037870122,0.00013892683,0.00010382398,0.00063227955,0.00007384981,0.00026148855],"category_scores_gemma":[0.000093788716,0.00007812141,0.00008070058,0.00087933755,0.00007897756,0.00022721165,0.00009058985,0.000028403596,0.000277293],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010674863,0.0050186417,0.009027869,0.00025927872,0.00014299787,0.0000029590653,0.0010516595,0.000050608578,0.19160995,0.17174302,0.033692222,0.58729404],"study_design_scores_gemma":[0.0027324602,0.00014768975,0.011280137,0.0000134775855,0.000015613768,0.0000011150288,0.000019025649,0.22331226,0.1016327,0.0015677459,0.65881,0.0004677419],"about_ca_topic_score_codex":0.000112791146,"about_ca_topic_score_gemma":0.000020046411,"teacher_disagreement_score":0.85398585,"about_ca_system_score_codex":0.00018993217,"about_ca_system_score_gemma":0.00007218571,"threshold_uncertainty_score":0.3564133},"labels":[],"label_agreement":null},{"id":"W2552057188","doi":"10.5539/mas.v11n1p222","title":"New Combined Technique for Fingerprint Image Enhancement","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Skeletonization; Artificial intelligence; Computer science; Normalization (sociology); Histogram equalization; Histogram; Pixel; Computer vision; Adaptive histogram equalization; Pattern recognition (psychology); Image processing; Image (mathematics)","score_opus":0.018084147240617514,"score_gpt":0.26213232546285165,"score_spread":0.24404817822223412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552057188","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":[0.0003717265,0.000009494862,0.99402213,0.0018690567,0.00020541878,0.0006350546,0.0000021487406,0.0001360301,0.0027489492],"genre_scores_gemma":[0.6455541,0.00000349856,0.3531731,0.00017652902,0.000017618542,0.00015870907,3.7493643e-7,0.0000039678607,0.0009120863],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99825263,0.000008005435,0.00021374607,0.00066139555,0.00048481932,0.0003793943],"domain_scores_gemma":[0.9987525,0.00006964033,0.000094405004,0.00077131856,0.00013270687,0.00017946347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010203035,0.00011146899,0.00011416949,0.0003073445,0.00026407355,0.00026481212,0.0018358425,0.000040001305,0.000030196969],"category_scores_gemma":[0.00006831651,0.00008036859,0.00004016131,0.0011863665,0.00020293519,0.0004013984,0.0003767559,0.000046504425,0.00010351221],"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":[0.000003930215,0.000026397438,0.0000020122532,0.0000030741649,9.1754737e-7,1.415809e-7,0.00012355884,1.2681275e-7,0.7819387,0.108309194,0.00066177064,0.10893019],"study_design_scores_gemma":[0.00038164115,0.000035461606,0.00020616912,0.000007735187,0.0000013033596,0.0000011672497,0.0000027380036,0.01175222,0.8985929,0.083901726,0.0049489415,0.00016801845],"about_ca_topic_score_codex":0.0000074966733,"about_ca_topic_score_gemma":0.0000013505949,"teacher_disagreement_score":0.6451824,"about_ca_system_score_codex":0.00013642017,"about_ca_system_score_gemma":0.0003034199,"threshold_uncertainty_score":0.34114826},"labels":[],"label_agreement":null},{"id":"W2564142045","doi":"10.1109/btas.2016.7791194","title":"Pitfalls in studying “big data” from operational scenarios","year":2016,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Institute of Standards and Technology; International Research and Exchanges Board","keywords":"Computer science; Big data; Data science; Risk analysis (engineering); Data mining; Medicine","score_opus":0.14997307436268104,"score_gpt":0.29761176686795593,"score_spread":0.1476386925052749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564142045","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":[0.054173283,0.00007064267,0.9371183,0.006150059,0.00069610117,0.00011081302,0.000029223955,0.00008026927,0.0015713059],"genre_scores_gemma":[0.98181105,0.000015054157,0.016229764,0.00061424053,0.00008116705,0.0000038305125,0.000019972156,0.000002164175,0.0012227905],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999053,0.00004948673,0.00017689685,0.00037122067,0.00023518878,0.00011421296],"domain_scores_gemma":[0.9989812,0.00013243586,0.000026613056,0.0007700695,0.00004514278,0.00004453643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003718605,0.000052901843,0.00006742554,0.00017538712,0.00004850064,0.00017219884,0.0012388706,0.00003099243,0.00020997279],"category_scores_gemma":[0.000115369934,0.000034472465,0.000010712734,0.00054635346,0.000018446925,0.0006558335,0.00049144885,0.00004012724,0.0005186802],"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":[0.000006967813,0.0006413783,0.0612009,0.000004377933,0.00003439693,0.000019333544,0.0012108203,0.0000040641635,0.015379873,0.16022922,0.044477206,0.71679145],"study_design_scores_gemma":[0.0028924844,0.00004018015,0.6572232,0.00005365576,0.000005231074,0.0000049929176,0.00012274727,0.09031326,0.00474179,0.008741925,0.23516394,0.00069654593],"about_ca_topic_score_codex":0.00043187756,"about_ca_topic_score_gemma":0.0004735552,"teacher_disagreement_score":0.92763776,"about_ca_system_score_codex":0.00003171421,"about_ca_system_score_gemma":0.000071452356,"threshold_uncertainty_score":0.66667575},"labels":[],"label_agreement":null},{"id":"W2567896738","doi":"10.1167/16.12.1404","title":"Psychophysics of Fingerprint Identification","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Similarity (geometry); Computer science; Identification (biology); Fingerprint (computing); Stimulus (psychology); Matching (statistics); Mathematics; Statistics; Image (mathematics); Psychology","score_opus":0.01656237172412203,"score_gpt":0.30143698226031185,"score_spread":0.28487461053618984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567896738","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":[0.18364051,0.000092774666,0.8134075,0.001985669,0.0007312282,0.000025961062,7.841215e-7,0.0000069676335,0.00010863862],"genre_scores_gemma":[0.99166524,0.000111045156,0.008031358,0.00002658596,0.000048231912,1.7767826e-7,8.579714e-8,0.00000183296,0.00011543205],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.999069,0.000039575334,0.00041575136,0.000082451406,0.0003368621,0.00005638691],"domain_scores_gemma":[0.9986782,0.00006843942,0.00057925185,0.0002644966,0.0003669732,0.000042617656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062382343,0.000037774993,0.000093781055,0.00026774433,0.000023717193,0.000039383332,0.00047909986,0.000028868331,0.000013151363],"category_scores_gemma":[0.000108100634,0.000023115175,0.00008039641,0.0004926298,0.000023740005,0.00048279628,0.000044867036,0.00004557384,0.000026742764],"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":[0.000009160492,0.00014090432,0.00021742139,0.000007454037,0.00000804032,0.0000011884531,0.00016780764,0.0000011607273,0.49792194,0.01258084,0.0024597067,0.48648435],"study_design_scores_gemma":[0.0015339702,0.0004913876,0.68536836,0.0003257471,0.00001873983,0.000059190144,0.000030877865,0.0021252597,0.23551142,0.043513868,0.030791782,0.00022937733],"about_ca_topic_score_codex":0.00000106292,"about_ca_topic_score_gemma":1.3654181e-7,"teacher_disagreement_score":0.80802476,"about_ca_system_score_codex":0.000026088293,"about_ca_system_score_gemma":0.00003372218,"threshold_uncertainty_score":0.094260946},"labels":[],"label_agreement":null},{"id":"W2570727278","doi":"10.1109/mci.2016.2627668","title":"Bridging the Gap Between Forensics and Biometric-Enabled Watchlists for e-Borders","year":2017,"lang":"en","type":"article","venue":"IEEE Computational Intelligence Magazine","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Bridging (networking); Categorization; Law enforcement; Computer security; Border Security; Key (lock); Data science; Artificial intelligence; Business","score_opus":0.08927622442931912,"score_gpt":0.35735098209734945,"score_spread":0.26807475766803035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2570727278","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":[0.0054640747,0.00015722564,0.98348314,0.009358111,0.00075083756,0.00036508992,0.000037044636,0.000072920135,0.00031158587],"genre_scores_gemma":[0.93971896,0.000036298403,0.05916702,0.0003602685,0.00022262671,0.000025319672,0.000030158466,0.000010646324,0.00042870754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984458,0.000041584633,0.00036391392,0.0004580539,0.00039970607,0.00029093874],"domain_scores_gemma":[0.99768955,0.00074893335,0.00029256556,0.00062895456,0.00052859704,0.00011140057],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008434254,0.00016397968,0.00019277734,0.0003965042,0.001031844,0.0011911341,0.0014185774,0.000066583496,0.0000070140536],"category_scores_gemma":[0.00052921247,0.00013074411,0.000082799896,0.00085851626,0.00031240698,0.00053332193,0.00023190049,0.00013469472,0.00006501449],"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":[0.000012638453,0.000057605554,0.0020196796,0.00006819665,0.00008738807,0.0000047578897,0.00062037644,0.0027400192,0.00013342348,0.1396147,0.014546352,0.84009486],"study_design_scores_gemma":[0.000313496,0.00007601679,0.044913877,0.000024392122,0.00002483563,0.000019841382,0.000026231017,0.75085,0.002140531,0.18104939,0.020195872,0.00036550366],"about_ca_topic_score_codex":0.000045725494,"about_ca_topic_score_gemma":0.000012206093,"teacher_disagreement_score":0.9342549,"about_ca_system_score_codex":0.000042538537,"about_ca_system_score_gemma":0.00008692778,"threshold_uncertainty_score":0.99984574},"labels":[],"label_agreement":null},{"id":"W2575991389","doi":"10.1049/iet-bmt.2016.0067","title":"Efficient multimodal ocular biometric system for person authentication based on iris texture and corneal shape","year":2017,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hôpital Maisonneuve-Rosemont; Université de Montréal; Montreal Heart Institute; Hôtel-Dieu de Montréal","funders":"","keywords":"Biometrics; Computer science; Zernike polynomials; Iris recognition; IRIS (biosensor); Artificial intelligence; Pattern recognition (psychology); Fingerprint (computing); Linear discriminant analysis; Authentication (law); Feature extraction; Identifier; Computer vision","score_opus":0.03938993771644497,"score_gpt":0.27877598299801654,"score_spread":0.23938604528157156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575991389","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":[0.115724124,0.000514325,0.8789758,0.0014605863,0.0017146071,0.00090919755,0.00017208932,0.00024713227,0.00028213617],"genre_scores_gemma":[0.9731756,0.000009210031,0.026412347,0.00011058517,0.000082605315,0.000044031272,0.000034682005,0.000016898119,0.00011402958],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766684,0.00006713247,0.00031707893,0.0008060938,0.00074880396,0.00039407334],"domain_scores_gemma":[0.9971212,0.0004118764,0.0004359476,0.0013896329,0.0004029502,0.00023835075],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001282305,0.0002375478,0.00027577236,0.006352381,0.0009163594,0.0012772063,0.0013581029,0.00024526767,0.0000070429146],"category_scores_gemma":[0.0014326208,0.0002149588,0.00015280311,0.008099459,0.0001456792,0.00016616256,0.00015123372,0.00013985804,0.00005628053],"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":[0.00013564451,0.0020201968,0.0070710806,0.0012496944,0.00014875938,0.000036378642,0.0012148523,0.00022895701,0.009010466,0.026592728,0.0053793606,0.9469119],"study_design_scores_gemma":[0.0010845931,0.00017674822,0.05864333,0.000026404494,0.00002602645,0.000005702167,0.000049472506,0.9343157,0.0012915734,0.000014267276,0.0041039973,0.00026219073],"about_ca_topic_score_codex":0.00005649843,"about_ca_topic_score_gemma":6.5751664e-7,"teacher_disagreement_score":0.9466497,"about_ca_system_score_codex":0.00021544151,"about_ca_system_score_gemma":0.00007264147,"threshold_uncertainty_score":0.99975955},"labels":[],"label_agreement":null},{"id":"W2591206973","doi":"10.1109/lsens.2017.2673551","title":"Wireless Biometric Individual Identification Utilizing Millimeter Waves","year":2017,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Google","keywords":"Biometrics; Computer science; Transmitter; Extremely high frequency; Radar; Identification (biology); Wireless; Authentication (law); Feature (linguistics); SIGNAL (programming language); Signal processing; Real-time computing; Artificial intelligence; Telecommunications; Computer security; Channel (broadcasting)","score_opus":0.04973186617853767,"score_gpt":0.2815374972757827,"score_spread":0.23180563109724503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591206973","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9322817,0.000057574798,0.057490457,0.006584503,0.0029557461,0.00019276008,0.00002271203,0.00016477391,0.00024978173],"genre_scores_gemma":[0.99438286,0.000027787253,0.0038049717,0.0010845887,0.0001867094,0.000008473948,0.00001338859,0.000014914517,0.00047631213],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99778855,0.000114436494,0.0003998747,0.00064670946,0.0006533919,0.00039703862],"domain_scores_gemma":[0.99746025,0.000109156696,0.00043275786,0.0017523564,0.00010820605,0.00013726787],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00074668933,0.00019411808,0.00019869347,0.0014072129,0.000821989,0.0019670427,0.0023360963,0.00010737645,0.000013075337],"category_scores_gemma":[0.00016473075,0.00019484734,0.00013233755,0.0013236129,0.00021665671,0.0010731355,0.00020107225,0.00018870596,0.00038171627],"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":[0.0000112292255,0.00026255383,0.00604283,0.000081253995,0.00019913445,0.00009490271,0.0029996212,0.000018827186,0.7552138,0.0045725917,0.05068889,0.17981434],"study_design_scores_gemma":[0.0013178021,0.000049027996,0.5126036,0.00005031843,0.00007735143,0.000063220985,0.00014646936,0.024553934,0.43060154,0.0005467505,0.028587516,0.0014025102],"about_ca_topic_score_codex":0.0000669996,"about_ca_topic_score_gemma":0.0000033720464,"teacher_disagreement_score":0.50656074,"about_ca_system_score_codex":0.00005817643,"about_ca_system_score_gemma":0.000022769535,"threshold_uncertainty_score":0.99906904},"labels":[],"label_agreement":null},{"id":"W2732080625","doi":"10.1017/9781316450840.017","title":"Joint Privacy and Security of Multiple Biometric Systems","year":2017,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Sketch; Biometrics; Theoretical computer science; Combinatorial design; Subspace topology; Computer security; Context (archaeology); Authentication (law); Information privacy; Mathematics; Algorithm; Artificial intelligence","score_opus":0.04468485170182053,"score_gpt":0.21466972062971762,"score_spread":0.1699848689278971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732080625","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00052052544,0.0012962117,0.020375235,0.000024503164,0.0008247547,0.0006266368,0.00036355527,0.00015281436,0.9758158],"genre_scores_gemma":[0.109733656,0.00034758737,0.00044787046,0.000009139912,0.000050864088,6.3273916e-7,0.000022698137,0.000017624578,0.8893699],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99835134,0.000053271582,0.00030199194,0.000652487,0.00041987305,0.00022104004],"domain_scores_gemma":[0.99706566,0.00010243266,0.0007361437,0.0015301481,0.00036108305,0.00020450841],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030720848,0.00028438756,0.0005220138,0.001425323,0.00026853927,0.00023889645,0.0015756262,0.00036480284,8.1127615e-7],"category_scores_gemma":[0.00008257508,0.0003239398,0.00016776717,0.000052102958,0.00034347383,0.00025080526,0.0014005267,0.00031060365,0.000006938334],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009448282,0.000021262713,0.000014289395,0.00032292443,0.000096822994,0.000058741465,0.00014398895,1.5801147e-7,0.0000752543,0.9885605,0.007608447,0.0030881728],"study_design_scores_gemma":[0.00061280146,0.000043789372,0.00033577345,0.00014640203,0.000068028654,0.000024791943,0.0000114957875,0.0029185885,0.0004762403,0.000038009737,0.9949159,0.0004081843],"about_ca_topic_score_codex":0.0005636215,"about_ca_topic_score_gemma":0.0000013232215,"teacher_disagreement_score":0.98852247,"about_ca_system_score_codex":0.00011606831,"about_ca_system_score_gemma":0.00011880622,"threshold_uncertainty_score":0.99992126},"labels":[],"label_agreement":null},{"id":"W2735991754","doi":"10.7274/3f462516j7w","title":"Effects of Segmentation Routine and Acquisition Environment on Iris Recognition","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Iris recognition; Artificial intelligence; IRIS (biosensor); Biometrics; Segmentation; Computer science; Computer vision; Process (computing); Image segmentation; Pattern recognition (psychology)","score_opus":0.02292948093886231,"score_gpt":0.22381532386121697,"score_spread":0.20088584292235467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2735991754","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.607383,0.0045521525,0.0505229,0.004961474,0.0018377083,0.0076280576,0.31823856,0.0010700169,0.0038061172],"genre_scores_gemma":[0.9762978,0.000008150839,0.0012329972,0.0003032557,0.000016477845,0.00021787439,0.021852318,0.0000037301556,0.000067377114],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994306,0.00006906565,0.00008755103,0.00015482362,0.00020310486,0.000054885666],"domain_scores_gemma":[0.99969476,0.0000768745,0.000082807375,0.000107918015,0.000014417352,0.000023231849],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000060821232,0.00004147651,0.000045406316,0.00012524644,0.00009326121,0.000025705029,0.00011107858,0.000015096614,0.010389312],"category_scores_gemma":[0.000043322754,0.00004537747,0.000017503187,0.00024145907,0.0000023867503,0.00010657415,0.00012478394,0.00004845063,0.00016116211],"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":[0.00005009318,0.0010163609,0.00012209015,0.0007726628,0.000044445642,0.000022485963,0.0031669904,0.00011522462,0.024782678,0.00064486323,0.22928102,0.73998106],"study_design_scores_gemma":[0.004930069,0.0021754298,0.28275135,0.0006626966,0.00004513689,0.000048202197,0.0003030374,0.036680635,0.52107036,0.003183121,0.14706835,0.0010816111],"about_ca_topic_score_codex":0.0000023398284,"about_ca_topic_score_gemma":5.2153165e-8,"teacher_disagreement_score":0.73889947,"about_ca_system_score_codex":0.000046207613,"about_ca_system_score_gemma":0.0000061755127,"threshold_uncertainty_score":0.99051535},"labels":[],"label_agreement":null},{"id":"W2737771956","doi":"","title":"Changeable and privacy preserving face recognition","year":2010,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Password; Computer science; Computer security; Random projection; Adversary; Identity theft; Authentication (law); Identity (music); Facial recognition system; Face (sociological concept); Information privacy; Internet privacy; Human–computer interaction; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.029564080498025714,"score_gpt":0.2528995618337236,"score_spread":0.22333548133569786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737771956","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7350967,0.0054734387,0.043701425,0.0025028775,0.0027670895,0.0011441961,0.000074209805,0.00045061004,0.20878947],"genre_scores_gemma":[0.8623142,0.0029204413,0.043479633,0.000033288234,0.00007734867,0.0000013029573,0.0005518039,0.000022326805,0.09059964],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991451,0.00004060352,0.00007725907,0.00035425322,0.00023662679,0.00014618823],"domain_scores_gemma":[0.99900204,0.000033644246,0.0002481016,0.00043534304,0.00019216174,0.00008872727],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018079209,0.00012281138,0.00019167725,0.00013233868,0.00018532664,0.00006691598,0.00076025445,0.00028107522,0.0025278039],"category_scores_gemma":[0.000039962913,0.00016474615,0.000059425965,0.00018353821,0.00003191742,0.00093084056,0.00018537289,0.0001864159,0.000025661737],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054574106,0.00015140923,0.00008621069,0.00058007287,0.00008690277,0.000009363088,0.090541065,2.1691024e-7,0.005532539,0.001920695,0.003964551,0.8970724],"study_design_scores_gemma":[0.002255869,0.00030899074,0.82416946,0.0004908934,0.00026749034,0.000015534004,0.04969455,0.014895104,0.004743107,0.00146003,0.099807926,0.0018910632],"about_ca_topic_score_codex":0.045883767,"about_ca_topic_score_gemma":0.05374521,"teacher_disagreement_score":0.89518136,"about_ca_system_score_codex":0.000069067806,"about_ca_system_score_gemma":0.000064211425,"threshold_uncertainty_score":0.998384},"labels":[],"label_agreement":null},{"id":"W2739414687","doi":"10.1049/iet-bmt.2017.0036","title":"Biometric‐enabled watchlists technology","year":2017,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Vysoké Učení Technické v Brně","keywords":"Biometrics; Computer science; Quality (philosophy); Identification (biology); Metric (unit); Fingerprint (computing); Data science; Computer security","score_opus":0.032436854570932476,"score_gpt":0.29562337529221294,"score_spread":0.26318652072128046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739414687","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":[0.0350012,0.0021726904,0.9220899,0.017801221,0.0057670316,0.00050306536,0.0000466781,0.0013035005,0.015314727],"genre_scores_gemma":[0.9581281,0.0002966235,0.038826533,0.0002058598,0.000089921654,0.000017658973,0.000009046495,0.000015262052,0.002410977],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99755543,0.000037520247,0.00041020635,0.0007319892,0.0006952997,0.0005695259],"domain_scores_gemma":[0.99565035,0.00013080915,0.00044629176,0.0030904668,0.00046580302,0.00021629779],"candidate_categories":["bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0010060259,0.00021675767,0.00029832282,0.015249691,0.0009234695,0.0016745423,0.005204969,0.00033952054,0.00004662625],"category_scores_gemma":[0.0029197938,0.00020223392,0.000123285,0.034208026,0.0002893294,0.00092469057,0.0012295044,0.00023869051,0.0007886501],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062589816,0.00058242877,0.019645892,0.000057024645,0.00008322291,0.00009672635,0.00013507846,3.8851894e-7,0.0066983593,0.13197714,0.029996121,0.81072134],"study_design_scores_gemma":[0.0012510841,0.00016351655,0.081096806,0.000014392716,0.000018902367,0.00006164516,0.000030700096,0.00433627,0.029781558,0.013313529,0.8691395,0.0007921356],"about_ca_topic_score_codex":0.00010113294,"about_ca_topic_score_gemma":0.000004625368,"teacher_disagreement_score":0.92312694,"about_ca_system_score_codex":0.00012825707,"about_ca_system_score_gemma":0.000098951095,"threshold_uncertainty_score":0.99998933},"labels":[],"label_agreement":null},{"id":"W2757862359","doi":"10.1109/mwscas.2017.8053086","title":"Palmprint recognition based on histograms of sparse codes","year":2017,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Hash function; Feature (linguistics); Pattern recognition (psychology); Histogram; Artificial intelligence; Feature extraction; Encoding (memory); Hash table; Image (mathematics)","score_opus":0.0816534731218663,"score_gpt":0.2859656008480693,"score_spread":0.20431212772620302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757862359","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":[0.033676837,0.000008545326,0.92136526,0.0018115204,0.0005302573,0.000119709344,0.0000054599645,0.00009472525,0.042387675],"genre_scores_gemma":[0.9709532,0.0000037713844,0.028505588,0.00017989465,0.00000946151,0.000003529412,0.0000040050786,0.0000015241051,0.0003389745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945337,0.000023325809,0.0001178865,0.00016320815,0.00017006909,0.00007214283],"domain_scores_gemma":[0.9989448,0.00003412918,0.00012894558,0.00075765,0.00009496422,0.000039467603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027412592,0.000043612676,0.00006822,0.00013947846,0.000121764395,0.00013213018,0.00057715253,0.00003194313,0.00008676636],"category_scores_gemma":[0.00012030565,0.00003766014,0.000040947063,0.00013158136,0.000055364602,0.00013142353,0.000057463218,0.00004003473,0.000110579014],"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":[0.0000104849,0.00043042804,0.0025905815,0.000024483146,0.0000069801163,0.0000030659305,0.00012334132,0.0000052534942,0.0014883281,0.04872907,0.0024735339,0.94411445],"study_design_scores_gemma":[0.0016105587,0.00032479962,0.3433274,0.00007715356,0.000014005579,0.0000033407828,0.000032885368,0.44745508,0.13608532,0.017022433,0.05350369,0.0005433336],"about_ca_topic_score_codex":0.00018185658,"about_ca_topic_score_gemma":0.000025544512,"teacher_disagreement_score":0.9435711,"about_ca_system_score_codex":0.000016402186,"about_ca_system_score_gemma":0.00002142018,"threshold_uncertainty_score":0.15357359},"labels":[],"label_agreement":null},{"id":"W2766451175","doi":"10.1049/iet-bmt.2017.0015","title":"Symmetric sum‐based biometric score fusion","year":2017,"lang":"ru","type":"article","venue":"IET Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"NIST; Biometrics; Computer science; Benchmark (surveying); Fingerprint (computing); Data mining; Sensor fusion; Artificial intelligence; Information retrieval; Pattern recognition (psychology); Speech recognition","score_opus":0.08945076789636883,"score_gpt":0.31199198973297076,"score_spread":0.22254122183660194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766451175","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":[0.10755987,0.041326363,0.75871533,0.014267998,0.04778475,0.0032839833,0.0014465814,0.0014499229,0.024165215],"genre_scores_gemma":[0.9762787,0.0023716937,0.015200234,0.00073010457,0.0006393582,0.000027598215,0.0001302298,0.00008865005,0.0045333817],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.990326,0.0003629419,0.001594763,0.0024295903,0.0035047363,0.0017819875],"domain_scores_gemma":[0.9867106,0.0011705228,0.0023351263,0.0069053764,0.0017126179,0.0011657533],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0043034027,0.0009578805,0.0011036955,0.05555829,0.0034197061,0.008502488,0.009589635,0.0011310824,0.00048823818],"category_scores_gemma":[0.011450691,0.00097176805,0.00076996814,0.12400006,0.0009099965,0.0019437791,0.002458601,0.0009287023,0.0032942728],"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":[0.00004779152,0.002431917,0.041339323,0.00053556287,0.00016743895,0.0002179407,0.00018179626,0.0000052064347,0.0011473584,0.011297789,0.035317738,0.9073101],"study_design_scores_gemma":[0.004162958,0.00086466543,0.48357,0.00022841217,0.00024397382,0.000046073365,0.0000487785,0.09431988,0.009422443,0.00081852893,0.40367034,0.0026039581],"about_ca_topic_score_codex":0.0011222971,"about_ca_topic_score_gemma":0.000021893418,"teacher_disagreement_score":0.9047062,"about_ca_system_score_codex":0.00070740597,"about_ca_system_score_gemma":0.00084503036,"threshold_uncertainty_score":0.9992733},"labels":[],"label_agreement":null},{"id":"W2766487071","doi":"10.1101/209726","title":"Multi-modal brain fingerprinting: a manifold approximation based framework","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"NIH Blueprint for Neuroscience Research; National Institutes of Health","keywords":"Pattern recognition (psychology); Artificial intelligence; Computer science; Nonlinear dimensionality reduction; Connectome; Discriminative model; Human Connectome Project; Modal; Resting state fMRI; Graph; Biometrics; Fingerprint (computing); Dimensionality reduction; Psychology; Theoretical computer science; Functional connectivity","score_opus":0.03029322372509963,"score_gpt":0.2592317771356145,"score_spread":0.2289385534105149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766487071","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":[0.036447607,0.00038497167,0.955531,0.002710099,0.0028997522,0.00085540925,0.000075452575,0.0010797003,0.000016031365],"genre_scores_gemma":[0.6801007,0.000020212123,0.3188914,0.0005200762,0.00024714562,0.00014936096,5.2003764e-7,0.00005628088,0.000014329411],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99561995,0.00026640765,0.0007481267,0.0018279377,0.0008314604,0.0007061019],"domain_scores_gemma":[0.9927732,0.00023904188,0.001173584,0.004739054,0.0007152314,0.00035992207],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0019819385,0.0006385839,0.0006124142,0.00095653115,0.0006090903,0.0025619636,0.003898517,0.0010445679,0.000033802058],"category_scores_gemma":[0.0021570956,0.0007318812,0.00029803874,0.0010369143,0.00012148375,0.00055364653,0.0015909189,0.0013819026,0.00023232994],"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":[0.000084521926,0.004939372,0.019557634,0.005308795,0.0008867005,0.00047227312,0.00047749534,0.00057114754,0.67442495,0.28361812,0.009218973,0.0004400105],"study_design_scores_gemma":[0.001441421,0.000058360445,0.28953996,0.0012432702,0.000096767115,4.948826e-8,0.000002671398,0.554675,0.124558516,0.00015439009,0.025329048,0.0029005734],"about_ca_topic_score_codex":0.00008091849,"about_ca_topic_score_gemma":0.0000022208865,"teacher_disagreement_score":0.6436531,"about_ca_system_score_codex":0.00032123926,"about_ca_system_score_gemma":0.0008582437,"threshold_uncertainty_score":0.9995132},"labels":[],"label_agreement":null},{"id":"W2768888666","doi":"10.3390/cryptography1030022","title":"Learning Global-Local Distance Metrics for Signature-Based Biometric Cryptosystems","year":2017,"lang":"en","type":"article","venue":"Cryptography","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Metric (unit); Cryptosystem; Computer science; Pattern recognition (psychology); Cryptography; Signature (topology); Data mining; Class (philosophy); Digital signature; Artificial intelligence; Error detection and correction; Machine learning; Algorithm; Mathematics; Computer security; Engineering","score_opus":0.022424117950557508,"score_gpt":0.2836702587214891,"score_spread":0.2612461407709316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768888666","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":[0.0021269915,0.0017345472,0.99193794,0.0005077114,0.001469644,0.00040675534,0.000042548894,0.00030121292,0.0014726518],"genre_scores_gemma":[0.9418184,0.000027593376,0.05780977,0.00012453679,0.00008476677,0.00006395633,0.000021614875,0.000012344953,0.000037022317],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977682,0.00008798351,0.00036950747,0.0006961567,0.00059399457,0.00048416303],"domain_scores_gemma":[0.9972048,0.0002632873,0.00048330994,0.001381975,0.00043882258,0.00022785082],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008707657,0.00023364251,0.00029884232,0.0018722056,0.001277758,0.0015332156,0.0024603596,0.00020910868,0.0000074243408],"category_scores_gemma":[0.00081453973,0.00022430472,0.00037720072,0.008443491,0.00024864147,0.0005409691,0.00017436944,0.0002090819,0.000026564405],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000614096,0.00043711183,0.057437714,0.0003041442,0.00014010727,0.000012442255,0.00012394163,0.00011848105,0.0004528766,0.58043355,0.0073297154,0.35314852],"study_design_scores_gemma":[0.0033360075,0.0006338031,0.08086392,0.000084217885,0.00006934719,0.000006692655,0.00010403485,0.19364218,0.0038159213,0.022735989,0.69336355,0.0013443632],"about_ca_topic_score_codex":0.00005006659,"about_ca_topic_score_gemma":0.000008536201,"teacher_disagreement_score":0.9396914,"about_ca_system_score_codex":0.00009296043,"about_ca_system_score_gemma":0.000089741465,"threshold_uncertainty_score":0.9995033},"labels":[],"label_agreement":null},{"id":"W2785374190","doi":"10.1109/crv.2017.29","title":"Manifold Learning of Overcomplete Feature Spaces in a Multimodal Biometric Recognition System of Iris and Palmprint","year":2017,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Computer science; Dimensionality reduction; Feature extraction; Discrete cosine transform; Discriminative model; Iris recognition; Biometrics; Feature vector; Gabor filter; Feature (linguistics); Singular value decomposition; Mathematics; Image (mathematics)","score_opus":0.03472622500887903,"score_gpt":0.2634050200451452,"score_spread":0.22867879503626617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785374190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9353621,0.00018048019,0.061985787,0.00029683052,0.00015753509,0.00016034277,0.0000066739026,0.00004057781,0.0018096515],"genre_scores_gemma":[0.9817336,0.000039427374,0.018079737,0.000004730586,0.0000075333314,0.0000025318495,0.0000021386745,0.0000024178978,0.0001278775],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992155,0.000058083097,0.00020214185,0.0002240043,0.00019684603,0.00010344681],"domain_scores_gemma":[0.9991169,0.00007210138,0.0003074986,0.00035247073,0.000112705,0.00003832832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005016912,0.00006832724,0.00017162919,0.0009944771,0.000086664266,0.0001432885,0.00039253174,0.00007065028,0.0000064809487],"category_scores_gemma":[0.0001910119,0.000060529535,0.000035146997,0.00089515705,0.00004285665,0.00027292466,0.00021140133,0.00009762562,0.000005461978],"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":[0.00004590504,0.00043115063,0.3422971,0.0017006728,0.00008535182,0.000024756713,0.003318177,0.000011680702,0.047687177,0.062470417,0.00032896647,0.5415986],"study_design_scores_gemma":[0.0006723763,0.000068344896,0.91358644,0.00010703451,0.000005846949,0.000011374341,0.00033559938,0.0735627,0.010826073,0.00016128359,0.00052432256,0.0001385926],"about_ca_topic_score_codex":0.0010861253,"about_ca_topic_score_gemma":0.00003225514,"teacher_disagreement_score":0.57128936,"about_ca_system_score_codex":0.000023712224,"about_ca_system_score_gemma":0.000013455191,"threshold_uncertainty_score":0.24683228},"labels":[],"label_agreement":null},{"id":"W2785901951","doi":"10.1109/ssci.2017.8285219","title":"Watchlist risk assessment using multiparametric cost and relative entropy","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Entropy (arrow of time); Kullback–Leibler divergence; Identification (biology); Detector; Key (lock); Risk analysis (engineering); Computer security; Artificial intelligence; Business; Telecommunications; Physics","score_opus":0.07501414709195132,"score_gpt":0.3593398202264209,"score_spread":0.2843256731344696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785901951","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":[0.015907645,0.00049733213,0.9775147,0.0006233594,0.0015610963,0.0006595919,0.000048998536,0.00014111376,0.0030461813],"genre_scores_gemma":[0.65743524,0.0007847624,0.34042123,0.00006152547,0.000067591325,0.000024926157,0.00002400468,0.000010848123,0.0011698926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976547,0.00023129334,0.00038385685,0.0009613332,0.00048504947,0.0002837978],"domain_scores_gemma":[0.99683946,0.0002534637,0.00075313175,0.0016662162,0.00029860303,0.00018913858],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010323233,0.0002657722,0.00036334302,0.0007883262,0.00054506474,0.0020229644,0.0014060631,0.0003124359,0.000029047382],"category_scores_gemma":[0.00054052065,0.00023620155,0.000119317454,0.00055608194,0.00015069236,0.0005465584,0.0026899942,0.0008750578,0.000027889337],"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":[0.000019842742,0.0011004005,0.11231732,0.00038797164,0.0008546819,0.000081739396,0.003307315,0.0005465675,0.0003369219,0.41362074,0.007944315,0.4594822],"study_design_scores_gemma":[0.0005176176,0.000019065257,0.11834315,0.000043212847,0.000062550396,0.000009195888,0.000021841588,0.84915143,0.0001375034,0.018386547,0.012808519,0.00049938704],"about_ca_topic_score_codex":0.002048247,"about_ca_topic_score_gemma":0.000025304564,"teacher_disagreement_score":0.84860486,"about_ca_system_score_codex":0.00028138814,"about_ca_system_score_gemma":0.00025053584,"threshold_uncertainty_score":0.999013},"labels":[],"label_agreement":null},{"id":"W2786433123","doi":"10.1109/dcoss.2017.19","title":"A Privacy Enhanced Facial Recognition Access Control System Using Biometric Encryption","year":2017,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Biometrics; Encryption; Computer science; Computer security; Information privacy; Key (lock); Access control; Scheme (mathematics); Internet privacy","score_opus":0.11144228489198048,"score_gpt":0.34136933439646194,"score_spread":0.22992704950448145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786433123","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":[0.14872772,0.000020919253,0.84726566,0.00017612634,0.0010359561,0.00025395345,0.000007886125,0.00019485524,0.0023169103],"genre_scores_gemma":[0.98766077,0.000008305977,0.012046132,0.000066592205,0.00010286158,0.000013986046,0.0000057030893,0.0000045419365,0.00009110181],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987052,0.00007322068,0.00027968388,0.0003899704,0.0003381356,0.00021378585],"domain_scores_gemma":[0.99841464,0.000045835084,0.00035971473,0.0008165212,0.00027095436,0.00009235038],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005061806,0.000108470354,0.00015985798,0.0010253472,0.0006760733,0.0021560318,0.001621955,0.00009561719,0.000038700695],"category_scores_gemma":[0.00031208675,0.000099968536,0.00007092955,0.0013396834,0.000045226065,0.0021313566,0.00025990332,0.00007586022,0.00020109162],"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":[0.000024565701,0.00018943059,0.0012548694,0.00012166912,0.00005345633,0.000008252278,0.0003702441,0.000004889054,0.0523925,0.011673674,0.00027170428,0.93363476],"study_design_scores_gemma":[0.005659716,0.00013666824,0.15134181,0.00015692056,0.00008055303,0.000059492355,0.00012488873,0.68942815,0.14328973,0.00385668,0.0045466754,0.0013187018],"about_ca_topic_score_codex":0.00028658786,"about_ca_topic_score_gemma":0.0000054221064,"teacher_disagreement_score":0.93231606,"about_ca_system_score_codex":0.00013435705,"about_ca_system_score_gemma":0.00006047223,"threshold_uncertainty_score":0.99887985},"labels":[],"label_agreement":null},{"id":"W2786741554","doi":"10.1109/btas.2017.8272677","title":"Risk assessment in the face-based watchlist screening in e-borders","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Biometrics; Task (project management); Component (thermodynamics); Modality (human–computer interaction); Facial recognition system; Identification (biology); Key (lock); Face (sociological concept); Scale (ratio); Face Recognition Grand Challenge; Computer security; Face detection; Artificial intelligence; Feature extraction; Engineering; Systems engineering","score_opus":0.05512815934277564,"score_gpt":0.3582858640297113,"score_spread":0.30315770468693565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786741554","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":[0.009398086,0.00011382347,0.9686783,0.014619065,0.0005265566,0.0005391405,0.000012614293,0.000072695104,0.006039749],"genre_scores_gemma":[0.95320785,0.00006976886,0.045851167,0.0005058519,0.000022739636,0.00007595824,0.000030841733,0.0000057500547,0.00023009637],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99771285,0.0004143446,0.00038538242,0.0006542762,0.000564747,0.00026838132],"domain_scores_gemma":[0.99732727,0.00020542489,0.00033270137,0.0020118754,0.00007674843,0.000046003563],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0029311196,0.0001926864,0.00023550031,0.0006658303,0.00018160579,0.0014406905,0.0038187755,0.00021042998,0.000025864254],"category_scores_gemma":[0.00017980575,0.00014108718,0.00010562993,0.0006526115,0.00007220288,0.00019670173,0.00087147724,0.001199557,0.000017291228],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020348001,0.0016226865,0.32690805,0.00028852845,0.00008990411,0.0001456087,0.011153426,0.014366415,0.000018972589,0.053144015,0.013332047,0.57891],"study_design_scores_gemma":[0.000497359,0.0000099599665,0.52505714,0.00005362308,0.0000049119926,6.384565e-7,0.00027256992,0.45928785,0.000029865887,0.0036098487,0.010899324,0.00027694434],"about_ca_topic_score_codex":0.007948119,"about_ca_topic_score_gemma":0.002792554,"teacher_disagreement_score":0.94380975,"about_ca_system_score_codex":0.00009194138,"about_ca_system_score_gemma":0.00035915902,"threshold_uncertainty_score":0.9995959},"labels":[],"label_agreement":null},{"id":"W2790644461","doi":"10.1109/tifs.2018.2807790","title":"Normalization and Weighting Techniques Based on Genuine-Impostor Score Fusion in Multi-Biometric Systems","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normalization (sociology); Biometrics; Weighting; Computer science; Word error rate; Pattern recognition (psychology); Artificial intelligence; Fingerprint (computing); Data mining","score_opus":0.019593229606397113,"score_gpt":0.24407388149218626,"score_spread":0.22448065188578914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790644461","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":[0.041412324,0.000035059147,0.9570029,0.00014025,0.0005663343,0.0003858049,0.000037704074,0.00014445955,0.00027519802],"genre_scores_gemma":[0.9907699,0.00010211535,0.0087133935,0.0003274433,0.000021919499,0.00002651343,0.000020887532,0.0000054521333,0.0000123274685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874735,0.00006024178,0.00044974277,0.00022541656,0.0003294847,0.00018776607],"domain_scores_gemma":[0.9991017,0.000062686064,0.00017594137,0.00027941633,0.0002804922,0.00009975446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000532666,0.00015857439,0.00015628991,0.00191268,0.00034354714,0.0003506938,0.00016590019,0.00015483118,0.000005696943],"category_scores_gemma":[0.000023747527,0.000149654,0.000035636298,0.002210721,0.000095754556,0.0012577699,0.000006354492,0.00018878996,0.000013930118],"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":[0.00026454573,0.0010445899,0.0024233926,0.00084324146,0.00005063406,0.000006846576,0.019235302,0.0011476189,0.00100508,0.054180745,0.0010739296,0.91872406],"study_design_scores_gemma":[0.0006636426,0.00026710896,0.00174716,0.000075676086,0.0000062595977,0.0000096837375,0.00009242686,0.98687273,0.004747586,0.00019205244,0.0051018223,0.00022382745],"about_ca_topic_score_codex":0.00015113474,"about_ca_topic_score_gemma":0.000048883943,"teacher_disagreement_score":0.98572516,"about_ca_system_score_codex":0.0000815229,"about_ca_system_score_gemma":0.000039155388,"threshold_uncertainty_score":0.61027133},"labels":[],"label_agreement":null},{"id":"W2790737570","doi":"10.5539/mas.v12n4p49","title":"A Novel Multi-Level Security Technique Based on IRIS Image Encoding","year":2018,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Encryption; Encoding (memory); IRIS (biosensor); Authentication (law); Mobile phone; Image (mathematics); Iris recognition; Access control; Computer security; Plaintext; Computer hardware; Embedded system; Computer vision; Artificial intelligence; Operating system; Biometrics","score_opus":0.057149464572927605,"score_gpt":0.296060192887671,"score_spread":0.2389107283147434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790737570","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":[0.0012383583,0.000003768914,0.9875053,0.00037472363,0.0002461169,0.00042158843,0.000014126191,0.00031144172,0.00988453],"genre_scores_gemma":[0.6544714,5.2792063e-7,0.3447517,0.0006292563,0.000031677184,0.000054183995,0.000001077609,0.00000616,0.000054012093],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971239,0.000023225055,0.00027156906,0.0010671071,0.0009769852,0.0005372142],"domain_scores_gemma":[0.998046,0.00007088729,0.00013717877,0.0012206034,0.00030374696,0.00022158939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019688073,0.00019584998,0.0001627916,0.0008356429,0.00075016235,0.0005908423,0.0026067079,0.00009235196,0.000023691615],"category_scores_gemma":[0.00015821503,0.00018428003,0.000053404445,0.0039037287,0.0009612688,0.0005167313,0.0004404397,0.00023610654,0.0002095518],"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":[0.0000054134166,0.00024888912,0.000013302675,0.00000742483,0.0000011088772,0.0000013526035,0.00085505174,0.0000056088147,0.96970093,0.017773852,0.00017677298,0.0112103],"study_design_scores_gemma":[0.00030335807,0.000031564065,0.0011715374,0.000009161228,0.0000014250354,0.0000042281335,0.000012004469,0.77627873,0.2188882,0.0023251604,0.0007481283,0.00022647537],"about_ca_topic_score_codex":0.00003300584,"about_ca_topic_score_gemma":0.000009827259,"teacher_disagreement_score":0.77627313,"about_ca_system_score_codex":0.00017874513,"about_ca_system_score_gemma":0.00032036696,"threshold_uncertainty_score":0.7514721},"labels":[],"label_agreement":null},{"id":"W2791250578","doi":"10.1117/12.2293353","title":"Computer-aided detection of basal cell carcinoma through blood content analysis in dermoscopy images","year":2018,"lang":"en","type":"article","venue":"Medical Imaging 2018: Computer-Aided Diagnosis","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"","keywords":"Basal cell carcinoma; Skin cancer; Lesion; Pathology; Computer-aided diagnosis; Medicine; Cancer; Basal cell; Computer science; Radiology; Internal medicine","score_opus":0.027913143335429807,"score_gpt":0.2699515861420118,"score_spread":0.24203844280658202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791250578","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":[0.3293664,0.0005061056,0.6663817,0.0016849316,0.0015406383,0.00026126168,0.000013219755,0.0001542926,0.00009146973],"genre_scores_gemma":[0.9255311,0.00013607854,0.07201327,0.0016999008,0.00048903556,0.000060916806,0.000022814334,0.00002526784,0.000021654876],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99445504,0.00057214434,0.0014032746,0.0013225114,0.0014521013,0.00079495256],"domain_scores_gemma":[0.99613637,0.0009477775,0.0005525828,0.0013368838,0.00058583374,0.0004405322],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013777909,0.000459444,0.000934223,0.0016316795,0.00023649746,0.00035547843,0.0020908273,0.00022871817,0.00015952699],"category_scores_gemma":[0.00027693008,0.00044789354,0.00042776312,0.0056965942,0.0006364899,0.00090225996,0.0009430759,0.0004767038,0.0000895879],"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":[0.000058456462,0.006017955,0.68223697,0.0003478286,0.0007567706,0.00048552165,0.0038689312,0.000056655947,0.004594392,0.0011728018,0.0197196,0.28068408],"study_design_scores_gemma":[0.0029736666,0.00040370988,0.3748786,0.000140773,0.00032697048,0.000036350662,0.000055547916,0.4789384,0.13955247,0.0006008054,0.0013583775,0.0007343389],"about_ca_topic_score_codex":0.0032420412,"about_ca_topic_score_gemma":0.0002358825,"teacher_disagreement_score":0.5961647,"about_ca_system_score_codex":0.00014480036,"about_ca_system_score_gemma":0.00018927388,"threshold_uncertainty_score":0.9997973},"labels":[],"label_agreement":null},{"id":"W2794104577","doi":"10.1109/tifs.2018.2804890","title":"Liveness Detection and Automatic Template Updating Using Fusion of ECG and Fingerprint","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Liveness; Computer science; Biometrics; Fingerprint (computing); Spoofing attack; Artificial intelligence; Fingerprint recognition; Pattern recognition (psychology); Word error rate; Classifier (UML); Computer vision; Speech recognition; Computer security","score_opus":0.0157950947077046,"score_gpt":0.24065781152854857,"score_spread":0.22486271682084397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794104577","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":[0.4465664,0.000009771116,0.55304766,0.000030996547,0.00018174044,0.000083815925,0.00000724873,0.00003209685,0.00004026794],"genre_scores_gemma":[0.98880714,0.00006329678,0.01105165,0.000059821148,0.000009029547,0.0000032941548,0.0000014968335,0.0000026683795,0.0000016147918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992179,0.00003425162,0.0003322845,0.00013653545,0.00017163716,0.00010737842],"domain_scores_gemma":[0.99934685,0.000054946388,0.00017839922,0.00016693298,0.00018573318,0.00006716317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033477397,0.0000974262,0.00012520491,0.00034474448,0.0003823749,0.00016794365,0.00007653333,0.000081466096,0.000006543282],"category_scores_gemma":[0.000011789243,0.00009516351,0.000024620927,0.00044782093,0.00013843276,0.0010956725,0.000009870789,0.00010782992,0.0000025411211],"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":[0.000023118107,0.0000494227,0.00011872044,0.00027320968,0.00002974864,3.265974e-7,0.012123582,0.000089418914,0.0023143534,0.0050681443,0.000008761219,0.9799012],"study_design_scores_gemma":[0.00032954232,0.00011244737,0.0020804736,0.000047676276,0.000014849788,0.000035213518,0.00028475112,0.96946585,0.024631854,0.0026110383,0.0002506316,0.00013565866],"about_ca_topic_score_codex":0.0001200644,"about_ca_topic_score_gemma":0.000035984984,"teacher_disagreement_score":0.97976553,"about_ca_system_score_codex":0.000022844299,"about_ca_system_score_gemma":0.000022279874,"threshold_uncertainty_score":0.38806552},"labels":[],"label_agreement":null},{"id":"W2794422176","doi":"10.1109/ipta.2017.8310109","title":"Combining left and right wrist vein images for personal verification","year":2017,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","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":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Wrist; Preprocessor; Biometrics; Computer science; Artificial intelligence; Feature extraction; Computer vision; Fuse (electrical); Norm (philosophy); Pattern recognition (psychology); Medicine; Radiology; Engineering","score_opus":0.027343156271624324,"score_gpt":0.2875618836180114,"score_spread":0.26021872734638707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794422176","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":[0.03029193,0.00012219875,0.9525441,0.007419188,0.0004950519,0.0001907908,0.000009539086,0.00010140129,0.008825778],"genre_scores_gemma":[0.97950804,0.00001309668,0.016685512,0.00010313893,0.000025595507,0.000005472682,0.000005611815,0.0000022607164,0.0036512876],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9994877,0.000011699317,0.000088550405,0.00021779377,0.00009928576,0.00009497639],"domain_scores_gemma":[0.99944556,0.000042989916,0.000078527606,0.0003093961,0.000077239994,0.000046263067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028051093,0.00004874904,0.00006029303,0.00007674143,0.0005823799,0.00080352556,0.0004046753,0.0000310398,0.000039138627],"category_scores_gemma":[0.00008801179,0.00004311096,0.000023219405,0.000048316622,0.00007445281,0.00047047134,0.00008163965,0.000035492492,0.000017988063],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017437924,0.00026966672,0.011378039,0.00009944022,0.00004452568,0.0000036901538,0.004931204,9.173696e-8,0.0154766645,0.56701267,0.072722405,0.32804418],"study_design_scores_gemma":[0.0014749328,0.000102317776,0.46939883,0.000014870778,0.00001285095,0.000023505745,0.00014644062,0.20658724,0.03554132,0.0087849675,0.27745724,0.00045548478],"about_ca_topic_score_codex":0.00003750477,"about_ca_topic_score_gemma":0.0000058970727,"teacher_disagreement_score":0.94921607,"about_ca_system_score_codex":0.000010382769,"about_ca_system_score_gemma":0.00001839366,"threshold_uncertainty_score":0.77484155},"labels":[],"label_agreement":null},{"id":"W2796417745","doi":"10.1007/s11042-019-7424-8","title":"11K Hands: Gender recognition and biometric identification using a large dataset of hand images","year":2019,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":171,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Biometrics; Artificial intelligence; Convolutional neural network; Classifier (UML); Pattern recognition (psychology); Identification (biology); Hand geometry; Support vector machine; Computer vision; Machine learning","score_opus":0.0826933588358688,"score_gpt":0.3007993664852606,"score_spread":0.2181060076493918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796417745","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":[0.19875847,0.000846917,0.79554325,0.000097570206,0.0000938779,0.00084278046,0.0036937962,0.00004681581,0.00007649444],"genre_scores_gemma":[0.9670877,0.00032664815,0.029993845,0.0000669076,0.000040751132,0.00009168997,0.002320878,0.000008196917,0.000063418185],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998917,0.00003696099,0.000323601,0.00039608343,0.00018030549,0.00014605466],"domain_scores_gemma":[0.99896795,0.00015114587,0.00020345984,0.0004465688,0.0001476427,0.00008321998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004260645,0.00009653476,0.00014099716,0.00053291966,0.00016463616,0.00032746443,0.00023476307,0.00006897061,0.00003648433],"category_scores_gemma":[0.000059741422,0.000094310904,0.000023843613,0.0015782908,0.00008892043,0.0005949411,0.00013801608,0.00006751585,0.00007743012],"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":[0.000009112909,0.000555988,0.0077181812,0.0003216677,0.00006328551,6.399762e-7,0.0010601471,0.000002266933,0.20121263,0.0036830797,0.0021414533,0.78323156],"study_design_scores_gemma":[0.005987272,0.00013667895,0.39412326,0.00008366695,0.00021000321,0.000069794754,0.00064515386,0.34910914,0.11934181,0.008836742,0.12002999,0.0014264542],"about_ca_topic_score_codex":0.000036528396,"about_ca_topic_score_gemma":0.0000016717684,"teacher_disagreement_score":0.7818051,"about_ca_system_score_codex":0.0000141790415,"about_ca_system_score_gemma":0.000025855185,"threshold_uncertainty_score":0.38458872},"labels":[],"label_agreement":null},{"id":"W2797298759","doi":"10.1007/s00500-018-3182-1","title":"Difference co-occurrence matrix using BP neural network for fingerprint liveness detection","year":2018,"lang":"en","type":"article","venue":"Soft Computing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":69,"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":"Liveness; Computer science; Artificial neural network; Artificial intelligence; Preprocessor; Fingerprint (computing); Pattern recognition (psychology); Fingerprint recognition; Spoofing attack; Data mining","score_opus":0.050992646134346974,"score_gpt":0.3298064939482945,"score_spread":0.2788138478139475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797298759","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":[0.2684812,0.00006657176,0.7293769,0.000042207957,0.0016655191,0.00016466249,0.0000053380923,0.00018402014,0.000013594243],"genre_scores_gemma":[0.95142406,9.450506e-7,0.047859002,0.00013265123,0.0005516954,0.0000037770585,0.0000065365944,0.000006460021,0.00001485992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848133,0.00008796905,0.000307189,0.00048664203,0.00021755301,0.00041930185],"domain_scores_gemma":[0.99873465,0.00031710282,0.00021705618,0.00038751445,0.0002566622,0.00008703906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062367774,0.00014535215,0.00016784525,0.00016469271,0.0007194069,0.0003563837,0.0007088459,0.000076072734,0.0000043638365],"category_scores_gemma":[0.00014459621,0.00015085643,0.00008062648,0.0010656404,0.00008935363,0.00017495494,0.00025989593,0.00014018152,0.000018688104],"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":[0.000033346834,0.00012946963,0.009625178,0.00015861291,0.000035248275,0.000005080772,0.00224927,0.005360885,0.007013146,0.006756854,0.00050411106,0.9681288],"study_design_scores_gemma":[0.0001609699,0.000054215296,0.009599178,0.00003234259,0.0000053900917,0.000018772356,0.000012680518,0.9856632,0.0020013584,0.0010123512,0.0012454219,0.0001941242],"about_ca_topic_score_codex":0.000044390912,"about_ca_topic_score_gemma":0.000009922058,"teacher_disagreement_score":0.98030233,"about_ca_system_score_codex":0.00006926847,"about_ca_system_score_gemma":0.000058925365,"threshold_uncertainty_score":0.61517465},"labels":[],"label_agreement":null},{"id":"W2797490324","doi":"10.1049/iet-bmt.2017.0128","title":"Fast and efficient minutia‐based palmprint matching","year":2018,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Minutiae; Computer science; Matching (statistics); Artificial intelligence; Orientation (vector space); Pattern recognition (psychology); Process (computing); Field (mathematics); Blossom algorithm; Computer vision; Fingerprint recognition; Mathematics; Fingerprint (computing)","score_opus":0.02307271373006373,"score_gpt":0.26031486706855594,"score_spread":0.23724215333849222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797490324","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":[0.14673306,0.000245107,0.85048616,0.0007046053,0.0007937258,0.0001092182,0.000008431704,0.0001448089,0.00077486655],"genre_scores_gemma":[0.9437993,0.0000122745105,0.055562526,0.00041078203,0.00007044365,0.0000038740227,0.0000036853028,0.0000063120497,0.0001308168],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985398,0.000047638,0.00024050011,0.00044993317,0.00044968017,0.00027245164],"domain_scores_gemma":[0.99882615,0.00014317162,0.0001030978,0.00055870827,0.00020648597,0.00016241663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007120657,0.00012220092,0.00012797827,0.0026029847,0.00020473676,0.000403437,0.0006322699,0.00008665036,0.000026568361],"category_scores_gemma":[0.00017910566,0.00011245274,0.00004869422,0.011763714,0.00014745191,0.00010637776,0.00028296822,0.000096003656,0.00017967523],"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":[0.000016259151,0.0008070801,0.004164271,0.000118470845,0.000043966214,0.000025593086,0.0027349093,0.000049714545,0.010115103,0.05132383,0.0050333,0.9255675],"study_design_scores_gemma":[0.0019618238,0.00046500104,0.14088488,0.000045861958,0.000027186472,0.00004997524,0.00019147337,0.66134465,0.0511087,0.0028156703,0.13991947,0.0011853307],"about_ca_topic_score_codex":0.000051672127,"about_ca_topic_score_gemma":0.0000032561325,"teacher_disagreement_score":0.92438215,"about_ca_system_score_codex":0.000055179993,"about_ca_system_score_gemma":0.000049335686,"threshold_uncertainty_score":0.5652075},"labels":[],"label_agreement":null},{"id":"W2799408155","doi":"10.1109/iscas.2018.8351048","title":"Weighted Hybrid Fusion for Multimodal Biometric Recognition System","year":2018,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Biometrics; Weighting; Computer science; Pattern recognition (psychology); Fusion; Artificial intelligence; Feature (linguistics); Word error rate; Hybrid system; Sensor fusion; Machine learning","score_opus":0.03360402175808133,"score_gpt":0.2628856255550034,"score_spread":0.22928160379692206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799408155","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":[0.03963333,0.00003342835,0.95492965,0.00029363116,0.0013415738,0.00033640352,0.000020914236,0.00041041922,0.0030006696],"genre_scores_gemma":[0.87158316,0.0000042169104,0.12745413,0.00016421272,0.0001704651,0.000028114133,0.00004037945,0.000005271523,0.000550058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894345,0.000040444247,0.00023186558,0.0003629181,0.00022510652,0.00019622271],"domain_scores_gemma":[0.9988897,0.000096083364,0.00008921283,0.00036052737,0.00047708518,0.000087379165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004630733,0.000086530184,0.0001030978,0.0012478633,0.00021973625,0.0001846098,0.00044750195,0.00005291461,0.00006499193],"category_scores_gemma":[0.00007194501,0.000073488416,0.00006411559,0.0033614836,0.0000378382,0.00030765633,0.000092644725,0.000036140344,0.0007412383],"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":[0.000016515944,0.00016177537,0.000059055874,0.00007052784,0.000017058263,0.0000018056576,0.00013241338,2.4673705e-8,0.0037631197,0.017844679,0.019883655,0.95804936],"study_design_scores_gemma":[0.0014372627,0.00032158938,0.0022385898,0.000030397905,0.000015204483,0.000043924752,0.00006901083,0.7248085,0.1790006,0.0022479622,0.089351326,0.00043559048],"about_ca_topic_score_codex":0.00005856985,"about_ca_topic_score_gemma":0.0000036627905,"teacher_disagreement_score":0.95761377,"about_ca_system_score_codex":0.0000700747,"about_ca_system_score_gemma":0.000029410627,"threshold_uncertainty_score":0.9527366},"labels":[],"label_agreement":null},{"id":"W2800128635","doi":"10.1117/12.2304814","title":"Deep ear biometrics","year":2018,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Softmax function; Computer science; Biometrics; Artificial intelligence; Deep learning; Residual; Feature extraction; Convolutional neural network; Extractor; Pattern recognition (psychology); Feature (linguistics); Human ear; Speech recognition; Algorithm; Engineering","score_opus":0.026839720613200238,"score_gpt":0.26752096526887176,"score_spread":0.24068124465567153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800128635","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":[0.0017153908,0.000054423697,0.9657839,0.00072107674,0.00058975094,0.000033855016,2.1698573e-7,0.00017411256,0.030927256],"genre_scores_gemma":[0.8659166,0.000010160147,0.1297555,0.00089131424,0.000095597534,0.0000015304621,9.911777e-7,0.0000025135382,0.0033258179],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993696,0.000016797494,0.000099348086,0.00018974076,0.00019584016,0.00012864821],"domain_scores_gemma":[0.99932283,0.000031173022,0.00002612515,0.0004045354,0.0001473121,0.0000680479],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022766921,0.00004205145,0.000045123226,0.00072860095,0.00008697311,0.00017098157,0.00062682776,0.000035703513,0.00027685968],"category_scores_gemma":[0.00008910753,0.000035371493,0.000024380246,0.006019322,0.000053576332,0.00021281777,0.00013504204,0.00003176395,0.0023345307],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.5036515e-7,0.00010309116,0.0009959618,0.0000037842378,0.000008767233,0.000002165435,0.00042206017,9.5255764e-8,0.0006164803,0.58634216,0.028613498,0.38289106],"study_design_scores_gemma":[0.000267878,0.00011317165,0.02790669,0.0000013994436,0.0000025006289,0.0000123077425,0.000029901325,0.1547404,0.015261276,0.0070029735,0.7943977,0.000263801],"about_ca_topic_score_codex":0.00002350367,"about_ca_topic_score_gemma":0.000006287425,"teacher_disagreement_score":0.8642012,"about_ca_system_score_codex":0.000015260466,"about_ca_system_score_gemma":0.000016814645,"threshold_uncertainty_score":0.9984423},"labels":[],"label_agreement":null},{"id":"W2805079381","doi":"10.5539/ass.v14n6p1","title":"Generating a Cancellable Fingerprint using Matrices Operations and Its Fingerprint Processing Requirements","year":2018,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Matrix (chemical analysis); Row; Row and column spaces; Kronecker product; Computer science; Biometrics; Fingerprint (computing); Set (abstract data type); Arithmetic; Matrix multiplication; Invertible matrix; Mathematics; Algorithm; Artificial intelligence; Kronecker delta; Pure mathematics","score_opus":0.0546574353404175,"score_gpt":0.3376935936164439,"score_spread":0.2830361582760264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805079381","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51626575,0.0005437646,0.4567197,0.0022495792,0.00096698425,0.00040754376,0.000004233927,0.0002226818,0.022619758],"genre_scores_gemma":[0.9511006,0.000007743233,0.04835937,0.00022696122,0.00019632767,0.0000061249975,3.1103326e-7,0.0000038860976,0.00009867832],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840343,0.000038480517,0.000221007,0.0005031808,0.00047952784,0.0003543563],"domain_scores_gemma":[0.99923927,0.000008607851,0.00009993523,0.00016890968,0.000371679,0.000111623114],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00088004704,0.000096488664,0.00010020125,0.00024664865,0.0026006158,0.0012771703,0.0007124146,0.000043389144,0.000015926598],"category_scores_gemma":[0.00012133175,0.0000968188,0.000019104216,0.0029583464,0.00033490095,0.0012644544,0.00039478994,0.00008104257,0.00001645959],"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":[0.0000019281104,0.00007787086,0.0003785901,0.00003904825,0.0000067126257,0.0000035591463,0.0213312,0.000018637353,0.26614472,0.051296208,0.00011341136,0.6605881],"study_design_scores_gemma":[0.0002738426,0.00005406303,0.0104346145,0.0000624232,0.000011335995,0.000018026944,0.0008014725,0.9437515,0.04021143,0.0009481705,0.0029011432,0.00053193734],"about_ca_topic_score_codex":0.00015936366,"about_ca_topic_score_gemma":0.000049140028,"teacher_disagreement_score":0.9437329,"about_ca_system_score_codex":0.00017089769,"about_ca_system_score_gemma":0.00046308662,"threshold_uncertainty_score":0.9997596},"labels":[],"label_agreement":null},{"id":"W282600021","doi":"10.1007/978-1-4471-5571-3_24","title":"Pattern Recognition for Biometrics and Bioinformatics","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Biometric Identification and Security","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":"Concordia University","funders":"","keywords":"Biometrics; Computer science; Computational biology; Pattern recognition (psychology); Biology; Artificial intelligence","score_opus":0.06218194342535285,"score_gpt":0.2414548120400677,"score_spread":0.17927286861471484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W282600021","genre_codex":"methods","genre_gemma":"other","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":[0.0000031925535,0.00019483107,0.83009815,0.0003418945,0.00040367845,0.00040693293,0.000073014045,0.000108899345,0.16836938],"genre_scores_gemma":[0.00031947566,0.0012898541,0.40187857,0.0016051607,0.00014668127,0.000048841823,0.00043832653,0.000033980705,0.5942391],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990305,0.0000039030883,0.00034113787,0.0002618402,0.00022134827,0.00014130132],"domain_scores_gemma":[0.99888796,0.00014552043,0.00021813795,0.00036241993,0.00028903357,0.000096944226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002216096,0.00016672767,0.00018115426,0.0014456584,0.00007727497,0.0004168938,0.00035231456,0.00025901815,0.000255232],"category_scores_gemma":[0.00004942559,0.00014826108,0.00007411896,0.0002811461,0.00004078725,0.00035484016,0.00015025285,0.0001011191,0.0007522473],"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":[2.2283258e-7,0.0000060046646,0.0000010966804,0.000083385195,0.000013500633,1.899705e-7,0.00004458328,2.8646672e-9,6.668108e-7,0.036139708,0.021320261,0.9423904],"study_design_scores_gemma":[0.0003471297,0.00008846749,0.000056381265,0.000043903947,0.000025622641,0.000014878351,0.000008102062,0.04474149,0.000065634165,0.063333064,0.89074063,0.0005347181],"about_ca_topic_score_codex":0.000011423901,"about_ca_topic_score_gemma":0.0000022737024,"teacher_disagreement_score":0.94185567,"about_ca_system_score_codex":0.00002306394,"about_ca_system_score_gemma":0.000028673147,"threshold_uncertainty_score":0.96688676},"labels":[],"label_agreement":null},{"id":"W2884597666","doi":"10.14393/ufu.te.2016.90","title":"Reconhecimento biométrico considerando a deformação não linear da íris humana","year":2016,"lang":"pt","type":"dissertation","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"Physics; Humanities; Computer science; Philosophy","score_opus":0.05949384021921911,"score_gpt":0.3223280772974418,"score_spread":0.2628342370782227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884597666","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":[0.037056126,0.0118631,0.8182403,0.0067330752,0.029693892,0.004840365,0.00053515687,0.0016716635,0.08936635],"genre_scores_gemma":[0.5411758,0.0034778607,0.010589123,0.0022755507,0.0007654238,0.00023220558,0.0020066577,0.00015973908,0.43931767],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9941591,0.00032154398,0.0016982212,0.0016739923,0.0012142063,0.00093294797],"domain_scores_gemma":[0.99505895,0.0004413213,0.0010682528,0.0017924404,0.0011271518,0.0005118989],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013372267,0.0008017908,0.00092185196,0.002785659,0.00087128556,0.001253155,0.001993347,0.00088560634,0.004645994],"category_scores_gemma":[0.00034766798,0.00064158166,0.00056315213,0.003590691,0.00016019959,0.0010678148,0.0002865335,0.0005313302,0.007003576],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002994762,0.0013980191,0.00052031974,0.00084850384,0.0009953614,0.0000994324,0.013246254,0.0000011804304,0.0040966277,0.049495764,0.09249566,0.8365034],"study_design_scores_gemma":[0.020073017,0.0013093189,0.005849595,0.0017543308,0.0009307937,0.00027100972,0.0056385547,0.06831192,0.11229574,0.009857735,0.7639696,0.009738406],"about_ca_topic_score_codex":0.000852585,"about_ca_topic_score_gemma":0.00024179202,"teacher_disagreement_score":0.826765,"about_ca_system_score_codex":0.00028114553,"about_ca_system_score_gemma":0.0006705496,"threshold_uncertainty_score":0.99978364},"labels":[],"label_agreement":null},{"id":"W2885607313","doi":"10.1109/memea.2018.8438797","title":"EER Calculation and DET Approximation in a Multi-Threshold Biometric System","year":2018,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Biometrics; Word error rate; Computer science; Identity (music); Biometric data; Authentication (law); Graph; Confidentiality; Theoretical computer science; Data mining; Pattern recognition (psychology); Artificial intelligence; Computer security","score_opus":0.044740645647380506,"score_gpt":0.28126789453364986,"score_spread":0.23652724888626936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885607313","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":[0.1009272,0.000072631636,0.89736295,0.00014645557,0.0001909479,0.00016530801,7.263029e-7,0.00011572615,0.0010180605],"genre_scores_gemma":[0.95658463,0.0000026870403,0.043106254,0.000054710068,0.000017970522,0.000008784921,0.000003134598,0.00000247915,0.00021936928],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991827,0.000034414108,0.00020114648,0.00026204964,0.00018352899,0.00013617073],"domain_scores_gemma":[0.9995179,0.000025297122,0.00005443451,0.0002473018,0.00010521078,0.00004983516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054343767,0.00006193522,0.000079463185,0.0014237113,0.00007130471,0.00016974319,0.00019705914,0.00006351538,0.0000061559144],"category_scores_gemma":[0.000047754984,0.000054325083,0.000014597231,0.004516009,0.00003706749,0.0004885627,0.00009146212,0.000039978884,0.00007321272],"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":[0.000012722544,0.00050456484,0.055564124,0.00026996384,0.000024836687,0.000008515724,0.0040690647,0.000010251279,0.0066581666,0.6751191,0.0016358881,0.25612277],"study_design_scores_gemma":[0.0003483768,0.000017481401,0.1295432,0.000007936544,0.0000013259315,0.000006900773,0.000038764592,0.86786556,0.0013741127,0.00008494495,0.00062436913,0.00008705304],"about_ca_topic_score_codex":0.00011995964,"about_ca_topic_score_gemma":0.000050141454,"teacher_disagreement_score":0.8678553,"about_ca_system_score_codex":0.00006401081,"about_ca_system_score_gemma":0.000014381323,"threshold_uncertainty_score":0.22153126},"labels":[],"label_agreement":null},{"id":"W2891482866","doi":"10.1049/iet-bmt.2018.5067","title":"Biometric ontology for semantic biometric‐as‐a‐service (BaaS) applications: a border security use case","year":2018,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Semtech (Canada)","funders":"","keywords":"Biometrics; Computer science; Ontology; Identification (biology); Focus (optics); Modalities; Cloud computing; Analytics; Data science; Computer security","score_opus":0.04949952315023018,"score_gpt":0.34526065150074237,"score_spread":0.29576112835051216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891482866","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":[0.043210067,0.0021564125,0.9465463,0.0022898908,0.002029266,0.0021697294,0.00034952344,0.00073079614,0.00051800703],"genre_scores_gemma":[0.90511113,0.00037005564,0.08932638,0.0031647682,0.0005095917,0.0005402067,0.00016479974,0.000066519744,0.0007465499],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99497265,0.0001882095,0.001094047,0.0016629003,0.0009639905,0.001118196],"domain_scores_gemma":[0.9913807,0.002060503,0.0006209354,0.0023151715,0.003045783,0.0005769043],"candidate_categories":["metaepi_narrow","bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.001894432,0.0005190381,0.0006305696,0.030306073,0.0008363664,0.0012036854,0.0024383585,0.00058406504,0.00012883697],"category_scores_gemma":[0.0033220851,0.00052357576,0.00028905587,0.18919358,0.00033888037,0.0012948582,0.00069829746,0.00028996097,0.0011498285],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013909405,0.005224829,0.0052640447,0.0010396027,0.00066672603,0.0005156781,0.0027091764,0.0000016520745,0.0026944715,0.12222129,0.049792707,0.8097307],"study_design_scores_gemma":[0.002056062,0.0007458706,0.002189825,0.000016361484,0.00012287695,0.001881471,0.00017335682,0.025380643,0.003915236,0.00638689,0.9559042,0.0012272199],"about_ca_topic_score_codex":0.0016507887,"about_ca_topic_score_gemma":0.00023153516,"teacher_disagreement_score":0.9061115,"about_ca_system_score_codex":0.00030816582,"about_ca_system_score_gemma":0.0003035053,"threshold_uncertainty_score":0.99983317},"labels":[],"label_agreement":null},{"id":"W2894269546","doi":"10.1049/iet-bmt.2018.5105","title":"Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset","year":2018,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Shared Services Canada; Public Health Agency of Canada","funders":"National Institute of Standards and Technology; Defence Research and Development Canada","keywords":"Nexus (standard); Computer science; Agency (philosophy); Interactive kiosk; Iris recognition; Data science; Artificial intelligence; World Wide Web; Biometrics; Social science; Sociology","score_opus":0.06918477058182675,"score_gpt":0.3102492522491896,"score_spread":0.24106448166736283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894269546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98774326,0.00006660031,0.011226609,0.000009336678,0.00024863065,0.000102752565,0.0005287059,0.000012509701,0.000061613726],"genre_scores_gemma":[0.99904215,0.000015870779,0.00079681806,0.00004707665,0.000015057276,8.3048826e-7,0.00006860705,0.0000037939785,0.000009771151],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989273,0.00011414084,0.00023595878,0.00023693146,0.00036663376,0.000119028],"domain_scores_gemma":[0.9988018,0.00025995044,0.00027184133,0.000534279,0.00009297905,0.00003915177],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00061984477,0.00009525664,0.00020411004,0.0035401818,0.00010293683,0.000072669056,0.0004801769,0.00006533021,0.000016991833],"category_scores_gemma":[0.000365228,0.00006250747,0.000074842,0.02315459,0.00019825641,0.00016015979,0.00014817152,0.00006217774,0.0000039436077],"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":[0.000056199726,0.00027186013,0.75853044,0.00027984468,0.0008226375,0.0000037909872,0.0016155294,0.000047158475,0.009808997,0.00012946017,0.002558095,0.22587597],"study_design_scores_gemma":[0.0003884875,0.0006145892,0.81868404,0.000043999695,0.00037391184,0.00000150518,0.000016827897,0.069802105,0.10569954,0.00003185121,0.0041326303,0.00021050315],"about_ca_topic_score_codex":0.00029492535,"about_ca_topic_score_gemma":0.000013067383,"teacher_disagreement_score":0.22566546,"about_ca_system_score_codex":0.000023378956,"about_ca_system_score_gemma":0.000011815105,"threshold_uncertainty_score":0.9976087},"labels":[],"label_agreement":null},{"id":"W2896190526","doi":"10.1002/spy2.44","title":"State of the art and perspectives on traditional and emerging biometrics: A survey","year":2018,"lang":"en","type":"article","venue":"Security and Privacy","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Biometrics; Modalities; State (computer science); Data science; Key (lock); Emerging technologies; Computer science; Computer security; Internet privacy; Human–computer interaction; Artificial intelligence; Sociology; Social science","score_opus":0.06399357638754868,"score_gpt":0.27498312453803625,"score_spread":0.21098954815048757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896190526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99077517,0.00086772034,0.0058493125,0.0017492169,0.0001355864,0.00010521642,0.00005956618,0.000018613518,0.00043957419],"genre_scores_gemma":[0.9989568,0.0003154397,0.0005545891,0.00009952297,0.000024041357,0.0000011997415,0.0000021854707,0.0000021072053,0.00004412235],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99920267,0.00012927069,0.00012266086,0.0002534177,0.00019215302,0.000099848075],"domain_scores_gemma":[0.99935144,0.00021245486,0.00007088963,0.00019827618,0.00010937726,0.00005756804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065182697,0.00006969269,0.00009215166,0.00030308956,0.00018737903,0.00011336118,0.0002342574,0.000028286631,0.0000107990045],"category_scores_gemma":[0.00028610654,0.000052777348,0.0000184573,0.0012921408,0.00031588716,0.00018920327,0.00014408407,0.00008927847,0.0000028151803],"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":[0.0001267119,0.0011086281,0.070702784,0.00022960326,0.00018014223,0.0000040584,0.20141742,3.3189647e-7,0.0021758163,0.4616839,0.006955413,0.2554152],"study_design_scores_gemma":[0.00032033818,0.00014506496,0.95698035,0.000015722695,0.000003880443,0.000010300377,0.0001743304,0.002189019,0.0014001434,0.03152608,0.007112427,0.00012235135],"about_ca_topic_score_codex":0.0000529896,"about_ca_topic_score_gemma":0.000018230405,"teacher_disagreement_score":0.88627756,"about_ca_system_score_codex":0.000008749613,"about_ca_system_score_gemma":0.000022635713,"threshold_uncertainty_score":0.21521978},"labels":[],"label_agreement":null},{"id":"W2905558153","doi":"10.1364/ao.57.010305","title":"Double random phase encoding for cancelable face and iris recognition","year":2018,"lang":"en","type":"article","venue":"Applied Optics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Biometrics; Computer science; Iris recognition; Artificial intelligence; Encryption; Face (sociological concept); Feature (linguistics); Pattern recognition (psychology); IRIS (biosensor); Encoding (memory); Feature extraction; Computer vision; Computer security","score_opus":0.055696951325145676,"score_gpt":0.30380914039956863,"score_spread":0.24811218907442295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905558153","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":[0.032922592,0.000065664826,0.9583463,0.00027351978,0.00032032194,0.0004158997,0.00001200411,0.0000826743,0.007561038],"genre_scores_gemma":[0.8727417,0.00007090705,0.12630992,0.00024975254,0.00013223637,0.000074845506,0.000019926634,0.000006531182,0.00039417917],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993465,0.0000058715295,0.0001414943,0.0002421511,0.0001041728,0.00015983898],"domain_scores_gemma":[0.9994772,0.00007864919,0.0000656948,0.00020473402,0.000111673755,0.00006206633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031052122,0.00006922418,0.00009682506,0.00010415749,0.00019794285,0.00018417799,0.00019623585,0.00005316772,0.000011242929],"category_scores_gemma":[0.000020209038,0.00006909008,0.000018523006,0.000434806,0.000058706402,0.00014439033,0.000057336365,0.000046358105,0.000045219793],"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":[0.00069337786,0.0005455179,0.00003232007,0.00016444681,0.00008816073,0.0000022251904,0.0077604502,0.000007874428,0.06548555,0.32541838,0.020307302,0.57949436],"study_design_scores_gemma":[0.03959021,0.00046192683,0.000068808484,0.00003216898,0.000085788204,0.000018260433,0.0005279216,0.22347558,0.38804638,0.038136974,0.30854228,0.0010137161],"about_ca_topic_score_codex":0.000015364896,"about_ca_topic_score_gemma":0.000006186479,"teacher_disagreement_score":0.83981913,"about_ca_system_score_codex":0.000021067295,"about_ca_system_score_gemma":0.000028672337,"threshold_uncertainty_score":0.28174117},"labels":[],"label_agreement":null},{"id":"W2907819165","doi":"10.1109/newcas.2018.8585495","title":"A Two-Stage Scheme for Fusion of Hash-Encoded Features in a Multimodal Biometric System","year":2018,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Biometrics; Computer science; Feature (linguistics); Pattern recognition (psychology); Artificial intelligence; Hash function; Fusion; Encoder; Scheme (mathematics); Encoding (memory); Feature extraction; Mathematics","score_opus":0.02620273275865393,"score_gpt":0.3006020952060511,"score_spread":0.27439936244739715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907819165","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":[0.14168894,0.00013336433,0.8542208,0.0002159464,0.00052118435,0.00049685134,0.000016782302,0.00014151994,0.002564593],"genre_scores_gemma":[0.83834255,0.0000020368961,0.16065402,0.000044992234,0.000033846478,0.000018787008,0.0000040489003,0.000003895737,0.000895809],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988129,0.00004591695,0.00031613267,0.0003431718,0.0002741165,0.00020774931],"domain_scores_gemma":[0.9989525,0.00011695984,0.00013115628,0.00046108954,0.00027438335,0.000063923886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006715746,0.00009469412,0.0001793756,0.0020067103,0.0000734586,0.000088728506,0.00065766496,0.000080777616,0.00001996657],"category_scores_gemma":[0.00016739834,0.000078624245,0.00006923564,0.006092724,0.000063703585,0.00021743833,0.00016504824,0.00005763662,0.00002646022],"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":[0.00013715462,0.0011577406,0.0060051805,0.0008267528,0.000057305355,0.000008981994,0.0037189198,0.000009134605,0.1801175,0.72367585,0.006672923,0.077612564],"study_design_scores_gemma":[0.0032155842,0.00027259934,0.020183234,0.00006706597,0.000005523697,0.00000900112,0.0004704147,0.84501934,0.12475716,0.00024788978,0.005408095,0.00034409025],"about_ca_topic_score_codex":0.0005940365,"about_ca_topic_score_gemma":0.00014399417,"teacher_disagreement_score":0.8450102,"about_ca_system_score_codex":0.00006715285,"about_ca_system_score_gemma":0.00005814868,"threshold_uncertainty_score":0.3206204},"labels":[],"label_agreement":null},{"id":"W2909129969","doi":"10.3166/ts.35.341-354","title":"An improved fingerprint image matching and multi-view fingerprint recognition algorithm","year":2018,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fingerprint (computing); Pattern recognition (psychology); Artificial intelligence; Matching (statistics); Fingerprint recognition; Computer science; Computer vision; Image (mathematics); Algorithm; Mathematics; Statistics","score_opus":0.028795924021948506,"score_gpt":0.27590997052648797,"score_spread":0.24711404650453947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909129969","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":[0.079561174,0.000061616316,0.91922003,0.00033896617,0.00024212297,0.00025343683,0.000013703584,0.00022598881,0.00008294742],"genre_scores_gemma":[0.6035981,0.000027075068,0.39578912,0.00038415976,0.00013399876,0.000023433646,0.000016636206,0.000008241227,0.000019233932],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985417,0.00010776852,0.00033217933,0.0005172807,0.00023587105,0.0002652014],"domain_scores_gemma":[0.9991612,0.000040883835,0.00012705423,0.00032731664,0.00018409724,0.00015946328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008212678,0.00015784672,0.00014983042,0.00022729088,0.00025466984,0.00049643725,0.0004384775,0.000060428265,0.00017334141],"category_scores_gemma":[0.000017382628,0.00015313119,0.000048199257,0.0004160647,0.00009729853,0.0006707297,0.00014875262,0.00012727731,0.00010248287],"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":[0.000005000795,0.00024773667,0.000029557375,0.000024059922,0.000015899072,0.0000038847884,0.0019968448,3.823212e-7,0.038468216,0.00051397545,0.00007623973,0.9586182],"study_design_scores_gemma":[0.0013922184,0.00041762646,0.01938385,0.00006612891,0.000023875995,0.00003204915,0.00017316833,0.94539744,0.025822315,0.0035094528,0.0031830843,0.0005988164],"about_ca_topic_score_codex":0.00011410032,"about_ca_topic_score_gemma":0.00002143826,"teacher_disagreement_score":0.9580194,"about_ca_system_score_codex":0.000047653186,"about_ca_system_score_gemma":0.000035714267,"threshold_uncertainty_score":0.62445086},"labels":[],"label_agreement":null},{"id":"W2909210083","doi":"10.1049/iet-ipr.2018.5642","title":"3D palmprint recognition using unsupervised convolutional deep learning network and SVM classifier","year":2019,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Support vector machine; Classifier (UML); Convolutional neural network; Deep learning; Machine learning","score_opus":0.03088436046542572,"score_gpt":0.2569254985288612,"score_spread":0.22604113806343548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909210083","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":[0.18760261,0.0009197558,0.8097578,0.00016712885,0.00029435428,0.00013652148,9.763279e-7,0.00014512289,0.0009757201],"genre_scores_gemma":[0.79566497,0.000028650067,0.20387661,0.00018268939,0.00009952197,0.0000045102292,0.000012764036,0.0000099358485,0.000120379154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987112,0.00009342728,0.00023697525,0.00041854352,0.00025975297,0.0002800868],"domain_scores_gemma":[0.9992974,0.000058963527,0.0001435775,0.00016903237,0.00024808606,0.00008294697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051443384,0.0001219299,0.00013872853,0.00016154576,0.00032912256,0.0006326863,0.00022581549,0.0000842989,0.00007376404],"category_scores_gemma":[0.000065288965,0.0001255625,0.00003397399,0.00087561185,0.000071396404,0.0009906283,0.00015652255,0.00024238796,0.0000929682],"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":[0.000016294493,0.00008531731,0.013661353,0.00024934413,0.000021251288,0.000009278249,0.0013314098,0.00033078223,0.016988503,0.00057807,0.00007574183,0.96665263],"study_design_scores_gemma":[0.0004165996,0.00001737364,0.013201566,0.000081400285,0.000009461028,0.00004512747,0.00010266302,0.98222995,0.00034157027,0.0016128856,0.0017085546,0.00023285032],"about_ca_topic_score_codex":0.000012515863,"about_ca_topic_score_gemma":0.0000014558032,"teacher_disagreement_score":0.98189914,"about_ca_system_score_codex":0.00005386757,"about_ca_system_score_gemma":0.000084632906,"threshold_uncertainty_score":0.61010087},"labels":[],"label_agreement":null},{"id":"W2912455255","doi":"10.1109/tcc.2018.2866405","title":"Efficient and Privacy-Preserving Online Fingerprint Authentication Scheme over Outsourced Data","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Cloud Computing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Biometrics; Computer security; Encryption; Fingerprint (computing); Authentication (law); Information privacy; Server; Homomorphic encryption; Cryptography; Computer network","score_opus":0.05653985901422063,"score_gpt":0.31239422709335213,"score_spread":0.2558543680791315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912455255","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":[0.3619953,0.0000219999,0.63617617,0.00060558604,0.00085038634,0.000117655065,0.000014068952,0.00018329102,0.000035570072],"genre_scores_gemma":[0.94087905,0.000003810248,0.0586357,0.00021501353,0.00017284886,0.0000017567548,0.0000075019443,0.0000115565745,0.00007279273],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981487,0.00009260607,0.00036749695,0.000736245,0.00036977293,0.00028518375],"domain_scores_gemma":[0.997695,0.00019805769,0.0001458465,0.0016913721,0.00014575536,0.00012391964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006357929,0.00016135786,0.000155206,0.00032521447,0.0005459634,0.00029313192,0.0015603772,0.0000821841,0.000034874847],"category_scores_gemma":[0.00006090606,0.00016583934,0.000045785193,0.0010527916,0.00010769792,0.00013268978,0.00011901698,0.00026043996,0.0000528413],"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":[0.00009831085,0.0041909353,0.0006390911,0.00029020125,0.00039133753,0.000014763752,0.027091129,0.014841827,0.039977547,0.011202273,0.0023610222,0.8989016],"study_design_scores_gemma":[0.00030860247,0.00003810459,0.0035593417,0.000044605542,0.000011691977,0.000008553999,0.0000481544,0.992184,0.0022038184,0.0001148425,0.0012966371,0.0001816721],"about_ca_topic_score_codex":0.00007564981,"about_ca_topic_score_gemma":0.000018840774,"teacher_disagreement_score":0.9773421,"about_ca_system_score_codex":0.000059080456,"about_ca_system_score_gemma":0.000048443704,"threshold_uncertainty_score":0.6762732},"labels":[],"label_agreement":null},{"id":"W2919309882","doi":"10.1109/access.2019.2901235","title":"Fingerprint Liveness Detection Using an Improved CNN With Image Scale Equalization","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Liveness; Fingerprint recognition; Fingerprint (computing); Spoofing attack; Biometrics; Pattern recognition (psychology); Authentication (law); Classifier (UML); Data mining; Computer vision; Machine learning; Computer security","score_opus":0.037321418879331165,"score_gpt":0.3095874375762189,"score_spread":0.27226601869688777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919309882","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":[0.4876166,0.0000052851406,0.51171845,0.000015337588,0.00038936138,0.00011688782,6.464527e-7,0.00007062815,0.0000668377],"genre_scores_gemma":[0.99324155,0.000002158671,0.006545408,0.00008552008,0.000048742455,0.0000075287894,0.0000032165954,0.000008764484,0.00005712418],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902,0.00007250358,0.00016025665,0.00037407462,0.00021179348,0.00016136338],"domain_scores_gemma":[0.99905115,0.000022572227,0.00013453956,0.0005215928,0.00020617532,0.00006395773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027773427,0.00009250481,0.00010179826,0.0002570143,0.00011791274,0.0007658115,0.0007178827,0.000059062713,0.000022877117],"category_scores_gemma":[0.00001046387,0.00008320075,0.000024137735,0.001244886,0.000024522695,0.00225322,0.00009612597,0.00007619688,0.000037167087],"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":[0.0000514599,0.00041881332,0.007835362,0.00015326246,0.000026726226,0.0000063697566,0.0018012493,0.0016130892,0.8918906,0.0007463228,0.0000124348335,0.09544434],"study_design_scores_gemma":[0.00032279204,0.00006449229,0.017092496,0.000016089347,0.0000064749584,0.000010971995,0.000028405499,0.63926625,0.3425772,0.0002486226,0.0001544861,0.00021169201],"about_ca_topic_score_codex":0.00041290114,"about_ca_topic_score_gemma":0.00009560691,"teacher_disagreement_score":0.6376532,"about_ca_system_score_codex":0.00006427021,"about_ca_system_score_gemma":0.000052555268,"threshold_uncertainty_score":0.7384738},"labels":[],"label_agreement":null},{"id":"W2938914726","doi":"10.2196/11472","title":"A Facial Recognition Mobile App for Patient Safety and Biometric Identification: Design, Development, and Validation","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Seoul National University Hospital; National Research Foundation of Korea; Korea Health Industry Development Institute; Seoul National University; National Research Foundation","keywords":"Identification (biology); Patient safety; Biometrics; Medicine; Health care; Medical emergency; Computer science; Artificial intelligence","score_opus":0.07627390721603412,"score_gpt":0.3389561442877376,"score_spread":0.2626822370717035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938914726","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61131877,0.0011914473,0.38407135,0.0005864745,0.00042055646,0.0022344515,0.000038589562,0.0000976921,0.000040695635],"genre_scores_gemma":[0.96699107,0.0009648192,0.031058738,0.00040857482,0.000039337017,0.00026320873,0.00013463687,0.000009510663,0.00013013123],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983718,0.000112690286,0.00050220627,0.0005206601,0.00021486137,0.00027780663],"domain_scores_gemma":[0.9989311,0.00017098102,0.00028543142,0.000204835,0.00015287164,0.00025474725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010790108,0.00012573697,0.00018665324,0.00071574934,0.0003977277,0.00020390212,0.00011695255,0.000097813594,0.000006989002],"category_scores_gemma":[0.000049806345,0.00012322907,0.00001652804,0.0012613114,0.00003587767,0.0003866799,0.00006610725,0.00008415311,0.000023061562],"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":[0.000059085283,0.0000785902,0.00072575966,0.0005456535,0.000005511602,1.5112903e-7,0.0023741012,4.9508503e-7,0.000108731954,0.00078425335,0.00024797834,0.9950697],"study_design_scores_gemma":[0.008868512,0.003013641,0.47742227,0.00018094841,0.000085280575,0.000114521754,0.0016166735,0.035926275,0.010360534,0.007287626,0.45320717,0.0019165801],"about_ca_topic_score_codex":0.000019047042,"about_ca_topic_score_gemma":0.0000027469875,"teacher_disagreement_score":0.9931531,"about_ca_system_score_codex":0.000081032726,"about_ca_system_score_gemma":0.0002035433,"threshold_uncertainty_score":0.5025135},"labels":[],"label_agreement":null},{"id":"W2943694314","doi":"10.1007/978-3-030-18419-3_4","title":"Mobile Travel Credentials","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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 Ottawa","funders":"","keywords":"Computer science; Computer graphics (images)","score_opus":0.022211530160034677,"score_gpt":0.2602474960792216,"score_spread":0.2380359659191869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943694314","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":[0.00003552971,0.0008726066,0.9846963,0.00020422072,0.0038280122,0.00053064723,0.000010089319,0.00012686416,0.009695722],"genre_scores_gemma":[0.38194737,0.00044465376,0.5878182,0.0041563874,0.0011528503,0.000040092975,0.00003895346,0.000101749436,0.02429973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99596477,0.000035519413,0.0005690977,0.0016227013,0.0012404013,0.0005675156],"domain_scores_gemma":[0.99706626,0.0002588113,0.00030890928,0.0019056041,0.00028703667,0.00017336648],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001084668,0.00042213337,0.0005203446,0.0017477367,0.00019358881,0.0009459199,0.004454466,0.00038563574,0.0001284369],"category_scores_gemma":[0.00006531439,0.00040009708,0.00017697216,0.0013731724,0.00048153836,0.0005894859,0.0011568305,0.00063543173,0.0005402275],"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":[0.0000028141073,0.00006142914,0.000025892235,0.000058881255,0.000014816864,0.000040461415,0.0010736007,0.0016035979,0.0005082562,0.07076555,0.00027412007,0.9255706],"study_design_scores_gemma":[0.0010489606,0.00060692243,0.0009284941,0.00048463728,0.00002870797,0.00020607351,8.577873e-7,0.6314642,0.011351013,0.26547894,0.085598275,0.0028029557],"about_ca_topic_score_codex":0.000023399592,"about_ca_topic_score_gemma":0.000019517145,"teacher_disagreement_score":0.92276764,"about_ca_system_score_codex":0.0002485202,"about_ca_system_score_gemma":0.00075776945,"threshold_uncertainty_score":0.9998451},"labels":[],"label_agreement":null},{"id":"W2944328653","doi":"10.22215/etd/2018-13180","title":"Effects of Sensors, Age, and Gender on Fingerprint Image Quality","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Fingerprint (computing); Fingerprint recognition; Quality (philosophy); USable; Biometrics; Vendor; Artificial intelligence; Pattern recognition (psychology); Computer science; Image quality; Multispectral image; Computer vision; Engineering; Image (mathematics); Multimedia","score_opus":0.03320760046724055,"score_gpt":0.33354582871752997,"score_spread":0.30033822825028944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944328653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8998193,0.000287412,0.052357983,0.00009136312,0.001971941,0.00056824286,0.0000088538745,0.00019138989,0.044703476],"genre_scores_gemma":[0.9382144,0.00014506638,0.04173749,0.00021451635,0.00008634239,0.000017913591,0.00012475942,0.00001855327,0.019440947],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99872625,0.000113560796,0.00028406837,0.00042168392,0.0003320725,0.00012234932],"domain_scores_gemma":[0.9988923,0.00020039071,0.00021607462,0.0004521993,0.00017643248,0.0000626123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038103745,0.00014417569,0.00022890423,0.00034131663,0.00005553273,0.00011874681,0.00036278905,0.00017015632,0.000031692452],"category_scores_gemma":[0.00021634724,0.00012624574,0.00006751388,0.0004411152,0.000042738844,0.000089670764,0.000057129444,0.00012854872,0.00005518606],"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":[0.00015407312,0.0015310947,0.00015762426,0.010655132,0.0003671227,0.000103395054,0.028677031,3.4380147e-7,0.09317859,0.59719217,0.037186973,0.23079643],"study_design_scores_gemma":[0.000767337,0.00017542283,0.6251049,0.0001255453,0.00004823399,0.0000031364111,0.00019901,0.0009290753,0.35150498,0.013591752,0.0067261374,0.00082447287],"about_ca_topic_score_codex":0.00009200054,"about_ca_topic_score_gemma":0.000026440652,"teacher_disagreement_score":0.62494725,"about_ca_system_score_codex":0.000018995526,"about_ca_system_score_gemma":0.000038553484,"threshold_uncertainty_score":0.51481515},"labels":[],"label_agreement":null},{"id":"W2944648082","doi":"10.1109/access.2019.2914992","title":"A Multi-Biometric System Based on Feature and Score Level Fusions","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Normalization (sociology); Biometrics; Weighting; Computer science; Pattern recognition (psychology); Artificial intelligence; Fusion; Feature (linguistics); Modalities; Modality (human–computer interaction); Sensor fusion; Fingerprint recognition; Fingerprint (computing)","score_opus":0.0899867567989089,"score_gpt":0.3107564861189591,"score_spread":0.22076972932005023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944648082","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":[0.43505535,0.00027642224,0.557423,0.0014172792,0.003327688,0.0007032029,0.00005961439,0.00036357134,0.0013739011],"genre_scores_gemma":[0.9935047,0.0000056025815,0.005083106,0.00041410164,0.000034239525,0.000011445882,0.0000051765996,0.0000066836155,0.00093493034],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99884367,0.00005452523,0.0001368177,0.0004391943,0.00033845197,0.00018732027],"domain_scores_gemma":[0.998898,0.00012246809,0.000091301496,0.00066166866,0.000114310664,0.00011219239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002634094,0.00011739943,0.00014730888,0.0012848426,0.00012642115,0.0005545888,0.0010651264,0.00010501022,0.000014162655],"category_scores_gemma":[0.000042584332,0.00009765896,0.000045321536,0.0044519063,0.000023296438,0.0004153298,0.00014639928,0.00014420606,0.00016042954],"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":[0.00015181082,0.0038278152,0.35759106,0.0037861944,0.0002404311,0.00026153767,0.0029924314,0.00081192947,0.050834768,0.048670385,0.09591286,0.43491876],"study_design_scores_gemma":[0.0013946831,0.00007085075,0.49510738,0.00011635897,0.000009628093,0.0000124048975,0.000031825264,0.49240163,0.004223822,0.000026150416,0.0062367185,0.00036855382],"about_ca_topic_score_codex":0.000061263454,"about_ca_topic_score_gemma":0.000008444391,"teacher_disagreement_score":0.5584494,"about_ca_system_score_codex":0.00005563077,"about_ca_system_score_gemma":0.000052451822,"threshold_uncertainty_score":0.5347913},"labels":[],"label_agreement":null},{"id":"W2950330842","doi":"10.48550/arxiv.quant-ph/0507048","title":"Optimal fingerprinting strategies with one-sided error","year":2005,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.0841803107695236,"score_gpt":0.2960604219358495,"score_spread":0.21188011116632594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950330842","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7354229,0.00022123291,0.25930703,0.0015434863,0.00051164784,0.00023972116,0.000004676095,0.00039108642,0.0023582436],"genre_scores_gemma":[0.9198784,0.00003736187,0.07882415,0.00021469453,0.00023525384,0.000037248843,0.000022456741,0.000020708932,0.00072967977],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997523,0.00008718408,0.00045400855,0.001002847,0.0005002441,0.0004326973],"domain_scores_gemma":[0.9977053,0.00009753908,0.00039208904,0.0014175841,0.00024866898,0.00013881127],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048983947,0.00032505742,0.00036265657,0.00044631973,0.0001969001,0.0008230552,0.0018529706,0.0002794977,0.000080372636],"category_scores_gemma":[0.00009954992,0.00030760234,0.00012882099,0.00084256404,0.00011321654,0.0005544023,0.0013972955,0.00082548335,0.0002900878],"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":[0.00026224172,0.005396558,0.4956583,0.0028338856,0.0027763895,0.00065370684,0.049570903,0.053826686,0.017920509,0.16514936,0.008880336,0.1970711],"study_design_scores_gemma":[0.0010172661,0.00014021668,0.9201042,0.00058242434,0.00010226746,0.000043921948,0.0007489807,0.049858406,0.010792558,0.0013735599,0.013107488,0.0021287124],"about_ca_topic_score_codex":0.0001809669,"about_ca_topic_score_gemma":0.00006290832,"teacher_disagreement_score":0.42444587,"about_ca_system_score_codex":0.000100469566,"about_ca_system_score_gemma":0.00046506833,"threshold_uncertainty_score":0.9999376},"labels":[],"label_agreement":null},{"id":"W2958552210","doi":"10.18280/rces.050203","title":"Fingerprint positioning based on piecewise filtering of received signal strength indices and space-scene constraints","year":2018,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Changzhou Institute of Technology; National Natural Science Foundation of China","keywords":"Fingerprint (computing); Signal strength; Piecewise; Computer vision; Artificial intelligence; SIGNAL (programming language); Space (punctuation); Computer science; Fingerprint recognition; Pattern recognition (psychology); Mathematics; Telecommunications; Mathematical analysis; Wireless","score_opus":0.01966173580468526,"score_gpt":0.2729383605807253,"score_spread":0.25327662477604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958552210","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":[0.011047219,0.026652407,0.9607763,0.00067258615,0.00040089423,0.00023020242,0.000008186174,0.00011634223,0.00009582172],"genre_scores_gemma":[0.83817065,0.0062184436,0.15543021,0.00011542386,0.000049314458,0.000006348695,0.0000018367891,0.0000056003223,0.00000216007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990135,0.00003977878,0.00036158095,0.00023521995,0.00021714902,0.000132802],"domain_scores_gemma":[0.999064,0.00022613961,0.00019343349,0.00025577037,0.00022382436,0.000036817426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041423476,0.00013698036,0.00038008852,0.00025647553,0.000052416133,0.000027973028,0.00025660495,0.000025578529,0.0000064508235],"category_scores_gemma":[0.00008638551,0.00012357639,0.000060629445,0.0005153774,0.00012990234,0.00009614586,0.00016421726,0.0000746772,0.0000018011577],"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":[0.00001481086,0.00035374958,0.0007416899,0.03195535,0.00061888614,0.000014550548,0.0031039114,0.0026730876,0.0039208736,0.009607829,0.0014386773,0.9455566],"study_design_scores_gemma":[0.0007179528,0.00061218976,0.014727075,0.037180398,0.0000726354,0.000016457067,0.000027585298,0.9197399,0.021723302,0.00007071104,0.0045601004,0.0005516723],"about_ca_topic_score_codex":0.0000016125895,"about_ca_topic_score_gemma":1.8864414e-7,"teacher_disagreement_score":0.94500494,"about_ca_system_score_codex":0.000021613421,"about_ca_system_score_gemma":0.00002085135,"threshold_uncertainty_score":0.5039299},"labels":[],"label_agreement":null},{"id":"W2965959584","doi":"10.11159/icbes19.146","title":"Multi-Mode Biometrics for Law Enforcement Operations","year":2019,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biometrics; Law enforcement; Computer security; Computer science; Mode (computer interface); Law; Human–computer interaction; Political science","score_opus":0.01353693703979808,"score_gpt":0.24343058209203855,"score_spread":0.22989364505224047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965959584","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":[0.36260557,0.001320634,0.620629,0.001078765,0.01003392,0.0029010242,0.000017286435,0.00034680314,0.001066973],"genre_scores_gemma":[0.9874945,0.000010978755,0.01172128,0.000081163795,0.00003728401,0.000023436703,1.8010529e-7,0.000004135924,0.00062702195],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988492,0.000003247508,0.00022217429,0.00036330224,0.00032801417,0.00023405634],"domain_scores_gemma":[0.9993373,0.00008297087,0.00006980113,0.00015167188,0.00026194786,0.00009627024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004737371,0.00010960535,0.00016833318,0.0006066833,0.00020871864,0.00055837585,0.00071797415,0.000028847295,2.6557973e-7],"category_scores_gemma":[0.000042009535,0.00007744507,0.00003527396,0.0028255917,0.0000849541,0.0003143368,0.00019817131,0.00009385401,7.4149057e-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":[0.0000020773637,0.000035376004,0.00022185381,0.00007782547,0.000007798311,4.0022954e-8,0.00006523949,0.0013917185,0.0057434463,0.9902006,0.00012280444,0.0021312186],"study_design_scores_gemma":[0.00024860224,0.00008565686,0.00062497635,0.000049462964,0.0000030412755,0.0000052532914,0.0000029896082,0.98918194,0.003284397,0.000026779171,0.006378956,0.00010795152],"about_ca_topic_score_codex":0.00004486905,"about_ca_topic_score_gemma":0.0000012499648,"teacher_disagreement_score":0.9901738,"about_ca_system_score_codex":0.000037242684,"about_ca_system_score_gemma":0.000026812382,"threshold_uncertainty_score":0.5384431},"labels":[],"label_agreement":null},{"id":"W2969725856","doi":"10.1109/est.2019.8806206","title":"Hybrid Score- and Rank-Level Fusion for Person Identification using Face and ECG Data","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Identification (biology); Computer science; Artificial intelligence; Face (sociological concept); Sensor fusion; RGB color model; Pattern recognition (psychology); Facial recognition system; Computer vision","score_opus":0.247999999857604,"score_gpt":0.3382935710822925,"score_spread":0.09029357122468853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969725856","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":[0.08666145,0.0010552462,0.9090382,0.00079158443,0.0010954648,0.00089350017,0.0003280146,0.000079667116,0.000056893492],"genre_scores_gemma":[0.8745071,0.00045745456,0.12293639,0.0001422808,0.00007005008,0.000015130055,0.0005575371,0.000015878502,0.0012981556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978139,0.00006361327,0.00033326805,0.0012877679,0.0003050371,0.00019640964],"domain_scores_gemma":[0.9974339,0.00010935863,0.0002945672,0.0018814395,0.0001866164,0.000094101866],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011427336,0.00020447314,0.00026474465,0.00042917713,0.00020111512,0.0011269308,0.0013837927,0.00016130129,0.000009788175],"category_scores_gemma":[0.00015384058,0.00019766648,0.000043853364,0.0002398509,0.000063008505,0.0006158067,0.0025070896,0.00019627372,0.00001114759],"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":[0.00007554073,0.00041578605,0.0037113584,0.003985362,0.0002619579,0.0000061743185,0.004925093,0.00020044718,0.025187958,0.01987388,0.032222953,0.9091335],"study_design_scores_gemma":[0.0003371788,0.000010260535,0.008846404,0.00006427201,0.000033029686,0.000016697484,0.00009004053,0.9862593,0.0012909826,0.0012310746,0.0015268187,0.00029394194],"about_ca_topic_score_codex":0.00031158622,"about_ca_topic_score_gemma":0.0000138589685,"teacher_disagreement_score":0.98605883,"about_ca_system_score_codex":0.00005021533,"about_ca_system_score_gemma":0.0001277664,"threshold_uncertainty_score":0.99991},"labels":[],"label_agreement":null},{"id":"W2975485695","doi":"10.1109/tcds.2019.2920364","title":"Deep Residual Network With Adaptive Learning Framework for Fingerprint Liveness Detection","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Liveness; Artificial intelligence; Spoofing attack; Convolutional neural network; Fingerprint (computing); Pattern recognition (psychology); Feature extraction; Deep learning; Residual; Fingerprint recognition; Artificial neural network; Multilayer perceptron; Feature (linguistics); Machine learning; Algorithm; Computer security","score_opus":0.02163336013942574,"score_gpt":0.23581662265953193,"score_spread":0.2141832625201062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975485695","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":[0.046336815,0.00010031,0.9517201,0.000016681244,0.00077894656,0.00070946314,0.000005721542,0.0000986071,0.00023336445],"genre_scores_gemma":[0.98426783,0.000023301845,0.015089031,0.00005758471,0.000043027656,0.0002017739,0.0000032089642,0.000012116184,0.00030211918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880195,0.00010135356,0.00020312924,0.00043292617,0.00022927359,0.0002313713],"domain_scores_gemma":[0.9990087,0.00054618093,0.00009693628,0.0000880634,0.00018013085,0.00007999332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002857691,0.00015375686,0.00017722796,0.00018584375,0.00043516772,0.0001957811,0.000119772856,0.0001077882,0.000011087647],"category_scores_gemma":[0.00001051579,0.00013927775,0.000037101003,0.00063489156,0.0000381648,0.0002533669,0.0000040559953,0.00022849329,0.000068582325],"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":[0.0023271593,0.00073740014,0.003094873,0.0006069772,0.0011528216,0.00003239526,0.015036417,0.02330656,0.0011724095,0.01398149,0.000042871863,0.9385086],"study_design_scores_gemma":[0.013563499,0.0084331585,0.048119605,0.0060865954,0.000377537,0.001118887,0.046382423,0.7350347,0.123441316,0.0040842327,0.0076593915,0.0056986194],"about_ca_topic_score_codex":0.000047808226,"about_ca_topic_score_gemma":0.00004221129,"teacher_disagreement_score":0.937931,"about_ca_system_score_codex":0.000087915956,"about_ca_system_score_gemma":0.000064596315,"threshold_uncertainty_score":0.5679582},"labels":[],"label_agreement":null},{"id":"W2992938135","doi":"10.1109/cw.2019.00054","title":"Multi-instance Cancelable Biometric System using Convolutional Neural Network","year":2019,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Random projection; Convolutional neural network; Pattern recognition (psychology); Authentication (law); Iris recognition; Identification (biology); Support vector machine; Artificial neural network; Projection (relational algebra); Machine learning; Deep learning; Data mining; Algorithm; Computer security","score_opus":0.038170001225772326,"score_gpt":0.2562951483954574,"score_spread":0.2181251471696851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992938135","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":[0.08477105,0.0011354772,0.9080188,0.000110722605,0.0033667872,0.00026342977,0.000006075033,0.00031513086,0.0020124912],"genre_scores_gemma":[0.8883946,0.0000053931903,0.109111816,0.00017316805,0.00006847557,0.0000037646766,0.0000035199148,0.000005215284,0.0022340422],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985932,0.00005897967,0.00025672172,0.00040337862,0.00034832582,0.00033937095],"domain_scores_gemma":[0.9990657,0.00006296977,0.00011716058,0.0004867907,0.00017091603,0.00009649294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038239616,0.00010854895,0.00015732614,0.0004904031,0.00015574975,0.0001966693,0.00065643113,0.0000669745,0.00007707249],"category_scores_gemma":[0.000016894975,0.00010001707,0.00005769398,0.006334363,0.000030030415,0.00045845346,0.00015185738,0.00009299818,0.00039289048],"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":[0.000021656599,0.0003942865,0.11012763,0.0003924108,0.00012556699,0.000027853821,0.0003100984,0.022332633,0.011032491,0.82566565,0.015495464,0.014074269],"study_design_scores_gemma":[0.00033368877,0.000010453516,0.011768984,0.000012236536,0.000002405716,0.000018112692,0.000023341549,0.9806446,0.00014100289,0.000020562184,0.0068774875,0.00014712813],"about_ca_topic_score_codex":0.0003433984,"about_ca_topic_score_gemma":0.000015804813,"teacher_disagreement_score":0.958312,"about_ca_system_score_codex":0.00024354231,"about_ca_system_score_gemma":0.00010958048,"threshold_uncertainty_score":0.50499433},"labels":[],"label_agreement":null},{"id":"W2996857901","doi":"","title":"Fast Minutia-based Palmprint Matching Using CNN and Generalized Hough Transform","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Minutiae; Artificial intelligence; Hough transform; Convolutional neural network; Computer science; Matching (statistics); Computer vision; Pattern recognition (psychology); Rotation (mathematics); Image (mathematics); Process (computing); Mathematics; Fingerprint recognition; Fingerprint (computing)","score_opus":0.013762015257125754,"score_gpt":0.2739707817555335,"score_spread":0.26020876649840774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996857901","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":[0.33946925,0.000753206,0.6582812,0.0007526891,0.00059136644,0.00009010527,0.0000016547444,0.000012656631,0.000047880367],"genre_scores_gemma":[0.96445775,0.000013275264,0.035218336,0.00020536061,0.000061482955,4.2361216e-7,0.0000018074212,0.0000058623345,0.000035726418],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860686,0.00010325264,0.00052801776,0.00016032207,0.00048012222,0.00012139768],"domain_scores_gemma":[0.999,0.0001398336,0.00035487444,0.00010644978,0.0002889633,0.00010984026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008070424,0.00010993318,0.00023752169,0.00044381316,0.00011574104,0.0005674777,0.00020258767,0.000030256899,0.0000055937567],"category_scores_gemma":[0.00001251929,0.00008859988,0.000070428476,0.00032158417,0.000033195793,0.0005886657,0.00003905265,0.00012693545,0.0000039133433],"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":[0.000250978,0.0007582943,0.020080294,0.0015238521,0.00032169072,0.00012687166,0.011760618,0.4237932,0.028515527,0.06750255,0.00223807,0.44312805],"study_design_scores_gemma":[0.0012404221,0.000041668383,0.0114215445,0.0001740638,0.000009061374,0.00038532342,0.00020204776,0.9823739,0.00013475279,0.0021413781,0.0017439211,0.00013189421],"about_ca_topic_score_codex":0.000029225945,"about_ca_topic_score_gemma":1.9670323e-7,"teacher_disagreement_score":0.6249885,"about_ca_system_score_codex":0.00004699052,"about_ca_system_score_gemma":0.00009906148,"threshold_uncertainty_score":0.54722005},"labels":[],"label_agreement":null},{"id":"W3005857660","doi":"10.1109/icb45273.2019.8987267","title":"Directed Adversarial Attacks on Fingerprints using Attributions","year":2019,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Fingerprint (computing); Minutiae; Computer science; Artificial intelligence; Fingerprint recognition; Pattern recognition (psychology); Fingerprint Verification Competition; Biometrics; Matching (statistics); Artificial neural network; Noise (video); Data mining; Mathematics; Image (mathematics); Statistics","score_opus":0.034283653592893135,"score_gpt":0.28181742330702175,"score_spread":0.2475337697141286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005857660","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":[0.35109708,0.000014144605,0.6246531,0.0008447169,0.0027002634,0.0002566046,0.000007988921,0.0005761876,0.019849895],"genre_scores_gemma":[0.9851368,0.0000013973516,0.012335927,0.00025970596,0.000036908306,0.0000013927147,0.0000057702455,0.0000028773284,0.0022191915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991818,0.00004184601,0.0001291974,0.0002734934,0.00021765719,0.00015600245],"domain_scores_gemma":[0.99928135,0.000067933885,0.000044327764,0.00046176283,0.0000863223,0.00005832009],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00017950175,0.0000663459,0.00008234271,0.0002208237,0.000093691604,0.00011789466,0.000409604,0.000055699707,0.00041640017],"category_scores_gemma":[0.00006287315,0.00006073246,0.000048192924,0.0011163615,0.000014359635,0.00020389658,0.0001229577,0.00008543973,0.0015152477],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037532962,0.0009571469,0.015604818,0.000032573585,0.0001062843,0.000016034528,0.001023926,0.0005499009,0.01963326,0.89164686,0.02447542,0.04591624],"study_design_scores_gemma":[0.0016397199,0.00010782159,0.11909044,0.000032879067,0.000013118407,0.0000195253,0.00003538177,0.7285295,0.017115552,0.0017025718,0.13102852,0.000684985],"about_ca_topic_score_codex":0.00007036259,"about_ca_topic_score_gemma":0.000003456698,"teacher_disagreement_score":0.8899443,"about_ca_system_score_codex":0.000071615366,"about_ca_system_score_gemma":0.000051144823,"threshold_uncertainty_score":0.9992622},"labels":[],"label_agreement":null},{"id":"W3012389411","doi":"","title":"Textured Contact Lenses Detection in Iris Recognition Using Weber Local Descriptor (WLD)","year":2019,"lang":"en","type":"article","venue":"Journal of Emerging Technologies and Innovative Research","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Iris recognition; IRIS (biosensor); Artificial intelligence; Computer science; Biometrics; Contact lens; Computer vision; Histogram; Lens (geology); Feature (linguistics); Pattern recognition (psychology); Feature extraction; Image (mathematics); Optics; Physics","score_opus":0.12528000105219245,"score_gpt":0.372359836236682,"score_spread":0.24707983518448956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012389411","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8577277,0.0006722882,0.13988662,0.0011995162,0.0002515903,0.00013477316,0.000001171025,0.000045166686,0.000081179816],"genre_scores_gemma":[0.9910753,0.0003315091,0.008530101,0.00001881085,0.000017275996,0.0000022170327,4.3740812e-7,0.0000044603985,0.000019885118],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99853456,0.00014360501,0.0004121168,0.00020324523,0.00044996318,0.00025652393],"domain_scores_gemma":[0.9979675,0.00014799066,0.0002326241,0.00019549637,0.0014344474,0.000021942422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022975479,0.000083939856,0.00019031853,0.0026355442,0.00011819885,0.00015063515,0.00042878266,0.00014927368,0.000011095046],"category_scores_gemma":[0.0008273144,0.00006809513,0.0000362088,0.006040079,0.00014988566,0.00073661475,0.00025284913,0.0008250268,0.000007638352],"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":[0.000050684615,0.000075770404,0.003900258,0.000034299024,0.000029367371,0.000015265858,0.00032391108,0.0000022023528,0.15079376,0.0020413785,0.00035184703,0.84238124],"study_design_scores_gemma":[0.0048904167,0.003389581,0.08799909,0.0012318267,0.000014178368,0.00055990566,0.04784257,0.075321615,0.6887419,0.0542621,0.034652457,0.0010943708],"about_ca_topic_score_codex":0.00007042433,"about_ca_topic_score_gemma":0.000010290372,"teacher_disagreement_score":0.8412869,"about_ca_system_score_codex":0.00019931747,"about_ca_system_score_gemma":0.00009838306,"threshold_uncertainty_score":0.3584377},"labels":[],"label_agreement":null},{"id":"W3029151513","doi":"","title":"Private Fingerprint Matching.","year":2012,"lang":"en","type":"preprint","venue":"Research Online (University of Wollongong)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Minutiae; Fingerprint (computing); Scalability; Protocol (science); Matching (statistics); Cryptography; Theoretical computer science; Modular design; Algorithm; Fingerprint recognition; Computer security; Mathematics; Programming language; Database","score_opus":0.10977779003576868,"score_gpt":0.34841784506908513,"score_spread":0.23864005503331645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029151513","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6246725,0.0011053926,0.36032307,0.009815773,0.0008899746,0.00078866835,0.0002305925,0.00031720195,0.0018568399],"genre_scores_gemma":[0.87884545,0.00087643607,0.1164264,0.00002926877,0.0001501441,9.527906e-7,0.00014515097,0.000017878743,0.0035083382],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9964602,0.0004951728,0.00025486998,0.0007498691,0.0013873068,0.0006525833],"domain_scores_gemma":[0.9965796,0.00023894785,0.00027002016,0.0018122481,0.00074055186,0.0003586418],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0026404231,0.00020852758,0.0004120416,0.0017053718,0.00035857814,0.0001571012,0.0044654612,0.0003949535,0.00017598775],"category_scores_gemma":[0.00016881274,0.00025566202,0.0002606063,0.0020339352,0.00034606614,0.00041266234,0.008732171,0.0017342143,0.00020229584],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020412772,0.008530409,0.0094554145,0.0040546847,0.0012460051,0.00043947814,0.046754967,0.00044709805,0.006884824,0.44996902,0.039222714,0.43279126],"study_design_scores_gemma":[0.0028256637,0.0002929743,0.48658,0.0012038876,0.00013251137,0.000047053152,0.0046181683,0.060309526,0.0014289368,0.0751295,0.36452076,0.0029109793],"about_ca_topic_score_codex":0.0019618315,"about_ca_topic_score_gemma":0.00020484181,"teacher_disagreement_score":0.4771246,"about_ca_system_score_codex":0.00026264077,"about_ca_system_score_gemma":0.0004973464,"threshold_uncertainty_score":0.99998957},"labels":[],"label_agreement":null},{"id":"W3036219156","doi":"10.1109/access.2020.3003869","title":"Cancelable Biometrics Using Deep Learning as a Cloud Service","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Cloud computing; Computer science; Scalability; Popularity; Software deployment; Computer security; Authentication (law); Service (business); Deep learning; Artificial intelligence; Distributed computing; Database; Software engineering; Operating system","score_opus":0.10615991042397781,"score_gpt":0.33525588142579127,"score_spread":0.22909597100181345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036219156","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":[0.18580583,0.00046006672,0.80816334,0.0028087313,0.0013463692,0.00012670319,0.000001919847,0.00027205813,0.0010149797],"genre_scores_gemma":[0.9873372,0.000052126365,0.0061878306,0.006072246,0.00023458875,0.0000045715947,0.0000029655166,0.00001122466,0.00009726642],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864554,0.000071804665,0.00021898845,0.00041659543,0.00038372757,0.00026335524],"domain_scores_gemma":[0.9990342,0.0000752519,0.0001443783,0.00030582902,0.00024554154,0.00019480025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023921608,0.000111757814,0.00014564296,0.00046210215,0.00022332536,0.0008686804,0.0017573534,0.00007563551,0.00007038146],"category_scores_gemma":[0.0001848643,0.00011670315,0.00004181664,0.013594013,0.00001867509,0.0010007328,0.00033103555,0.0002073909,0.0002817558],"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":[0.00014026831,0.0011240527,0.055635933,0.0018612419,0.0005121622,0.0005090906,0.040040653,0.04153337,0.09625695,0.041252285,0.03751458,0.68361944],"study_design_scores_gemma":[0.0004111813,0.000041960975,0.0010816233,0.000012681628,0.000014233293,0.0000108473605,0.000084323976,0.89707464,0.017722884,0.0007231838,0.08244721,0.00037520946],"about_ca_topic_score_codex":0.0012378608,"about_ca_topic_score_gemma":0.000027688728,"teacher_disagreement_score":0.8555413,"about_ca_system_score_codex":0.00006713295,"about_ca_system_score_gemma":0.00010515026,"threshold_uncertainty_score":0.8376705},"labels":[],"label_agreement":null},{"id":"W3036300525","doi":"10.1007/978-3-030-50347-5_20","title":"Deep Learning for Partial Fingerprint Inpainting and Recognition","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"Université de Moncton","funders":"","keywords":"Inpainting; Fingerprint (computing); Artificial intelligence; Computer science; Pattern recognition (psychology); Deep learning; Computer vision; Image (mathematics)","score_opus":0.033280241797872326,"score_gpt":0.25262300029936824,"score_spread":0.21934275850149593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036300525","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":[0.000099526624,0.00025170654,0.9969847,0.0010731751,0.0007982186,0.00031558215,0.0000014824628,0.00013148463,0.0003440882],"genre_scores_gemma":[0.33057728,0.00007118292,0.66709894,0.0015300818,0.0005592907,0.000020687454,0.000018557566,0.00002601864,0.00009793605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774283,0.00003315651,0.00036309083,0.0011092934,0.00042221887,0.00032942052],"domain_scores_gemma":[0.9985577,0.0005114884,0.0002401307,0.00035053352,0.00020335981,0.00013680581],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00095225335,0.00024498763,0.00028148334,0.0006296243,0.00030333985,0.00070047745,0.0009591496,0.00019669114,0.000008431261],"category_scores_gemma":[0.00050564914,0.00024968633,0.00007706685,0.00062560814,0.00026321033,0.0003385243,0.00069591834,0.00055171945,0.000019232048],"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":[0.000002961962,0.0000067928772,0.000021932372,0.00004130971,0.000004243776,0.0000054190796,0.0007934141,0.0008901628,0.000082054976,0.0052397544,0.0000040071423,0.99290794],"study_design_scores_gemma":[0.00017444181,0.0000918777,0.00008942558,0.00009355945,0.0000054817397,0.000015027778,3.0516725e-7,0.9095837,0.00083256775,0.08497744,0.0038058534,0.0003303162],"about_ca_topic_score_codex":0.0000079577185,"about_ca_topic_score_gemma":0.0000114659615,"teacher_disagreement_score":0.9925776,"about_ca_system_score_codex":0.00009594262,"about_ca_system_score_gemma":0.00013044802,"threshold_uncertainty_score":0.9999955},"labels":[],"label_agreement":null},{"id":"W3043510852","doi":"10.17762/ijritcc.v7i8.5348","title":"Face Liveness Detection using Feature Fusion Using Block Truncation Code Technique","year":2019,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Compute Canada","funders":"","keywords":"Spoofing attack; Computer science; Liveness; Biometrics; Facial recognition system; Password; Artificial intelligence; Face (sociological concept); Feature (linguistics); Block (permutation group theory); Three-dimensional face recognition; Computer vision; Pattern recognition (psychology); AdaBoost; Face detection; Computer security; Support vector machine","score_opus":0.051209439150043924,"score_gpt":0.34776656516203386,"score_spread":0.2965571260119899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043510852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7040598,0.00018443025,0.29262885,0.002017435,0.0006736496,0.00012359822,0.0000020056993,0.00004437528,0.00026590357],"genre_scores_gemma":[0.977817,0.0005339503,0.021266954,0.0002305525,0.000059563896,0.0000020941595,0.000028602963,0.000007860916,0.000053416592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985429,0.00021069887,0.00046857764,0.00026663992,0.00038168178,0.00012947165],"domain_scores_gemma":[0.9984641,0.00008957475,0.0005044059,0.00028777213,0.00061931903,0.000034850385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011265391,0.00013458406,0.00014070023,0.0022316808,0.00025950556,0.0004400356,0.0005089374,0.00012878317,0.000011422874],"category_scores_gemma":[0.00006292773,0.00013487307,0.000026439271,0.002409134,0.000031613246,0.0005374599,0.00021366998,0.00045888725,0.0000017357518],"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":[0.00005431346,0.00016999769,0.0056652594,0.000010938302,0.000030479656,0.0000017754206,0.000770478,0.002083541,0.023488455,0.019408789,0.000031076343,0.9482849],"study_design_scores_gemma":[0.0011421489,0.0000931172,0.036011674,0.0002813079,0.000007667352,0.00033180945,0.00019639595,0.94178957,0.0067759557,0.0023278769,0.0107333055,0.00030915445],"about_ca_topic_score_codex":0.000024107321,"about_ca_topic_score_gemma":0.000006085287,"teacher_disagreement_score":0.94797575,"about_ca_system_score_codex":0.00025429795,"about_ca_system_score_gemma":0.00004037427,"threshold_uncertainty_score":0.54999644},"labels":[],"label_agreement":null},{"id":"W3046431518","doi":"10.1007/978-3-030-54407-2_35","title":"Fingerprint Liveness Detection Based on Multi-modal Fine-Grained Feature Fusion","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"Liveness; Computer science; Fingerprint (computing); Modal; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Fingerprint recognition; Fusion; Biometrics; Computer vision; Theoretical computer science","score_opus":0.022986774298710353,"score_gpt":0.24775437194198766,"score_spread":0.2247675976432773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046431518","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":[0.00006977574,0.0001126824,0.9927812,0.0034075656,0.0024308476,0.0004522886,0.000010178558,0.00027669477,0.0004587165],"genre_scores_gemma":[0.69650936,0.000011138419,0.2994211,0.003212765,0.00042657484,0.000016451577,0.000020762363,0.00003785985,0.00034399825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959813,0.0000719942,0.00041209042,0.0018625533,0.0012057893,0.0004663097],"domain_scores_gemma":[0.99741226,0.0003987832,0.0003030953,0.0013398308,0.00030617148,0.00023987117],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000749292,0.00052259007,0.00045407304,0.0016020641,0.0003768653,0.00067595526,0.002713837,0.00049416895,0.000021529726],"category_scores_gemma":[0.00028459588,0.00048031184,0.00020019623,0.0020034956,0.00031766677,0.00029747715,0.00081953575,0.0011642317,0.000092658025],"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":[0.000027035068,0.000100121666,0.000019975681,0.00007154946,0.0000073152555,0.00010536863,0.00045868906,0.016774373,0.0015066649,0.003288103,0.000036462607,0.9776043],"study_design_scores_gemma":[0.00043418055,0.00019866928,0.00081465323,0.00018277315,0.0000057329094,0.000017197644,9.295953e-8,0.9835639,0.0062339325,0.0046466785,0.0033445784,0.0005575932],"about_ca_topic_score_codex":0.00002617646,"about_ca_topic_score_gemma":0.00011272878,"teacher_disagreement_score":0.9770467,"about_ca_system_score_codex":0.00040982736,"about_ca_system_score_gemma":0.00044187257,"threshold_uncertainty_score":0.99976486},"labels":[],"label_agreement":null},{"id":"W3049178586","doi":"10.1145/3389683","title":"Deep Learning for Multi-instance Biometric Privacy","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Management Information Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Computer science; Artificial intelligence; Random projection; Convolutional neural network; Modality (human–computer interaction); Deep learning; Authentication (law); Identification (biology); Machine learning; Support vector machine; Pattern recognition (psychology); Computer security","score_opus":0.06197770605970799,"score_gpt":0.2771877586914677,"score_spread":0.21521005263175974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049178586","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":[0.00006972057,0.00006796002,0.99588746,0.0011189771,0.00065074733,0.00096508424,0.000007728052,0.00042846185,0.00080385903],"genre_scores_gemma":[0.87928057,0.00014429275,0.1186695,0.00092359254,0.000028101831,0.00031267313,0.000045170418,0.000009335824,0.00058674155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857163,0.00005187847,0.00051827,0.00023461672,0.0004097786,0.00021383952],"domain_scores_gemma":[0.99888194,0.000081702216,0.00023002712,0.00051894586,0.0001706965,0.00011667332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036775376,0.00013826278,0.00014782201,0.0011797069,0.0003485611,0.0006146691,0.0010402155,0.00006146287,0.000011029198],"category_scores_gemma":[0.000084973326,0.00014067774,0.0000965935,0.003986732,0.000014870249,0.002029474,0.000027911343,0.00013100785,0.0004896928],"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":[0.00003296819,0.00015999582,0.000059105467,0.0009492428,0.00017338454,0.0000012902849,0.0050660674,0.032423124,0.00001860652,0.04218498,0.0011724414,0.9177588],"study_design_scores_gemma":[0.000753118,0.000062539344,0.00032322382,0.00001290632,0.000011738234,0.0000011879989,0.00063320395,0.6251929,0.000069407186,0.00002046252,0.37276572,0.0001535868],"about_ca_topic_score_codex":0.000007739329,"about_ca_topic_score_gemma":3.9549124e-7,"teacher_disagreement_score":0.9176052,"about_ca_system_score_codex":0.000085843574,"about_ca_system_score_gemma":0.000011682522,"threshold_uncertainty_score":0.6294173},"labels":[],"label_agreement":null},{"id":"W3107636056","doi":"10.1109/access.2020.3041519","title":"Iris Segmentation Using Interactive Deep Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":38,"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":"Aalborg Universitet; Deutscher Akademischer Austauschdienst; Indian Statistical Institute; University of Alberta; Uppsala Universitet; Institut national de recherche en informatique et en automatique (INRIA); University Grants Commission; Indian National Science Academy; Meiji University; Intel Corporation","keywords":"Computer science; Deep learning; Segmentation; Artificial intelligence; Machine learning; Component (thermodynamics); Biometrics; IRIS (biosensor); Iris recognition","score_opus":0.08561528923686526,"score_gpt":0.3554876378840964,"score_spread":0.2698723486472311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107636056","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":[0.13906078,0.000032439195,0.859513,0.0005888074,0.00039037038,0.00006506158,4.478274e-7,0.000094663366,0.00025447065],"genre_scores_gemma":[0.988808,0.000007985507,0.010254444,0.00081962434,0.000076329656,0.0000030169797,0.000002615454,0.000003963815,0.000024046398],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993026,0.00006640298,0.00012903743,0.00023048697,0.00016986675,0.00010155354],"domain_scores_gemma":[0.99958974,0.000045585068,0.00010154812,0.00011474365,0.00008069794,0.00006767913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000090796624,0.000058478545,0.000068582085,0.00011927033,0.00010913536,0.00050891866,0.00059982133,0.000027392736,0.00004093145],"category_scores_gemma":[0.000063691,0.000059952054,0.000028947376,0.0011076638,0.000013333154,0.0013984776,0.00012537223,0.0001241127,0.00007051929],"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":[0.000048793438,0.00022353172,0.026518477,0.0001231116,0.00013063454,0.00005327328,0.037913904,0.008373826,0.13563685,0.0025183624,0.0040043625,0.7844549],"study_design_scores_gemma":[0.00027262516,0.00002905872,0.0057561914,0.000007266418,0.000007899254,0.000004744431,0.000224447,0.94481784,0.043362167,0.00023967224,0.005095298,0.00018277195],"about_ca_topic_score_codex":0.00005801498,"about_ca_topic_score_gemma":0.0000021560293,"teacher_disagreement_score":0.93644404,"about_ca_system_score_codex":0.000041293177,"about_ca_system_score_gemma":0.000018451812,"threshold_uncertainty_score":0.49075145},"labels":[],"label_agreement":null},{"id":"W3112503563","doi":"10.1109/tvt.2020.3043203","title":"Accurate Image-Based Pedestrian Detection With Privacy Preservation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Biometric Identification and Security","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":"Queen's University; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Encryption; Pedestrian detection; Computer science; Support vector machine; Histogram; Pedestrian; Kernel (algebra); Overhead (engineering); Feature extraction; Block (permutation group theory); Artificial intelligence; Computer vision; Homomorphic encryption; Pattern recognition (psychology); Image (mathematics); Mathematics; Engineering; Computer network","score_opus":0.024523704978946025,"score_gpt":0.24034494850724908,"score_spread":0.21582124352830306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112503563","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":[0.043662544,0.000019054096,0.9392305,0.015570394,0.0001574278,0.0003141922,0.000005198395,0.0010093225,0.000031351155],"genre_scores_gemma":[0.97842723,0.000009833233,0.02090267,0.0005174749,0.000014344122,0.00008332049,0.0000028340116,0.000013044253,0.00002923462],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987051,0.00006454377,0.00022885011,0.0005061806,0.0002736021,0.00022174923],"domain_scores_gemma":[0.9989488,0.000037992406,0.000113376205,0.00061803614,0.00018837076,0.00009339649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000114739705,0.00015849249,0.00015258008,0.00065842306,0.00024331424,0.00013612426,0.0007137419,0.00021613331,0.000022258646],"category_scores_gemma":[0.000030692998,0.00014645045,0.00006780415,0.0038539732,0.00010385791,0.00054386945,0.000004402027,0.00040502026,0.00011450033],"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":[0.00045986837,0.0016455571,0.00021110188,0.0002460536,0.00031412585,0.00017359483,0.0010785306,0.030475372,0.357913,0.005076641,0.000453767,0.6019524],"study_design_scores_gemma":[0.0010440744,0.0005172929,0.0003029437,0.000012301673,0.000025886096,0.000019147385,0.000026915706,0.38423577,0.6046478,0.00033156897,0.008575764,0.00026051022],"about_ca_topic_score_codex":0.000021792268,"about_ca_topic_score_gemma":0.000021956337,"teacher_disagreement_score":0.9347647,"about_ca_system_score_codex":0.00006332891,"about_ca_system_score_gemma":0.00008982888,"threshold_uncertainty_score":0.5972076},"labels":[],"label_agreement":null},{"id":"W3115967792","doi":"10.1109/csp51677.2021.9357588","title":"Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Fingerprint (computing); Artificial intelligence; Term (time); Focus (optics); Convolutional neural network; Machine learning; Pattern recognition (psychology); Fingerprint recognition; Recall; Convolution (computer science); Variety (cybernetics); Psychology; Cognitive psychology; Artificial neural network","score_opus":0.0480085940930848,"score_gpt":0.27467371184911,"score_spread":0.2266651177560252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115967792","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":[0.02707323,0.0012270685,0.96627706,0.0006409801,0.0030132276,0.00083986187,0.00008523761,0.0002777189,0.00056559395],"genre_scores_gemma":[0.8953978,0.0003730019,0.09542438,0.0007328504,0.00039600415,0.00046892624,0.0053175683,0.000031543805,0.0018579571],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974005,0.00014158034,0.0005681447,0.0010863348,0.0004222748,0.00038113206],"domain_scores_gemma":[0.9977276,0.000273995,0.0002161448,0.000932435,0.00069190556,0.00015797338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007445981,0.0003024791,0.00037167626,0.0003248176,0.00023399226,0.0009785203,0.0010419211,0.000448185,0.00017077403],"category_scores_gemma":[0.00015634287,0.0003222824,0.00031011173,0.00055544934,0.00010009027,0.00031910624,0.0012215499,0.0005453686,0.000015457577],"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":[0.000030394694,0.00074437296,0.0015518215,0.0005794085,0.00042454808,0.000032253072,0.0006446548,0.0005550232,0.0009304179,0.005053745,0.10001353,0.8894398],"study_design_scores_gemma":[0.0014755178,0.00010743314,0.049844973,0.00051918556,0.00020366127,0.00008240655,0.0002116158,0.91177154,0.01769145,0.010426115,0.0048875813,0.0027785236],"about_ca_topic_score_codex":0.000059787046,"about_ca_topic_score_gemma":0.000095421616,"teacher_disagreement_score":0.9112165,"about_ca_system_score_codex":0.00016646217,"about_ca_system_score_gemma":0.00026341464,"threshold_uncertainty_score":0.99992293},"labels":[],"label_agreement":null},{"id":"W3118296927","doi":"","title":"Palmprint authentication technologies, systems and applications","year":2004,"lang":"en","type":"article","venue":"PolyU Institutional Research Archive (Hong Kong Polytechnic University)","topic":"Biometric Identification and Security","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":"University of Waterloo","funders":"","keywords":"Biometrics; Computer science; Password; Robustness (evolution); Authentication (law); Process (computing); Artificial intelligence; Computer security; Computer vision","score_opus":0.04342827606327832,"score_gpt":0.28161585558115315,"score_spread":0.23818757951787484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118296927","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":[0.0031870091,0.00051464443,0.9871803,0.0021995986,0.00010280214,0.00065068953,0.000043387277,0.00058337057,0.0055381875],"genre_scores_gemma":[0.98270756,0.00043908937,0.016149286,0.0000141122455,0.000033696626,0.000026194071,0.000030078312,0.000006057429,0.000593911],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99796957,0.00011974516,0.00019175191,0.0005973272,0.0006853561,0.00043624904],"domain_scores_gemma":[0.9985843,0.00017304509,0.00007573745,0.0007376482,0.0002535245,0.0001757648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065166,0.00013293135,0.00012905657,0.0023587209,0.0011011139,0.00022854755,0.0014071212,0.000118904936,0.0000030891952],"category_scores_gemma":[0.0001935341,0.00014348437,0.00005248587,0.0035418184,0.0011870909,0.0005476251,0.0009464968,0.0005081118,0.00010022392],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059230397,0.00011018522,0.0001330689,0.000022224418,0.000015318967,0.000018151683,0.00007848081,0.00006175239,0.0035192107,0.98228717,0.000058767793,0.013689762],"study_design_scores_gemma":[0.0021931974,0.00022898063,0.030234471,0.0002562246,0.00003150474,0.0002980273,0.0013976155,0.013602059,0.0062038996,0.22542118,0.71914446,0.0009884144],"about_ca_topic_score_codex":0.0005092155,"about_ca_topic_score_gemma":0.00004879388,"teacher_disagreement_score":0.97952056,"about_ca_system_score_codex":0.00050871004,"about_ca_system_score_gemma":0.0006680855,"threshold_uncertainty_score":0.84689885},"labels":[],"label_agreement":null},{"id":"W3118412211","doi":"","title":"Palmprint authentication system for civil applications","year":2004,"lang":"en","type":"article","venue":"PolyU Institutional Research Archive (Hong Kong Polytechnic University)","topic":"Biometric Identification and Security","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":"University of Waterloo","funders":"","keywords":"Biometrics; Computer science; Preprocessor; Authentication (law); Artificial intelligence; Feature extraction; Process (computing); Computer vision; Feature (linguistics); Identity (music); Matching (statistics); Pattern recognition (psychology); Computer security","score_opus":0.04975351090910918,"score_gpt":0.29524898296542085,"score_spread":0.24549547205631167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118412211","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":[0.0010707757,0.00007696563,0.9866678,0.0016413074,0.00016517616,0.0010151324,0.00013449606,0.00037321643,0.008855099],"genre_scores_gemma":[0.9450655,0.00004239051,0.053776186,0.000026087953,0.00008801629,0.00006612467,0.00010134686,0.000009100606,0.0008252399],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976614,0.00013225406,0.00023791815,0.000648458,0.00078109,0.00053887506],"domain_scores_gemma":[0.99806505,0.00029253555,0.00009648181,0.0007923818,0.0004727975,0.0002807317],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00090928737,0.00014618793,0.00014619935,0.00216448,0.0014915271,0.00018129818,0.0017727,0.000100171485,0.0000071472064],"category_scores_gemma":[0.00017629855,0.00016404559,0.00014309144,0.0033695751,0.0006426245,0.0005401456,0.0005415208,0.0003651082,0.00017618109],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017635433,0.00015914734,0.000035725086,0.000054396212,0.00002232431,0.000009567677,0.00013370303,0.000103002625,0.007072905,0.98661613,0.00013453684,0.005640923],"study_design_scores_gemma":[0.003887748,0.00029460227,0.01347445,0.00033942296,0.000051898027,0.00014272121,0.00082112546,0.019847628,0.018564858,0.1491089,0.7924136,0.0010530751],"about_ca_topic_score_codex":0.00026867914,"about_ca_topic_score_gemma":0.00013172299,"teacher_disagreement_score":0.94399476,"about_ca_system_score_codex":0.0010766522,"about_ca_system_score_gemma":0.0011819993,"threshold_uncertainty_score":0.9998084},"labels":[],"label_agreement":null},{"id":"W3118603879","doi":"10.18280/ts.370602","title":"Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biometrics; Artificial intelligence; Fingerprint (computing); Computer science; Fingerprint recognition; Computer vision; Landmark; Pattern recognition (psychology); Palm print","score_opus":0.07392427419079446,"score_gpt":0.2528065594042479,"score_spread":0.17888228521345345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118603879","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":[0.22460763,0.00021959648,0.7740041,0.0005747082,0.00014708829,0.00020423581,0.0000067735996,0.0001729779,0.00006289529],"genre_scores_gemma":[0.9749133,0.000010759972,0.024671482,0.00032187367,0.000057161247,0.000007880234,0.000002389333,0.0000063876073,0.000008755557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998641,0.00007549175,0.00032387694,0.00041553337,0.0003311117,0.00021296815],"domain_scores_gemma":[0.9993362,0.000065195454,0.00012189489,0.00018422844,0.00009186457,0.00020062218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035081882,0.00013929163,0.00019153269,0.00037006603,0.00015134762,0.00032556034,0.00044018548,0.000051832078,0.00003042232],"category_scores_gemma":[0.000028290437,0.00013095506,0.00005438255,0.0015693823,0.00003798349,0.00022555946,0.00023214013,0.00009935788,0.000027588938],"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":[0.00013496721,0.0017688161,0.048301958,0.0028569237,0.0007469201,0.00032931063,0.039273597,0.00095227885,0.24726453,0.321425,0.0016059126,0.3353398],"study_design_scores_gemma":[0.0011972687,0.0000912939,0.036023445,0.000036603145,0.000023007982,0.00002369339,0.00046614444,0.95023733,0.007863521,0.00002086056,0.0036731656,0.00034368748],"about_ca_topic_score_codex":0.000049006714,"about_ca_topic_score_gemma":0.0000016688412,"teacher_disagreement_score":0.94928503,"about_ca_system_score_codex":0.000077479395,"about_ca_system_score_gemma":0.000039007882,"threshold_uncertainty_score":0.53401923},"labels":[],"label_agreement":null},{"id":"W3133596308","doi":"10.1007/978-3-319-98734-7","title":"Biometric-Based Physical and Cybersecurity Systems","year":2018,"lang":"en","type":"book","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Victoria","funders":"","keywords":"Biometrics; Computer security; Computer science; Engineering","score_opus":0.020565909954554894,"score_gpt":0.24764428301516145,"score_spread":0.22707837306060655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133596308","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015049358,0.0014918755,0.43868765,0.0003451243,0.0027174207,0.0006520736,0.00007718458,0.000712988,0.5551652],"genre_scores_gemma":[0.020276016,0.000052229872,0.0061334837,0.00035767152,0.0008508945,0.000025796764,0.00010041508,0.00003497354,0.9721685],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99798024,0.00007623214,0.00028513614,0.0007704435,0.00062885496,0.000259118],"domain_scores_gemma":[0.9981006,0.0002499508,0.00021056486,0.00096055144,0.0002713528,0.00020702483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004367433,0.00027492867,0.0003939959,0.0017546166,0.00012905036,0.0006519845,0.0009997163,0.000308977,0.00003650075],"category_scores_gemma":[0.000074680844,0.00023898947,0.00011880631,0.0018056115,0.00020881923,0.00020237411,0.00029016557,0.00023721383,0.0005365429],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022555566,0.00020416499,0.000008316255,0.0002146062,0.000050245428,0.000011036051,0.00019968186,2.284026e-7,0.000012034386,0.49614334,0.4958131,0.007341014],"study_design_scores_gemma":[0.00026541628,0.00010834933,0.00007435646,0.000039534334,0.000021411384,0.000008960722,0.0000042007096,0.051926974,0.00006855012,0.004762848,0.94227535,0.00044407675],"about_ca_topic_score_codex":0.000060004266,"about_ca_topic_score_gemma":0.0000037458028,"teacher_disagreement_score":0.49138048,"about_ca_system_score_codex":0.00014817342,"about_ca_system_score_gemma":0.0003438008,"threshold_uncertainty_score":0.97457075},"labels":[],"label_agreement":null},{"id":"W3143652836","doi":"10.1109/ultsym.2009.5441399","title":"High resolution ultrasonic method for 3D fingerprint recognizable characteristics in biometrics identification","year":2009,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Biometrics; Fingerprint (computing); Identification (biology); Computer science; Artificial intelligence; Visualization; Computer vision; Fingerprint recognition; Ultrasonic sensor; Crime scene; Usability; Pattern recognition (psychology); Human–computer interaction; Geography; Acoustics","score_opus":0.0302804846346709,"score_gpt":0.30072136648390796,"score_spread":0.27044088184923704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143652836","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":[0.0045189145,0.00007151012,0.9919636,0.0020386993,0.00066491036,0.0003830506,0.000014782057,0.00015954672,0.00018497327],"genre_scores_gemma":[0.3568558,0.00008458449,0.64185864,0.00033674776,0.000044246426,0.000025785126,0.00006248538,0.000005024731,0.0007266664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820256,0.00009846458,0.0005958628,0.0005260071,0.00027018305,0.00030694244],"domain_scores_gemma":[0.99855673,0.000285159,0.00023414117,0.0005665613,0.00027989852,0.00007752834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002114266,0.00012441237,0.00020348978,0.0016020652,0.00011997122,0.0003361282,0.0006307988,0.00013281884,0.000023285505],"category_scores_gemma":[0.00096351816,0.00012751142,0.0000630606,0.0056394893,0.000014981023,0.00046430132,0.000045713237,0.00011805004,0.000062988736],"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":[0.000011960659,0.0003324367,0.00021509723,0.000022899943,0.000006785453,0.0000011456926,0.00020045192,0.000014408776,0.009003426,0.104490414,0.001706251,0.8839947],"study_design_scores_gemma":[0.0009535807,0.00017425895,0.23179473,0.00002524012,0.000016228176,0.000011131318,0.000028085982,0.6913151,0.02084988,0.02244977,0.031835455,0.00054652296],"about_ca_topic_score_codex":0.00009801884,"about_ca_topic_score_gemma":0.000011658297,"teacher_disagreement_score":0.8834482,"about_ca_system_score_codex":0.00017957743,"about_ca_system_score_gemma":0.00006752781,"threshold_uncertainty_score":0.5199765},"labels":[],"label_agreement":null},{"id":"W3143770518","doi":"10.1109/iciap.2007.4362749","title":"Collarette Area Localization and Asymmetrical Support Vector Machines for Efficient Iris Recognition","year":2007,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Iris recognition; Artificial intelligence; Pattern recognition (psychology); Computer science; IRIS (biosensor); Feature (linguistics); Feature extraction; Feature vector; Computer vision; Biometrics","score_opus":0.03148888884385603,"score_gpt":0.27323387984769154,"score_spread":0.2417449910038355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143770518","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":[0.011873441,0.000047296257,0.9855013,0.0006695859,0.00034932652,0.0002784555,0.00001312612,0.00011640944,0.0011510722],"genre_scores_gemma":[0.9537166,0.0000111290365,0.044792026,0.000901274,0.000054180902,0.000013221939,0.000080665704,0.0000066243865,0.0004242834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903744,0.000020983767,0.00023672395,0.00029381402,0.00022883162,0.00018220492],"domain_scores_gemma":[0.9992103,0.00020402519,0.000069188645,0.00017258673,0.00023816449,0.0001057256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008103046,0.000080861784,0.00009657819,0.00051242823,0.00013728891,0.00015699117,0.00016631675,0.000075233125,0.00003798859],"category_scores_gemma":[0.00026935042,0.0000714033,0.000036876518,0.0018573457,0.000030083285,0.00011700632,0.000054430755,0.00004234998,0.000020415415],"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":[0.0000631591,0.00056990393,0.0048852474,0.00009697877,0.00003647412,0.000009669128,0.0007662299,0.000026130398,0.0009970143,0.051619798,0.045204934,0.8957245],"study_design_scores_gemma":[0.0012370585,0.00025086818,0.038124647,0.000009377758,0.00002302243,0.000037422356,0.000055063505,0.8651264,0.011423,0.0025947795,0.08068454,0.00043387536],"about_ca_topic_score_codex":0.000019966019,"about_ca_topic_score_gemma":0.000012501703,"teacher_disagreement_score":0.94184315,"about_ca_system_score_codex":0.000043168155,"about_ca_system_score_gemma":0.000025748355,"threshold_uncertainty_score":0.2911742},"labels":[],"label_agreement":null},{"id":"W3163472867","doi":"10.18280/ts.380206","title":"Modeling Fingerprint Presentation Attack Detection Through Transient Liveness Factor-A Person Specific Approach","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Liveness; Computer science; Fingerprint (computing); Artificial intelligence; Anomaly detection; Sample (material); Set (abstract data type); Support vector machine; Local outlier factor; Transient (computer programming); Scheme (mathematics); Data mining; Outlier; Fingerprint recognition; Pattern recognition (psychology); Machine learning; Theoretical computer science; Mathematics","score_opus":0.11149010923708791,"score_gpt":0.280902521376921,"score_spread":0.1694124121398331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163472867","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":[0.20405783,0.00024803838,0.7943774,0.0002311642,0.0003156224,0.00019919632,0.00000571397,0.00010480416,0.00046022862],"genre_scores_gemma":[0.9810203,0.00006159327,0.018571364,0.00008396697,0.00011303267,0.000033649576,0.000034355966,0.000009610177,0.000072185336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812424,0.00015178691,0.00030737196,0.0005911484,0.00057212135,0.00025332105],"domain_scores_gemma":[0.9992949,0.000034529567,0.00007089484,0.00032216063,0.00020066829,0.000076856995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002434822,0.00015782572,0.00015389026,0.00015035746,0.0002230582,0.00035909185,0.0003518902,0.00007614695,0.0001634721],"category_scores_gemma":[0.000008299508,0.00016385931,0.00012708893,0.0009114364,0.000024414054,0.0005979088,0.000049652608,0.0001513749,0.00003120189],"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":[0.00012295948,0.0035133774,0.00039675125,0.00050585176,0.00030295324,0.000069011345,0.08527984,0.18160103,0.26556355,0.02235688,0.0008129963,0.4394748],"study_design_scores_gemma":[0.0005347096,0.00004050198,0.0033170308,0.000013801853,0.0000100733,0.000016003212,0.0008356582,0.9609092,0.03105039,0.00011566233,0.0029135693,0.00024339002],"about_ca_topic_score_codex":0.000043527187,"about_ca_topic_score_gemma":0.00000982245,"teacher_disagreement_score":0.7793082,"about_ca_system_score_codex":0.0001313111,"about_ca_system_score_gemma":0.000056774406,"threshold_uncertainty_score":0.6681989},"labels":[],"label_agreement":null},{"id":"W3165673144","doi":"10.1007/s12083-021-01120-7","title":"MASK: Efficient and privacy-preserving m-tree based biometric identification over cloud","year":2021,"lang":"en","type":"article","venue":"Peer-to-Peer Networking and Applications","topic":"Biometric Identification and Security","field":"Computer Science","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":"University of New Brunswick","funders":"","keywords":"Biometrics; Computer science; Identification (biology); Homomorphic encryption; Cloud computing; Computer security; Service provider; Tree (set theory); Encryption; Server; Information privacy; Data mining; Service (business); Computer network","score_opus":0.02137866558049927,"score_gpt":0.2765908388230916,"score_spread":0.2552121732425923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165673144","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":[0.033615854,0.0008181185,0.94704694,0.016517235,0.0005259237,0.0005634777,0.000028021366,0.00021699531,0.00066745706],"genre_scores_gemma":[0.9661013,0.000036927653,0.029926939,0.0009357144,0.00040277684,0.00025507275,0.000098458855,0.000019744793,0.0022230488],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99743783,0.000087294946,0.00042196357,0.0009330969,0.00074191805,0.00037791714],"domain_scores_gemma":[0.9974319,0.0002729926,0.00013529837,0.0012045216,0.00059047586,0.0003648299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010857943,0.00019196399,0.00020170245,0.0008650827,0.0005344037,0.0009834643,0.0007937955,0.00009907779,0.00001736103],"category_scores_gemma":[0.00029258884,0.00020944381,0.000057254507,0.008068752,0.000048746828,0.000120743636,0.00073603756,0.00016602043,0.000073055366],"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":[0.000013868437,0.00077935227,0.009557202,0.00016754106,0.0000852637,0.000009980664,0.0024083601,0.0026441019,0.014939681,0.055296954,0.0527315,0.8613662],"study_design_scores_gemma":[0.00035930707,0.000018465587,0.06890039,0.00003521334,0.00002963091,0.000010594387,0.000062599465,0.17897908,0.0014426858,0.0013545791,0.74839103,0.00041644336],"about_ca_topic_score_codex":0.00003718341,"about_ca_topic_score_gemma":0.000013000884,"teacher_disagreement_score":0.93248546,"about_ca_system_score_codex":0.00006559183,"about_ca_system_score_gemma":0.000065029584,"threshold_uncertainty_score":0.9483569},"labels":[],"label_agreement":null},{"id":"W3165954958","doi":"10.1109/syscon48628.2021.9447102","title":"A Generic Model for Privacy-Preserving Authentication on Smartphones","year":2021,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Overhead (engineering); Mobile device; Biometrics; Authentication (law); Smart card; Computer security; Operating system","score_opus":0.07708049882556135,"score_gpt":0.2920613533705506,"score_spread":0.21498085454498927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165954958","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":[0.011850058,0.00005716419,0.9827159,0.003782739,0.00023366102,0.000104597784,0.0000024429885,0.00010579775,0.0011476456],"genre_scores_gemma":[0.7397995,0.0000140216125,0.24833715,0.00083682645,0.000028017834,0.000037149104,0.000014975474,0.000004743624,0.010927633],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925184,0.000022830767,0.00013728133,0.00029487922,0.0001649288,0.0001282158],"domain_scores_gemma":[0.99898964,0.0000635378,0.000040150975,0.00067982805,0.0001775095,0.00004931561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001743752,0.000057403624,0.000065766595,0.0001275906,0.00010226948,0.00018663873,0.00056028273,0.000035847712,0.000033204105],"category_scores_gemma":[0.00018790789,0.000053171898,0.00005265952,0.0008527903,0.000008450218,0.00019135917,0.00020888443,0.00003272364,0.000051084407],"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":[0.0000065272725,0.00041983594,0.00009979518,0.000060706134,0.000025856081,0.0000020159903,0.0024360747,0.00059591525,0.026437933,0.8641272,0.036640402,0.0691477],"study_design_scores_gemma":[0.00012558287,0.0000068891086,0.000726404,0.000002564926,0.0000023196376,0.0000018746284,0.000011560473,0.96967,0.009708789,0.012864069,0.0068075457,0.00007240576],"about_ca_topic_score_codex":0.0000048547113,"about_ca_topic_score_gemma":0.000005985353,"teacher_disagreement_score":0.9690741,"about_ca_system_score_codex":0.00002450913,"about_ca_system_score_gemma":0.00007608189,"threshold_uncertainty_score":0.21682872},"labels":[],"label_agreement":null},{"id":"W3171430437","doi":"","title":"A Review on Iris Based Diabetes Detection Using Machine Leaning Techniques","year":2021,"lang":"en","type":"review","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"IRIS (biosensor); Computer science; Enhanced Data Rates for GSM Evolution; Edge detection; Artificial intelligence; Ideal (ethics); Computer vision; Machine learning; Image (mathematics); Image processing; Biometrics","score_opus":0.10094493788644764,"score_gpt":0.36509392622331094,"score_spread":0.2641489883368633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171430437","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.3119865e-8,0.7563738,0.24243046,0.000055369346,0.00020256994,0.000415856,0.0000044765056,0.0002819846,0.00023541934],"genre_scores_gemma":[0.0000010719104,0.95828474,0.040270712,0.0011662402,0.00004873874,0.000058873105,0.000040542327,0.000019516834,0.00010958385],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977743,0.00042872078,0.00060026994,0.00066231913,0.00030933894,0.00022502472],"domain_scores_gemma":[0.9982439,0.00019767391,0.00038975032,0.0009534279,0.00013886667,0.00007636982],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009805388,0.00031543698,0.001115344,0.0007739597,0.00014167994,0.00026945426,0.0007616797,0.00020233603,0.00008971294],"category_scores_gemma":[0.00024700197,0.0002478404,0.00049428263,0.0037557122,0.000020368681,0.00015783607,0.00018467776,0.0003797787,0.0000493295],"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":[3.058945e-8,0.000027292936,2.0016236e-7,0.015740374,0.0000148539975,0.000001910757,0.0000012472585,2.3871369e-8,0.0000024213666,0.00022313438,0.00040221275,0.9835863],"study_design_scores_gemma":[0.00001713613,0.00001937871,1.17029316e-7,0.04601481,0.00011798158,0.000007695072,2.0676275e-7,0.0037184462,0.00017547647,0.000008062101,0.94965786,0.00026284443],"about_ca_topic_score_codex":0.000032329208,"about_ca_topic_score_gemma":0.000003891497,"teacher_disagreement_score":0.98332345,"about_ca_system_score_codex":0.00019435772,"about_ca_system_score_gemma":0.00019309866,"threshold_uncertainty_score":0.9999974},"labels":[],"label_agreement":null},{"id":"W3171707793","doi":"10.22215/etd/2016-11435","title":"Protocols for Evaluation of an Interactive Video Tracking System Utilizing Face Recognition","year":2016,"lang":"en","type":"dissertation","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Tracking (education); Facial motion capture; Artificial intelligence; Facial recognition system; Computer vision; Precision and recall; Video tracking; Tracking system; Face (sociological concept); Protocol (science); Active appearance model; Face detection; Pattern recognition (psychology); Video processing; Image (mathematics)","score_opus":0.14158282465014457,"score_gpt":0.41546547753443525,"score_spread":0.2738826528842907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171707793","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":[0.040304985,0.000069035814,0.88093114,0.000071177994,0.002265666,0.05627513,0.00014212697,0.00045180984,0.019488925],"genre_scores_gemma":[0.96517295,0.0000019722072,0.012380075,0.000013966682,0.00010023854,0.020522326,0.00093385903,0.00002473839,0.00084987946],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976879,0.00027778122,0.0005859615,0.0005508951,0.0007381643,0.00015929506],"domain_scores_gemma":[0.9958761,0.0001921195,0.00082429475,0.0004662767,0.0025854371,0.000055780558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022900426,0.00017511258,0.00026782192,0.0006405633,0.000117790594,0.00022593566,0.00056294526,0.00023104568,0.000036822214],"category_scores_gemma":[0.0004277557,0.00014805033,0.00012285601,0.00054855586,0.000010842228,0.001203699,0.00002004607,0.00010131592,0.000023090373],"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":[0.0000782724,0.0001128679,0.0000026874452,0.000706253,0.00003903414,1.6457322e-7,0.0022897462,8.2249045e-7,0.0050312527,0.0037838186,0.000096292555,0.9878588],"study_design_scores_gemma":[0.0043069436,0.000490929,0.0031144628,0.004609016,0.00025550826,0.000010850713,0.012717284,0.27977282,0.683409,0.008527515,0.0017220363,0.0010636821],"about_ca_topic_score_codex":0.000034442928,"about_ca_topic_score_gemma":0.00006549485,"teacher_disagreement_score":0.9867951,"about_ca_system_score_codex":0.00025872196,"about_ca_system_score_gemma":0.00027082398,"threshold_uncertainty_score":0.6037317},"labels":[],"label_agreement":null},{"id":"W3175579268","doi":"10.1109/tbiom.2021.3065914","title":"An Efficient Convolutional Neural Network for Fingerprint Pore Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Biometrics Behavior and Identity Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fingerprint (computing); Computer science; Convolutional neural network; Artificial intelligence; Centroid; Pattern recognition (psychology); Scheme (mathematics); Artificial neural network; Mathematics","score_opus":0.03557229552737199,"score_gpt":0.31229875863497947,"score_spread":0.27672646310760746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175579268","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":[0.30902302,0.00008432993,0.6881996,0.00008937002,0.0022727726,0.0001989057,0.00003922327,0.000085515036,0.000007261408],"genre_scores_gemma":[0.9822124,0.00003785528,0.017454337,0.00010961922,0.000043241125,0.00005878481,0.0000065559593,0.000006391945,0.00007081795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975453,0.00005664171,0.00031136483,0.00081779284,0.00084961543,0.00041930037],"domain_scores_gemma":[0.99821305,0.00011189445,0.00010312724,0.0005545956,0.00072787,0.00028945177],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011460092,0.00015031283,0.00015270642,0.0019387398,0.0013856497,0.0011245825,0.0007278976,0.000095616546,0.000016094416],"category_scores_gemma":[0.00006381473,0.00016177306,0.000111405934,0.015280385,0.0003380046,0.0013108463,0.000014851051,0.00018497475,0.000009251496],"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":[0.000043515134,0.004255706,0.0020257512,0.00007089904,0.000031592615,0.000042650117,0.000606031,0.01751088,0.17811406,0.019793348,0.00012243904,0.77738315],"study_design_scores_gemma":[0.0010086069,0.00044543113,0.17542462,0.000014917103,0.00008430929,0.0001998643,0.00012622665,0.68415636,0.13618258,0.0007368653,0.000911196,0.00070902205],"about_ca_topic_score_codex":0.0000628035,"about_ca_topic_score_gemma":0.00006425631,"teacher_disagreement_score":0.7766741,"about_ca_system_score_codex":0.00018096715,"about_ca_system_score_gemma":0.00021514542,"threshold_uncertainty_score":0.9999144},"labels":[],"label_agreement":null},{"id":"W3202418359","doi":"10.18280/ts.380429","title":"Integration of Two-Dimensional Kernel Principal Component Analysis Plus Two-Dimensional Linear Discriminant Analysis with Convolutional Neural Network for Finger Vein Recognition","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Pattern recognition (psychology); Linear discriminant analysis; Principal component analysis; Artificial intelligence; Convolutional neural network; Kernel Fisher discriminant analysis; Kernel principal component analysis; Feature extraction; Dimensionality reduction; Computer science; Kernel (algebra); Redundancy (engineering); Feature (linguistics); Mathematics; Kernel method; Support vector machine; Facial recognition system","score_opus":0.04626990891323786,"score_gpt":0.28098110615002797,"score_spread":0.23471119723679013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202418359","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":[0.48731148,0.00008445536,0.51147807,0.0004725691,0.00016657237,0.00026672723,0.00016078162,0.00004159452,0.000017722794],"genre_scores_gemma":[0.9038103,0.0000016800412,0.09265417,0.00026725544,0.00015670239,0.00006593567,0.0029260681,0.00001009431,0.00010775149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99674743,0.00025351247,0.0008289726,0.0007587402,0.0010220708,0.00038925384],"domain_scores_gemma":[0.9976063,0.00032473553,0.00045131656,0.0003876127,0.001063432,0.000166614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090326066,0.00026958119,0.0005667693,0.0007716615,0.00029001856,0.00012297306,0.00034442864,0.0000732801,0.00036354788],"category_scores_gemma":[0.000037559832,0.00022985434,0.0005193485,0.004349658,0.00012557226,0.00034110807,0.00014053215,0.0001711157,0.000008589337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041729282,0.0011434018,0.0034224666,0.000044340184,0.0038834112,0.000027804937,0.0005655988,0.95620334,0.018488666,0.010113297,0.00060510443,0.00508526],"study_design_scores_gemma":[0.0018354576,0.0001455368,0.051307827,0.000024738347,0.001672333,0.000011176882,0.000027944883,0.94043803,0.0037572002,0.0003955126,0.00009748743,0.00028672925],"about_ca_topic_score_codex":0.00018367163,"about_ca_topic_score_gemma":0.0004449036,"teacher_disagreement_score":0.41882393,"about_ca_system_score_codex":0.00016677206,"about_ca_system_score_gemma":0.0002658212,"threshold_uncertainty_score":0.9373188},"labels":[],"label_agreement":null},{"id":"W3208320265","doi":"10.1155/2021/2313389","title":"An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree","year":2021,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Ministry of Public Security of the People's Republic of China; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Biometrics; Homomorphic encryption; Identification (biology); Scheme (mathematics); Tree (set theory); Cloud computing; Server; Service provider; Computer security; Encryption; Data mining; Service (business); Computer network; Mathematics","score_opus":0.018720105840357465,"score_gpt":0.25326406443671234,"score_spread":0.23454395859635488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208320265","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":[0.4388365,0.010070345,0.5217104,0.026036026,0.00033056605,0.00060003565,0.000011616972,0.0003501398,0.0020544128],"genre_scores_gemma":[0.99385184,0.00067216234,0.0044041174,0.0009149917,0.000029351804,0.00002862406,0.00006561385,0.000007565837,0.000025732576],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801564,0.00066817296,0.00032873743,0.00045240944,0.0003245098,0.00021054066],"domain_scores_gemma":[0.99624354,0.00072449615,0.00019706103,0.0024457825,0.00026909355,0.00012004306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016175924,0.00013934723,0.00013524415,0.0003022182,0.0008213631,0.0009344507,0.001438337,0.00012053501,0.000024427416],"category_scores_gemma":[0.00040240702,0.00012136745,0.000042920805,0.0029294358,0.00014802182,0.00028082638,0.0006908659,0.00036039003,0.000006056136],"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":[0.000040064453,0.0021056624,0.0067014857,0.0001442079,0.000086402055,0.00000832823,0.007872545,0.003883639,0.0015307512,0.7838954,0.0038629372,0.18986861],"study_design_scores_gemma":[0.00020667525,0.000020318173,0.018257864,0.00003225793,0.0000067988885,0.000004769888,0.00022459813,0.97534233,0.0003613342,0.0028623133,0.002532471,0.00014827635],"about_ca_topic_score_codex":0.000033703123,"about_ca_topic_score_gemma":0.000031198906,"teacher_disagreement_score":0.9714587,"about_ca_system_score_codex":0.000035339293,"about_ca_system_score_gemma":0.000052646283,"threshold_uncertainty_score":0.901093},"labels":[],"label_agreement":null},{"id":"W3211334395","doi":"10.18280/ts.380506","title":"Comparative Experimental Investigation of Deep Convolutional Neural Networks for Latent Fingerprint Pattern Classification","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Fingerprint (computing); Computer science; Pattern recognition (psychology); Transfer of learning; Feature extraction; Deep learning; Feature (linguistics); Realization (probability); Process (computing); Fingerprint recognition; Mathematics","score_opus":0.07787189327582635,"score_gpt":0.2888918027930747,"score_spread":0.21101990951724836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211334395","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":[0.18596625,0.0002598924,0.812629,0.0005892741,0.00024607818,0.00023866954,0.000010685094,0.00003319136,0.000026999547],"genre_scores_gemma":[0.9931606,0.0000028984014,0.0062257503,0.00027258563,0.00006775092,0.000077855206,0.00017235099,0.0000039069773,0.00001629279],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874157,0.00010244791,0.0003851285,0.0003142199,0.00029924675,0.00015740337],"domain_scores_gemma":[0.99915093,0.00010599797,0.0001958706,0.00018400495,0.00028792137,0.0000752837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025751055,0.00010504535,0.00015724193,0.00010570571,0.00011135025,0.00008615392,0.000253319,0.000049369093,0.000101211495],"category_scores_gemma":[0.000008647603,0.00010928953,0.00008796021,0.0004074767,0.00007537123,0.00022247818,0.00006786734,0.000068152316,0.000004168962],"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":[0.00017475763,0.0028989115,0.040813018,0.00022064468,0.00049691513,0.000013452323,0.026257563,0.025458045,0.49837813,0.30063263,0.0056581367,0.09899781],"study_design_scores_gemma":[0.00051500445,0.000064137836,0.08162392,0.000006593942,0.00000733053,0.0000029276248,0.00012494577,0.8751384,0.0419842,0.00023877647,0.00018786048,0.00010589948],"about_ca_topic_score_codex":0.000008251361,"about_ca_topic_score_gemma":0.0000066915477,"teacher_disagreement_score":0.84968036,"about_ca_system_score_codex":0.000080681246,"about_ca_system_score_gemma":0.00005288396,"threshold_uncertainty_score":0.4456698},"labels":[],"label_agreement":null},{"id":"W4206100981","doi":"10.1142/s0219467823500195","title":"Multimodal Biometric Person Authentication Using Face, Ear and Periocular Region Based on Convolution Neural Networks","year":2021,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Feature (linguistics); Identification (biology); Convolutional neural network; Face (sociological concept); Feature extraction; Pattern recognition (psychology); Modality (human–computer interaction); Authentication (law); Facial recognition system; Convolution (computer science); Artificial neural network; Computer vision; Computer security","score_opus":0.028528613818144755,"score_gpt":0.27689701224312946,"score_spread":0.2483683984249847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206100981","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":[0.31508994,0.0007459463,0.68152887,0.0020673815,0.0005133061,0.000033301294,0.0000022870422,0.0000075868024,0.000011376866],"genre_scores_gemma":[0.9909286,0.0002782141,0.008317632,0.00036859926,0.00008368572,4.1627135e-7,0.000006216577,0.0000040050572,0.0000126130235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898005,0.00009179831,0.00023306656,0.0001770847,0.00042384778,0.00009414848],"domain_scores_gemma":[0.998687,0.00008761309,0.00024293785,0.000121160374,0.00078073156,0.00008054224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003260219,0.00008223602,0.000105936895,0.0010413728,0.00009865921,0.00038149787,0.0002341527,0.00006331258,0.0000037514785],"category_scores_gemma":[0.00018310349,0.00007682555,0.00007816491,0.0010102929,0.0000855966,0.0004907089,0.000045429446,0.00017411358,4.1301143e-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":[0.0007325778,0.0034651302,0.2281596,0.000251204,0.00130513,0.003637819,0.00816846,0.008758284,0.07000663,0.09270636,0.0022213063,0.5805875],"study_design_scores_gemma":[0.00050344074,0.000049846425,0.067120075,0.000028369841,0.000014729212,0.0003087651,0.000072954535,0.9309736,0.00043592337,0.00015316313,0.00026001435,0.00007910391],"about_ca_topic_score_codex":0.000015370453,"about_ca_topic_score_gemma":9.87589e-7,"teacher_disagreement_score":0.92221534,"about_ca_system_score_codex":0.000037420483,"about_ca_system_score_gemma":0.000048803788,"threshold_uncertainty_score":0.36787927},"labels":[],"label_agreement":null},{"id":"W4212961748","doi":"10.1007/978-1-4419-5906-5_730","title":"Biometrics: Terms and Definitions","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Biometric Identification and Security","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":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Biometrics; Computer science; Computer security","score_opus":0.11685889938184123,"score_gpt":0.2338723942101268,"score_spread":0.11701349482828556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212961748","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000001119843,0.00048397522,0.17294893,0.00018357333,0.0002995135,0.00008138023,0.000019941814,0.0001473881,0.82583416],"genre_scores_gemma":[0.003546077,0.0025379127,0.032585185,0.000618507,0.000059420017,0.000006853664,0.000038142254,0.000020096893,0.9605878],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990276,0.000006597551,0.00021990483,0.0004084989,0.0002096259,0.00012776359],"domain_scores_gemma":[0.9990392,0.000063773405,0.00011025246,0.00057993823,0.000088788365,0.00011804896],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014827772,0.00015850224,0.00016409518,0.0019359841,0.0000936008,0.00020526483,0.0005479822,0.00022306132,0.00069742167],"category_scores_gemma":[0.000024665722,0.0001417556,0.000068661204,0.00042465387,0.000094056064,0.00017316338,0.00028408188,0.00015243437,0.0008952417],"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":[1.5837449e-7,0.000009434565,0.00000487054,0.000007870753,0.000009741434,0.0000035029566,0.000040783074,6.011215e-10,0.0000015041811,0.93530345,0.0053013656,0.059317328],"study_design_scores_gemma":[0.00006638674,0.000020566953,0.00024909884,0.0000095598425,0.000009805991,0.000021572621,7.3733344e-7,0.00004412777,0.000018548373,0.3799876,0.6193573,0.0002146808],"about_ca_topic_score_codex":0.000019621013,"about_ca_topic_score_gemma":0.000005165117,"teacher_disagreement_score":0.61405593,"about_ca_system_score_codex":0.000021337604,"about_ca_system_score_gemma":0.000031506697,"threshold_uncertainty_score":0.9998827},"labels":[],"label_agreement":null},{"id":"W4229446550","doi":"10.18280/ts.390232","title":"A Robust Automatic Fingerprint Recognition System Using Multi-Connection Hopfield Neural Network","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fingerprint (computing); Computer science; Fingerprint recognition; Noise (video); Artificial neural network; Artificial intelligence; Pattern recognition (psychology); NIST; Content-addressable memory; Associative property; Speech recognition; Mathematics","score_opus":0.0943491844124644,"score_gpt":0.24813707458077638,"score_spread":0.15378789016831199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229446550","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":[0.26448613,0.00005875128,0.7334194,0.00023204237,0.0010876661,0.0003177069,0.0000069645807,0.00032378858,0.0000675305],"genre_scores_gemma":[0.9699346,9.865779e-7,0.029533723,0.00026253334,0.00013739099,0.00007666528,0.00002137164,0.00000800702,0.00002476163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983172,0.000273588,0.0003717265,0.00035868838,0.00041469847,0.0002641117],"domain_scores_gemma":[0.9993622,0.000072975345,0.00019479262,0.00023276183,0.000065919034,0.00007136421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008620371,0.00012203034,0.00014026176,0.00025955902,0.0006356481,0.00020931716,0.00040346332,0.000036208952,0.00034209347],"category_scores_gemma":[0.000013748754,0.00013369447,0.00008902191,0.0011519936,0.00001559758,0.00026909288,0.00020111029,0.00018808048,0.000021667218],"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":[0.000066757355,0.001553098,0.0020460477,0.0004783832,0.00022563401,0.00012350902,0.0053690765,0.403984,0.0056886286,0.009277598,0.00449248,0.5666948],"study_design_scores_gemma":[0.00044555898,0.000068762216,0.0021064854,0.000018902318,0.000015718406,0.00006308514,0.00024794336,0.9962267,0.00017413544,0.00006345757,0.0004058024,0.00016343448],"about_ca_topic_score_codex":0.00010587979,"about_ca_topic_score_gemma":0.000008704325,"teacher_disagreement_score":0.70544845,"about_ca_system_score_codex":0.00026918965,"about_ca_system_score_gemma":0.000044588793,"threshold_uncertainty_score":0.5451902},"labels":[],"label_agreement":null},{"id":"W4231980254","doi":"10.1007/978-3-319-14346-0_32","title":"Security Imaging: Biometrics and Recognition Technology","year":2016,"lang":"en","type":"book-chapter","venue":"Handbook of Visual Display Technology","topic":"Biometric Identification and Security","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":"Advanced Micro Devices (Canada)","funders":"","keywords":"Biometrics; Eigenface; Facial recognition system; Computer science; Fingerprint (computing); Artificial intelligence; Pattern recognition (psychology); Signature recognition; Face (sociological concept); Three-dimensional face recognition; Principal component analysis; Fingerprint recognition; Computer vision; Face detection","score_opus":0.015665300592305968,"score_gpt":0.2678236032764503,"score_spread":0.2521583026841443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231980254","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":[0.0029409702,0.07621599,0.7709085,0.008991086,0.0026338175,0.0021765963,0.0007641705,0.003713534,0.1316554],"genre_scores_gemma":[0.6297131,0.074163884,0.077652745,0.0013645965,0.0007138627,0.00044809873,0.0005715324,0.000675603,0.21469653],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997543,0.000020090696,0.0006821049,0.00096900517,0.00038597084,0.0003998752],"domain_scores_gemma":[0.99769926,0.00012599227,0.0006545318,0.00091532356,0.0004983568,0.0001065293],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003046567,0.00042236166,0.0006541234,0.009347839,0.00012750138,0.00006676484,0.001126839,0.0012863972,0.000103706996],"category_scores_gemma":[0.00023159181,0.00038267297,0.00012427359,0.0015982821,0.001162887,0.00023072587,0.0009467959,0.00052804383,0.00021077707],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007903821,0.00008396421,0.00014686734,0.00008720904,0.000057849797,0.000025441535,0.000029095758,2.2312503e-9,0.0023669954,0.5517856,0.0007673696,0.44464168],"study_design_scores_gemma":[0.00086626696,0.00038079103,0.000036767364,0.00075025746,0.00007331825,0.00018422016,0.000015485057,0.00031989184,0.023953069,0.80795085,0.16465801,0.00081107847],"about_ca_topic_score_codex":0.000003496848,"about_ca_topic_score_gemma":0.0000036551817,"teacher_disagreement_score":0.6932557,"about_ca_system_score_codex":0.00009529656,"about_ca_system_score_gemma":0.00011131568,"threshold_uncertainty_score":0.9998625},"labels":[],"label_agreement":null},{"id":"W4232427071","doi":"10.1007/978-981-32-9945-0_5","title":"Fingerprint Classification","year":2019,"lang":"en","type":"book-chapter","venue":"Cognitive intelligence and robotics","topic":"Biometric Identification and Security","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 Toronto","funders":"","keywords":"Biometrics; Fingerprint (computing); Consistency (knowledge bases); Impression; Reliability (semiconductor); Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; World Wide Web; Physics","score_opus":0.08498569158122893,"score_gpt":0.28745897260911957,"score_spread":0.20247328102789064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232427071","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003032175,0.0010193405,0.8072372,0.00041998373,0.00058204203,0.00024592035,0.000013615768,0.00006849436,0.19041036],"genre_scores_gemma":[0.1268626,0.010592842,0.019485915,0.0013317709,0.0002737754,0.000012442117,0.00015498603,0.00006585329,0.84121984],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985775,0.000018578128,0.0003410287,0.00060060865,0.0002860072,0.00017627429],"domain_scores_gemma":[0.99853027,0.00026359537,0.000235282,0.00041545942,0.00045698634,0.00009841844],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020656399,0.00023853735,0.00024819712,0.00036315605,0.000107654145,0.00022825914,0.0004963547,0.00030690865,0.00008811008],"category_scores_gemma":[0.000094892326,0.00023112941,0.000089095534,0.00014355149,0.00017065382,0.00016913524,0.0002808593,0.00037439453,0.0013887062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018892791,0.000015866322,0.000016485707,0.00003374015,0.000025509236,0.000004445508,0.00015043635,0.000010174434,0.000005122777,0.7710426,0.00019543362,0.22849832],"study_design_scores_gemma":[0.0004125758,0.0005565548,0.0035884145,0.0020203271,0.00035753995,0.0001436119,0.0004547468,0.18480752,0.0016964658,0.48936164,0.31293517,0.003665398],"about_ca_topic_score_codex":0.0000027220058,"about_ca_topic_score_gemma":0.0000027522985,"teacher_disagreement_score":0.7877513,"about_ca_system_score_codex":0.00004378878,"about_ca_system_score_gemma":0.0001428875,"threshold_uncertainty_score":0.9993888},"labels":[],"label_agreement":null},{"id":"W4239493594","doi":"10.1007/978-1-4471-7452-3_29","title":"Pattern Recognition for Biometrics and Bioinformatics","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Biometric Identification and Security","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":"Concordia University","funders":"","keywords":"Biometrics; Computer science; Bioinformatics; Data science; Biology; Computer security","score_opus":0.0597677556804989,"score_gpt":0.24663705230309224,"score_spread":0.18686929662259333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239493594","genre_codex":"methods","genre_gemma":"other","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":[0.000002889781,0.00019516503,0.8304313,0.00026653384,0.00060376024,0.00039672272,0.00011672916,0.00009125896,0.16789566],"genre_scores_gemma":[0.00044726022,0.0012850647,0.23373695,0.0018177421,0.00013739687,0.000015983787,0.00060635834,0.000039104838,0.76191413],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990343,0.0000037918255,0.00031898945,0.0002770707,0.0002322233,0.0001336344],"domain_scores_gemma":[0.9989041,0.00017088915,0.00021931775,0.00040918062,0.00022607709,0.0000704487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027381856,0.0001609687,0.00019433377,0.0014994397,0.00005721792,0.0002953602,0.0003563999,0.00026042896,0.00008702059],"category_scores_gemma":[0.000044007833,0.0001468944,0.00008157804,0.00028505112,0.000030421174,0.00027062197,0.0001561255,0.0001036932,0.00052611897],"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":[5.703823e-7,0.000006490461,0.00000383615,0.00014429487,0.000016546699,2.4505775e-7,0.00004902984,8.392871e-9,7.6317264e-7,0.04841406,0.008963133,0.942401],"study_design_scores_gemma":[0.00044462696,0.00010989428,0.00006542611,0.00005426925,0.000028475604,0.000014325321,0.000007663982,0.04119025,0.00006215895,0.027998092,0.92949295,0.0005318937],"about_ca_topic_score_codex":0.0000047105145,"about_ca_topic_score_gemma":0.0000025044965,"teacher_disagreement_score":0.94186914,"about_ca_system_score_codex":0.00002508635,"about_ca_system_score_gemma":0.000053765976,"threshold_uncertainty_score":0.67623705},"labels":[],"label_agreement":null},{"id":"W4239752405","doi":"10.1504/ijbm.2018.095292","title":"Bidirectional aggregated features fusion from CNN for palmprint recognition","year":2018,"lang":"en","type":"article","venue":"International Journal of Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Hong Kong Polytechnic University; National Natural Science Foundation of China","keywords":"Convolutional neural network; Computer science; Encoding (memory); Artificial intelligence; Pattern recognition (psychology); Multispectral image; ENCODE; Matching (statistics); Representation (politics); Fusion; Computer vision; Mathematics","score_opus":0.04271574981743429,"score_gpt":0.30755343040782185,"score_spread":0.26483768059038754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239752405","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":[0.11050053,0.00058732665,0.8730253,0.0030408173,0.012099946,0.00012242407,0.00013400942,0.000052659958,0.00043699515],"genre_scores_gemma":[0.88146716,0.00030716532,0.115495734,0.0005144289,0.00188652,0.0000039231695,0.00008073624,0.000009421672,0.00023492804],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982501,0.000048744983,0.00048922043,0.00021268762,0.0008683477,0.00013089112],"domain_scores_gemma":[0.9955225,0.00035132372,0.0006066025,0.00015772493,0.003257949,0.00010391466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006926725,0.000099989455,0.00013920834,0.0034478724,0.000096039344,0.00032970207,0.0010584753,0.00010542237,0.00009607491],"category_scores_gemma":[0.0011821585,0.000088489316,0.00016399671,0.0030623798,0.00006306832,0.00047867085,0.00012544294,0.00013364143,0.000060749517],"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":[0.00012330763,0.00030737193,0.0006884105,0.0000037132554,0.00020740209,0.000013618353,0.0002553424,0.0000014177214,0.0101026725,0.0016535082,0.016988494,0.96965474],"study_design_scores_gemma":[0.0045937733,0.0010545974,0.122225456,0.00018101168,0.0000728765,0.00044587828,0.000111299465,0.013596034,0.24593191,0.0573811,0.5537036,0.00070246024],"about_ca_topic_score_codex":0.000054975262,"about_ca_topic_score_gemma":0.0000063598313,"teacher_disagreement_score":0.9689523,"about_ca_system_score_codex":0.00016504078,"about_ca_system_score_gemma":0.00011036922,"threshold_uncertainty_score":0.36084896},"labels":[],"label_agreement":null},{"id":"W4241115028","doi":"10.4018/978-1-61350-129-0","title":"Continuous Authentication Using Biometrics","year":2011,"lang":"en","type":"book","venue":"IGI Global eBooks","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Biometrics; Authentication (law); Computer science; Computer security","score_opus":0.049604531631955745,"score_gpt":0.2743686299158355,"score_spread":0.22476409828387975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241115028","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007447203,0.00058298884,0.2498374,0.000022735443,0.0018464781,0.00032087023,0.00006894351,0.0003099798,0.74693614],"genre_scores_gemma":[0.14316262,0.000024291332,0.086918905,0.0010947143,0.0007654993,0.000036171754,0.00005853053,0.00009951051,0.7678397],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977179,0.0000604022,0.00051361247,0.0007174646,0.0006161742,0.00037443006],"domain_scores_gemma":[0.99766695,0.000040861854,0.00048853696,0.0012287553,0.00037491214,0.00019996717],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035834705,0.0003206994,0.00038563096,0.00093113596,0.00014820018,0.00041319564,0.0017045885,0.00050882885,0.000021544969],"category_scores_gemma":[0.0000841695,0.00033694808,0.00020576463,0.00095119706,0.000115950286,0.00014614096,0.0004241917,0.00023288764,0.0004724628],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001879053,0.00003207042,0.000011706961,0.000028345166,0.000039912367,0.000012431013,0.00012265117,6.434783e-8,0.00003375889,0.9690719,0.009325575,0.021319652],"study_design_scores_gemma":[0.0004866995,0.000086367836,0.000352969,0.00011478446,0.00014052655,0.000119689365,0.000009084029,0.0033194874,0.00034177979,0.603928,0.38997266,0.0011278852],"about_ca_topic_score_codex":0.00011070036,"about_ca_topic_score_gemma":0.0000063610637,"teacher_disagreement_score":0.3806471,"about_ca_system_score_codex":0.0006305959,"about_ca_system_score_gemma":0.000759318,"threshold_uncertainty_score":0.99990827},"labels":[],"label_agreement":null},{"id":"W4244264779","doi":"10.22215/etd/2012-07136","title":"A fingerprint identification system","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Identification (biology); Fingerprint (computing); Computer science; Humanities; Art; Artificial intelligence","score_opus":0.016586200354087345,"score_gpt":0.2608265831912023,"score_spread":0.24424038283711494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244264779","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":[0.011133653,0.0018703227,0.8044719,0.00023907193,0.016118184,0.0007728821,0.000011188011,0.0017817123,0.16360109],"genre_scores_gemma":[0.93805903,0.000017097962,0.0037526963,0.000025977828,0.00012936274,0.00006714576,0.00038417493,0.000014505068,0.057550024],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99838847,0.000056591398,0.00044646088,0.00043718916,0.0004469103,0.00022438627],"domain_scores_gemma":[0.9983741,0.000031740845,0.00033326304,0.0009165842,0.00023684102,0.00010745013],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000530752,0.00017133639,0.00018592598,0.0005935016,0.00013247077,0.00041599502,0.0010703175,0.0002565936,0.00008700833],"category_scores_gemma":[0.000037503938,0.00016457826,0.00011216825,0.0010411895,0.000007878764,0.00038975017,0.000055259075,0.00017927113,0.0019537585],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036773658,0.00012150584,0.00005933958,0.00054817856,0.00004287905,0.0000022160514,0.0019373015,3.3525205e-7,0.0026278042,0.8608785,0.006829636,0.12694857],"study_design_scores_gemma":[0.0009334697,0.000058195736,0.5138386,0.00056566513,0.00026771447,0.00008889183,0.0046612998,0.03757301,0.0818449,0.0030664036,0.35324064,0.0038612387],"about_ca_topic_score_codex":0.00008854678,"about_ca_topic_score_gemma":0.000042691277,"teacher_disagreement_score":0.92692536,"about_ca_system_score_codex":0.00014467693,"about_ca_system_score_gemma":0.000090694215,"threshold_uncertainty_score":0.99882334},"labels":[],"label_agreement":null},{"id":"W4245968210","doi":"10.1049/pbse008e_ch1","title":"General introduction","year":2018,"lang":"en","type":"book-chapter","venue":"Institution of Engineering and Technology eBooks","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Liveness; Biometrics; Spoofing attack; Computer science; Fingerprint (computing); Finger print; Artificial intelligence; Computer security","score_opus":0.009918994234946684,"score_gpt":0.19693909154192263,"score_spread":0.18702009730697594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245968210","genre_codex":"methods","genre_gemma":"other","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":[0.004192999,0.0015783246,0.7770638,0.0017847152,0.0050246697,0.00043789035,0.000021352746,0.0020004893,0.20789579],"genre_scores_gemma":[0.30579197,0.00034479346,0.10641551,0.00007413289,0.0017748757,0.000037439924,0.00004224034,0.000063874475,0.5854552],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.999314,0.0000018381062,0.00020915351,0.00027766335,0.00010187264,0.000095453695],"domain_scores_gemma":[0.9993291,0.000005515369,0.00010340567,0.00039454884,0.00013905608,0.00002835644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009771035,0.00013057976,0.00016816723,0.0011133656,0.00005053779,0.00002188959,0.0002698279,0.000391881,0.000011929641],"category_scores_gemma":[0.00002923638,0.00013807238,0.00003080642,0.00009771129,0.00025014693,0.000051818864,0.00012591411,0.0002060638,0.000016058255],"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":[6.088596e-7,0.0000026565563,6.871911e-7,0.000022086377,0.000018902407,0.0000019893869,0.000016316242,0.0000046551613,0.0008121793,0.9779684,0.00051752315,0.020633984],"study_design_scores_gemma":[0.00012147856,0.00006913183,0.0000075798016,0.0000428017,0.000013106098,0.00007832986,9.3042604e-7,0.0035869891,0.00619895,0.02510803,0.9645763,0.00019637767],"about_ca_topic_score_codex":0.000001977074,"about_ca_topic_score_gemma":8.1768053e-7,"teacher_disagreement_score":0.96405876,"about_ca_system_score_codex":0.000030511377,"about_ca_system_score_gemma":0.000043492833,"threshold_uncertainty_score":0.5630428},"labels":[],"label_agreement":null},{"id":"W4246395638","doi":"10.32920/ryerson.14646801","title":"Sequential subspace estimator for an efficient multibiometrics authentication and encryption","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Computer science; Biometrics; Subspace topology; Encryption; Authentication (law); Data mining; Cryptography; Noise (video); Fingerprint (computing); Pattern recognition (psychology); Computer security; Algorithm; Artificial intelligence; Image (mathematics)","score_opus":0.0719615744055088,"score_gpt":0.3346970098399241,"score_spread":0.26273543543441535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246395638","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":[0.11824749,0.0003211081,0.8786155,0.00049604464,0.0013785923,0.0006442708,0.000024476009,0.00021444364,0.000058076825],"genre_scores_gemma":[0.6657556,0.000040215633,0.333593,0.000048333586,0.00005245666,0.000056268556,0.00026159015,0.000010180005,0.00018233819],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784935,0.00010926259,0.0003724988,0.0010124691,0.00041566422,0.00024078628],"domain_scores_gemma":[0.9978905,0.00012525254,0.00024737674,0.0009890156,0.00055730675,0.00019059179],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008924153,0.0002144987,0.00024659035,0.0011229406,0.00019481046,0.001388532,0.00069581746,0.00030774914,0.000015515483],"category_scores_gemma":[0.00032230065,0.00021890347,0.00011109419,0.0015071677,0.000061450366,0.00024808297,0.00076918607,0.0001955057,0.0000084030535],"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":[0.000054315387,0.0042207213,0.001027738,0.0028124605,0.00038180393,0.000014259521,0.016062658,0.0036122005,0.04110156,0.60590816,0.003136497,0.32166764],"study_design_scores_gemma":[0.00029585307,0.000036197525,0.004046209,0.000020148625,0.000033413853,0.000006743549,0.000107500884,0.9894628,0.0040745004,0.00088338,0.00073378434,0.0002995028],"about_ca_topic_score_codex":0.00014177152,"about_ca_topic_score_gemma":0.000022257744,"teacher_disagreement_score":0.9858506,"about_ca_system_score_codex":0.00012066444,"about_ca_system_score_gemma":0.000202608,"threshold_uncertainty_score":0.9996481},"labels":[],"label_agreement":null},{"id":"W4247107964","doi":"10.1016/s0969-4765(11)70117-5","title":"Canadian police get real-time fingerprint and palm ID system","year":2011,"lang":"en","type":"article","venue":"Biometric Technology Today","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Biometrics; Fingerprint (computing); Law enforcement; Identification (biology); Computer security; Service (business); Computer science; Business; Law; Political science","score_opus":0.015394505266241382,"score_gpt":0.21378326420223887,"score_spread":0.19838875893599747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247107964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.833894,0.0039530755,0.06138999,0.009099617,0.003094159,0.0016283221,0.00017216036,0.0075997435,0.07916892],"genre_scores_gemma":[0.9794628,0.000094622344,0.01975685,0.00007785856,0.000020271364,0.000022717215,0.000004397012,0.000010739504,0.00054973294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9983849,0.000047468086,0.00030507555,0.00056588126,0.00019695655,0.00049975014],"domain_scores_gemma":[0.99845594,0.000053435968,0.00013301376,0.000948992,0.00012495951,0.00028365038],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0005079325,0.00017035306,0.00023425747,0.011988105,0.00021001347,0.00009247485,0.0012805793,0.00040765532,0.00004954345],"category_scores_gemma":[0.000112781425,0.0001651475,0.000037733756,0.017072227,0.00015476545,0.00016078481,0.0003503315,0.00016862081,0.00047712683],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063837037,0.00021807235,0.030479547,0.000112780755,0.00014390165,0.0001236506,0.0020635263,7.305549e-8,0.005455641,0.5094602,0.008306427,0.44362977],"study_design_scores_gemma":[0.0023890964,0.0008987819,0.51052153,0.0001414206,0.00012564902,0.0016282481,0.001599572,0.011929444,0.03958218,0.0076190997,0.4202616,0.0033033686],"about_ca_topic_score_codex":0.118661575,"about_ca_topic_score_gemma":0.0030294787,"teacher_disagreement_score":0.5018411,"about_ca_system_score_codex":0.00030534316,"about_ca_system_score_gemma":0.00016796462,"threshold_uncertainty_score":0.9992102},"labels":[],"label_agreement":null},{"id":"W4248002105","doi":"10.22215/etd/2019-13751","title":"Biometric Quality and its Impact on Template Ageing in a Longitudinal Fingerprint Study","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biometrics; Fingerprint (computing); Computer science; Quality (philosophy); Resampling; Artificial intelligence; Statistics; Data mining; Mathematics","score_opus":0.08511586875190842,"score_gpt":0.4089081374064014,"score_spread":0.323792268654493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248002105","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945546,0.00032317257,0.0013942885,0.000047163747,0.00068530283,0.0006846894,0.0000067006795,0.00007771626,0.002226369],"genre_scores_gemma":[0.9971298,0.00003901115,0.00016672368,0.000026637403,0.000014601751,0.000019232628,0.000043893426,0.000010635992,0.0025494567],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975391,0.00019135498,0.0005627142,0.0008495707,0.0005825426,0.0002747295],"domain_scores_gemma":[0.99858856,0.00027776856,0.00029940685,0.00059138174,0.00014238417,0.00010050134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014199434,0.00027922163,0.00043977544,0.004297818,0.0000836315,0.00044257604,0.00067137246,0.00019404128,0.000055550925],"category_scores_gemma":[0.0002599388,0.00022867673,0.000098519864,0.0057384465,0.000006801792,0.0002810986,0.00012005374,0.0003636829,0.00015044317],"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":[0.00061671296,0.010031349,0.4899679,0.0019262221,0.0008248871,0.00049878296,0.041719023,0.00009418546,0.0023223953,0.034012485,0.00084586913,0.4171402],"study_design_scores_gemma":[0.00042278037,0.00019537346,0.99539936,0.000039632087,0.0000070040082,0.0000018299261,0.0002473845,0.0031012637,0.00018295142,0.000083233914,0.00003422603,0.00028498957],"about_ca_topic_score_codex":0.0013902237,"about_ca_topic_score_gemma":0.00037769586,"teacher_disagreement_score":0.5054314,"about_ca_system_score_codex":0.00016114512,"about_ca_system_score_gemma":0.00011382079,"threshold_uncertainty_score":0.93251663},"labels":[],"label_agreement":null},{"id":"W4248006368","doi":"10.1007/978-3-319-98734-7_1","title":"Introduction","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Biometric Identification and Security","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":"Toronto Metropolitan University; University of Victoria","funders":"","keywords":"Biometrics; Computer security; Computer science; Government (linguistics); Population; Field (mathematics); Mobile device; Data science; World Wide Web; Mathematics; Sociology","score_opus":0.022884482340949062,"score_gpt":0.2295430372201878,"score_spread":0.20665855487923873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248006368","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.0237956e-7,0.000062553336,0.26165822,0.002448803,0.0015458548,0.000049642193,0.0000011877064,0.00016256887,0.73407084],"genre_scores_gemma":[0.00003695377,0.000039072474,0.010840917,0.00023365345,0.0013154626,9.2363894e-7,0.000013351869,0.000006090943,0.9875136],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99920994,0.000004578305,0.00014289052,0.00035844147,0.00020723716,0.0000769116],"domain_scores_gemma":[0.9990482,0.0000089363475,0.00007849372,0.0006745128,0.00014719857,0.00004266847],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00014975568,0.000095242154,0.000092779126,0.0003193605,0.000047911908,0.00013428563,0.0005104454,0.00014595428,0.0084463395],"category_scores_gemma":[0.000012385764,0.000086225016,0.00005400506,0.000097032855,0.000046736695,0.00013690967,0.0001302266,0.00010250596,0.00921686],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.339852e-7,0.0000020108528,3.7875736e-8,0.0000017810609,0.0000039694514,3.1937313e-7,0.000016596083,4.021103e-9,0.0000025735142,0.6028734,0.38298136,0.014117827],"study_design_scores_gemma":[0.000021621237,0.000011912634,0.0000049353794,0.0000017639844,0.0000024381434,0.000006075366,4.089775e-7,0.00018780673,0.00007661373,0.059178334,0.940408,0.00010012533],"about_ca_topic_score_codex":0.0000025187258,"about_ca_topic_score_gemma":0.0000028130746,"teacher_disagreement_score":0.5574266,"about_ca_system_score_codex":0.000035240355,"about_ca_system_score_gemma":0.000031526342,"threshold_uncertainty_score":0.9924601},"labels":[],"label_agreement":null},{"id":"W4249027838","doi":"10.1109/tsmca.2012.2201465","title":"Palm-Print Classification by Global Features","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Palm print; Palm; Artificial intelligence; Computer science; Pattern recognition (psychology); Search engine indexing; Linear discriminant analysis; Biometrics; Support vector machine; Computer vision; Mathematics","score_opus":0.01607433311781229,"score_gpt":0.22888818199703415,"score_spread":0.21281384887922186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249027838","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":[0.014246993,0.0014938407,0.97586995,0.0005125636,0.0031867148,0.0009279265,0.00006049446,0.00028650518,0.0034149932],"genre_scores_gemma":[0.99339044,0.00014637389,0.00023118591,0.00007416829,0.00005529295,0.00022036505,0.000006094413,0.000012296634,0.0058637876],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790764,0.00021487604,0.0004986338,0.0005689409,0.00050651206,0.00030340115],"domain_scores_gemma":[0.9985682,0.0000638755,0.0001900876,0.000730385,0.00020197243,0.00024551176],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00029357366,0.00023481151,0.00026699516,0.00019468971,0.00024901924,0.0012404725,0.0005308314,0.00020886658,0.000012548514],"category_scores_gemma":[0.0000039485226,0.0002131455,0.00007596051,0.0007364161,0.00007570933,0.00028672192,0.0000051557804,0.0001935298,0.00036595055],"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":[0.00004632158,0.0025635383,0.0020720237,0.0017182584,0.0008014135,0.00002128976,0.0044459463,0.003582288,0.020566788,0.44363236,0.27288112,0.24766862],"study_design_scores_gemma":[0.003504213,0.0007464393,0.048952848,0.0006061868,0.00015940449,0.00087728107,0.0051728794,0.692595,0.004044339,0.0011060172,0.23909388,0.0031414975],"about_ca_topic_score_codex":0.0021632889,"about_ca_topic_score_gemma":0.00002537461,"teacher_disagreement_score":0.97914344,"about_ca_system_score_codex":0.00015043814,"about_ca_system_score_gemma":0.000034031153,"threshold_uncertainty_score":0.99979633},"labels":[],"label_agreement":null},{"id":"W4250937911","doi":"10.1007/978-0-387-73003-5_63","title":"Encryption, Biometric","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Biometrics","topic":"Biometric Identification and Security","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":"Privacy Analytics (Canada)","funders":"","keywords":"Biometrics; Computer science; Encryption; Computer security","score_opus":0.0193091266025697,"score_gpt":0.237420298091565,"score_spread":0.2181111714889953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250937911","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013750134,0.010557424,0.1113189,0.00028784553,0.0019998436,0.000382015,0.000101927515,0.00028036992,0.87505794],"genre_scores_gemma":[0.0015953208,0.04386783,0.05804702,0.00028805676,0.00046959877,0.000008915773,0.00021473983,0.00007137719,0.8954371],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958288,0.000036674388,0.0011813174,0.00094654306,0.0015803009,0.0004263706],"domain_scores_gemma":[0.99584866,0.0003797237,0.0010635975,0.0016596905,0.00075871835,0.0002895903],"candidate_categories":["metaepi_narrow","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0008670712,0.0005074967,0.0007506157,0.025875058,0.000113338145,0.00015717951,0.0024989047,0.0007924124,0.00029482544],"category_scores_gemma":[0.0006157766,0.00051928044,0.00041096605,0.016750572,0.00016883595,0.00039993465,0.00039228742,0.0004587652,0.0006966574],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002234064,0.000114109585,0.000022247763,0.000066140856,0.00005050437,0.000015208923,0.000066277105,2.6257038e-7,0.000020317677,0.24696101,0.030076917,0.72260475],"study_design_scores_gemma":[0.00023327852,0.00016618613,0.00069215754,0.000035651934,0.00004073655,0.000012424248,0.0000018533663,0.000101982165,0.00009721991,0.02689687,0.97117174,0.00054988277],"about_ca_topic_score_codex":0.000015486661,"about_ca_topic_score_gemma":0.0000011140622,"teacher_disagreement_score":0.9410948,"about_ca_system_score_codex":0.0002009776,"about_ca_system_score_gemma":0.00029810524,"threshold_uncertainty_score":0.9997259},"labels":[],"label_agreement":null},{"id":"W4252195007","doi":"10.4172/plastic-surgery.1000822","title":"Characterizing the lateral slope of the aging female eyebrow","year":2013,"lang":"en","type":"article","venue":"Plastic Surgery","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Western University","funders":"","keywords":"Eyebrow; Geology; Anatomy; Biology; Computer science; Psychology; Communication","score_opus":0.027430609020429892,"score_gpt":0.21986812397959193,"score_spread":0.19243751495916203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252195007","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.984819,0.000059756527,0.0082484605,0.0019125511,0.0046427203,0.00007839384,0.0000019914219,0.000043202268,0.00019393745],"genre_scores_gemma":[0.9994336,0.0000069755574,0.00013738776,0.00020182897,0.000057095353,0.0000111324525,0.0000011355934,0.0000036035365,0.00014726495],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991283,0.00009188676,0.00022442664,0.000153908,0.00022906349,0.00017242845],"domain_scores_gemma":[0.9960926,0.003218969,0.00013785381,0.0004422866,0.00007358481,0.000034714816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003977739,0.00006841942,0.000109439774,0.00011655657,0.00013645626,0.000178246,0.00057080283,0.000026502754,0.00009863906],"category_scores_gemma":[0.00086503895,0.00003786848,0.000086093234,0.0008330816,0.00006207161,0.0002679758,0.00018823585,0.000092586786,0.00012768834],"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":[0.000008485574,0.0003693243,0.7307443,0.0002440927,0.0001583889,0.000010855016,0.009120132,0.000024394818,0.0542226,0.016403805,0.036397118,0.15229653],"study_design_scores_gemma":[0.000035754987,0.000001922357,0.98266834,0.00003106214,0.0000037674663,0.0000064164306,0.000028308956,0.006284618,0.0044917334,0.000112895585,0.006252389,0.00008277195],"about_ca_topic_score_codex":0.00007127225,"about_ca_topic_score_gemma":0.0000019953784,"teacher_disagreement_score":0.25192407,"about_ca_system_score_codex":0.000012233549,"about_ca_system_score_gemma":0.000043301134,"threshold_uncertainty_score":0.17188303},"labels":[],"label_agreement":null},{"id":"W4255215843","doi":"10.32920/ryerson.14646801.v1","title":"Sequential subspace estimator for an efficient multibiometrics authentication and encryption","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Toronto Metropolitan University","funders":"","keywords":"Computer science; Biometrics; Subspace topology; Encryption; Authentication (law); Data mining; Cryptography; Noise (video); Fingerprint (computing); Pattern recognition (psychology); Computer security; Algorithm; Artificial intelligence; Image (mathematics)","score_opus":0.0719615744055088,"score_gpt":0.3346970098399241,"score_spread":0.26273543543441535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255215843","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":[0.11824749,0.0003211081,0.8786155,0.00049604464,0.0013785923,0.0006442708,0.000024476009,0.00021444364,0.000058076825],"genre_scores_gemma":[0.6657556,0.000040215633,0.333593,0.000048333586,0.00005245666,0.000056268556,0.00026159015,0.000010180005,0.00018233819],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784935,0.00010926259,0.0003724988,0.0010124691,0.00041566422,0.00024078628],"domain_scores_gemma":[0.9978905,0.00012525254,0.00024737674,0.0009890156,0.00055730675,0.00019059179],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008924153,0.0002144987,0.00024659035,0.0011229406,0.00019481046,0.001388532,0.00069581746,0.00030774914,0.000015515483],"category_scores_gemma":[0.00032230065,0.00021890347,0.00011109419,0.0015071677,0.000061450366,0.00024808297,0.00076918607,0.0001955057,0.0000084030535],"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":[0.000054315387,0.0042207213,0.001027738,0.0028124605,0.00038180393,0.000014259521,0.016062658,0.0036122005,0.04110156,0.60590816,0.003136497,0.32166764],"study_design_scores_gemma":[0.00029585307,0.000036197525,0.004046209,0.000020148625,0.000033413853,0.000006743549,0.000107500884,0.9894628,0.0040745004,0.00088338,0.00073378434,0.0002995028],"about_ca_topic_score_codex":0.00014177152,"about_ca_topic_score_gemma":0.000022257744,"teacher_disagreement_score":0.9858506,"about_ca_system_score_codex":0.00012066444,"about_ca_system_score_gemma":0.000202608,"threshold_uncertainty_score":0.9996481},"labels":[],"label_agreement":null},{"id":"W4256605964","doi":"10.1007/978-0-387-73003-5_64","title":"Template Security","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Biometrics","topic":"Biometric Identification and Security","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":"Computer science; Computer security; Business","score_opus":0.01835228898464001,"score_gpt":0.24085319441867648,"score_spread":0.22250090543403647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256605964","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000028913735,0.004262108,0.020974899,0.00031861622,0.0015980223,0.00030611586,0.00011253539,0.00022001025,0.97217876],"genre_scores_gemma":[0.0043361285,0.021688811,0.027232759,0.00029113336,0.00044262246,0.0000066857942,0.00016990796,0.00006635276,0.9457656],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99683595,0.00003013147,0.00088764983,0.0007661517,0.0011343459,0.00034577874],"domain_scores_gemma":[0.9968926,0.0002509146,0.000788628,0.0014006428,0.00043439845,0.0002328367],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067636784,0.00040966782,0.00062969676,0.006785094,0.000091580274,0.00011913607,0.0020153667,0.0006660242,0.00022825116],"category_scores_gemma":[0.00026123904,0.0004217832,0.00032469822,0.004168732,0.00013101113,0.00030307713,0.0003785387,0.000489576,0.00039261777],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040185223,0.00013650223,0.000040286035,0.000111962974,0.00006716435,0.000028586117,0.0002507374,2.2267031e-7,0.000008573247,0.43503052,0.05228257,0.5120388],"study_design_scores_gemma":[0.00017930378,0.00010135875,0.00037831775,0.00003340839,0.000027270975,0.000011349074,0.0000016246197,0.000113133065,0.00007336832,0.05993579,0.938708,0.000437049],"about_ca_topic_score_codex":0.000018062388,"about_ca_topic_score_gemma":0.000002849789,"teacher_disagreement_score":0.88642544,"about_ca_system_score_codex":0.000117377545,"about_ca_system_score_gemma":0.00021776772,"threshold_uncertainty_score":0.9998234},"labels":[],"label_agreement":null},{"id":"W4280517983","doi":"10.3390/s22103620","title":"RETRACTED: Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":19,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Qassim University","keywords":"Biometrics; Artificial intelligence; Iris recognition; Pattern recognition (psychology); Computer science; Classifier (UML); Feature extraction; Fuzzy logic; Artificial neural network; Computer vision","score_opus":0.10446551961661214,"score_gpt":0.3190579954491928,"score_spread":0.21459247583258062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280517983","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93621147,0.000017859245,0.062558375,0.00034052946,0.0004459089,0.00023857153,0.000031871383,0.00004636351,0.000109028544],"genre_scores_gemma":[0.98725295,0.000005754595,0.012277349,0.00007143753,0.00002902248,0.0000072131384,0.000016723956,0.000008011795,0.00033150704],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99873704,0.00011984801,0.00028744875,0.00032777354,0.0003815564,0.00014631751],"domain_scores_gemma":[0.9991575,0.00008406692,0.00023054519,0.00034975918,0.00012953508,0.00004861634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006816542,0.00008235206,0.0001296952,0.00033801913,0.00041070924,0.00012210356,0.00031864512,0.00005437294,0.0000329312],"category_scores_gemma":[0.00012043958,0.000091541464,0.000057931255,0.0008450866,0.000038778482,0.00029190487,0.00032786172,0.0001551645,0.0000019586128],"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":[0.00005871697,0.0008227268,0.0119503485,0.0003882227,0.00007054394,0.0000164156,0.007847754,0.0012199832,0.8034973,0.13704368,0.00422136,0.03286291],"study_design_scores_gemma":[0.002233041,0.0004290073,0.4995957,0.000044979894,0.00009567894,0.000097954086,0.0011434837,0.3324331,0.088472284,0.036482245,0.03789494,0.0010775827],"about_ca_topic_score_codex":0.00021530314,"about_ca_topic_score_gemma":0.0000105272775,"teacher_disagreement_score":0.71502507,"about_ca_system_score_codex":0.00006164119,"about_ca_system_score_gemma":0.000029356244,"threshold_uncertainty_score":0.37329528},"labels":[],"label_agreement":null},{"id":"W4285121428","doi":"10.1109/jstsp.2022.3174655","title":"FLD-SRC: Fingerprint Liveness Detection for AFIS Based on Spatial Ridges Continuity","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Ministry of Public Security of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Fingerprint (computing); Liveness; Pattern recognition (psychology); Spoofing attack; Fingerprint recognition; Computer vision; Feature extraction; Minutiae; Pruning","score_opus":0.021552847069399722,"score_gpt":0.2652068262916054,"score_spread":0.2436539792222057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285121428","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":[0.14102986,0.00010017165,0.85764265,0.0004084773,0.00061778363,0.0001336189,0.0000029560713,0.000024634675,0.000039844497],"genre_scores_gemma":[0.99409366,0.000002677402,0.0054247705,0.00016928166,0.0002529516,0.000014745795,0.0000011652643,0.000007106658,0.00003362079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984294,0.00017612679,0.0004722879,0.0002136226,0.00050803804,0.00020051573],"domain_scores_gemma":[0.9985831,0.0001756565,0.0004907128,0.00011737185,0.00057583826,0.000057334066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010959876,0.000104591214,0.00020139068,0.00067741936,0.00030966336,0.0001896125,0.00054244726,0.000057128105,0.00001375615],"category_scores_gemma":[0.00015663927,0.00010516234,0.00007690676,0.0015557155,0.00002290717,0.0002456033,0.000041036034,0.0004795651,4.3976289e-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":[0.00030841905,0.00059895107,0.0011364025,0.0001310251,0.000018465918,0.00003839062,0.0012339937,0.012136457,0.024123205,0.00018357443,0.00010407628,0.95998704],"study_design_scores_gemma":[0.0018037113,0.00092535367,0.018337978,0.00010523056,0.000019674206,0.00008797088,0.00007794599,0.83739936,0.13421576,0.0016830178,0.005037391,0.00030662958],"about_ca_topic_score_codex":0.000023001001,"about_ca_topic_score_gemma":0.000024286519,"teacher_disagreement_score":0.95968044,"about_ca_system_score_codex":0.0002802178,"about_ca_system_score_gemma":0.0003806802,"threshold_uncertainty_score":0.42883956},"labels":[],"label_agreement":null},{"id":"W4289823448","doi":"10.1109/tii.2022.3196343","title":"Privacy Preserving Ear Recognition System Using Transfer Learning in Industry 4.0","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Lenovo Group; National Institute of Technology Rourkela","keywords":"Biometrics; Computer science; Unavailability; Convolutional neural network; Encoding (memory); Feature extraction; Artificial intelligence; Deep learning; Feature (linguistics); Transfer of learning; Computation; Speech recognition; Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.11236552943275933,"score_gpt":0.27408732734616736,"score_spread":0.16172179791340802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289823448","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":[0.4182466,0.0000043392524,0.57940644,0.0001035546,0.00126264,0.00034775346,0.000028819773,0.00020545341,0.0003944167],"genre_scores_gemma":[0.9973606,0.0000037089173,0.0022746418,0.00008960911,0.00004825381,0.00006662712,0.000009370979,0.000011907826,0.00013526682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978053,0.00030572587,0.00079808314,0.00018533116,0.00059757126,0.00030798215],"domain_scores_gemma":[0.99918664,0.00012754096,0.00014335885,0.00036695556,0.000072793584,0.000102723156],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001058918,0.00015954727,0.00020547456,0.0011250214,0.0006672479,0.00024911328,0.0006835609,0.00029835466,0.00011390127],"category_scores_gemma":[0.000023218825,0.00018553037,0.00009158949,0.0029946752,0.000026302063,0.0010975812,0.000015413798,0.0023848268,0.000024864803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014140939,0.0007088793,0.00031321184,0.00020278331,0.00010325803,0.000021737937,0.02190002,0.6908655,0.00036602045,0.0007577339,0.000471325,0.2841481],"study_design_scores_gemma":[0.0024349326,0.00022542277,0.000056963636,0.00013246194,0.000033267057,0.00009096307,0.009870354,0.9738885,0.006771369,0.0000482774,0.005949525,0.0004979765],"about_ca_topic_score_codex":0.00021658704,"about_ca_topic_score_gemma":0.0000058074534,"teacher_disagreement_score":0.579114,"about_ca_system_score_codex":0.00055949035,"about_ca_system_score_gemma":0.00025456605,"threshold_uncertainty_score":0.99991673},"labels":[],"label_agreement":null},{"id":"W4293863222","doi":"10.1109/siu55565.2022.9864940","title":"Dynamic ROI Extraction for Palmprints using MediaPipe Hands","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Extraction (chemistry); Pattern recognition (psychology)","score_opus":0.06065998108639392,"score_gpt":0.3340515873877862,"score_spread":0.27339160630139225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293863222","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":[0.002834756,0.0020659731,0.9909581,0.0024715285,0.00005243435,0.0007367467,0.000054436117,0.00020137306,0.0006246748],"genre_scores_gemma":[0.9183088,0.000300492,0.07842173,0.00018494086,0.000017462547,0.002127059,0.00020367767,0.000015614045,0.0004202112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819255,0.00017091382,0.00045035026,0.0005557459,0.00035963889,0.000270829],"domain_scores_gemma":[0.99746406,0.00022822541,0.0003617476,0.0014059728,0.0004153366,0.00012464561],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00080580125,0.00017712354,0.00019624099,0.00044655576,0.0032094389,0.0005029357,0.002129404,0.000069365095,0.00008039793],"category_scores_gemma":[0.000024779705,0.0002052094,0.00006579211,0.0018551501,0.000210158,0.00049806753,0.0009196018,0.00041454798,0.000007825316],"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":[0.000012839614,0.00052895467,0.00018314198,0.000082839164,0.000030309848,3.3121015e-7,0.0014400141,0.00014018688,0.009542902,0.11416197,0.0002532105,0.8736233],"study_design_scores_gemma":[0.00040695927,0.00004891029,0.00048278895,0.000013461307,0.000034717654,0.00003273774,0.00087114365,0.9075157,0.00021271296,0.01553033,0.074555136,0.00029540667],"about_ca_topic_score_codex":0.000043320866,"about_ca_topic_score_gemma":0.000021445267,"teacher_disagreement_score":0.91547406,"about_ca_system_score_codex":0.00016138557,"about_ca_system_score_gemma":0.00043878314,"threshold_uncertainty_score":0.99808824},"labels":[],"label_agreement":null},{"id":"W4298335400","doi":"10.1007/978-1-4419-5906-5_91","title":"Personal Identification Number (PIN)","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Biometric Identification and Security","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 Ottawa","funders":"","keywords":"Identification (biology); Psychology; Biology; Botany","score_opus":0.04569554378307625,"score_gpt":0.24861838834405223,"score_spread":0.20292284456097598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298335400","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003415816,0.00011062113,0.22466983,0.0002854599,0.0008571519,0.00012286929,0.000009784241,0.00021311225,0.7737278],"genre_scores_gemma":[0.0080598,0.000119126795,0.0035396824,0.0003123038,0.00011328846,0.0000071867703,0.000049330614,0.000018993296,0.9877803],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983527,0.000014566614,0.00037723168,0.0006171178,0.00046532825,0.000173054],"domain_scores_gemma":[0.9985806,0.000027178883,0.00024659224,0.00078965083,0.0002427885,0.00011318948],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002955205,0.00021763064,0.00019324472,0.00032669515,0.000109522145,0.0002770006,0.0010436416,0.00030858698,0.007315218],"category_scores_gemma":[0.000014703815,0.00021158338,0.00017172379,0.00015816338,0.00007398317,0.00034901325,0.00020055257,0.00024550315,0.01598619],"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":[7.3979203e-7,0.000016300577,0.000006302228,0.000010924698,0.000020748848,0.00000303171,0.00022873066,2.9868916e-9,0.000010013481,0.9354441,0.029736545,0.03452252],"study_design_scores_gemma":[0.00008497045,0.000007546783,0.00046792987,0.000013378095,0.000015028465,0.000026531383,0.0000029939781,0.00044364837,0.000115933464,0.10002296,0.8984389,0.00036020824],"about_ca_topic_score_codex":0.00003308637,"about_ca_topic_score_gemma":0.00000930831,"teacher_disagreement_score":0.86870235,"about_ca_system_score_codex":0.000067128414,"about_ca_system_score_gemma":0.000091635906,"threshold_uncertainty_score":0.9935922},"labels":[],"label_agreement":null},{"id":"W4299797133","doi":"10.48550/arxiv.1711.04322","title":"11K Hands: Gender recognition and biometric identification using a large\\n dataset of hand images","year":2017,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; Convolutional neural network; Identification (biology); Pattern recognition (psychology); Task (project management); Feature (linguistics); Support vector machine; Feature extraction; Speech recognition; Computer vision","score_opus":0.28252680327757007,"score_gpt":0.25538921255629476,"score_spread":0.027137590721275306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299797133","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":[0.3888115,0.00055284443,0.6045217,0.000022218554,0.00075321307,0.00046681866,0.0047487514,0.00003486115,0.0000881382],"genre_scores_gemma":[0.9928438,0.0028635021,0.0014153287,0.000024130291,0.000071931536,0.0000010976672,0.0023466772,0.000021778553,0.00041172275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957216,0.00044059398,0.00083539827,0.0021518976,0.00031703836,0.00053347484],"domain_scores_gemma":[0.99347645,0.00019399147,0.0023035747,0.0026674075,0.0010415283,0.0003170658],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0021465395,0.00049033907,0.0006505467,0.00400567,0.0012145776,0.0014521948,0.0022067796,0.00055568415,0.00013791306],"category_scores_gemma":[0.00046463334,0.0006163216,0.00022850824,0.004154692,0.00087324047,0.0020111625,0.0024712598,0.00052142603,0.00013406608],"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":[0.0027730465,0.022977624,0.14054368,0.028421033,0.010835044,0.002838226,0.027755357,0.008179307,0.15454252,0.087481566,0.036488954,0.47716364],"study_design_scores_gemma":[0.0047627776,0.00016288084,0.060871534,0.0004898863,0.0013547945,0.00007033315,0.0005436555,0.8786671,0.016371185,0.03090146,0.003713494,0.0020908606],"about_ca_topic_score_codex":0.00071257906,"about_ca_topic_score_gemma":0.000026190097,"teacher_disagreement_score":0.8704878,"about_ca_system_score_codex":0.00021756237,"about_ca_system_score_gemma":0.000334178,"threshold_uncertainty_score":0.99962884},"labels":[],"label_agreement":null},{"id":"W4310528321","doi":"10.1109/icaee53772.2022.9962020","title":"An Efficient Palmprint Authentication System based on One-Class SVM and HOG Descriptor","year":2022,"lang":"en","type":"article","venue":"2022 2nd International Conference on Advanced Electrical Engineering (ICAEE)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Biometrics; Support vector machine; Artificial intelligence; Histogram; Classifier (UML); Pattern recognition (psychology); Authentication (law); Machine learning; Data mining; Image (mathematics); Computer security","score_opus":0.018060314152061226,"score_gpt":0.24217911663570746,"score_spread":0.22411880248364624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310528321","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":[0.06167565,0.0000654215,0.9315848,0.0022384448,0.0015727814,0.000461715,0.000052059564,0.0005710972,0.0017780741],"genre_scores_gemma":[0.9931957,0.000010742885,0.00608212,0.00027265085,0.000042368312,0.00015620339,0.00005660735,0.000016444055,0.00016715827],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759877,0.000103404556,0.0003420917,0.0006758449,0.0009779051,0.00030197],"domain_scores_gemma":[0.99885994,0.00012822711,0.00012853484,0.0005109172,0.00017935452,0.00019304495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041019323,0.00020156619,0.00018988084,0.0007393766,0.00021989741,0.00024214249,0.0009977047,0.00005414912,0.00016631582],"category_scores_gemma":[0.00015922492,0.00021998177,0.0000645829,0.0010316154,0.000024334571,0.0001621063,0.00014678492,0.0004814129,0.000023888584],"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":[0.00006183312,0.00051980466,0.000026645566,0.00001930624,0.000023414348,0.0000064039114,0.000122009005,0.077530555,0.03048302,0.87083054,0.00006602319,0.020310448],"study_design_scores_gemma":[0.0004581278,0.0003441857,0.0019439302,0.000021135842,0.000005161275,0.000007280402,0.000027437845,0.99072045,0.0015708215,0.00016299791,0.004498051,0.00024041609],"about_ca_topic_score_codex":0.00000925343,"about_ca_topic_score_gemma":4.688724e-7,"teacher_disagreement_score":0.93152004,"about_ca_system_score_codex":0.0006826176,"about_ca_system_score_gemma":0.0000941015,"threshold_uncertainty_score":0.89705956},"labels":[],"label_agreement":null},{"id":"W4313016332","doi":"10.1109/dasc/picom/cbdcom/cy55231.2022.9927942","title":"Enhancing Biometric Security with Combinatorial and Permutational Multi-Fingerprint Authentication Strategies","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Chicoutimi; Concordia University of Edmonton","funders":"","keywords":"Biometrics; Computer science; Fingerprint (computing); Authentication (law); Fingerprint recognition; Computer security; Scheme (mathematics); Data mining; Mathematics","score_opus":0.03987941056858585,"score_gpt":0.28339134634909996,"score_spread":0.2435119357805141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313016332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91613173,0.0014260741,0.06948834,0.0028876176,0.0063358005,0.0019823404,0.0003488348,0.000817016,0.00058224157],"genre_scores_gemma":[0.99426407,0.0005394472,0.0021497188,0.0022084333,0.00036867135,0.000047789417,0.00018261482,0.00009365474,0.00014559836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.987515,0.0007473993,0.0023071463,0.005355398,0.002133521,0.0019415843],"domain_scores_gemma":[0.9905341,0.002163926,0.0021630172,0.0026597586,0.0016065849,0.0008725748],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","sts"],"category_scores_codex":[0.0052763573,0.0017433297,0.0018949847,0.004946306,0.004644323,0.00340177,0.004859765,0.0007892735,0.00010717008],"category_scores_gemma":[0.0010922691,0.001675987,0.00018178289,0.0052084294,0.0050175264,0.0009983609,0.0056718653,0.0035974174,0.00003801429],"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":[0.00056563143,0.0017109228,0.004258261,0.00034675532,0.0006481916,0.00031005152,0.010384195,0.00070592307,0.0030309747,0.6615975,0.0013794665,0.3150621],"study_design_scores_gemma":[0.0069090407,0.007729919,0.007738097,0.0015346028,0.00035078716,0.0017253696,0.014203508,0.8813706,0.018399509,0.013957264,0.040705252,0.0053760572],"about_ca_topic_score_codex":0.00071476784,"about_ca_topic_score_gemma":0.00018607237,"teacher_disagreement_score":0.88066465,"about_ca_system_score_codex":0.0005574294,"about_ca_system_score_gemma":0.0015192141,"threshold_uncertainty_score":0.99953127},"labels":[],"label_agreement":null},{"id":"W4315646247","doi":"10.18280/isi.270608","title":"Finger Veins Verification by Exploiting the Deep Learning Technique","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chinese Academy of Sciences; University of Mosul","keywords":"Preprocessor; Artificial intelligence; Computer science; Biometrics; Convolutional neural network; Pooling; Deep learning; Pattern recognition (psychology)","score_opus":0.013808858356872124,"score_gpt":0.22066702319000706,"score_spread":0.20685816483313493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315646247","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":[0.012295439,0.00017104583,0.9829064,0.00041440464,0.00030666488,0.00036343888,0.000006917651,0.000352171,0.00318352],"genre_scores_gemma":[0.99482393,0.000020547028,0.0040243194,0.00039080874,0.000020692058,0.0004577026,0.00012229342,0.0000059363356,0.00013374977],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99859565,0.00021205278,0.0004131848,0.00014577432,0.00042426575,0.00020904212],"domain_scores_gemma":[0.99900955,0.00008825465,0.00034871994,0.00036930642,0.00014471999,0.000039454433],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0013310711,0.00010147725,0.00008915216,0.00029999833,0.001515044,0.00052092096,0.00076979457,0.0000446359,0.000060744165],"category_scores_gemma":[0.00026262406,0.000092662194,0.000049722817,0.0016226993,0.000058939568,0.0022866088,0.00026984108,0.00031084978,0.00006222344],"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":[0.000013003797,0.00012641287,0.0005099429,0.000121776684,0.0000328365,0.0000014323164,0.0465122,0.0018091112,0.0052613136,0.07006904,0.009212847,0.8663301],"study_design_scores_gemma":[0.0003432845,0.00018957285,0.003186112,0.000023509103,0.000010497392,0.000099445955,0.007641145,0.2189455,0.009165782,0.004865031,0.75503224,0.00049790676],"about_ca_topic_score_codex":0.00005115179,"about_ca_topic_score_gemma":9.4713204e-7,"teacher_disagreement_score":0.9825285,"about_ca_system_score_codex":0.00030834996,"about_ca_system_score_gemma":0.0000462845,"threshold_uncertainty_score":0.9997848},"labels":[],"label_agreement":null},{"id":"W4315786042","doi":"10.18280/i2m.210603","title":"Thinning Algorithms Analysis Minutiae Extraction with Terminations and Bifurcation Extraction from the Single-Pixeled Thinned Biometric Image","year":2022,"lang":"en","type":"article","venue":"Instrumentation Mesure Métrologie","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Minutiae; Biometrics; Computer science; Thinning; Artificial intelligence; Feature extraction; Identification (biology); Pattern recognition (psychology); Computer vision; Fingerprint recognition; Fingerprint (computing)","score_opus":0.03101903213075002,"score_gpt":0.2965696306453887,"score_spread":0.2655505985146387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315786042","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":[0.45804027,0.00020198022,0.5329174,0.007613337,0.00047176905,0.00036207982,0.00006156398,0.00022882619,0.0001027834],"genre_scores_gemma":[0.9510312,0.00003474967,0.047864996,0.00041487883,0.000049573187,0.0001299621,0.00038650847,0.000009490856,0.00007862133],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99738574,0.00056647154,0.00043209916,0.0006238704,0.0007573769,0.00023441491],"domain_scores_gemma":[0.9980622,0.0005972301,0.0005295383,0.0005425449,0.00020271583,0.00006575312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012417769,0.00018565328,0.00020098985,0.0015817949,0.0010444502,0.0006211927,0.0005774654,0.00007628152,0.00012226739],"category_scores_gemma":[0.00017419129,0.00014891605,0.00009042082,0.008900778,0.00010940664,0.001373149,0.00016937475,0.00033332163,0.000008936032],"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":[0.0001627138,0.0010778281,0.062005512,0.00001713539,0.0012401473,0.000032453885,0.011457125,0.0010979106,0.07028198,0.0037412215,0.0016904569,0.8471955],"study_design_scores_gemma":[0.0015754871,0.00037766102,0.8643211,0.0000047764793,0.0006115137,0.00006841537,0.003947769,0.115362115,0.0064077093,0.0024005403,0.004444955,0.00047796304],"about_ca_topic_score_codex":0.0003803568,"about_ca_topic_score_gemma":0.00007126422,"teacher_disagreement_score":0.84671754,"about_ca_system_score_codex":0.00026746903,"about_ca_system_score_gemma":0.00007872468,"threshold_uncertainty_score":0.8033172},"labels":[],"label_agreement":null},{"id":"W4319990869","doi":"10.18280/ts.390611","title":"An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Deep learning; Computer science; Grayscale; Pattern recognition (psychology); Focus (optics); Feature extraction; Biometrics; Computer vision; Pixel","score_opus":0.03336318269592882,"score_gpt":0.27021955088713373,"score_spread":0.23685636819120492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319990869","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":[0.051954754,0.000030535073,0.9463488,0.00094772555,0.000168067,0.00034729938,0.000015753702,0.00015359082,0.00003349319],"genre_scores_gemma":[0.9813498,0.0000012768696,0.016787428,0.0004913738,0.000052340063,0.00041643772,0.00036070327,0.000010990859,0.0005296055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981675,0.0001538785,0.00036135118,0.00049440033,0.00059019565,0.00023264959],"domain_scores_gemma":[0.99914557,0.000083300685,0.00021058747,0.00030688028,0.00016965179,0.00008400078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010210766,0.000115537405,0.000108375534,0.0003520564,0.00075902825,0.00023300658,0.0007244772,0.00003326975,0.00021798779],"category_scores_gemma":[0.000020740556,0.00013481668,0.000111804184,0.0006875988,0.000029069884,0.00038666584,0.00005747701,0.00014597167,0.000014048417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001127854,0.0016365941,0.00035095876,0.00006251598,0.000054027918,0.000002080958,0.0037555504,0.5186296,0.36100477,0.053461216,0.0032295035,0.05770041],"study_design_scores_gemma":[0.0006130509,0.00011748847,0.0015969963,0.0000022761449,0.000009173287,9.0819447e-7,0.0000893261,0.99144924,0.0023629535,0.0010805235,0.0025113807,0.00016667794],"about_ca_topic_score_codex":0.000007421205,"about_ca_topic_score_gemma":0.0000047748667,"teacher_disagreement_score":0.9295614,"about_ca_system_score_codex":0.00013385067,"about_ca_system_score_gemma":0.000104427665,"threshold_uncertainty_score":0.58379084},"labels":[],"label_agreement":null},{"id":"W4328054402","doi":"10.18280/ts.400109","title":"Finger Vein Recognition Based on Multi-Features Fusion","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fusion; Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Speech recognition","score_opus":0.05554437092001527,"score_gpt":0.2713129360742676,"score_spread":0.21576856515425233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328054402","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":[0.16015363,0.000056850135,0.8256684,0.0080637615,0.0015281657,0.0007598448,0.000069026326,0.0014399897,0.002260327],"genre_scores_gemma":[0.9906678,0.000007213025,0.00731991,0.001151018,0.00008434651,0.000035788686,0.0001404443,0.000007635578,0.0005858278],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879944,0.000084019215,0.00017921008,0.00032202151,0.00041612267,0.00019920131],"domain_scores_gemma":[0.99946433,0.00010228641,0.00006111077,0.00022884161,0.00007148361,0.00007196172],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00052111456,0.00010157784,0.00008252311,0.0005320802,0.0001543401,0.00015999393,0.00033868395,0.000059007903,0.00033220986],"category_scores_gemma":[0.00004059449,0.00009287019,0.000065184,0.0013942694,0.00001944646,0.0001576129,0.00005076646,0.00010438406,0.00078551326],"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":[0.00006980546,0.0012897064,0.0005261809,0.00007521731,0.00003092864,0.00005836443,0.0017031805,0.0007420313,0.026779382,0.0036916018,0.11367119,0.8513624],"study_design_scores_gemma":[0.0019559094,0.00027381626,0.14657386,0.00006329661,0.00001097692,0.0000025115978,0.000044772365,0.80167085,0.015899856,0.0006866226,0.032400705,0.0004168524],"about_ca_topic_score_codex":0.000015357917,"about_ca_topic_score_gemma":0.0000068153267,"teacher_disagreement_score":0.85094553,"about_ca_system_score_codex":0.000038365542,"about_ca_system_score_gemma":0.000029853842,"threshold_uncertainty_score":0.9999925},"labels":[],"label_agreement":null},{"id":"W4353100313","doi":"10.18280/ts.400138","title":"Biometric User Authentication System via Fingerprints Using Novel Hybrid Optimization Tuned Deep Learning Strategy","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biometrics; Computer science; Authentication (law); Artificial intelligence; Deep learning; Fingerprint (computing); Pattern recognition (psychology); Computer security","score_opus":0.03930864097657536,"score_gpt":0.25838280541882674,"score_spread":0.2190741644422514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353100313","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":[0.07222366,0.000033423188,0.92639965,0.00008262978,0.0003430985,0.00027658377,0.0000058011146,0.0005407477,0.00009440845],"genre_scores_gemma":[0.9736797,0.00000730169,0.0259532,0.000025875826,0.00006736827,0.000021252055,0.0000997833,0.000016254016,0.00012925453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979184,0.000119081815,0.0004875224,0.00050702126,0.0006179806,0.0003499753],"domain_scores_gemma":[0.9990023,0.00009507163,0.00025926196,0.00030801696,0.00021611516,0.00011927056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009874727,0.00017109394,0.0001733515,0.0018567971,0.0003440178,0.00044493238,0.0005717623,0.000065594475,0.00012592916],"category_scores_gemma":[0.00005069091,0.0001806361,0.000080831065,0.006179016,0.00003322553,0.0005479184,0.0001439114,0.00012844366,0.00019030331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003167343,0.00079459505,0.0016425445,0.00039895182,0.00022654579,0.00004227843,0.0024150845,0.7162458,0.13640788,0.028690156,0.00021705411,0.112887435],"study_design_scores_gemma":[0.0004143187,0.00003797079,0.0051178597,0.00002183867,0.000018886736,0.000018451716,0.000109400986,0.99177676,0.0019006383,0.00003144028,0.00035362793,0.00019879182],"about_ca_topic_score_codex":0.000046633042,"about_ca_topic_score_gemma":9.4518373e-7,"teacher_disagreement_score":0.90145606,"about_ca_system_score_codex":0.00020659964,"about_ca_system_score_gemma":0.00005320643,"threshold_uncertainty_score":0.7366126},"labels":[],"label_agreement":null},{"id":"W4360989104","doi":"10.18280/ria.370116","title":"Artificial Neural Network-Based Fingerprint Classification and Recognition","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Artificial intelligence; Fingerprint (computing); Pattern recognition (psychology); Computer science","score_opus":0.1197279100865883,"score_gpt":0.2983767486331179,"score_spread":0.17864883854652963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360989104","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":[0.1652347,0.00012943016,0.82755363,0.0043019345,0.0010716749,0.00034197455,0.000007369456,0.0006798728,0.0006794013],"genre_scores_gemma":[0.9941637,0.00006189571,0.0049214982,0.00026564696,0.00014118635,0.00004345427,0.000048533107,0.00001085796,0.0003432186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831176,0.00010584848,0.00044957962,0.00055364624,0.00021755454,0.0003616029],"domain_scores_gemma":[0.99879533,0.00025736095,0.00013886615,0.00054867094,0.00014176038,0.000118013166],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00091381447,0.0001366573,0.00014425763,0.0003748298,0.00029780963,0.000304988,0.00044519958,0.00009855923,0.000062845116],"category_scores_gemma":[0.00019308718,0.00014634099,0.00006972067,0.0029155177,0.00009361085,0.00027315886,0.00012674746,0.00017151884,0.0012722539],"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":[0.000012363339,0.000109075445,0.00076428556,0.00004975644,0.00000840496,0.000012258299,0.0007339945,0.01493754,0.0042519714,0.027911315,0.0024909219,0.94871813],"study_design_scores_gemma":[0.000018344379,0.000035323956,0.0033487407,0.000021032749,0.0000045651254,0.0000060245566,0.00009405248,0.97066766,0.00894382,0.012955308,0.003723267,0.00018187289],"about_ca_topic_score_codex":0.000020893347,"about_ca_topic_score_gemma":0.000011507052,"teacher_disagreement_score":0.9557301,"about_ca_system_score_codex":0.000036594476,"about_ca_system_score_gemma":0.000040410185,"threshold_uncertainty_score":0.9995054},"labels":[],"label_agreement":null},{"id":"W4367316159","doi":"10.1007/s11265-023-01870-y","title":"A Fast and Light Fingerprint-Matching Model Based on Deep Learning Approaches","year":2023,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Biometric Identification and Security","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":"University of Toronto","funders":"","keywords":"Computer science; Biometrics; Artificial intelligence; Preprocessor; Convolutional neural network; Identification (biology); Fingerprint (computing); Segmentation; Pattern recognition (psychology); Matching (statistics); Architecture; Deep learning; Computer vision; Machine learning","score_opus":0.044774177126011165,"score_gpt":0.2497754268995254,"score_spread":0.20500124977351422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367316159","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":[0.014408255,0.0006088213,0.983524,0.0007596452,0.0001543835,0.00006213648,2.8886188e-7,0.00008005164,0.0004023778],"genre_scores_gemma":[0.9945337,0.000007647657,0.005106904,0.000042951775,0.00009956199,0.0000029155324,5.2924867e-7,0.000009849399,0.00019590788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984505,0.000114570044,0.00045428227,0.00020979079,0.0005797578,0.00019106313],"domain_scores_gemma":[0.9989562,0.00011231855,0.00052987295,0.000112947055,0.0001751384,0.00011352398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001751102,0.000115475064,0.00022756546,0.00077694934,0.00027478172,0.00081668,0.00041328702,0.00006927067,8.308247e-7],"category_scores_gemma":[0.000059177368,0.00009253401,0.00006168727,0.0011220105,0.000023090974,0.00052287587,0.00005932615,0.00034735055,0.000008426336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020974061,0.00008781042,0.00032265027,0.00050536805,0.000021864358,0.000038708058,0.0056590373,0.7639283,0.0022149482,0.0015790961,0.0001436872,0.22547756],"study_design_scores_gemma":[0.00020642247,0.00005656271,0.00019240132,0.00030529866,0.000006407393,0.000051218358,0.000465708,0.99780446,0.00017769575,0.00045109694,0.00017756653,0.00010514449],"about_ca_topic_score_codex":0.000003587205,"about_ca_topic_score_gemma":2.7483642e-7,"teacher_disagreement_score":0.9801255,"about_ca_system_score_codex":0.000049598933,"about_ca_system_score_gemma":0.00012112841,"threshold_uncertainty_score":0.7875264},"labels":[],"label_agreement":null},{"id":"W4377082039","doi":"10.1016/bs.adcom.2023.04.004","title":"A realtime fingerprint liveness detection method for fingerprint authentication systems","year":2023,"lang":"en","type":"book-chapter","venue":"Advances in computers","topic":"Biometric Identification and Security","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 Windsor","funders":"","keywords":"Liveness; Fingerprint (computing); Computer science; Authentication (law); Artificial intelligence; Feature (linguistics); Fingerprint recognition; Local binary patterns; Pattern recognition (psychology); Computer vision; Binary number; Feature extraction; Spoofing attack; Computer security; Image (mathematics); Mathematics","score_opus":0.03196692965208851,"score_gpt":0.31206979892920833,"score_spread":0.2801028692771198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377082039","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":[0.00000528019,0.0011644235,0.988423,0.00020617811,0.0058117183,0.0009042344,0.000019466537,0.0003869106,0.003078775],"genre_scores_gemma":[0.027051851,0.0049921395,0.8476079,0.0004459543,0.0012108753,0.0012233629,0.00025797912,0.00029368114,0.11691623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750876,0.00008294496,0.0006988863,0.0009899077,0.00039774078,0.00032174264],"domain_scores_gemma":[0.9973,0.0009598186,0.00053865276,0.0008848556,0.00022503534,0.0000916178],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011542039,0.0003166063,0.00046391672,0.0011052815,0.00014025057,0.00025851358,0.0010949966,0.00028315213,0.000002895742],"category_scores_gemma":[0.00009144123,0.0003492211,0.00018413182,0.0004692441,0.000050964834,0.00041411186,0.00033234016,0.00030602058,0.00009473202],"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":[0.000012133303,0.000028055083,0.0000021166168,0.00037740215,0.00003465355,0.000009363886,0.0004150597,0.0030234542,0.000042992113,0.39992872,0.00010677469,0.59601927],"study_design_scores_gemma":[0.00031996312,0.00007633829,0.00011491793,0.00046873846,0.000020016221,0.00001841423,0.000011035,0.7105277,0.000138155,0.050369974,0.23741603,0.0005187198],"about_ca_topic_score_codex":0.000060394257,"about_ca_topic_score_gemma":0.0000552512,"teacher_disagreement_score":0.7075043,"about_ca_system_score_codex":0.0003389233,"about_ca_system_score_gemma":0.00008476207,"threshold_uncertainty_score":0.999896},"labels":[],"label_agreement":null},{"id":"W4377832583","doi":"10.18280/ts.400243","title":"Effects of Training Parameters of AlexNet Architecture on Wound Image Classification","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Konya Teknik Üniversitesi","keywords":"Training (meteorology); Computer science; Artificial intelligence; Pattern recognition (psychology); Architecture; Image (mathematics); Computer vision; Geography; Art; Visual arts; Meteorology","score_opus":0.040532059575007225,"score_gpt":0.2639019404748058,"score_spread":0.2233698808997986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377832583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5831231,0.00002309412,0.41512734,0.000708706,0.00019820372,0.0003602193,0.000012846082,0.000119509314,0.0003269669],"genre_scores_gemma":[0.9917423,0.000008243053,0.008032634,0.00010043377,0.000019191973,0.000022880538,0.000018498124,0.0000057731436,0.000050047372],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987033,0.00010508735,0.00031785565,0.00026409893,0.0004315169,0.00017811102],"domain_scores_gemma":[0.9990789,0.00036594513,0.00018807607,0.00024889328,0.000061182946,0.00005695333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004722919,0.00010143301,0.00016731035,0.0005419104,0.000047114332,0.000043328866,0.00045437083,0.00004894275,0.000019558336],"category_scores_gemma":[0.000049105638,0.00009313883,0.000085806045,0.0014333745,0.000083837665,0.00010785038,0.000041641506,0.00009645813,0.00002333017],"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":[0.00008035839,0.0005912163,0.0002317406,0.00059836847,0.00013038206,0.000016631038,0.013231269,0.00040989908,0.49188524,0.05461263,0.0031327063,0.43507957],"study_design_scores_gemma":[0.004039482,0.0017764329,0.36000428,0.00033056695,0.000090127185,0.0000106216885,0.00076068,0.22047679,0.393315,0.013485397,0.0048451084,0.0008655399],"about_ca_topic_score_codex":0.000008167351,"about_ca_topic_score_gemma":0.0000012926689,"teacher_disagreement_score":0.43421403,"about_ca_system_score_codex":0.000021930473,"about_ca_system_score_gemma":0.00003528793,"threshold_uncertainty_score":0.3798091},"labels":[],"label_agreement":null},{"id":"W4385386616","doi":"10.18280/ria.370319","title":"Advancements in Biometric Authentication Systems: A Comprehensive Survey on Internal Traits, Multimodal Systems, and Vein Pattern Biometrics","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biometrics; Authentication (law); Computer science; Computer security","score_opus":0.09368449571320188,"score_gpt":0.3202770140850375,"score_spread":0.22659251837183564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385386616","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":[0.38508657,0.0015517527,0.60973865,0.00025735504,0.0020351226,0.0008539041,0.00012670155,0.0002417068,0.000108225286],"genre_scores_gemma":[0.9984771,0.00041040493,0.00020062832,0.00004485547,0.000035497644,0.00005609076,0.000083850166,0.000016359974,0.00067523826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970359,0.00035147797,0.00083771325,0.000805951,0.0005032243,0.00046571103],"domain_scores_gemma":[0.9975994,0.0010674315,0.00027226692,0.0006059197,0.00029562856,0.00015931389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014351113,0.00023306976,0.00033980396,0.005280206,0.0001435025,0.0004884002,0.00087981106,0.00012829092,0.000010330134],"category_scores_gemma":[0.0006396371,0.00023698679,0.000055130535,0.016787533,0.0000861671,0.00032916784,0.00026270433,0.00022119374,0.0006851814],"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":[0.00011137352,0.0027403499,0.090362854,0.0016743952,0.00017503345,0.0002115275,0.009622094,0.0306418,0.007076109,0.024461351,0.005403804,0.8275193],"study_design_scores_gemma":[0.00014662906,0.00012426902,0.12868212,0.00013051277,0.000003848971,0.000010685364,0.00049253035,0.8660694,0.0006467431,0.00005527569,0.00337449,0.00026353414],"about_ca_topic_score_codex":0.0013495357,"about_ca_topic_score_gemma":0.00004113507,"teacher_disagreement_score":0.8354276,"about_ca_system_score_codex":0.00016620367,"about_ca_system_score_gemma":0.00003789714,"threshold_uncertainty_score":0.9664041},"labels":[],"label_agreement":null},{"id":"W4385390235","doi":"10.18280/ria.370328","title":"Age-Dependent Palm Print Recognition Using Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Mosul","keywords":"Convolutional neural network; Palm; Computer science; Palm print; Pattern recognition (psychology); Artificial intelligence; Biometrics; Physics","score_opus":0.13401685656837023,"score_gpt":0.3067798837220277,"score_spread":0.17276302715365746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385390235","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":[0.11064187,0.00017208671,0.8845215,0.00089900155,0.0021114973,0.00030082779,0.00000964316,0.0005097771,0.00083382305],"genre_scores_gemma":[0.98869616,0.0000813464,0.009481734,0.00019565121,0.0002562628,0.000022205448,0.000047457255,0.000013080161,0.0012060995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980818,0.000116119336,0.00048366367,0.00053259195,0.00033194307,0.00045386114],"domain_scores_gemma":[0.99886197,0.00015637215,0.00014625047,0.0005558618,0.00015433884,0.00012518137],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008976946,0.00014346674,0.000164705,0.00031481156,0.0003483856,0.00026134675,0.000676621,0.00009222422,0.000166706],"category_scores_gemma":[0.00013502028,0.00015364154,0.00010929384,0.0028974777,0.000086038766,0.0002956536,0.00028975192,0.00020160078,0.002358172],"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":[0.000026496378,0.00039867868,0.0024786172,0.00013390188,0.00006846857,0.00027763148,0.0028716435,0.38728338,0.008963226,0.0650133,0.0077814297,0.52470326],"study_design_scores_gemma":[0.00003169644,0.000023701321,0.0011724004,0.00002790841,0.0000054895704,0.000038817518,0.000116257455,0.9788809,0.004328602,0.010214764,0.004953097,0.00020635477],"about_ca_topic_score_codex":0.000048997706,"about_ca_topic_score_gemma":0.000012178631,"teacher_disagreement_score":0.8780543,"about_ca_system_score_codex":0.000089351684,"about_ca_system_score_gemma":0.000048824153,"threshold_uncertainty_score":0.9984186},"labels":[],"label_agreement":null},{"id":"W4386325276","doi":"10.18280/ts.400443","title":"Enhanced Palmprint Recognition via Curvi-Linear Anisotropic Gaussian Filter-Based Combined Differential Concavity and Infirmity Codes","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gaussian; Pattern recognition (psychology); Differential (mechanical device); Anisotropy; Filter (signal processing); Artificial intelligence; Gaussian filter; Computer science; Mathematics; Algorithm; Computer vision; Physics; Image (mathematics); Optics","score_opus":0.03733996666316639,"score_gpt":0.2621622292364692,"score_spread":0.22482226257330282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386325276","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5155616,0.0000062576914,0.4833928,0.00033492473,0.0002244146,0.00020930717,0.000029413002,0.00019996222,0.000041340623],"genre_scores_gemma":[0.99666935,0.000009182885,0.0028122128,0.0001498307,0.000066671244,0.000046175795,0.00019727786,0.000008406003,0.0000409112],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983357,0.00017274338,0.000356744,0.00047639932,0.00035639346,0.00030201036],"domain_scores_gemma":[0.9991733,0.00012161572,0.00014103879,0.0002874654,0.00011277689,0.00016381046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032772197,0.00018490339,0.00021626074,0.00028529044,0.00027245842,0.00024582862,0.0003647714,0.00008622189,0.00038912488],"category_scores_gemma":[0.000029623983,0.00017773562,0.00007516537,0.0008757806,0.00013272683,0.00023405935,0.00011596191,0.00014387637,0.00013879444],"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":[0.00033887604,0.0023394506,0.020830851,0.00063820294,0.000280062,0.00007742229,0.0040326538,0.00009942616,0.39853054,0.020641992,0.002336593,0.5498539],"study_design_scores_gemma":[0.004127583,0.00049739296,0.43140867,0.000050999115,0.000038714206,0.0000035657458,0.000048429087,0.42683065,0.1318205,0.0035516866,0.0010056364,0.00061618764],"about_ca_topic_score_codex":0.000042924046,"about_ca_topic_score_gemma":0.000023124585,"teacher_disagreement_score":0.5492377,"about_ca_system_score_codex":0.000036808968,"about_ca_system_score_gemma":0.000056780013,"threshold_uncertainty_score":0.72478485},"labels":[],"label_agreement":null},{"id":"W4386325756","doi":"10.18280/ts.400431","title":"Deep Learning Based Gender Identification Using Ear Images","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Artificial intelligence; Computer science; Deep learning; Pattern recognition (psychology); Computer vision; Speech recognition; Biology","score_opus":0.05668951340752716,"score_gpt":0.2837552247068762,"score_spread":0.22706571129934905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386325756","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":[0.052757956,0.000052359657,0.94588447,0.0003254217,0.00025646045,0.00014701713,0.0000022974305,0.00041100258,0.00016300839],"genre_scores_gemma":[0.99216026,0.000008204034,0.00731761,0.00013684707,0.00006759302,0.0000142791605,0.000039199884,0.000009830972,0.0002461817],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984312,0.00013791824,0.00030217826,0.00037587385,0.0004896844,0.00026313323],"domain_scores_gemma":[0.99933666,0.00007455598,0.00012589648,0.00026198974,0.000120309924,0.000080611586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009360229,0.00010951347,0.000099004414,0.00057393004,0.00029744417,0.0003679997,0.00048486175,0.00004775386,0.00022749987],"category_scores_gemma":[0.00004188666,0.00011467391,0.000073659794,0.0019948392,0.000036307894,0.00040090884,0.00008254327,0.00011679455,0.000385946],"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":[0.000040840998,0.0009008996,0.017532978,0.00035564133,0.00022172353,0.00011635073,0.008375046,0.1080077,0.50970936,0.033107534,0.010416665,0.31121525],"study_design_scores_gemma":[0.00027896528,0.000015111569,0.058600605,0.000004796777,0.000009123563,0.0000021669125,0.0000832436,0.9302902,0.0073425737,0.00032749292,0.0028940053,0.00015168637],"about_ca_topic_score_codex":0.00001619726,"about_ca_topic_score_gemma":9.90337e-7,"teacher_disagreement_score":0.9394023,"about_ca_system_score_codex":0.000059062608,"about_ca_system_score_gemma":0.000042337844,"threshold_uncertainty_score":0.49606836},"labels":[],"label_agreement":null},{"id":"W4386985880","doi":"10.1007/978-981-99-1431-9_38","title":"Comparative Analysis of Segmentation and Generative Models for Fingerprint Retrieval Task","year":2023,"lang":"en","type":"book-chapter","venue":"Algorithms for intelligent systems","topic":"Biometric Identification and Security","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":"Dalhousie University","funders":"","keywords":"Computer science; Fingerprint (computing); Segmentation; Artificial intelligence; Task (project management); Authentication (law); Noise (video); Inpainting; Deep learning; Biometrics; Generative model; Pattern recognition (psychology); Quality (philosophy); Generative grammar; Machine learning; Image (mathematics); Computer security; Engineering","score_opus":0.1311845778895276,"score_gpt":0.3341990610092101,"score_spread":0.2030144831196825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386985880","genre_codex":"methods","genre_gemma":"other","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":[0.00002289579,0.0010757737,0.99314755,0.000069148264,0.0012136837,0.00199315,0.001183221,0.00007720458,0.0012173565],"genre_scores_gemma":[0.0642894,0.003572273,0.2018118,0.00019722595,0.0010772082,0.001570854,0.007428768,0.0002351686,0.7198173],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768233,0.000041205953,0.00084124826,0.000750459,0.000469092,0.00021564233],"domain_scores_gemma":[0.9971719,0.00057122926,0.000795154,0.00049155677,0.00086961413,0.000100563564],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008217184,0.0003074528,0.0009450377,0.0014993728,0.0001435448,0.00020466278,0.00047357203,0.00025845456,0.0000037121786],"category_scores_gemma":[0.00003203887,0.00029777698,0.0004079507,0.00067723077,0.00008765968,0.00018864221,0.00012657778,0.000117045216,0.000009797621],"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":[0.000043865406,0.000051761042,0.000002291772,0.00039139256,0.0048760585,0.0000012750439,0.00510935,0.0081230085,0.00020382706,0.96986896,0.002051093,0.009277111],"study_design_scores_gemma":[0.00017940119,0.0001513901,0.000004508842,0.000068943926,0.0006491557,0.0000014134799,0.00022771218,0.9745845,0.0018367405,0.011219791,0.010756782,0.0003196182],"about_ca_topic_score_codex":0.00006019975,"about_ca_topic_score_gemma":0.000019239655,"teacher_disagreement_score":0.96646154,"about_ca_system_score_codex":0.00015106238,"about_ca_system_score_gemma":0.00008049692,"threshold_uncertainty_score":0.9999474},"labels":[],"label_agreement":null},{"id":"W4387164795","doi":"10.21203/rs.3.rs-3371756/v1","title":"A Novel Deep Learning-Based Method for Real-Time Face Spoof Detection","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Spoofing attack; Face (sociological concept); Naive Bayes classifier; Support vector machine; Face detection; Deep learning; Facial recognition system; Replay attack; Pattern recognition (psychology); Computer vision; Machine learning; Authentication (law); Computer security","score_opus":0.1326177828230057,"score_gpt":0.4365214268693856,"score_spread":0.30390364404637993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387164795","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":[0.0005182135,0.00009312265,0.9946302,0.0016867978,0.0004993225,0.0015811065,0.00006989758,0.0007332058,0.00018811591],"genre_scores_gemma":[0.29004306,0.0003737534,0.680007,0.00008876846,0.0007494142,0.0033270114,0.0008894533,0.00022456083,0.024297012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995086,0.0009272459,0.0004136132,0.001299486,0.001530012,0.00074362045],"domain_scores_gemma":[0.9945799,0.0021911727,0.000198576,0.0013345436,0.0014524595,0.00024332177],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008369253,0.00024375423,0.00035461062,0.0022559296,0.0005477752,0.0008518984,0.001859185,0.00050896325,0.00003762598],"category_scores_gemma":[0.0029634426,0.00025350583,0.0002991242,0.00352841,0.00008307513,0.00014017319,0.0014451144,0.0015082483,0.00048294145],"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":[0.00035722167,0.0015348915,0.00021875478,0.007506432,0.00039208386,0.000054354576,0.0057551484,0.055465437,0.07861621,0.010389787,0.010050674,0.829659],"study_design_scores_gemma":[0.00041870977,0.00016748354,0.002410987,0.00010461705,0.0000073502647,0.0000021839435,0.000091799106,0.9742525,0.005780811,0.0022331318,0.014249959,0.000280445],"about_ca_topic_score_codex":0.0012392367,"about_ca_topic_score_gemma":0.00016079971,"teacher_disagreement_score":0.91878706,"about_ca_system_score_codex":0.0004364894,"about_ca_system_score_gemma":0.0005031071,"threshold_uncertainty_score":0.9999917},"labels":[],"label_agreement":null},{"id":"W4387591862","doi":"10.1016/j.jisa.2023.103624","title":"Privacy-preserving model for biometric-based authentication and Key Derivation Function","year":2023,"lang":"en","type":"article","venue":"Journal of Information Security and Applications","topic":"Biometric Identification and Security","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":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Key (lock); Biometrics; Authentication (law); Computer security; Code (set theory); Encryption; Cryptosystem; Function (biology); Key generation; Data mining; Database","score_opus":0.027409876350624758,"score_gpt":0.269683699779569,"score_spread":0.24227382342894427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387591862","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":[0.016537176,0.00008940213,0.9799943,0.0027867267,0.00007616637,0.00036534638,0.000020455576,0.000070959664,0.000059484013],"genre_scores_gemma":[0.9682242,0.00013463828,0.031006737,0.00041125828,0.000049159884,0.00008482471,0.00006834323,0.0000040031637,0.000016827393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892515,0.000022330343,0.0005484239,0.0001093967,0.0002748197,0.000119897],"domain_scores_gemma":[0.9983369,0.00017657586,0.0005332482,0.0002370342,0.0006190991,0.00009711157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009749153,0.00008154397,0.000116991396,0.0014319594,0.00028806768,0.00035720522,0.000321024,0.00007905274,0.0000022542142],"category_scores_gemma":[0.000273835,0.00007850253,0.000050510476,0.002373187,0.00003563429,0.002245641,0.000088321634,0.000095312025,0.000009089345],"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":[0.00007000652,0.00022185301,0.0006547642,0.0006347625,0.00007462305,1.7153272e-7,0.011612545,0.0022757493,0.0018552134,0.7619938,0.011069658,0.2095368],"study_design_scores_gemma":[0.00047278337,0.000038381528,0.0042780456,0.000011242777,0.000017032371,0.0000049678856,0.0001631573,0.9077025,0.00025442042,0.0371104,0.049857907,0.0000891417],"about_ca_topic_score_codex":0.0000029171927,"about_ca_topic_score_gemma":7.6726013e-7,"teacher_disagreement_score":0.95168704,"about_ca_system_score_codex":0.00003280771,"about_ca_system_score_gemma":0.00008289868,"threshold_uncertainty_score":0.34445384},"labels":[],"label_agreement":null},{"id":"W4387953197","doi":"10.18280/mmep.100518","title":"Storage Space Reduction of Biometric Iris Databases by Successive Images Differences and Quadtree Decomposition","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quadtree; Biometrics; IRIS (biosensor); Computer science; Reduction (mathematics); Biometric data; Space (punctuation); Database; Artificial intelligence; Pattern recognition (psychology); Computer vision; Decomposition; Mathematics; Geometry","score_opus":0.03569384515857665,"score_gpt":0.25553360933794933,"score_spread":0.21983976417937268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387953197","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":[0.15932487,0.0005843256,0.83960456,0.00018633883,0.00005534575,0.00007893415,0.000012384796,0.00014152049,0.000011732092],"genre_scores_gemma":[0.94903,0.00039964647,0.050467495,0.0000014227669,0.000009637779,0.00001136113,0.000011495663,0.0000066044927,0.00006232548],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992047,0.000017032991,0.0002129552,0.00023949186,0.00017851437,0.00014729012],"domain_scores_gemma":[0.9994514,0.00019985744,0.00006445938,0.00017160542,0.000040679275,0.000071995266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028434917,0.00010142946,0.00016735516,0.00054882537,0.000064519605,0.000112121,0.0001362789,0.000038999624,0.0000020273849],"category_scores_gemma":[0.000055337037,0.00008919645,0.000019989875,0.0013921006,0.000041841642,0.0002531977,0.000083482526,0.00007433573,0.000004193063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025378004,0.0012858011,0.00064281136,0.014009942,0.00035783352,0.000018151486,0.014284049,0.37946683,0.19551824,0.34521136,0.004179866,0.044999756],"study_design_scores_gemma":[0.000081678445,0.00002202693,0.00014287911,0.00009073371,0.000007917926,0.00000545154,0.000028019664,0.9950907,0.001519521,0.0028512792,0.00005294152,0.00010685255],"about_ca_topic_score_codex":0.0000336706,"about_ca_topic_score_gemma":7.8389526e-8,"teacher_disagreement_score":0.78970516,"about_ca_system_score_codex":0.000009484905,"about_ca_system_score_gemma":0.0000048575553,"threshold_uncertainty_score":0.36373258},"labels":[],"label_agreement":null},{"id":"W4388038321","doi":"10.1139/bcb-2023-0183","title":"Early pigment spot segmentation and classification from iris cellular image analysis with explainable deep learning and multiclass support vector machine","year":2023,"lang":"en","type":"article","venue":"Biochemistry and Cell Biology","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; IRIS (biosensor); Computer science; Segmentation; Support vector machine; Benchmark (surveying); Pattern recognition (psychology); Multiclass classification; Computer vision; Cartography","score_opus":0.010763374564610137,"score_gpt":0.2342001542029026,"score_spread":0.22343677963829248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388038321","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94398123,0.0004883115,0.055012114,0.00018918305,0.000028979559,0.000078818746,0.000018185337,0.00006477367,0.00013838125],"genre_scores_gemma":[0.9941646,0.00035045604,0.004377253,0.000019784933,0.000017390083,0.000015238209,0.00047055262,0.0000042067345,0.00058055343],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904823,0.000062114,0.0001594946,0.00048247937,0.00008252457,0.00016518238],"domain_scores_gemma":[0.999504,0.000073007446,0.00010903718,0.00017388293,0.0000461376,0.000093914045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002140928,0.00011497331,0.00014523383,0.00015207697,0.00016293919,0.00012263509,0.00011012241,0.000102927705,0.000029458912],"category_scores_gemma":[0.000016636066,0.00010153088,0.000022233724,0.00073249085,0.000109443776,0.00009690767,0.0000988721,0.00009335406,0.000009799367],"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":[0.000014765884,0.000030627867,0.028295808,0.000031236403,0.000056364803,0.0000061430296,0.00048306456,0.0000010873376,0.9641194,0.000059738224,0.000023212036,0.006878574],"study_design_scores_gemma":[0.0010793902,0.00018670413,0.12300918,0.000005395009,0.0001778193,0.000004617277,0.001017764,0.085436605,0.7852398,0.000098197816,0.0033601788,0.0003843126],"about_ca_topic_score_codex":0.000104189254,"about_ca_topic_score_gemma":0.0000040339037,"teacher_disagreement_score":0.17887954,"about_ca_system_score_codex":0.00001986128,"about_ca_system_score_gemma":0.000013715787,"threshold_uncertainty_score":0.4140309},"labels":[],"label_agreement":null},{"id":"W4388249333","doi":"10.1063/5.0175614","title":"Dynamic fingerprint construction and tree generation approach for biometric template protection","year":2023,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Biometric Identification and Security","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":"University of Regina","funders":"","keywords":"Biometrics; Computer science; Fingerprint (computing); Tree (set theory); Privacy protection; Fingerprint recognition; Artificial intelligence; Computer security; Mathematics","score_opus":0.06531570147612929,"score_gpt":0.26786064026822026,"score_spread":0.20254493879209096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388249333","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":[0.07436855,0.000027172346,0.92336553,0.0007751943,0.00023462642,0.00059332856,0.0000041973317,0.00035214555,0.00027922392],"genre_scores_gemma":[0.9124976,0.00008093466,0.086854465,0.000029916533,0.000033733388,0.00024899765,0.000025651108,0.0000071644745,0.00022157263],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882156,0.000009012475,0.00022618867,0.0005136855,0.00020999367,0.00021955921],"domain_scores_gemma":[0.99927324,0.000020617703,0.00014066893,0.00011947447,0.00037452846,0.00007147126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004258735,0.000121893725,0.00012842446,0.0012760785,0.00026566483,0.00057220476,0.00026496593,0.00010888929,0.0000027723638],"category_scores_gemma":[0.00016847928,0.00012118136,0.00003539795,0.0037431899,0.00006788967,0.00060998305,0.00010486864,0.0000983214,0.000014335684],"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":[0.000011731428,0.0000616603,0.0014076168,0.00021208944,0.000026210115,3.0953308e-7,0.0017324582,0.000002344477,0.17102528,0.062448427,0.00077326316,0.76229864],"study_design_scores_gemma":[0.0002781837,0.00007472161,0.0064900243,0.000007216324,0.0000069570583,0.000020901136,0.00017415422,0.98522496,0.0028554725,0.0031239863,0.001574004,0.0001694352],"about_ca_topic_score_codex":0.000019737818,"about_ca_topic_score_gemma":0.0000020851287,"teacher_disagreement_score":0.9852226,"about_ca_system_score_codex":0.000058499365,"about_ca_system_score_gemma":0.00004991794,"threshold_uncertainty_score":0.5517784},"labels":[],"label_agreement":null},{"id":"W4388339813","doi":"10.31893/multiscience.2024049","title":"Soft computing approach for feature extraction of palm biometric","year":2023,"lang":"en","type":"article","venue":"Multidisciplinary Science Journal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nutrasource","funders":"","keywords":"Biometrics; Convolutional neural network; Computer science; Palm print; Palm; Artificial intelligence; Feature extraction; Identification (biology); Pattern recognition (psychology); Feature (linguistics); Artificial neural network; Computer vision","score_opus":0.0532522156398122,"score_gpt":0.3464110241375755,"score_spread":0.2931588084977633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388339813","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":[0.12502931,0.00011358812,0.8722582,0.00070429087,0.0013676865,0.00021736635,0.000008667117,0.000114323455,0.00018659823],"genre_scores_gemma":[0.7764116,0.00001655455,0.22317417,0.000010769549,0.00012582494,0.00000318156,0.000008263411,0.000005672653,0.00024394438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976497,0.000045838624,0.00041282905,0.00047023478,0.000935302,0.00048608292],"domain_scores_gemma":[0.9981822,0.00020963253,0.00043421512,0.00039355858,0.0005665094,0.00021391704],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0045450986,0.00012736043,0.00019631269,0.0037670846,0.0013075148,0.00046488762,0.0017299473,0.000080578204,0.0000030490892],"category_scores_gemma":[0.0004225534,0.00010668989,0.0001454853,0.019296732,0.00029925982,0.0013220325,0.0004479602,0.000274628,0.000014282609],"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":[0.00006697232,0.0014871857,0.048895404,0.00029095123,0.000068785936,0.000045519344,0.011092146,0.009859398,0.278877,0.008687911,0.014695607,0.6259331],"study_design_scores_gemma":[0.0003456382,0.000095631745,0.23129673,0.0000115688845,0.0000047375543,0.00015570578,0.00040757444,0.76280814,0.0036472147,0.00066240906,0.0004191702,0.00014547099],"about_ca_topic_score_codex":0.0000037744749,"about_ca_topic_score_gemma":3.3222673e-7,"teacher_disagreement_score":0.75294876,"about_ca_system_score_codex":0.00011223153,"about_ca_system_score_gemma":0.0002963394,"threshold_uncertainty_score":0.99999267},"labels":[],"label_agreement":null},{"id":"W4390937848","doi":"10.1049/2024/4924184","title":"Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds","year":2024,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"International Civil Aviation Organization","funders":"China Agricultural University; South China Agricultural University; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Region of interest; Feature extraction; Word error rate; Rotation (mathematics); Palm; Pattern recognition (psychology); Computation; Computer vision; Convolution (computer science); Feature (linguistics); Algorithm; Artificial neural network","score_opus":0.042801900204153344,"score_gpt":0.30889905348523766,"score_spread":0.2660971532810843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390937848","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":[0.023576066,0.0016030008,0.9509161,0.0056792945,0.0057208417,0.0007141663,0.000095598545,0.0009595517,0.010735394],"genre_scores_gemma":[0.9874637,0.00007371623,0.010878015,0.0005522965,0.00013729186,0.000022707956,0.00010548048,0.000019620862,0.000747144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975633,0.00012484346,0.00049204443,0.00076852844,0.0006449118,0.00040634887],"domain_scores_gemma":[0.9983061,0.00055320066,0.00010887202,0.0007457334,0.00012203989,0.0001640587],"candidate_categories":["bibliometrics","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011070612,0.00023294121,0.00024338157,0.00819113,0.00009896625,0.0010760075,0.0008571443,0.00022068543,0.00014931468],"category_scores_gemma":[0.0001998574,0.00021565547,0.00014010625,0.029237112,0.000041400403,0.00062068674,0.000099666366,0.0003674364,0.0004416626],"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":[0.000082737395,0.0025357383,0.003009271,0.00041191338,0.0000869384,0.00034851008,0.0007772882,0.000059724756,0.041770574,0.044702247,0.07649809,0.829717],"study_design_scores_gemma":[0.0005093231,0.00022119701,0.03527711,0.000027888007,0.0000070737724,0.000008289179,0.000013415528,0.61800545,0.0028049343,0.00033948082,0.34246093,0.00032491065],"about_ca_topic_score_codex":0.00019980923,"about_ca_topic_score_gemma":0.000038335016,"teacher_disagreement_score":0.96388763,"about_ca_system_score_codex":0.00043847278,"about_ca_system_score_gemma":0.00015783792,"threshold_uncertainty_score":0.99996096},"labels":[],"label_agreement":null},{"id":"W4392366110","doi":"10.18280/ria.380110","title":"Comparison of Fine-Tuned Networks on Generalization for Face Spoofing Detection","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generalization; Spoofing attack; Computer science; Face (sociological concept); Artificial intelligence; Pattern recognition (psychology); Computer security; Mathematics","score_opus":0.07705725297770775,"score_gpt":0.3404162803428674,"score_spread":0.26335902736515965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392366110","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":[0.0066468827,0.00077551743,0.9900895,0.0003991618,0.0013609063,0.00027844674,0.0000038684248,0.0001506705,0.00029508528],"genre_scores_gemma":[0.9941656,0.000035736397,0.0048297867,0.00003681588,0.00009402922,0.000026543656,0.0000146003895,0.0000098877035,0.00078701443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877715,0.000043391246,0.0004517899,0.00038758805,0.00015471487,0.00018539111],"domain_scores_gemma":[0.99908656,0.00024523167,0.000107168504,0.0003880461,0.00012658403,0.00004638691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004472603,0.00010711211,0.0001627881,0.00033186612,0.00012733943,0.00017801768,0.00038487423,0.00008830385,0.000021661828],"category_scores_gemma":[0.00010875575,0.00010709206,0.000106249616,0.0016412641,0.000033196095,0.00019006782,0.000047562975,0.00011208653,0.000066890294],"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":[0.000014557741,0.00014663549,0.000058624097,0.00015484061,0.000018043422,0.0000011139839,0.0017421992,0.25404212,0.014036095,0.1006594,0.0011028029,0.62802356],"study_design_scores_gemma":[0.000013754034,0.00007545703,0.000025750809,0.000045819226,0.0000049770797,0.0000014964702,0.000064095446,0.73558396,0.25456095,0.00066007476,0.008879718,0.000083923784],"about_ca_topic_score_codex":0.000012261688,"about_ca_topic_score_gemma":0.000012721311,"teacher_disagreement_score":0.9875187,"about_ca_system_score_codex":0.00004907275,"about_ca_system_score_gemma":0.000022807806,"threshold_uncertainty_score":0.43670875},"labels":[],"label_agreement":null},{"id":"W4393144217","doi":"10.1007/978-3-031-47504-7_16","title":"The Matching Lattice and Optimal Ear Decompositions","year":2024,"lang":"en","type":"book-chapter","venue":"Algorithms and computation in mathematics","topic":"Biometric Identification and Security","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":"University of Waterloo","funders":"","keywords":"Matching (statistics); Lattice (music); Computer science; Mathematics; Acoustics; Physics; Statistics","score_opus":0.022246070789752905,"score_gpt":0.27873140022903137,"score_spread":0.2564853294392785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393144217","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":[0.00036756456,0.0037117472,0.96197194,0.0014732397,0.0005200795,0.00034294304,0.000017523138,0.00013715036,0.031457826],"genre_scores_gemma":[0.0040689246,0.0027880028,0.86140054,0.00021738058,0.0001813504,0.000037101898,0.000041451975,0.00007159837,0.13119367],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99893004,0.00001401939,0.00038226787,0.00030238976,0.00024061334,0.00013065926],"domain_scores_gemma":[0.9990635,0.0004624691,0.00013608192,0.00020634207,0.000073817624,0.00005779512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040457497,0.00017047802,0.00018526004,0.0002547761,0.00024913964,0.0008337128,0.00024215603,0.000117531476,0.000004412464],"category_scores_gemma":[0.000009532157,0.0001363991,0.000042198026,0.00015486099,0.000091795846,0.00013788174,0.00023620285,0.00028146475,0.00004487506],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.9046157e-7,0.000010658251,3.3162988e-7,0.000094370764,0.00002451306,0.00000884697,0.0019238127,0.000066314766,7.5102184e-7,0.9693574,0.00018235986,0.028330393],"study_design_scores_gemma":[0.00007071692,0.000011885448,0.000028059483,0.0001039202,0.000017163133,0.000054609794,0.00010192883,0.4419992,8.86445e-7,0.5505041,0.0069738515,0.0001336746],"about_ca_topic_score_codex":0.000008511475,"about_ca_topic_score_gemma":0.000004142769,"teacher_disagreement_score":0.4419329,"about_ca_system_score_codex":0.00003859953,"about_ca_system_score_gemma":0.000034515208,"threshold_uncertainty_score":0.8039512},"labels":[],"label_agreement":null},{"id":"W4393323061","doi":"10.1051/e3sconf/202450701037","title":"Fingerprint-based biometric smart electronic voting machine using IoT and advanced interdisciplinary approaches","year":2024,"lang":"en","type":"article","venue":"E3S Web of Conferences","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Biometrics; Fingerprint (computing); Internet of Things; Computer science; Electronic voting; Voting; Artificial intelligence; Computer security; Political science","score_opus":0.05124489052714802,"score_gpt":0.29307651371735954,"score_spread":0.2418316231902115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393323061","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":[0.46319628,0.008551492,0.52452284,0.0012170998,0.0005424304,0.00018713581,0.000009564298,0.00020252324,0.0015706313],"genre_scores_gemma":[0.99053735,0.000030249248,0.009317927,0.00001912573,0.000016816135,0.000005325541,0.0000054230304,0.0000060178504,0.000061749604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987073,0.00006811388,0.00030078372,0.00042623348,0.0002514349,0.00024614038],"domain_scores_gemma":[0.99931073,0.0002249453,0.00010670798,0.0002324679,0.00006298666,0.00006213323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005856866,0.00013808357,0.00020199241,0.001417808,0.0001102587,0.000293935,0.00049868907,0.000060791164,0.000030598803],"category_scores_gemma":[0.00006350432,0.00011919103,0.000064628926,0.0029627762,0.00012428079,0.00021467438,0.00031628567,0.0001746748,0.0000054975835],"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":[0.000017594988,0.0001566934,0.005870924,0.00043941833,0.00008858385,0.000008001243,0.0006917427,0.00010450145,0.009163059,0.25110397,0.000056088993,0.73229945],"study_design_scores_gemma":[0.00016847506,0.000094571944,0.004277117,0.000104208695,0.000013380362,0.000008283446,0.00008018273,0.98616797,0.0046839807,0.0017217408,0.0025039783,0.0001761383],"about_ca_topic_score_codex":0.000040124196,"about_ca_topic_score_gemma":0.000027809925,"teacher_disagreement_score":0.9860634,"about_ca_system_score_codex":0.00004124263,"about_ca_system_score_gemma":0.00070398534,"threshold_uncertainty_score":0.4860469},"labels":[],"label_agreement":null},{"id":"W4396219654","doi":"10.1007/978-981-97-0700-3_25","title":"A Review on Facial Anti-spoofing Techniques","year":2024,"lang":"en","type":"review","venue":"Lecture notes in networks and systems","topic":"Biometric Identification and Security","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 the Fraser Valley","funders":"","keywords":"Spoofing attack; Computer science; Artificial intelligence; Computer security","score_opus":0.051018711202501066,"score_gpt":0.33671385021238825,"score_spread":0.2856951390098872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396219654","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.253774e-8,0.80800104,0.18929724,0.00008995776,0.0014658774,0.0008871306,0.000007338784,0.0001574798,0.00009389725],"genre_scores_gemma":[0.0001452373,0.9984958,0.00026901715,0.00036428554,0.00050497235,0.00014059136,0.000030777705,0.000024263236,0.000025043333],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974789,0.00034758775,0.00076849456,0.0008629445,0.00023996421,0.00030208807],"domain_scores_gemma":[0.99848896,0.0003098139,0.00026230063,0.0008264635,0.000040420862,0.00007205577],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012851624,0.000404933,0.0015168111,0.0005977323,0.00007879598,0.00056840584,0.00071205763,0.0005185663,0.0000022045394],"category_scores_gemma":[0.0001424545,0.00027081888,0.0002513837,0.0026504623,0.000033343807,0.00007376723,0.00019582601,0.0008518308,0.00002083466],"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":[1.4887885e-7,0.0000094445795,6.8796317e-7,0.032619767,0.000020705893,0.00001786993,0.000018696,0.000010044845,1.5842947e-8,0.0011547009,0.0019336351,0.96421427],"study_design_scores_gemma":[0.000017069691,0.000018075687,3.5870139e-7,0.108383186,0.000079350124,0.00006822103,1.6925388e-7,0.0065364265,8.884948e-8,0.00007512214,0.88456374,0.0002581907],"about_ca_topic_score_codex":0.000037885213,"about_ca_topic_score_gemma":0.000008139738,"teacher_disagreement_score":0.9639561,"about_ca_system_score_codex":0.0000914032,"about_ca_system_score_gemma":0.00007403644,"threshold_uncertainty_score":0.9999744},"labels":[],"label_agreement":null},{"id":"W4396494547","doi":"10.18280/ts.410230","title":"Enhanced Security in Biometrics: A Cancelable Multi-Instance Iris Authentication Utilizing Quotient Filter","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biometrics; IRIS (biosensor); Iris recognition; Computer science; Authentication (law); Quotient; Computer security; Computer vision; Pattern recognition (psychology); Artificial intelligence; Mathematics; Combinatorics","score_opus":0.034368004139552245,"score_gpt":0.2803599408457881,"score_spread":0.24599193670623584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396494547","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":[0.11742908,0.0017433208,0.87770885,0.000737995,0.00095718814,0.00045766053,0.000030674662,0.0003001031,0.00063510315],"genre_scores_gemma":[0.9898036,0.00010392952,0.009468726,0.00017434484,0.000052672145,0.00008688179,0.000021127978,0.000011375711,0.0002773182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977346,0.00009962509,0.0005211062,0.00069795497,0.00054349005,0.0004032282],"domain_scores_gemma":[0.9991774,0.000113056485,0.00009181638,0.00038939802,0.0001134217,0.00011491377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008669734,0.0001871537,0.00018664844,0.0015214066,0.00010743319,0.0004839599,0.0006593363,0.00008468438,0.00026464954],"category_scores_gemma":[0.000045106746,0.00018474672,0.000091683556,0.0056269,0.000055131382,0.00069805887,0.00012266592,0.00020697131,0.000171792],"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":[0.00008747617,0.0044697416,0.0034472523,0.001499783,0.00029441607,0.00018746732,0.06480921,0.0002915073,0.17536302,0.27433124,0.014509562,0.4607093],"study_design_scores_gemma":[0.0014216728,0.00012406547,0.012714038,0.000263189,0.000022854047,0.000007538756,0.0005516077,0.89366984,0.03869037,0.0017856727,0.05005693,0.0006922155],"about_ca_topic_score_codex":0.00022168517,"about_ca_topic_score_gemma":0.00010582978,"teacher_disagreement_score":0.8933783,"about_ca_system_score_codex":0.00034977484,"about_ca_system_score_gemma":0.00012562891,"threshold_uncertainty_score":0.75337523},"labels":[],"label_agreement":null},{"id":"W4396552723","doi":"10.22214/ijraset.2024.60664","title":"Eye Based Secure Authentication System","year":2024,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Computer science; Computer security; Authentication (law)","score_opus":0.04732955192066598,"score_gpt":0.38815031029014324,"score_spread":0.3408207583694773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396552723","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":[0.016383346,0.00037804473,0.9643148,0.015903184,0.0020600494,0.00025906047,0.0000050298286,0.00032170734,0.00037478437],"genre_scores_gemma":[0.97879326,0.00002217834,0.021023493,0.0000151807635,0.00006009975,0.000057298334,0.0000010369063,0.000004495575,0.00002298183],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841636,0.000008403643,0.0001859219,0.00029534215,0.0007955363,0.0002984205],"domain_scores_gemma":[0.9991808,0.00011003343,0.000020631964,0.00016905794,0.0004446766,0.00007482606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038680742,0.000058397407,0.00006506008,0.0048912754,0.00015922007,0.0008750044,0.0014685749,0.000071325674,0.0000014886375],"category_scores_gemma":[0.00025121073,0.000052915595,0.000018179668,0.003591541,0.0001852066,0.0002668786,0.00019174856,0.00040081775,0.000011258147],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026455536,0.000013719462,0.00001720813,0.000035522247,0.0000054862176,0.000017231936,0.00008901364,0.0000951229,0.027289538,0.9275622,0.00016982328,0.04470253],"study_design_scores_gemma":[0.00015811592,0.000022287422,0.00015950242,0.000092379174,7.7102527e-7,0.000071624425,0.00010169068,0.93668157,0.0037103726,0.012366875,0.04656158,0.000073255185],"about_ca_topic_score_codex":0.000002473023,"about_ca_topic_score_gemma":5.2872116e-7,"teacher_disagreement_score":0.9624099,"about_ca_system_score_codex":0.0004298306,"about_ca_system_score_gemma":0.00028479355,"threshold_uncertainty_score":0.8437688},"labels":[],"label_agreement":null},{"id":"W4397000326","doi":"10.1109/scc59637.2023.10527686","title":"Enhancing Biometric Authentication Efficiency: A Hybrid Approach Exploiting Iris Modality and Leveraging One-Class SVM","year":2023,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Biometrics; Iris recognition; Artificial intelligence; Convolutional neural network; Support vector machine; Feature extraction; Machine learning; Pattern recognition (psychology); Modalities; Data mining","score_opus":0.05701446500888871,"score_gpt":0.2665079956918101,"score_spread":0.2094935306829214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397000326","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":[0.2592713,0.00007045694,0.73796576,0.0008598863,0.00016680201,0.00016145146,0.0000020887721,0.0005504275,0.0009518356],"genre_scores_gemma":[0.96777487,0.000026585438,0.03150071,0.00011975704,0.000033589924,0.000025202788,0.000017341685,0.00000899729,0.0004929434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978585,0.000118979515,0.0003881851,0.00070390076,0.0005364798,0.0003939482],"domain_scores_gemma":[0.99880195,0.00021562053,0.00013408135,0.0005778226,0.00013167174,0.00013886901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020375152,0.00014148769,0.00018014721,0.0020846166,0.00036260334,0.00054329314,0.00060100126,0.00005184147,0.000012882969],"category_scores_gemma":[0.0003656434,0.00014174992,0.00005652567,0.009774018,0.000063997984,0.0005702238,0.00041102179,0.00013381906,0.00013545516],"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":[0.000013347493,0.0016839638,0.0070458637,0.0010431971,0.00019389525,0.000026521104,0.029216576,0.0001884317,0.17550737,0.30614123,0.0028921627,0.47604746],"study_design_scores_gemma":[0.00030954217,0.000019830515,0.031098612,0.000016714845,0.0000113540345,0.000015262674,0.00052148267,0.9453917,0.017126659,0.0044795508,0.0006709308,0.0003383498],"about_ca_topic_score_codex":0.00012744835,"about_ca_topic_score_gemma":0.0000011923258,"teacher_disagreement_score":0.9452033,"about_ca_system_score_codex":0.00007849826,"about_ca_system_score_gemma":0.000058828733,"threshold_uncertainty_score":0.57803935},"labels":[],"label_agreement":null},{"id":"W4399392634","doi":"10.1007/s13042-024-02232-1","title":"Different gaze direction (DGNet) collaborative learning for iris segmentation","year":2024,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Biometric Identification and Security","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":"Simon Fraser University","funders":"","keywords":"IRIS (biosensor); Gaze; Artificial intelligence; Computer science; Computational intelligence; Segmentation; Computer vision; Pattern recognition (psychology); Biometrics","score_opus":0.010080295198782754,"score_gpt":0.2948244639995781,"score_spread":0.2847441688007954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399392634","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":[0.103129454,0.006245587,0.8840841,0.0033941667,0.0025079595,0.00011423824,0.0000067905175,0.00008608563,0.0004315898],"genre_scores_gemma":[0.98809695,0.0016294891,0.007990823,0.00004660325,0.00024836874,0.000003766459,0.000017302866,0.000008702882,0.0019579625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896806,0.000112763555,0.00030184668,0.0001529402,0.00037614763,0.0000882089],"domain_scores_gemma":[0.9989435,0.00028351208,0.00021104154,0.000039294275,0.00046514292,0.000057475318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044025254,0.00009133906,0.00011398165,0.00039220313,0.0000901885,0.0005499995,0.00022446457,0.000044598935,0.000019977215],"category_scores_gemma":[0.0002520045,0.00007619247,0.00006947332,0.0002804861,0.000024728437,0.0002468887,0.000055218457,0.00031290812,0.000004007884],"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":[0.00008955641,0.00014777618,0.011705912,0.000058892194,0.00041969496,0.00003589285,0.0055643604,0.0022448823,0.007112565,0.019522928,0.0019901162,0.95110744],"study_design_scores_gemma":[0.0009547075,0.000549558,0.009781924,0.00015234455,0.000051656596,0.0002021856,0.0003315955,0.5646303,0.0026107524,0.0023848158,0.41813165,0.00021846556],"about_ca_topic_score_codex":0.000012551603,"about_ca_topic_score_gemma":0.0000040852747,"teacher_disagreement_score":0.95088893,"about_ca_system_score_codex":0.00007791518,"about_ca_system_score_gemma":0.000043182343,"threshold_uncertainty_score":0.53036577},"labels":[],"label_agreement":null},{"id":"W4401508449","doi":"10.1109/infocom52122.2024.10621407","title":"Utility-Preserving Face Anonymization via Differentially Private Feature Operations","year":2024,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; McMaster University","funders":"","keywords":"Computer science; Face (sociological concept); Feature (linguistics); Artificial intelligence; Data mining; Pattern recognition (psychology)","score_opus":0.018987108904704068,"score_gpt":0.26314794530867164,"score_spread":0.24416083640396757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401508449","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":[0.0048104543,0.00031238882,0.98513645,0.0057818973,0.00078916026,0.00015855177,0.000004719387,0.0005347545,0.0024716093],"genre_scores_gemma":[0.95061904,0.000030874326,0.036143918,0.00022658172,0.000053518288,0.000008295761,0.000041067946,0.0000066591774,0.012870027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899423,0.00006374704,0.00015594273,0.00038340234,0.0002510977,0.00015160322],"domain_scores_gemma":[0.99926865,0.000038064405,0.000013582013,0.0005269393,0.00008419189,0.00006854437],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00017686753,0.0000951025,0.00007886976,0.00027363218,0.0001714362,0.001235894,0.00070818525,0.00007789979,0.00032220857],"category_scores_gemma":[0.00005117998,0.00007932909,0.000051703977,0.0017111326,0.00001943398,0.00081156334,0.00029189046,0.0001379229,0.00018341761],"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":[0.0000025362474,0.00017857255,0.00054756267,0.00013885114,0.00006770326,0.000011831122,0.0020504051,0.00019602652,0.018426763,0.7887792,0.037621647,0.15197888],"study_design_scores_gemma":[0.000058588146,0.0000071507066,0.00667102,0.000010124683,0.0000047683548,0.0000046677283,0.000009071196,0.92845654,0.0037552747,0.0017339718,0.05917154,0.000117266114],"about_ca_topic_score_codex":0.000040575447,"about_ca_topic_score_gemma":0.000082056154,"teacher_disagreement_score":0.94899255,"about_ca_system_score_codex":0.000020771467,"about_ca_system_score_gemma":0.000051375766,"threshold_uncertainty_score":0.9998009},"labels":[],"label_agreement":null},{"id":"W4402063433","doi":"10.1016/b978-0-443-13223-0.00072-2","title":"Biometrics","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Biometric Identification and Security","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":"Simon Fraser University","funders":"","keywords":"Biometrics; Computer science; Computer security","score_opus":0.026563007372054404,"score_gpt":0.253332378501814,"score_spread":0.2267693711297596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402063433","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.910089e-7,0.0057979375,0.0022154155,0.00034669906,0.0026569068,0.00023252408,0.00003212844,0.00040203697,0.98831594],"genre_scores_gemma":[0.0001153676,0.0001949349,0.0030022787,0.0003602463,0.0002276994,0.000010793716,0.000016951182,0.000042810476,0.9960289],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979284,0.00001155303,0.00043107834,0.0007503371,0.00062173215,0.00025689576],"domain_scores_gemma":[0.9982686,0.00007129839,0.00016107835,0.0011818939,0.00015381585,0.00016330797],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038954685,0.0003226115,0.00033211664,0.002509416,0.000089947695,0.00054764404,0.0013999842,0.0003796032,0.00014993403],"category_scores_gemma":[0.000024998591,0.00030066734,0.00028195028,0.0004228571,0.000094215815,0.000085551066,0.00057130115,0.0005054782,0.005334363],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.0199238e-7,0.00000293343,9.963062e-8,0.00004210476,0.000028770506,0.00002547879,0.000053593616,8.140957e-9,0.000002954386,0.3771088,0.0015974557,0.6211376],"study_design_scores_gemma":[0.000046979538,0.000016760183,0.0000024387773,0.0000689668,0.00002728245,0.000019190105,6.359469e-7,0.00013355642,0.000027507567,0.111502886,0.88784915,0.0003046522],"about_ca_topic_score_codex":3.1201918e-7,"about_ca_topic_score_gemma":0.0000021383187,"teacher_disagreement_score":0.8862517,"about_ca_system_score_codex":0.00012395348,"about_ca_system_score_gemma":0.00014393528,"threshold_uncertainty_score":0.99994457},"labels":[],"label_agreement":null},{"id":"W4402362360","doi":"10.62036/isd.2024.54","title":"Finger Vein Presentation Attack Detection Method Using a Hybridized Gray-Level Co-Occurrence Matrix Feature with Light-Gradient Boosting Machine Model","year":2024,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Information Systems Development","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Computer science; Gray level; Gradient boosting; Co-occurrence matrix; Pattern recognition (psychology); Boosting (machine learning); Computer vision; Feature extraction; Gray (unit); Pixel; Image processing; Image (mathematics); Radiology; Medicine; Image texture","score_opus":0.0738367536876496,"score_gpt":0.33142293864267824,"score_spread":0.25758618495502866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402362360","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":[0.043688465,0.00005254611,0.9485205,0.0016922174,0.0014525328,0.0008086239,0.00010510079,0.00019913627,0.0034809075],"genre_scores_gemma":[0.952393,0.000008549567,0.04685635,0.00007684039,0.000029603265,0.00008137337,0.000043688986,0.0000069129314,0.0005036815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763376,0.000020373296,0.0006621038,0.0002948719,0.0012011927,0.00018769188],"domain_scores_gemma":[0.997975,0.000049778522,0.0006011919,0.00014208881,0.0011668957,0.000065066866],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00094532984,0.00020825576,0.00018591505,0.0007202604,0.00022116509,0.0012852415,0.0009186587,0.00007868496,0.000005563888],"category_scores_gemma":[0.0001083029,0.00014767417,0.00006355523,0.00085426733,0.000026132751,0.002144636,0.00017323406,0.00025004992,0.000022502783],"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":[0.000579586,0.00044431235,0.0020647668,0.004437403,0.0012413561,0.000004597528,0.06278471,0.025679277,0.08509177,0.6913861,0.0173443,0.10894182],"study_design_scores_gemma":[0.00026720203,0.000023542845,0.00028217462,0.00051366107,0.000010630157,0.00005829639,0.0003002893,0.9544856,0.034857545,0.0001422641,0.008861068,0.00019772071],"about_ca_topic_score_codex":0.000033090757,"about_ca_topic_score_gemma":0.0000031289123,"teacher_disagreement_score":0.9288063,"about_ca_system_score_codex":0.00041057842,"about_ca_system_score_gemma":0.00032084514,"threshold_uncertainty_score":0.9997515},"labels":[],"label_agreement":null},{"id":"W4402632135","doi":"10.1007/978-3-031-71602-7_18","title":"Palmprint Classification via Filter Faces and Feature Extraction","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Computer vision; Filter (signal processing)","score_opus":0.027984307750574915,"score_gpt":0.27302305516569564,"score_spread":0.24503874741512072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402632135","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":[0.00008862635,0.0014070091,0.9891927,0.0039526564,0.0025060778,0.00028424128,0.000006019394,0.00019172997,0.0023709333],"genre_scores_gemma":[0.6445637,0.000541275,0.34429756,0.0015451505,0.00080647605,0.000028797353,0.000032960397,0.00005733086,0.008126777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99708766,0.00002334385,0.00034062943,0.0014754524,0.00073743676,0.0003354641],"domain_scores_gemma":[0.99832386,0.00020123017,0.00019728833,0.0009487988,0.00019100252,0.0001378211],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00069053646,0.00035163603,0.00028203975,0.0013685882,0.0002056813,0.0012437104,0.0013568908,0.00039731592,0.000022851327],"category_scores_gemma":[0.000042302432,0.00030923987,0.00008164156,0.0010520633,0.00042543365,0.0006190633,0.0006760534,0.00087548373,0.00011477191],"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":[0.000002119538,0.000019627454,0.00003024153,0.00007047483,0.000010134835,0.00002483512,0.00058228266,0.00014045628,0.0011926631,0.050532788,0.00013482147,0.94725955],"study_design_scores_gemma":[0.00013799773,0.000071505354,0.0026699337,0.00021936613,0.000014860634,0.0001701501,5.1468174e-7,0.72031796,0.0017241556,0.23497488,0.039017193,0.0006814778],"about_ca_topic_score_codex":0.000015886082,"about_ca_topic_score_gemma":0.000039370847,"teacher_disagreement_score":0.9465781,"about_ca_system_score_codex":0.0002207449,"about_ca_system_score_gemma":0.00015687897,"threshold_uncertainty_score":0.999936},"labels":[],"label_agreement":null},{"id":"W4402690151","doi":"10.3233/faia240370","title":"Hands and Palms Recognition by Transfer Learning for Forensics: A Comparative Study","year":2024,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton; Université Laval","funders":"","keywords":"Palm; Computer science; Physics; Astronomy","score_opus":0.08335853146016137,"score_gpt":0.30739585509180534,"score_spread":0.22403732363164397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402690151","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":[0.00018662805,0.001972307,0.9907698,0.00022705721,0.00021389796,0.001566526,0.00007015966,0.00007067714,0.004922952],"genre_scores_gemma":[0.7938279,0.007090754,0.048095312,0.00037300208,0.00078115484,0.0054739653,0.0014018203,0.00015818789,0.14279789],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985838,0.000018091087,0.00043384384,0.00065476843,0.00014843994,0.00016104062],"domain_scores_gemma":[0.9994411,0.000096611504,0.00006633406,0.00020906643,0.000112275964,0.00007461796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029273404,0.0002043284,0.00029575956,0.00043634576,0.00021808919,0.0003152738,0.00023073873,0.00016274558,0.0000082105435],"category_scores_gemma":[0.000008339484,0.00021263321,0.000057419566,0.00029145452,0.00017072783,0.00014008237,0.00005593619,0.000323221,0.000025739213],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013682575,0.000085226115,0.000023629294,0.00005146553,0.000055543434,8.295981e-7,0.0030077614,0.000007131308,0.000016300186,0.31180274,0.0025246537,0.682411],"study_design_scores_gemma":[0.000079286634,0.00022653237,0.00000775691,0.00006208404,0.000072833645,0.000002266172,0.0019008964,0.027655026,0.0005265964,0.81194097,0.15713026,0.00039548345],"about_ca_topic_score_codex":0.000014155318,"about_ca_topic_score_gemma":0.00005796759,"teacher_disagreement_score":0.94267446,"about_ca_system_score_codex":0.000038390193,"about_ca_system_score_gemma":0.00002808656,"threshold_uncertainty_score":0.8670931},"labels":[],"label_agreement":null},{"id":"W4402811598","doi":"10.1109/iccc62479.2024.10681904","title":"Establishing Secure Region for Covert Communication Based on Frequency Diverse Array","year":2024,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Science Foundation of Fujian Province; Hainan University; National Natural Science Foundation of China","keywords":"Covert; Computer science; Computer security; Computer network","score_opus":0.038303803275472996,"score_gpt":0.27628061691174877,"score_spread":0.23797681363627576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402811598","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":[0.0004149812,0.00020494217,0.98186076,0.0063969633,0.00059823075,0.00023713247,0.00000809715,0.00036151893,0.009917403],"genre_scores_gemma":[0.9375397,0.00003085571,0.06015421,0.0012257983,0.000042693777,0.00003266134,0.00005231063,0.000007358613,0.0009144275],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991351,0.000058438167,0.00014885265,0.00031104186,0.000214002,0.00013258525],"domain_scores_gemma":[0.9987026,0.00037929235,0.00003810758,0.00072320085,0.000102777245,0.000054042488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040486045,0.000081962244,0.00007224532,0.0003362361,0.00017249171,0.0007252076,0.0007646398,0.0000700485,0.000030617113],"category_scores_gemma":[0.00013661325,0.00007186011,0.000072883624,0.0011141874,0.000024677387,0.0007692285,0.000049254693,0.00012341171,0.00005880609],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008502103,0.0001019058,0.00012996068,0.00009711395,0.000010374487,0.000003932357,0.0010141787,0.000023921135,0.00033101797,0.87329954,0.08007423,0.044905342],"study_design_scores_gemma":[0.0004980232,0.00011802798,0.0005991674,0.00017038643,0.000012419748,0.000004861424,0.00016616932,0.7252723,0.0023373032,0.02685907,0.24363193,0.00033034958],"about_ca_topic_score_codex":0.00011207419,"about_ca_topic_score_gemma":0.00002689094,"teacher_disagreement_score":0.9371247,"about_ca_system_score_codex":0.00008476124,"about_ca_system_score_gemma":0.00007391719,"threshold_uncertainty_score":0.69931936},"labels":[],"label_agreement":null},{"id":"W4403052684","doi":"10.1109/mwc.015.2300550","title":"New Frontier of Communication Security on Radio Frequency Fingerprints Concealment","year":2024,"lang":"en","type":"article","venue":"IEEE Wireless Communications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Computer security; Frontier; Telecommunications; Computer network","score_opus":0.03305879344446542,"score_gpt":0.2943349567157282,"score_spread":0.26127616327126274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403052684","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":[0.030983882,0.016419716,0.9049878,0.022058113,0.0021324882,0.0009111047,0.00009183309,0.0009828215,0.021432232],"genre_scores_gemma":[0.9571653,0.0018759804,0.0402262,0.00013389604,0.000027377244,0.000041629402,0.000040143324,0.000013722095,0.00047577277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982073,0.0003363335,0.00053295586,0.0003486459,0.00037535935,0.00019945958],"domain_scores_gemma":[0.9943983,0.00046310967,0.00016978766,0.0046645296,0.00017320091,0.00013110424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005831084,0.0001618504,0.0002306937,0.00043421617,0.00025522272,0.00024503347,0.004104817,0.00011655861,0.000050776256],"category_scores_gemma":[0.000043322274,0.00016622573,0.00013064734,0.0015780241,0.00019564369,0.00045269608,0.0004221406,0.00043213254,0.0001875497],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027963206,0.00025168995,0.00022233176,0.00003796853,0.00007117445,0.0000011579492,0.004061145,0.000011882614,0.0014527622,0.8922095,0.014286353,0.08739128],"study_design_scores_gemma":[0.0017525563,0.0002896148,0.01559826,0.0014891536,0.00014444368,0.00004376394,0.000554306,0.40424377,0.03389379,0.2086026,0.3315969,0.0017908097],"about_ca_topic_score_codex":0.0005350626,"about_ca_topic_score_gemma":0.00008303459,"teacher_disagreement_score":0.9261814,"about_ca_system_score_codex":0.00016579374,"about_ca_system_score_gemma":0.00029912646,"threshold_uncertainty_score":0.76278394},"labels":[],"label_agreement":null},{"id":"W4403289372","doi":"10.1016/j.forsciint.2024.112244","title":"Examiner consistency in perceptions of fingerprint minutia rarity","year":2024,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Institute of Standards and Technology; Iowa State University; University of California, Irvine; University of Virginia; University of Nebraska-Lincoln; West Virginia University; University of Pennsylvania; Center for Statistics and Applications in Forensic Evidence; Swarthmore College; Carnegie Mellon University; Duke University","keywords":"Minutiae; Fingerprint (computing); Consistency (knowledge bases); Perception; Medicine; Computer science; Psychology; Computer security; Artificial intelligence; Fingerprint recognition; Neuroscience","score_opus":0.023652968736374284,"score_gpt":0.2973523762522613,"score_spread":0.273699407515887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403289372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81900775,0.00016182446,0.15835881,0.0032221542,0.0047487896,0.00013808116,0.000019917507,0.00011488741,0.014227758],"genre_scores_gemma":[0.9845726,0.000009746074,0.01489213,0.000074148025,0.000034569108,0.000006536388,0.0000039697125,0.0000019072146,0.00040443148],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9985744,0.000017600842,0.00027759175,0.00037940376,0.0005972197,0.00015382035],"domain_scores_gemma":[0.9992812,0.00009350269,0.000043207558,0.00029203467,0.00023761242,0.000052424763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008263912,0.00006505693,0.00008165234,0.0011232098,0.00005684523,0.00025454126,0.0010586106,0.00003139319,0.00016736684],"category_scores_gemma":[0.0002774297,0.000058878333,0.000053557516,0.002379363,0.00050160004,0.00071996084,0.00024637225,0.00010883081,0.00007503524],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002272022,0.00010703581,0.009639839,0.000012120548,0.0000085869915,0.000014396411,0.0028652712,0.000025146492,0.009174934,0.8931819,0.0012551585,0.083713345],"study_design_scores_gemma":[0.0002476501,0.000047598354,0.60324544,0.0001159522,0.000004380757,0.000075790704,0.00030055034,0.34945434,0.007982088,0.026657905,0.011610158,0.00025816823],"about_ca_topic_score_codex":0.00014514013,"about_ca_topic_score_gemma":0.0000727101,"teacher_disagreement_score":0.866524,"about_ca_system_score_codex":0.00016262579,"about_ca_system_score_gemma":0.00027642932,"threshold_uncertainty_score":0.24545471},"labels":[],"label_agreement":null},{"id":"W4404359167","doi":"10.18280/ts.410538","title":"Discrete Cosine Transform-Based Kernel Discriminant Analysis for Enhanced Biometric Recognition","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biometrics; Discrete cosine transform; Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Kernel (algebra); Computer science; Lapped transform; Discriminant; Mathematics; Modified discrete cosine transform; Speech recognition; Transform coding; Image (mathematics); Pure mathematics","score_opus":0.03863323725399241,"score_gpt":0.2831849754690255,"score_spread":0.24455173821503307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404359167","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":[0.010436552,0.0002872314,0.9859718,0.0017657646,0.00029532658,0.0005224803,0.00020845914,0.00024063858,0.00027174407],"genre_scores_gemma":[0.98494893,0.00002479187,0.013926776,0.00017303148,0.00007748283,0.0001801268,0.00046732364,0.000011464028,0.00019009897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814504,0.000053976455,0.00044661007,0.0005859836,0.00045250976,0.00031588637],"domain_scores_gemma":[0.999199,0.00020707349,0.00007319043,0.0002598791,0.00013773555,0.00012315337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006719085,0.00018395706,0.00023651197,0.0026128688,0.0001650796,0.000531026,0.00045301314,0.0000672807,0.000246298],"category_scores_gemma":[0.000024883704,0.00015448306,0.00038225492,0.0090331165,0.00004764997,0.00044452108,0.000022788357,0.000084112704,0.000053291238],"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":[0.0001346428,0.0008675305,0.00013124403,0.0006787402,0.0012690309,0.000017773658,0.00229555,0.00032502974,0.05005936,0.01619475,0.0024232208,0.92560315],"study_design_scores_gemma":[0.001296964,0.0003764112,0.0073333536,0.000077376775,0.00069058174,0.0000020804443,0.00005792526,0.8680075,0.1050394,0.0028928032,0.013663379,0.00056226965],"about_ca_topic_score_codex":0.00003352273,"about_ca_topic_score_gemma":0.000020194959,"teacher_disagreement_score":0.97451234,"about_ca_system_score_codex":0.00010254745,"about_ca_system_score_gemma":0.00007921296,"threshold_uncertainty_score":0.62996364},"labels":[],"label_agreement":null},{"id":"W4405086841","doi":"10.2196/54921","title":"Long-Term Experiences of Health Care Providers Using Iris Scanning as an Identification Tool in a Vaccine Trial in the Democratic Republic of the Congo: Qualitative Study","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprint; Democracy; Identification (biology); Qualitative research; Coronavirus disease 2019 (COVID-19); Health care; Tanzania; Medicine; Political science; Socioeconomics; Disease; Sociology; Biology; Law; Computer science; Internal medicine; Social science","score_opus":0.15333309572380557,"score_gpt":0.5162591568118162,"score_spread":0.3629260610880106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405086841","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932997,0.00037135882,0.0027216626,0.0007065085,0.00019102468,0.0026681286,0.0000034026993,0.000013898728,0.000024275156],"genre_scores_gemma":[0.99943936,0.000005597923,0.00008195681,0.00001880987,0.000011958539,0.0004175549,0.000004882201,0.000004268879,0.00001559468],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9920062,0.005158616,0.00094665465,0.00034004133,0.0012620502,0.00028646816],"domain_scores_gemma":[0.9979744,0.00066195463,0.00028204493,0.0006154992,0.0004369175,0.000029186363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011702218,0.00009488837,0.0002268893,0.001203517,0.00025180384,0.00044074678,0.0012506471,0.0000423382,0.0000066214984],"category_scores_gemma":[0.0005512149,0.000058543093,0.000055139935,0.0068934252,0.00017764738,0.0015966245,0.00022598886,0.00037641978,0.0000018227081],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106123865,0.00035427624,0.0005521758,0.0001900627,0.000009742042,0.0000014474361,0.9896642,0.0000028096051,0.00007178398,0.0019888186,0.000071024406,0.0069875205],"study_design_scores_gemma":[0.0020281295,0.0008152795,0.052088674,0.00016270325,0.0000017746336,0.0000027052436,0.9345484,0.009084306,0.00037124028,0.00081355055,0.000006027592,0.000077204866],"about_ca_topic_score_codex":0.00070252916,"about_ca_topic_score_gemma":0.00021712275,"teacher_disagreement_score":0.055115808,"about_ca_system_score_codex":0.00027129462,"about_ca_system_score_gemma":0.00078984257,"threshold_uncertainty_score":0.42501312},"labels":[],"label_agreement":null},{"id":"W4405303824","doi":"10.1109/tbiom.2024.3516634","title":"A Deep CNN-Based Feature Extraction and Matching of Pores for Fingerprint Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Biometrics Behavior and Identity Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fingerprint (computing); Pattern recognition (psychology); Artificial intelligence; Matching (statistics); Feature extraction; Computer science; Feature (linguistics); Extraction (chemistry); Mathematics; Chemistry; Chromatography; Statistics; Philosophy","score_opus":0.04040861572360703,"score_gpt":0.3303098214017909,"score_spread":0.28990120567818384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405303824","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":[0.19440843,0.00029732025,0.80362135,0.00020701182,0.0010798727,0.000253179,0.00004208009,0.00008126089,0.000009512192],"genre_scores_gemma":[0.96527016,0.00016930167,0.03440876,0.00003318535,0.000011795336,0.000044677694,0.000003231032,0.000005843015,0.00005306091],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984642,0.000024469984,0.00023291532,0.0005439783,0.00053570134,0.00019868181],"domain_scores_gemma":[0.9990766,0.00021993497,0.00007989032,0.00024033486,0.00025924115,0.0001239984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010232556,0.0001191612,0.00012956331,0.004580627,0.0004330031,0.0009787777,0.0003413092,0.00009267442,0.0000081808475],"category_scores_gemma":[0.000049505175,0.00011695685,0.0000798402,0.009363505,0.00026167874,0.0017893278,0.0000070579154,0.00017313521,0.0000047993294],"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":[0.000013914562,0.00041545395,0.00015455828,0.0002316136,0.000010970371,0.000005453605,0.0005146522,0.000048186565,0.09384731,0.0018332688,0.000034515233,0.9028901],"study_design_scores_gemma":[0.0014646873,0.0009403115,0.092317455,0.00033295294,0.0003564118,0.00016028194,0.00045941072,0.3682148,0.52512544,0.008037771,0.0013397334,0.0012507272],"about_ca_topic_score_codex":0.00011658334,"about_ca_topic_score_gemma":0.000032428637,"teacher_disagreement_score":0.9016394,"about_ca_system_score_codex":0.000084701336,"about_ca_system_score_gemma":0.00010707745,"threshold_uncertainty_score":0.94383746},"labels":[],"label_agreement":null},{"id":"W4405790736","doi":"10.30564/aia.v6i1.8128","title":"A Novel Fingerprint Recognition Framework with Attention Mechanism Based on Domain Adaptation for Improving Applicability in Overpressured Situations","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Advances","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Computer science; Generalizability theory; Robustness (evolution); Fingerprint (computing); Domain adaptation; Artificial intelligence; Adaptation (eye); Domain (mathematical analysis); Biometrics; Feature (linguistics); Fingerprint recognition; Machine learning; Word error rate; Reliability (semiconductor); Data mining; Pattern recognition (psychology)","score_opus":0.05617945385564249,"score_gpt":0.3135405589163647,"score_spread":0.2573611050607222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405790736","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":[0.007032948,0.000106217485,0.9903081,0.0010364444,0.0005445789,0.00072636124,0.000026655865,0.00017373769,0.000044978686],"genre_scores_gemma":[0.7298016,0.000008471249,0.26973668,0.00008757999,0.000051982803,0.00027988505,0.000022463117,0.000007255075,0.0000040661444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984065,0.000051001087,0.0003815847,0.00063442875,0.00030935757,0.00021710891],"domain_scores_gemma":[0.9987171,0.00066712475,0.00010470156,0.0003065003,0.00015558332,0.000048987207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007811057,0.00013741378,0.00012721213,0.00041652087,0.00015934191,0.00033225198,0.0002784823,0.00008889367,0.000009408873],"category_scores_gemma":[0.00028908826,0.00012743221,0.00006642069,0.0015686357,0.00005287657,0.0005982633,0.00002807193,0.00017761659,0.000032892225],"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":[0.00005496386,0.00020192914,0.00000882292,0.00007068024,0.000004208833,0.0000011258564,0.0006583466,0.00378672,0.0058680363,0.26656023,6.9008576e-7,0.7227842],"study_design_scores_gemma":[0.000030689014,0.00009672859,0.00011403218,0.000085469015,0.0000053801873,6.393876e-7,0.00024588977,0.5765275,0.013354254,0.4092826,0.00013226234,0.0001246049],"about_ca_topic_score_codex":0.000073652955,"about_ca_topic_score_gemma":0.00037768768,"teacher_disagreement_score":0.72276866,"about_ca_system_score_codex":0.00010874201,"about_ca_system_score_gemma":0.0001007955,"threshold_uncertainty_score":0.5196535},"labels":[],"label_agreement":null},{"id":"W4405908418","doi":"10.1109/wf-iot62078.2024.10811233","title":"Palmprint Biometrics: Online Learning with Differential Evolution and Contrastive Representation","year":2024,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Ministry of Education","keywords":"Biometrics; Computer science; Representation (politics); Artificial intelligence; Natural language processing","score_opus":0.01938358418100016,"score_gpt":0.27300844230696697,"score_spread":0.2536248581259668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405908418","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":[0.14145052,0.00030165445,0.8570301,0.0004456228,0.00020978242,0.00008324211,0.0000023411646,0.00023052646,0.0002462036],"genre_scores_gemma":[0.99251324,0.000045121094,0.006615917,0.000015967007,0.000038386268,0.0000042278543,0.000016364025,0.0000036679617,0.0007470804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991548,0.00004872473,0.00012619195,0.00033835002,0.00022215884,0.00010979601],"domain_scores_gemma":[0.99957764,0.00011585766,0.000030848063,0.00012994134,0.00008855103,0.00005718307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012607608,0.00007001569,0.00007593088,0.000806964,0.00008029961,0.00041341083,0.00012644738,0.000036664864,0.000028571521],"category_scores_gemma":[0.00007240812,0.000052010924,0.00002023631,0.0028120042,0.000044211465,0.00033016517,0.00008303082,0.000120013916,0.00001854227],"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":[0.000022782373,0.0002282041,0.016908402,0.0000817011,0.000120422985,0.000028370336,0.0016736761,0.000044732635,0.00831235,0.39048448,0.00043312678,0.58166176],"study_design_scores_gemma":[0.000362512,0.0001338466,0.2836113,0.000026286863,0.00001644534,0.000030210402,0.0002754221,0.7100528,0.0013715525,0.0014633199,0.002474909,0.0001814286],"about_ca_topic_score_codex":0.000087010965,"about_ca_topic_score_gemma":0.000011394553,"teacher_disagreement_score":0.8510628,"about_ca_system_score_codex":0.000045943587,"about_ca_system_score_gemma":0.000026440082,"threshold_uncertainty_score":0.39865303},"labels":[],"label_agreement":null},{"id":"W4405982974","doi":"10.1007/978-3-031-74491-4_18","title":"Vehicle Anti-Theft Systems Using Vision Transformer and Iris Identification","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Biometric Identification and Security","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":"Kingston Health Sciences Centre","funders":"","keywords":"Iris recognition; Identification (biology); Computer science; Computer vision; IRIS (biosensor); Transformer; Artificial intelligence; Biometrics; Computer security; Engineering; Electrical engineering","score_opus":0.020777450984457248,"score_gpt":0.2562958646704098,"score_spread":0.23551841368595258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405982974","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":[0.000985967,0.092092685,0.900237,0.00019288756,0.0037065025,0.0007518636,0.000019279776,0.00011736471,0.0018964018],"genre_scores_gemma":[0.9922566,0.0025133116,0.00014480874,0.00003990559,0.0004389612,0.000012711061,0.000029794439,0.000042672174,0.0045212684],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774593,0.0000695131,0.00071610365,0.00081841694,0.00037028443,0.0002797439],"domain_scores_gemma":[0.99885434,0.00019099624,0.0002456562,0.00050310296,0.00010145817,0.00010446544],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00082066155,0.00034414858,0.0005277662,0.0006019597,0.00016777148,0.001415097,0.00032378224,0.00067521085,0.000003609153],"category_scores_gemma":[0.000013995681,0.00029511083,0.000083086146,0.00040046533,0.00009204134,0.00024705782,0.00007881921,0.00053937564,0.000010149836],"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":[0.000043999782,0.00012908854,0.0010810256,0.0068628956,0.0005858115,0.0002437719,0.004933395,0.12296132,0.0033988906,0.62424225,0.0017214118,0.23379615],"study_design_scores_gemma":[0.00015804544,0.000027234499,0.00016056714,0.00094096846,0.000044040044,0.000098561795,0.000007778947,0.96559656,0.000009333933,0.002672933,0.029906634,0.00037732633],"about_ca_topic_score_codex":0.00023623208,"about_ca_topic_score_gemma":0.000032176536,"teacher_disagreement_score":0.9912706,"about_ca_system_score_codex":0.00009328138,"about_ca_system_score_gemma":0.000032460943,"threshold_uncertainty_score":0.9999501},"labels":[],"label_agreement":null},{"id":"W4406016024","doi":"10.18280/ijsse.140612","title":"A Comprehensive Method for Fingerprint Classification Based on Gabor Filters and Machine Learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fingerprint (computing); Artificial intelligence; Computer science; Pattern recognition (psychology); Gabor filter; Machine learning; Feature extraction","score_opus":0.021787936020938238,"score_gpt":0.28253432376211685,"score_spread":0.2607463877411786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406016024","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":[0.0028574262,0.0009654031,0.9909699,0.0042610234,0.00079860934,0.00006368256,0.000017291171,0.000038786566,0.000027891918],"genre_scores_gemma":[0.93441314,0.00041654403,0.064854205,0.00017121853,0.00011292598,0.0000025720221,0.0000075366165,0.0000070112487,0.000014850558],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920154,0.000033059598,0.00027562468,0.0001613785,0.00024470512,0.00008369035],"domain_scores_gemma":[0.9989944,0.0005675568,0.00008789827,0.000065572895,0.00021822519,0.00006631331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051465747,0.000087328735,0.00012192067,0.0004852466,0.00004733025,0.00024602978,0.00022905284,0.00004375684,0.000004645949],"category_scores_gemma":[0.00015520118,0.000080353806,0.000066542656,0.00019583396,0.000013766836,0.00024778515,0.000044549626,0.00024090683,7.2687453e-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":[0.0004812125,0.00020701524,0.00029416315,0.00069642917,0.00056162337,0.0001198592,0.0064215693,0.06591355,0.013015156,0.36170283,0.0003882597,0.5501983],"study_design_scores_gemma":[0.00029994507,0.00006182141,0.0016347475,0.00010631259,0.0000070613764,0.0000617879,0.000022831735,0.91751283,0.00032630938,0.00039179684,0.07949948,0.00007506909],"about_ca_topic_score_codex":0.000005717554,"about_ca_topic_score_gemma":5.8990656e-7,"teacher_disagreement_score":0.9315557,"about_ca_system_score_codex":0.00005789483,"about_ca_system_score_gemma":0.000031855652,"threshold_uncertainty_score":0.32767332},"labels":[],"label_agreement":null},{"id":"W4406257159","doi":"10.1007/978-3-030-71522-9_730","title":"Biometrics: Terms and Definitions","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Biometric Identification and Security","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":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Computer security","score_opus":0.05440030538304853,"score_gpt":0.24342726427078495,"score_spread":0.1890269588877364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406257159","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.5138146e-7,0.00059421326,0.20444024,0.0007482043,0.0003393177,0.00008588775,0.00003144901,0.00014024244,0.7936201],"genre_scores_gemma":[0.0004949315,0.0015037834,0.024333343,0.00075564714,0.000029284733,0.000004827044,0.000037208883,0.000006396263,0.9728346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990373,0.0000068256536,0.00021881869,0.00041110348,0.00021263801,0.00011331726],"domain_scores_gemma":[0.9990594,0.00013634517,0.00008061819,0.00054957846,0.00009513604,0.00007896318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013793686,0.00014750217,0.00016835755,0.0025959467,0.00010628165,0.00030236453,0.0005226559,0.00021119055,0.00022532117],"category_scores_gemma":[0.000047847745,0.00013646026,0.000069463626,0.00071595813,0.00006861367,0.00012587795,0.0003426842,0.00015606284,0.0002440759],"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":[1.16851815e-7,0.000006784838,0.0000032868447,0.000015532747,0.000010871198,0.0000024683725,0.000011534187,2.764291e-9,0.0000010891895,0.8957187,0.023523284,0.08070634],"study_design_scores_gemma":[0.000073047886,0.000010843417,0.00010941431,0.000020772319,0.0000112189355,0.0000074863674,8.9728934e-7,0.00026709086,0.000011872862,0.19540128,0.8039112,0.00017484344],"about_ca_topic_score_codex":0.00001067005,"about_ca_topic_score_gemma":0.000004740794,"teacher_disagreement_score":0.78038794,"about_ca_system_score_codex":0.00003157163,"about_ca_system_score_gemma":0.00006095071,"threshold_uncertainty_score":0.5564688},"labels":[],"label_agreement":null},{"id":"W4406257415","doi":"10.1007/978-3-030-71522-9_731","title":"Biometrics for Forensics","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Biometric Identification and Security","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":"University of Toronto","funders":"","keywords":"Biometrics; Computer science; Computer security","score_opus":0.039837912103223905,"score_gpt":0.2660888204868983,"score_spread":0.2262509083836744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406257415","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.463831e-8,0.00028747183,0.55394673,0.0005224743,0.0008619013,0.00019075394,0.00003536012,0.00011128702,0.44404396],"genre_scores_gemma":[0.000017922033,0.00013016931,0.13430849,0.00067802885,0.00006196728,0.000008958938,0.000060318387,0.000008490116,0.86472565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988111,0.000003642836,0.00028650975,0.00046035202,0.00027463952,0.00016371801],"domain_scores_gemma":[0.99841624,0.00024514287,0.0001317946,0.000762278,0.00037666966,0.00006788657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024717627,0.00017683512,0.00022740941,0.0021414636,0.000086984364,0.00022085065,0.0009904403,0.00031218832,0.00007532816],"category_scores_gemma":[0.000112664675,0.00016618281,0.00020184442,0.0008174203,0.000039480296,0.00010003377,0.00025571891,0.00012314023,0.00010975891],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.016641e-7,0.0000034870093,1.7243207e-7,0.000025560772,0.000014706832,4.1625094e-7,0.0000049430273,1.9094434e-8,5.150175e-7,0.66064477,0.1571323,0.18217269],"study_design_scores_gemma":[0.00009472546,0.00001705025,0.0000023851642,0.000010539981,0.000011107467,9.211241e-7,4.3664963e-7,0.0018413767,0.000058350506,0.183009,0.81479657,0.00015751379],"about_ca_topic_score_codex":0.000004631949,"about_ca_topic_score_gemma":0.000004361946,"teacher_disagreement_score":0.6576643,"about_ca_system_score_codex":0.00006884532,"about_ca_system_score_gemma":0.00015755219,"threshold_uncertainty_score":0.6776738},"labels":[],"label_agreement":null},{"id":"W4406266487","doi":"10.1109/vtc2024-fall63153.2024.10757826","title":"GAN-Assisted Secret Key Generation Against Eavesdropping In Dynamic Indoor LiFi Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Eavesdropping; Key (lock); Computer science; Key generation; Computer network; Computer security; Encryption","score_opus":0.023618116979898386,"score_gpt":0.2653110163722929,"score_spread":0.2416928993923945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406266487","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":[0.04149287,0.0007576118,0.95101315,0.001348241,0.0014031783,0.00014890669,0.0000016010355,0.00033792478,0.0034965435],"genre_scores_gemma":[0.9917769,0.00010511536,0.005899084,0.0005518574,0.00008536484,0.000012685602,0.00005370799,0.000007866677,0.0015074174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874026,0.0000848942,0.0003047099,0.00043655207,0.00021324516,0.00022032399],"domain_scores_gemma":[0.99943686,0.00005261195,0.00003704761,0.00035176135,0.00005041418,0.0000713164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048259355,0.0001089451,0.00011099761,0.000637039,0.00007860836,0.0007803157,0.00040230472,0.00011026998,0.00003549198],"category_scores_gemma":[0.000032811895,0.00010081707,0.00005218811,0.0029293755,0.000021822108,0.00046820537,0.00008216708,0.00020457683,0.0000950852],"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":[0.000004649477,0.0002200043,0.0009919703,0.00010212799,0.00006339258,0.00015767512,0.0025286323,0.0043788655,0.019509565,0.09883144,0.015364663,0.85784703],"study_design_scores_gemma":[0.00010216174,0.000008117447,0.0063755885,0.000022648268,0.0000017164123,0.0000060465172,0.000016917027,0.9860352,0.0002824027,0.00012384617,0.006892415,0.0001329353],"about_ca_topic_score_codex":0.00005758129,"about_ca_topic_score_gemma":0.00029916587,"teacher_disagreement_score":0.9816563,"about_ca_system_score_codex":0.00014114317,"about_ca_system_score_gemma":0.0000822411,"threshold_uncertainty_score":0.75246024},"labels":[],"label_agreement":null},{"id":"W4406271187","doi":"10.1007/978-3-030-71522-9_91","title":"Personal Identification Number (PIN)","year":2025,"lang":"en","type":"book-chapter","venue":"","topic":"Biometric Identification and Security","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":"University of Ottawa","funders":"","keywords":"Identification (biology); Computer science; Biology","score_opus":0.0215720114232154,"score_gpt":0.25835062545344195,"score_spread":0.23677861403022654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406271187","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000001081593,0.00013413322,0.25748298,0.0011345994,0.00096286053,0.00012870645,0.000015195862,0.00020075713,0.7399397],"genre_scores_gemma":[0.0017426097,0.000106403975,0.0040253852,0.0005738678,0.00008490672,0.000007650362,0.00007234398,0.000009205574,0.9933776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983642,0.000015052607,0.00037515163,0.00062034716,0.0004715098,0.00015375235],"domain_scores_gemma":[0.99867404,0.000057635858,0.00018131972,0.0007501781,0.00026081462,0.000076026336],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00027562192,0.0002026804,0.00019819482,0.0004369137,0.00012420233,0.0004061848,0.0009968469,0.00029292345,0.0023910524],"category_scores_gemma":[0.000028354538,0.00020362862,0.00017345099,0.00026354598,0.000054277454,0.0002551346,0.00024087996,0.00025086058,0.004471945],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.112473e-7,0.000010985893,0.0000040061454,0.000019940713,0.000021523938,0.0000019765887,0.00006085784,1.2476095e-8,0.0000068017475,0.8427544,0.112938896,0.04418008],"study_design_scores_gemma":[0.00008220572,0.0000035163232,0.00018203167,0.000025323057,0.000015060354,0.000008159553,0.0000031807026,0.0023210521,0.00006548359,0.03783855,0.95919716,0.00025827758],"about_ca_topic_score_codex":0.000018132656,"about_ca_topic_score_gemma":0.000008545067,"teacher_disagreement_score":0.8462583,"about_ca_system_score_codex":0.000098680444,"about_ca_system_score_gemma":0.00017568744,"threshold_uncertainty_score":0.9985209},"labels":[],"label_agreement":null},{"id":"W4407596847","doi":"10.1016/j.eswa.2025.126825","title":"A multi-task minutiae transformer network for fingerprint recognition of young children","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Biometric Identification and Security","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":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Minutiae; Computer science; Fingerprint (computing); Fingerprint recognition; Pattern recognition (psychology); Transformer; Task (project management); Artificial intelligence; Machine learning; Engineering; Electrical engineering","score_opus":0.02056990255885342,"score_gpt":0.2677597977606301,"score_spread":0.24718989520177667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407596847","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":[0.0007385223,0.0012868069,0.9948287,0.00046635963,0.0001846399,0.0020074842,0.000048944974,0.00010310082,0.00033543524],"genre_scores_gemma":[0.8231549,0.00006125608,0.17065513,0.0001311697,0.00010409612,0.005250499,0.00012842253,0.000010253399,0.00050424767],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998931,0.000033600038,0.00035747033,0.00035731442,0.00013987251,0.00018076485],"domain_scores_gemma":[0.9989504,0.00007955473,0.00014396964,0.00053080136,0.00024465026,0.000050573883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026478563,0.00010795665,0.00017840054,0.000206463,0.00018309632,0.00008296833,0.00045119546,0.00007394145,0.0000022192867],"category_scores_gemma":[0.0000118641665,0.00009422809,0.000065955566,0.0013373864,0.000044720557,0.0001304906,0.000020927795,0.000057124023,0.000011744456],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001856521,0.0036347057,0.017974192,0.0011959008,0.0013982635,0.0000010260156,0.017864246,0.0009197487,0.02896054,0.35269397,0.07151945,0.5036523],"study_design_scores_gemma":[0.009726434,0.00041010903,0.11300006,0.0012601452,0.00024436146,0.000114494054,0.0017942606,0.25162295,0.023586856,0.00489274,0.59085846,0.0024891223],"about_ca_topic_score_codex":0.00029534573,"about_ca_topic_score_gemma":0.000043449807,"teacher_disagreement_score":0.82417357,"about_ca_system_score_codex":0.000044296845,"about_ca_system_score_gemma":0.00008419527,"threshold_uncertainty_score":0.384251},"labels":[],"label_agreement":null},{"id":"W4407784647","doi":"10.1109/ecbios61468.2024.10885503","title":"Face Anti-Spoofing Framework Based on Optical Flow Field Texture Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Optical flow; Texture (cosmology); Face (sociological concept); Field (mathematics); Artificial intelligence; Computer vision; Spoofing attack; Flow (mathematics); Computer security; Image (mathematics); Mathematics; Geometry","score_opus":0.014474170983601061,"score_gpt":0.28173551479946374,"score_spread":0.2672613438158627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407784647","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":[0.00066279864,0.00011816869,0.9820352,0.011722571,0.00048723497,0.000052559622,0.0000024868962,0.00031726088,0.00460172],"genre_scores_gemma":[0.88633513,0.0000052727796,0.11042161,0.002425131,0.00004720373,0.0000028679694,0.0000051493826,0.0000034919303,0.00075414544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885046,0.000033814573,0.00015725556,0.00043894548,0.0003445439,0.0001749894],"domain_scores_gemma":[0.9987206,0.0005486783,0.000015379315,0.00058728794,0.000038840542,0.000089220826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028814445,0.0000973314,0.00013682798,0.00074079074,0.00007920773,0.00067999767,0.00053820596,0.00014729856,0.00050759595],"category_scores_gemma":[0.00017796356,0.00007662794,0.00017275328,0.0057133483,0.000016520164,0.00015086446,0.00007126094,0.0002844626,0.00031424518],"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":[0.0000050590024,0.00020739295,0.0009863513,0.00005638113,0.00027298898,0.000084137915,0.0006471433,0.007031877,0.000136285,0.58516777,0.02327017,0.38213444],"study_design_scores_gemma":[0.000028288609,0.000021022892,0.0021310872,0.000015090686,0.000033688255,7.424517e-7,0.000010282715,0.9855545,0.0015172724,0.000807734,0.009769315,0.000110980094],"about_ca_topic_score_codex":0.000013909158,"about_ca_topic_score_gemma":0.0000043966547,"teacher_disagreement_score":0.9785226,"about_ca_system_score_codex":0.000023929568,"about_ca_system_score_gemma":0.00003974905,"threshold_uncertainty_score":0.6557233},"labels":[],"label_agreement":null},{"id":"W4408323871","doi":"10.1109/access.2025.3550302","title":"Toward Secure and Transparent Global Authentication: A Blockchain-Based System Integrating Biometrics and Subscriber Identification Module","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Blockchain; Computer science; Biometrics; Authentication (law); Identification (biology); Computer security","score_opus":0.04788453032489781,"score_gpt":0.31646230101210865,"score_spread":0.26857777068721084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408323871","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":[0.22666045,0.00077391247,0.76935565,0.001714017,0.0008294755,0.00028761162,0.000028826113,0.0001723234,0.00017772402],"genre_scores_gemma":[0.99712694,0.000015014441,0.0025310875,0.0001770167,0.00002558581,0.000045505363,0.000009655451,0.000004434106,0.00006474368],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983894,0.000096891636,0.00041952266,0.00057383924,0.00031954033,0.00020081186],"domain_scores_gemma":[0.9988784,0.000096522854,0.00015985023,0.00049045286,0.0002717904,0.00010298922],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00053717813,0.00015734717,0.00018993499,0.0007266301,0.00022316055,0.0011002297,0.0008221277,0.00011059611,0.0000025609934],"category_scores_gemma":[0.00008353785,0.00014981598,0.000044783923,0.005048329,0.00008464325,0.0002356753,0.00009795839,0.000102110265,0.000004595882],"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":[0.000051423765,0.0007596748,0.051264137,0.0033125454,0.0002546585,0.000027229731,0.0044075507,0.0002977956,0.0046119434,0.62865233,0.0031867807,0.30317393],"study_design_scores_gemma":[0.0009736096,0.000027438406,0.09998306,0.00019370196,0.000081297905,0.000017928822,0.00030687705,0.88196963,0.011588732,0.0031857998,0.0012532824,0.0004186355],"about_ca_topic_score_codex":0.00013938891,"about_ca_topic_score_gemma":0.00003068863,"teacher_disagreement_score":0.88167185,"about_ca_system_score_codex":0.00014403647,"about_ca_system_score_gemma":0.00009177251,"threshold_uncertainty_score":0.9999367},"labels":[],"label_agreement":null},{"id":"W4408760431","doi":"10.5566/ias.3409","title":"Palmprint Classification with Multiple Filter Faces, Fourier Features and Voting Technique","year":2025,"lang":"en","type":"article","venue":"Image Analysis & Stereology","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Voting; Pattern recognition (psychology); Artificial intelligence; Computer science; Fourier transform; Filter (signal processing); Computer vision; Mathematics","score_opus":0.01183216367631722,"score_gpt":0.26566054582296744,"score_spread":0.2538283821466502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408760431","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":[0.026265983,0.0001617469,0.9695498,0.0026945781,0.00004338976,0.00017386673,0.0000031026404,0.000091168455,0.0010164053],"genre_scores_gemma":[0.89903873,0.000015031794,0.09945592,0.0003784864,0.000008971765,0.00004650932,0.000016242999,0.0000031144186,0.0010369865],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99876964,0.00015174931,0.00024214963,0.00051222916,0.00013404821,0.00019019077],"domain_scores_gemma":[0.99891317,0.00014081558,0.00013185198,0.0005815821,0.00018446379,0.000048120073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036840184,0.00012402408,0.00024210915,0.0010385842,0.00013985833,0.0002133029,0.00044036645,0.000100772704,0.000023205514],"category_scores_gemma":[0.00009488625,0.00010001468,0.000073010124,0.0026962652,0.00014287277,0.00023623739,0.00021829751,0.00015998831,0.000007739814],"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":[0.00004684226,0.00026789037,0.7374559,0.000088983674,0.0016969187,0.0000406032,0.0012880994,0.000024007222,0.03093477,0.078026146,0.0033757822,0.14675409],"study_design_scores_gemma":[0.00036179196,0.000029435085,0.91188174,0.000010107847,0.00026946422,0.000020638807,0.00009107523,0.07320863,0.0074326396,0.0006737315,0.0057985913,0.00022213908],"about_ca_topic_score_codex":0.0001442521,"about_ca_topic_score_gemma":0.00042508644,"teacher_disagreement_score":0.87277275,"about_ca_system_score_codex":0.000028019107,"about_ca_system_score_gemma":0.00003281738,"threshold_uncertainty_score":0.40784803},"labels":[],"label_agreement":null},{"id":"W4409787721","doi":"10.61091/jcmcc127a-502","title":"Extraction and mapping of multidimensional data from computer terminals based on transfer probability matrix Markov chain attack graphs","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Markov chain; Computer science; Matrix (chemical analysis); Chain (unit); Transfer (computing); Data extraction; Theoretical computer science; Data mining; Machine learning; Parallel computing","score_opus":0.03742939894858483,"score_gpt":0.30469042473563446,"score_spread":0.26726102578704963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409787721","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5362183,0.00019887017,0.45552972,0.0003703988,0.0072720377,0.00030870686,0.000015058094,0.000028134482,0.000058782156],"genre_scores_gemma":[0.9130485,0.000019830526,0.08664148,0.00004919457,0.00021955217,0.0000011498778,0.0000076009565,0.000009786501,0.0000028945774],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99700737,0.0002739724,0.0013191265,0.0004508036,0.00072324957,0.00022548721],"domain_scores_gemma":[0.9961009,0.0018491031,0.0006787526,0.00070092035,0.00053539523,0.00013492537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029421595,0.00025768863,0.00071340747,0.0006222023,0.00022384462,0.00024219672,0.00087978283,0.00018177468,0.0000046630616],"category_scores_gemma":[0.00036581542,0.0002322883,0.0001310723,0.00084301917,0.00012107598,0.00037933767,0.00042477998,0.00042582187,6.335174e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027452488,0.0029286803,0.0011728901,0.00088070316,0.00025914185,0.000025054895,0.0011260037,0.00013826256,0.0025189004,0.96474105,0.0009482906,0.024986492],"study_design_scores_gemma":[0.0053605326,0.0004268451,0.00465683,0.0008953924,0.00009272925,0.000022052842,0.000075016214,0.71578336,0.0009524885,0.27048472,0.0009193448,0.00033071107],"about_ca_topic_score_codex":0.000021470247,"about_ca_topic_score_gemma":5.0738515e-7,"teacher_disagreement_score":0.7156451,"about_ca_system_score_codex":0.000058053243,"about_ca_system_score_gemma":0.00019164555,"threshold_uncertainty_score":0.94724417},"labels":[],"label_agreement":null},{"id":"W4409787823","doi":"10.61091/jcmcc127a-517","title":"Commercial password modification for face recognition systems using quantum key distribution networks","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Password; Key (lock); Computer science; S/KEY; Facial recognition system; Face (sociological concept); Quantum key distribution; Quantum; Computer security; Pattern recognition (psychology); Artificial intelligence; Physics; Quantum mechanics","score_opus":0.04401672863453637,"score_gpt":0.2970817358893259,"score_spread":0.25306500725478953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409787823","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":[0.09542884,0.00039320363,0.8796685,0.00021646249,0.02373545,0.00046046395,0.000008776635,0.000045214118,0.000043116834],"genre_scores_gemma":[0.99163574,0.00003816563,0.0075050783,0.000021344631,0.000759379,0.0000054715174,0.000018991374,0.000012241483,0.0000035789315],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975465,0.00018119624,0.0012544321,0.0002686147,0.00044973748,0.00029951145],"domain_scores_gemma":[0.99621284,0.0008033409,0.0012994277,0.0003047799,0.0012564007,0.00012318391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024626798,0.00022446601,0.0005753756,0.00033380237,0.00051736005,0.0007630206,0.0006339793,0.00023138929,4.5065605e-7],"category_scores_gemma":[0.000612983,0.00022011809,0.00017454856,0.0010506603,0.000058190966,0.0004183774,0.00018517807,0.0003376691,6.7595596e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005254408,0.00038835077,0.000039813287,0.00019394285,0.00007494485,0.0000014019188,0.00031339796,0.00051374204,0.000121121426,0.9889944,0.0006406318,0.008665709],"study_design_scores_gemma":[0.0022189512,0.00017387689,0.00013950562,0.00028452577,0.00008388485,0.000021357017,0.00014885404,0.7185867,0.00016678004,0.2764628,0.0015054316,0.00020733211],"about_ca_topic_score_codex":0.000021945974,"about_ca_topic_score_gemma":2.2116753e-7,"teacher_disagreement_score":0.8962069,"about_ca_system_score_codex":0.00021488678,"about_ca_system_score_gemma":0.00018158923,"threshold_uncertainty_score":0.89761555},"labels":[],"label_agreement":null},{"id":"W4410192277","doi":"10.26034/la.cfs.2025.5554","title":"Déjouer le déverrouillage biométrique d’appareils mobiles par empreintes digitales : Une revue des méthodes applicables en contexte opérationnel policier","year":2025,"lang":"fr","type":"article","venue":"Criminologie Forensique et Sécurité","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Humanities; Art; Political science","score_opus":0.13083290911456105,"score_gpt":0.35659552693471125,"score_spread":0.2257626178201502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410192277","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":[0.09872933,0.023726126,0.81894356,0.043538585,0.0020893011,0.0014514583,0.00021310966,0.00084357435,0.010464953],"genre_scores_gemma":[0.93023545,0.008944189,0.040034004,0.002252616,0.00022640261,0.00033094388,0.00014428055,0.000058504436,0.017773597],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99451894,0.0011419465,0.0012521561,0.0015382568,0.0003631168,0.0011855717],"domain_scores_gemma":[0.9943054,0.002217533,0.00045151904,0.0017493804,0.0009934014,0.00028276676],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013806825,0.0007745569,0.0009563237,0.0010573425,0.00083623035,0.001359579,0.0020549844,0.0009306377,0.00006161237],"category_scores_gemma":[0.002713491,0.00080598914,0.0004395849,0.003155946,0.0027218398,0.0017025596,0.0011693378,0.00074311363,0.0001744485],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000320532,0.0005600134,0.00068611774,0.0007750993,0.00015455778,0.000058263024,0.0046922425,0.00013621364,0.003093964,0.82017064,0.01204708,0.15759377],"study_design_scores_gemma":[0.0014943946,0.0003056712,0.017791536,0.0011837187,0.00017936032,0.0002694021,0.0033433174,0.01213035,0.10648186,0.6262491,0.22889124,0.0016800748],"about_ca_topic_score_codex":0.0022847073,"about_ca_topic_score_gemma":0.0011699141,"teacher_disagreement_score":0.83150613,"about_ca_system_score_codex":0.00024452255,"about_ca_system_score_gemma":0.0009400963,"threshold_uncertainty_score":0.9999922},"labels":[],"label_agreement":null},{"id":"W4410546854","doi":"10.18280/isi.300406","title":"Efficient and Robust Iris Localization Framework for Real-World Noisy Images","year":2025,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Mustansiriyah University","keywords":"IRIS (biosensor); Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Biometrics","score_opus":0.024853215267403837,"score_gpt":0.2756593173614653,"score_spread":0.25080610209406146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410546854","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":[0.0023503168,0.002136869,0.98414063,0.002201703,0.0030143326,0.0008210941,0.00009400471,0.0001734585,0.0050676055],"genre_scores_gemma":[0.74393326,0.0012020196,0.24444206,0.002050633,0.00026078933,0.0002496613,0.00031254473,0.000024991206,0.007524028],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99798566,0.00011525367,0.00088369637,0.00030249954,0.00030548987,0.00040741783],"domain_scores_gemma":[0.99771243,0.00037834744,0.0004358885,0.00045976037,0.00089676044,0.00011680497],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010245001,0.00024181722,0.0002799377,0.0012096928,0.00070165505,0.0016321552,0.00038384835,0.0002720612,0.000030789015],"category_scores_gemma":[0.0009948609,0.00026767908,0.00009515573,0.0036332372,0.00035709306,0.0017963188,0.00019727505,0.00018942993,0.000057047357],"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":[0.000025494055,0.00007714532,0.00085775723,0.0018239225,0.000047053978,6.473445e-7,0.008362815,0.007653492,0.00000877861,0.6871575,0.011511517,0.28247383],"study_design_scores_gemma":[0.00044987403,0.000052026455,0.011182485,0.0007349731,0.000054522403,0.000006646738,0.0005095029,0.844533,0.00044629784,0.037755284,0.10396417,0.00031119544],"about_ca_topic_score_codex":0.0005549019,"about_ca_topic_score_gemma":0.00004155606,"teacher_disagreement_score":0.83687955,"about_ca_system_score_codex":0.00046610978,"about_ca_system_score_gemma":0.00024088653,"threshold_uncertainty_score":0.9999775},"labels":[],"label_agreement":null},{"id":"W4411011718","doi":"10.1063/5.0269069","title":"Criminal face recognition using machine learning","year":2025,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Biometric Identification and Security","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Face (sociological concept); Facial recognition system; Artificial intelligence; Machine learning; Speech recognition; Pattern recognition (psychology)","score_opus":0.07069472454664232,"score_gpt":0.2942461936532021,"score_spread":0.22355146910655976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411011718","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":[0.15819864,0.000120118864,0.8344995,0.0010636197,0.00026179475,0.00013111602,0.0000018413792,0.00024801915,0.0054753777],"genre_scores_gemma":[0.9786801,0.00003933211,0.020193916,0.00030138568,0.00001866476,0.000008373943,0.0000060035636,0.000003825075,0.0007484438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898666,0.000014833227,0.00020583357,0.00037588328,0.00020214087,0.00021467566],"domain_scores_gemma":[0.999239,0.000028901826,0.00010725322,0.00010356118,0.00046191245,0.000059323484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033692594,0.000115312956,0.00012388256,0.00042282703,0.00024370397,0.00054173265,0.00053432304,0.00007780283,0.000044070883],"category_scores_gemma":[0.0002221915,0.0001190463,0.000039448234,0.0013684243,0.000046847566,0.0006504419,0.00019394346,0.00024647254,0.000058361882],"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":[0.000041474123,0.00035385796,0.073626585,0.00055243756,0.00008703615,0.000007671159,0.014678694,0.000002808441,0.10780243,0.2075246,0.0016671323,0.5936553],"study_design_scores_gemma":[0.0003532001,0.0000533076,0.006547193,0.000116142895,0.00003233648,0.000018873528,0.00081648486,0.9622682,0.0095822895,0.010332727,0.009567012,0.0003122661],"about_ca_topic_score_codex":0.00011068103,"about_ca_topic_score_gemma":0.0000032582361,"teacher_disagreement_score":0.9622654,"about_ca_system_score_codex":0.00006163657,"about_ca_system_score_gemma":0.000110001696,"threshold_uncertainty_score":0.522394},"labels":[],"label_agreement":null},{"id":"W4411658734","doi":"10.51847/z7bkiiwcqa","title":"10.51847/z7bKiIWCqA","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"IRIS (biosensor); Identity (music); Authentication (law); Biometrics; Iris recognition; Computer science; Communication; Psychology; Computer security; Art; Aesthetics","score_opus":0.009833810475010661,"score_gpt":0.19296047460975493,"score_spread":0.18312666413474427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411658734","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002546101,0.00003989168,0.0007832601,0.0011529766,0.000007711367,0.0000906012,0.000003031915,0.00022465293,0.99744326],"genre_scores_gemma":[0.00081827125,2.9717233e-7,0.002068399,0.00018567541,0.000035747722,0.0000057910174,0.000003277527,0.000004155726,0.9968784],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999258,0.000029896182,0.00012118886,0.00023362997,0.00019186514,0.00016542201],"domain_scores_gemma":[0.9993276,0.000025177895,0.000017551918,0.00046520217,0.000043638305,0.000120840996],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00017315976,0.000065956825,0.00007508653,0.00016746606,0.0000719563,0.00015069847,0.0006603954,0.00003582871,0.9625341],"category_scores_gemma":[0.000022134072,0.00006544824,0.000035125984,0.001137533,0.0000150443075,0.00017931123,0.00006658958,0.000049162707,0.98761594],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002277032,0.000030147543,8.3671644e-8,0.0000010221216,0.0000024830208,0.0000015235119,0.000022777547,0.000003007001,0.000024687315,0.000090379624,0.0450633,0.9547583],"study_design_scores_gemma":[0.00007397268,0.000025619303,0.0002023224,0.0000017410647,0.0000013168535,0.000004242451,1.4050732e-7,0.0029895497,0.000048247657,0.000015766671,0.9965459,0.00009117467],"about_ca_topic_score_codex":0.0000144098385,"about_ca_topic_score_gemma":5.1828042e-8,"teacher_disagreement_score":0.95466715,"about_ca_system_score_codex":0.00002104774,"about_ca_system_score_gemma":0.000021118285,"threshold_uncertainty_score":0.2668902},"labels":[],"label_agreement":null},{"id":"W4412468137","doi":"10.1016/j.patrec.2025.06.017","title":"Multimodal biometric authentication using camera-based PPG and fingerprint fusion","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Biometric Identification and Security","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":"University of Toronto","funders":"King Abdullah University of Science and Technology","keywords":"Fingerprint (computing); Biometrics; Artificial intelligence; Computer science; Computer vision; Authentication (law); Fusion; Fingerprint recognition; Pattern recognition (psychology); Computer security","score_opus":0.031026008493534953,"score_gpt":0.2712023509819325,"score_spread":0.24017634248839756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412468137","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":[0.4211911,0.000037341422,0.57454896,0.0036355453,0.00033980227,0.00013621635,0.000008127986,0.00007656656,0.000026372518],"genre_scores_gemma":[0.974295,0.000011293484,0.020140043,0.0054397397,0.000027907583,0.000018903942,0.00004511789,0.000005989867,0.00001600814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998757,0.0001094473,0.0002754375,0.00043919715,0.00022282977,0.00019606174],"domain_scores_gemma":[0.99925375,0.00013110803,0.00012737191,0.00031130746,0.0001103587,0.0000660912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002739887,0.00012819807,0.00012249533,0.0017693044,0.00019146292,0.000273355,0.00026924233,0.00006806749,0.000040109844],"category_scores_gemma":[0.00008637456,0.00013593062,0.000055983128,0.0023278384,0.00005922284,0.0002578908,0.00010926175,0.000115433984,0.000059149963],"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":[0.0000054667953,0.00013766474,0.0072714686,0.00008839639,0.00002283846,0.0000044244102,0.00025973533,0.000015024062,0.06293126,0.000052628795,0.0004476469,0.92876345],"study_design_scores_gemma":[0.0013801379,0.000027304217,0.16307639,0.00016658352,0.000052296706,0.000009565405,0.000038441074,0.8044366,0.027767232,0.00044997488,0.0020937626,0.00050171016],"about_ca_topic_score_codex":0.00019104482,"about_ca_topic_score_gemma":0.000004167619,"teacher_disagreement_score":0.92826176,"about_ca_system_score_codex":0.00009228336,"about_ca_system_score_gemma":0.000034858018,"threshold_uncertainty_score":0.554309},"labels":[],"label_agreement":null},{"id":"W4413177255","doi":"10.18280/ts.420422","title":"Generative Information Hiding of Iris Feature Data via Gaussian Fuzzy Processing and Advanced Encryption Standard-Based Encryption","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Huanghuai University","keywords":"Encryption; Computer science; IRIS (biosensor); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Information hiding; Fuzzy logic; Data mining; Computer vision; Image (mathematics); Biometrics; Computer security","score_opus":0.01901174749178876,"score_gpt":0.27149809068529973,"score_spread":0.252486343193511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413177255","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":[0.014673784,0.00022966308,0.9828054,0.0014720033,0.000118492004,0.0002851895,0.00004620192,0.00006287623,0.00030639657],"genre_scores_gemma":[0.91752434,0.000021887181,0.08191804,0.0002371896,0.000020351865,0.000013826787,0.00024372729,0.0000024060978,0.000018211935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988383,0.00007035225,0.0003279636,0.0002675602,0.00035883932,0.00013696759],"domain_scores_gemma":[0.99918246,0.000034816374,0.00023111659,0.0002996933,0.00020725268,0.00004463964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056294753,0.00011486545,0.00014148823,0.00045562594,0.00019337374,0.0002402449,0.0004075415,0.000066702065,0.000016003922],"category_scores_gemma":[0.000035347544,0.000107701555,0.000023311226,0.0010021618,0.000049125953,0.002006259,0.000113229275,0.000098694756,0.0000021355556],"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":[0.000096341224,0.000099107645,0.00052649213,0.00033768636,0.000030383122,8.3833874e-7,0.0018543431,0.00031761936,0.04344149,0.016323788,0.0019190155,0.9350529],"study_design_scores_gemma":[0.0026018815,0.00020666503,0.0201042,0.00025272154,0.00005063408,0.0000030068422,0.00037361708,0.90937865,0.044633307,0.0032656933,0.018758006,0.00037160312],"about_ca_topic_score_codex":0.000008504646,"about_ca_topic_score_gemma":0.000004886754,"teacher_disagreement_score":0.9346813,"about_ca_system_score_codex":0.00008070871,"about_ca_system_score_gemma":0.00014827598,"threshold_uncertainty_score":0.4391942},"labels":[],"label_agreement":null},{"id":"W4413181373","doi":"10.1109/isscs66034.2025.11105629","title":"Benchmarking Lightweight Deep Learning Models for In-Vehicle Face Anti-Spoofing","year":2025,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Benchmarking; Computer science; Face (sociological concept); Deep learning; Artificial intelligence; Spoofing attack; Computer security; Business","score_opus":0.027490968546536654,"score_gpt":0.26577889908311636,"score_spread":0.2382879305365797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413181373","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":[0.020296983,0.00031160595,0.96987826,0.00093272416,0.00031748594,0.00017785549,3.1672138e-7,0.0001290827,0.007955663],"genre_scores_gemma":[0.95224035,0.00003335219,0.045354653,0.00029244108,0.00001743803,0.000017210712,0.0000030662598,0.0000035115652,0.002038007],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895674,0.00004244716,0.0002420832,0.00037060605,0.00014140294,0.0002467426],"domain_scores_gemma":[0.99939495,0.00017320324,0.000053113366,0.0002607832,0.000079697755,0.000038251797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005049237,0.000087208304,0.00013203954,0.0005722842,0.00018584858,0.00024690386,0.0005498261,0.00007065387,0.000013228654],"category_scores_gemma":[0.000056911496,0.00008443945,0.000056674682,0.0018874349,0.00001569757,0.00050213974,0.00017666706,0.00012996916,0.000009861694],"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":[0.0000038032433,0.000096646785,0.004038122,0.000054459033,0.000016100164,0.000002405765,0.0018374333,0.0049220948,0.0014894025,0.7442031,0.0005390667,0.24279739],"study_design_scores_gemma":[0.00026120336,0.000009615324,0.0032911017,0.000017600762,0.0000020386426,4.597253e-7,0.00007838026,0.9761287,0.0033791792,0.0070324815,0.009690286,0.00010893613],"about_ca_topic_score_codex":0.000043146116,"about_ca_topic_score_gemma":0.000030611856,"teacher_disagreement_score":0.9712066,"about_ca_system_score_codex":0.00005132031,"about_ca_system_score_gemma":0.000039935796,"threshold_uncertainty_score":0.34433407},"labels":[],"label_agreement":null},{"id":"W4413770722","doi":"10.1016/j.inffus.2025.103661","title":"Synthetic generation of finger-vein region by feature fusion-based enhanced U-transformer for finger-vein recognition","year":2025,"lang":"en","type":"article","venue":"Information Fusion","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Information Technology Research Centre; Ministry of Science and ICT, South Korea","keywords":"Computer science; Transformer; Fusion; Pattern recognition (psychology); Feature (linguistics); Artificial intelligence; Vein; Medicine; Engineering; Electrical engineering; Surgery; Voltage","score_opus":0.02130937938655922,"score_gpt":0.24550758976494633,"score_spread":0.2241982103783871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413770722","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":[0.03074267,0.00007949372,0.96202403,0.0035861735,0.00078626955,0.00074717693,0.000059369617,0.00012877933,0.001846061],"genre_scores_gemma":[0.97992647,0.0001160234,0.015774747,0.0015422201,0.00004569462,0.00015526259,0.0014924317,0.000007683505,0.00093948387],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99851125,0.00006747165,0.00060363906,0.00025041343,0.00036546026,0.00020177805],"domain_scores_gemma":[0.9983802,0.00014947166,0.00037359478,0.00040934145,0.0006325673,0.000054842538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005249474,0.00016316152,0.00018801882,0.00079786306,0.00029161593,0.00020304107,0.000366421,0.00023846983,0.000033307115],"category_scores_gemma":[0.00027783716,0.00015782179,0.0001238741,0.0014256126,0.000037427973,0.0014048076,0.000034232307,0.00012904177,0.000033671746],"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":[0.000073377865,0.000158253,0.000010648546,0.00030948943,0.000014100734,1.3209487e-7,0.0013335989,0.00010259913,0.16058934,0.0027074777,0.06675848,0.7679425],"study_design_scores_gemma":[0.0012850626,0.00012756443,0.0002251572,0.0001957432,0.000022499202,0.0000014717874,0.00008245693,0.20785862,0.66524297,0.0005950768,0.12407938,0.00028398458],"about_ca_topic_score_codex":0.00002976534,"about_ca_topic_score_gemma":0.000010159512,"teacher_disagreement_score":0.94918376,"about_ca_system_score_codex":0.00010061747,"about_ca_system_score_gemma":0.00014726781,"threshold_uncertainty_score":0.6435786},"labels":[],"label_agreement":null},{"id":"W4413918824","doi":"10.1109/icdici66477.2025.11135171","title":"AI-Driven Secure Authentication: A Deep Learning Approach for Multi-Modal Biometric Systems","year":2025,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Biometrics; Modal; Computer science; Authentication (law); Deep learning; Artificial intelligence; Computer security","score_opus":0.0364247351576691,"score_gpt":0.30367191221382944,"score_spread":0.26724717705616036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413918824","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":[0.00030423157,0.000621568,0.99534965,0.0007083519,0.0006157823,0.0005865921,0.0000030497806,0.00031690462,0.0014938606],"genre_scores_gemma":[0.7765544,0.000012956104,0.21320078,0.00023756415,0.000037768994,0.00016650988,0.000038282295,0.000006749748,0.009744964],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985637,0.00009104955,0.00032029065,0.00051968766,0.0002461629,0.00025910285],"domain_scores_gemma":[0.99888504,0.00010919393,0.000104481434,0.0004883815,0.00033148756,0.000081422484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005174728,0.00012857141,0.00018133438,0.0017525789,0.00028451934,0.00054688676,0.00090388965,0.0001282106,0.000008601622],"category_scores_gemma":[0.00019358196,0.00011634503,0.000100870435,0.006919744,0.000039645245,0.00028568498,0.0001772032,0.00015060072,0.000034837623],"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":[0.0000068334202,0.00055769796,0.0015884103,0.00036670343,0.000113648544,8.336219e-7,0.0013508236,0.00077516993,0.00055718806,0.9509707,0.004444634,0.03926732],"study_design_scores_gemma":[0.0004503483,0.000020006955,0.0015258738,0.000006292976,0.0000119881615,0.000002834069,0.00014000268,0.96395063,0.000135879,0.00014417093,0.0334793,0.00013265337],"about_ca_topic_score_codex":0.00003823111,"about_ca_topic_score_gemma":0.00000194647,"teacher_disagreement_score":0.9631755,"about_ca_system_score_codex":0.00008147914,"about_ca_system_score_gemma":0.00007136168,"threshold_uncertainty_score":0.5273642},"labels":[],"label_agreement":null},{"id":"W4415283044","doi":"10.1038/s41598-025-20275-4","title":"A probabilistic detection-based approach to skin and freckle segmentation","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Division of Human Resource Development; Korea Evaluation Institute of Industrial Technology; Ministry of Science and ICT, South Korea; Korea Health Industry Development Institute; National Research Foundation of Korea; Institute for Information and Communications Technology Promotion; National IT Industry Promotion Agency; Ministry of Trade, Industry and Energy; National Research Foundation","keywords":"Segmentation; Pattern recognition (psychology); Probabilistic logic; Image segmentation; Histogram; Region growing; Scale-space segmentation; Process (computing)","score_opus":0.012880769356582148,"score_gpt":0.2492460782114828,"score_spread":0.23636530885490067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415283044","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":[0.062274765,0.000058336725,0.9301211,0.0003372568,0.0038571695,0.00054835196,7.949603e-7,0.00013505701,0.0026671325],"genre_scores_gemma":[0.96654475,2.862754e-7,0.03069241,0.00015770455,0.000010957731,0.0000891826,0.0000116849715,0.000002910117,0.0024901035],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982936,0.00005419285,0.00031349942,0.0008218458,0.00034570944,0.0001711932],"domain_scores_gemma":[0.99877495,0.00003952446,0.000105852385,0.00078358507,0.00019608498,0.00010000592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013027478,0.00008769037,0.00009869735,0.00075736665,0.00036747952,0.0009509129,0.00023235506,0.00004461601,0.0000058009773],"category_scores_gemma":[0.0002610062,0.00008363713,0.000036346555,0.0035739902,0.000093933966,0.00020760985,0.000105500185,0.000061882216,0.000011966969],"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":[0.00002596343,0.002047754,0.0059981896,0.00074667303,0.00008486289,0.00007270315,0.0059453584,0.0030261728,0.09806648,0.049851406,0.06403076,0.7701037],"study_design_scores_gemma":[0.0007834156,0.00009141704,0.03182712,0.000086568994,0.000051036015,0.00014157152,0.00032219672,0.42701063,0.25999326,0.07893591,0.19980676,0.00095015106],"about_ca_topic_score_codex":0.000046473546,"about_ca_topic_score_gemma":0.000024660807,"teacher_disagreement_score":0.90427,"about_ca_system_score_codex":0.00007673982,"about_ca_system_score_gemma":0.0001653457,"threshold_uncertainty_score":0.9169675},"labels":[],"label_agreement":null},{"id":"W4415515869","doi":"10.1007/978-3-032-07373-0_3","title":"Facial Spoof Detection Using Deep Learning Techniques for Enhanced Biometric Security","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Biometric Identification and Security","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":"Université de Moncton","funders":"","keywords":"Liveness; Spoofing attack; Biometrics; Convolutional neural network; Deep learning; Authentication (law); Vulnerability (computing); Facial recognition system","score_opus":0.03878836484766509,"score_gpt":0.3157351913628646,"score_spread":0.27694682651519953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415515869","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":[0.00007987694,0.00030175172,0.9648908,0.00012875372,0.00039913086,0.0006189316,0.000012951932,0.00017147519,0.03339634],"genre_scores_gemma":[0.3813896,0.0038411848,0.6119665,0.00063307944,0.00009919145,0.00013057436,0.00013772727,0.000016007893,0.0017861215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818635,0.0000444017,0.0007429847,0.00037110917,0.00041099064,0.00024414406],"domain_scores_gemma":[0.9970273,0.00029493685,0.0004711791,0.0012846937,0.00084165565,0.00008025278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015819478,0.00021572303,0.00026775533,0.005493619,0.0009822585,0.00087712577,0.0025102461,0.00022492651,0.0000034785264],"category_scores_gemma":[0.00022765066,0.00023903142,0.00007851557,0.003027764,0.0004939041,0.0045722476,0.0015575826,0.0004539081,0.000008108674],"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":[0.0000026549285,0.000015858783,0.000005582349,0.000054016426,0.000005302145,4.4354913e-8,0.0010745154,0.000021056841,0.00008982729,0.14647391,0.00001307698,0.85224414],"study_design_scores_gemma":[0.00022998729,0.00005705961,0.00016681901,0.000106615,0.000009314985,0.000006553966,0.000023404149,0.7585339,0.0015892821,0.008413796,0.23052341,0.00033985797],"about_ca_topic_score_codex":0.000024362593,"about_ca_topic_score_gemma":0.000014243575,"teacher_disagreement_score":0.8519043,"about_ca_system_score_codex":0.00032582722,"about_ca_system_score_gemma":0.00026682968,"threshold_uncertainty_score":0.9747418},"labels":[],"label_agreement":null},{"id":"W4415910874","doi":"10.21608/erurj.2025.323692.1184","title":"MACET: A Novel Approach to Secure Multimodal Biometric Authentication with Cancellable Templates","year":2025,"lang":"en","type":"article","venue":"ERU Research Journal","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Biometrics; Spoofing attack; Robustness (evolution); Authentication (law); Affine transformation; Iris recognition; Signature recognition; Pattern recognition (psychology); Access control","score_opus":0.0696566173848934,"score_gpt":0.37301783434652563,"score_spread":0.3033612169616322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415910874","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":[0.017597029,0.0005935353,0.9665444,0.005687399,0.0003159148,0.00044866785,0.000008447403,0.000059758393,0.008744878],"genre_scores_gemma":[0.870196,0.00006673496,0.122299924,0.00014749418,0.000091045,0.00004413374,0.0000045443608,0.000009291012,0.007140823],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720436,0.0001953724,0.00029149148,0.0004543374,0.0012592794,0.00059513864],"domain_scores_gemma":[0.9977028,0.0001965802,0.00007966185,0.00055136817,0.001137676,0.00033194557],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003282199,0.00012144355,0.00016034758,0.0048263078,0.00072204595,0.0012796929,0.001572774,0.00008628578,0.00003645036],"category_scores_gemma":[0.0004805655,0.00009379876,0.000048561968,0.017870847,0.00010503721,0.00039703908,0.00031256693,0.0007314802,0.00010118019],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039084224,0.0058396603,0.00877657,0.0005154021,0.0006490297,0.00007832221,0.013547552,0.0010552922,0.07220063,0.40193632,0.25453597,0.2404744],"study_design_scores_gemma":[0.0059441333,0.00083306816,0.05879068,0.00040222172,0.000037192916,0.0006815365,0.0020041806,0.46752045,0.024013424,0.009326925,0.4292581,0.0011880709],"about_ca_topic_score_codex":0.00018029289,"about_ca_topic_score_gemma":0.000013819717,"teacher_disagreement_score":0.85259897,"about_ca_system_score_codex":0.00032786978,"about_ca_system_score_gemma":0.0006748705,"threshold_uncertainty_score":0.99975705},"labels":[],"label_agreement":null},{"id":"W4416339255","doi":"10.18280/isi.300912","title":"An Explainable Deep Neural Network Based PCANet Framework with Illumination-Invariant Features for Finger Knuckle Print Recognition System","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Knuckle; Artificial neural network; Feature (linguistics); Pattern recognition (psychology); Convolutional neural network; Feature extraction","score_opus":0.014441486445719515,"score_gpt":0.23743869325105477,"score_spread":0.22299720680533525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416339255","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":[0.012861388,0.00034651477,0.97908527,0.0004588726,0.002497024,0.0024034504,0.000109883105,0.00043518515,0.0018024221],"genre_scores_gemma":[0.9066031,0.000016377895,0.090605624,0.0009323944,0.00024154897,0.00063744147,0.0008489309,0.000022404336,0.00009217782],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99590194,0.00039675913,0.0014744035,0.0006383002,0.00070034235,0.0008882496],"domain_scores_gemma":[0.9948845,0.00052541617,0.0011760091,0.0010793587,0.0020922222,0.00024252392],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002166818,0.00052229397,0.00056234916,0.0012796202,0.001785869,0.0033623802,0.0009945643,0.0005730917,0.000041571813],"category_scores_gemma":[0.00069217815,0.00051077723,0.00017205176,0.004389226,0.0002622888,0.0062033082,0.000128105,0.00045292,0.000043524757],"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":[0.00075853936,0.00036271042,0.00068204326,0.0073383464,0.00025403625,0.000010512687,0.016372291,0.051542327,0.00003855615,0.16507931,0.0023775052,0.7551838],"study_design_scores_gemma":[0.0012785443,0.0004812367,0.007604742,0.0016433791,0.00013075091,0.0000368101,0.0029255324,0.97513855,0.0011193515,0.003945948,0.004983545,0.000711601],"about_ca_topic_score_codex":0.00021883391,"about_ca_topic_score_gemma":0.0000749733,"teacher_disagreement_score":0.92359626,"about_ca_system_score_codex":0.0011098637,"about_ca_system_score_gemma":0.00059877645,"threshold_uncertainty_score":0.9997344},"labels":[],"label_agreement":null},{"id":"W4416339674","doi":"10.7717/peerj-cs.3360","title":"A keyless multimodal-based user authentication scheme using generative adversarial networks","year":2025,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Biometrics; Authentication (law); Adversarial system; Code (set theory); Pattern recognition (psychology); Iris recognition; Transformation (genetics); Generative adversarial network","score_opus":0.020864543104417627,"score_gpt":0.29217110066434265,"score_spread":0.27130655755992505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416339674","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":[0.020540956,0.00005435276,0.9747012,0.0012263079,0.00291758,0.00025283513,0.0000018721253,0.00018600357,0.000118877266],"genre_scores_gemma":[0.5431724,0.0000011012505,0.45589572,0.00073691964,0.00009064208,0.000005500095,0.0000032407256,0.0000031031484,0.00009138084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975918,0.00010307463,0.00033247564,0.0008916633,0.00064527284,0.00043570955],"domain_scores_gemma":[0.99804276,0.00010880555,0.00014570737,0.0009449882,0.0006154948,0.00014224068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011424841,0.00017538453,0.0001830414,0.00088176096,0.0006726676,0.0010230474,0.0021881429,0.00008286862,0.00000878447],"category_scores_gemma":[0.000088227054,0.0001713508,0.000081133134,0.0057109436,0.0004043141,0.00095724844,0.0006289161,0.00017306402,0.000019255152],"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":[0.000048172486,0.0013324984,0.006887986,0.000092906965,0.000101247475,0.000023653034,0.0038618133,0.13402194,0.047505517,0.6140305,0.004755438,0.18733835],"study_design_scores_gemma":[0.0004223819,0.000014936047,0.007624547,0.00002113614,0.000006109666,0.0000019008115,0.0000056154813,0.9869067,0.0032139109,0.00031030655,0.0012885464,0.0001839209],"about_ca_topic_score_codex":0.00006744075,"about_ca_topic_score_gemma":0.0000048525526,"teacher_disagreement_score":0.85288477,"about_ca_system_score_codex":0.0002222815,"about_ca_system_score_gemma":0.0006827212,"threshold_uncertainty_score":0.9865269},"labels":[],"label_agreement":null},{"id":"W4417117410","doi":"10.22214/ijraset.2025.76083","title":"Blood Group and Diabetes Detection from Fingerprints Using CNN","year":2025,"lang":"","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Minutiae; Convolutional neural network; Fingerprint (computing); Automation; Pattern recognition (psychology); Biometrics; Fingerprint recognition; Analytics; Feature extraction","score_opus":0.05022799213694187,"score_gpt":0.3663620613460377,"score_spread":0.31613406920909587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417117410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.707911,0.0010601967,0.28657508,0.0021961231,0.0018781786,0.000274445,0.0000064899887,0.0000422343,0.00005624266],"genre_scores_gemma":[0.98117906,0.00048232282,0.018163089,0.0000359818,0.000088428365,0.00003431887,6.288357e-7,0.0000062799704,0.000009893101],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99739647,0.000025304073,0.00042112893,0.0006381091,0.00088979077,0.00062917836],"domain_scores_gemma":[0.99854696,0.0003300483,0.00008918215,0.00025157246,0.00065038056,0.00013185856],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0047697495,0.0001433008,0.0001887916,0.0076688803,0.00057714235,0.0012682248,0.0017069429,0.00020838439,0.0000033482395],"category_scores_gemma":[0.0011178773,0.00015359405,0.000027714892,0.00498874,0.0008292768,0.00041226263,0.0011117675,0.0009248421,0.0000014558906],"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":[0.000021717779,0.00010922042,0.001112465,0.00003693112,0.000067210065,0.00001600045,0.00014048941,0.0001963564,0.43860805,0.07273112,0.000009272427,0.48695117],"study_design_scores_gemma":[0.0009963991,0.000075740776,0.0037364662,0.0002539792,0.000010662758,0.000052873853,0.00013547257,0.8694935,0.052728567,0.06904902,0.0032650896,0.00020224428],"about_ca_topic_score_codex":0.000050860715,"about_ca_topic_score_gemma":0.0000110828305,"teacher_disagreement_score":0.86929715,"about_ca_system_score_codex":0.00040201104,"about_ca_system_score_gemma":0.00028166967,"threshold_uncertainty_score":0.99976856},"labels":[],"label_agreement":null},{"id":"W4417282434","doi":"10.1109/iai68403.2025.11277220","title":"Iris Recognition with Selectively Denoising, Data Augmentation and CNNs","year":2025,"lang":"","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Iris recognition; IRIS (biosensor); Convolutional neural network; Pattern recognition (psychology); Noise (video); Feature (linguistics); Smoothing","score_opus":0.06697277962257346,"score_gpt":0.3095983604343484,"score_spread":0.24262558081177493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417282434","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":[0.022567809,0.0006687958,0.9677051,0.003079121,0.00041762853,0.00040148332,0.00005500474,0.00009017186,0.005014941],"genre_scores_gemma":[0.92980653,0.00063771737,0.06172804,0.0012589131,0.000045528628,0.000012402069,0.00024835323,0.00000889031,0.006253607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979674,0.0001597857,0.0003338527,0.0009812574,0.00031974967,0.00023791385],"domain_scores_gemma":[0.99840915,0.00012032438,0.00015976744,0.0008370548,0.00038025033,0.000093471484],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006907139,0.00017410993,0.00016603275,0.0007111203,0.00041109265,0.0011032274,0.00074311264,0.000105349834,0.00013444282],"category_scores_gemma":[0.00011695993,0.00016147735,0.000016743283,0.0039151884,0.00014636523,0.0015774006,0.00051894336,0.0001685069,0.000060971328],"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":[0.000052335523,0.00028099015,0.002417058,0.00010336297,0.00013312603,0.0000034898837,0.0011142206,7.113062e-7,0.0005385705,0.0063686986,0.017804198,0.97118324],"study_design_scores_gemma":[0.006606983,0.000812851,0.3218765,0.0006342484,0.00071471307,0.000107584296,0.0022159107,0.5410827,0.020179534,0.014939009,0.08897379,0.0018561871],"about_ca_topic_score_codex":0.000471785,"about_ca_topic_score_gemma":0.00026114966,"teacher_disagreement_score":0.96932703,"about_ca_system_score_codex":0.00009153449,"about_ca_system_score_gemma":0.00031652173,"threshold_uncertainty_score":0.9999337},"labels":[],"label_agreement":null},{"id":"W52731893","doi":"10.1007/11424758_77","title":"A Novel Topology-Based Matching Algorithm for Fingerprint Recognition in the Presence of Elastic Distortions","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minutiae; Computer science; Delaunay triangulation; Matching (statistics); Fingerprint (computing); Blossom algorithm; Algorithm; Artificial intelligence; Fingerprint recognition; Transformation (genetics); Set (abstract data type); Pattern recognition (psychology); Scheme (mathematics); Mathematics","score_opus":0.03428365610204902,"score_gpt":0.27262104970909634,"score_spread":0.2383373936070473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W52731893","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":[0.00009978448,0.00012924382,0.99686843,0.0012822807,0.0008663278,0.0005354416,0.000034110522,0.00003549416,0.00014885965],"genre_scores_gemma":[0.13488048,0.0000105446425,0.86410457,0.0007383565,0.0001581512,0.000043107964,0.000016235577,0.000009809636,0.00003872014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778664,0.000038455346,0.0005113652,0.0007726668,0.00056560047,0.0003252818],"domain_scores_gemma":[0.9972728,0.0013782553,0.0003138435,0.0007561988,0.00022901193,0.00004991579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013440304,0.00022275427,0.00027763646,0.0010862687,0.00017257215,0.00019405382,0.0021696782,0.00018835098,0.000008311209],"category_scores_gemma":[0.00022119712,0.00018283645,0.00011187018,0.0010306684,0.00047962638,0.00026125304,0.0002577521,0.00044095103,0.000005531765],"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":[0.0000026557761,0.000105588595,0.0000064993956,0.000035157365,0.0000033985173,0.0000030648202,0.0011645803,0.0058466694,0.00015144954,0.008314146,0.000008069561,0.9843587],"study_design_scores_gemma":[0.00030683086,0.00008779533,0.00046163352,0.00021669966,0.000007538023,0.000021720489,8.967512e-7,0.91438794,0.0005066389,0.08291087,0.0008079732,0.00028345486],"about_ca_topic_score_codex":0.000067704575,"about_ca_topic_score_gemma":0.00020645629,"teacher_disagreement_score":0.98407525,"about_ca_system_score_codex":0.0001798295,"about_ca_system_score_gemma":0.00035686695,"threshold_uncertainty_score":0.7455854},"labels":[],"label_agreement":null},{"id":"W59149218","doi":"10.1007/978-3-642-33564-8_71","title":"Multibiometric System Using Distance Regularized Level Set Method and Particle Swarm Optimization","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","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 Waterloo","funders":"","keywords":"Biometrics; Computer science; Particle swarm optimization; Level set (data structures); Fitness function; Pattern recognition (psychology); Artificial intelligence; Iris recognition; Feature (linguistics); Segmentation; Fingerprint (computing); IRIS (biosensor); Set (abstract data type); Computer vision; Algorithm; Genetic algorithm; Machine learning","score_opus":0.06781017050512292,"score_gpt":0.2988291711658156,"score_spread":0.23101900066069267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W59149218","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":[0.000098511875,0.0013501089,0.9964839,0.00014478246,0.0013373598,0.00036464207,0.000016868555,0.00012707716,0.00007671366],"genre_scores_gemma":[0.20832202,0.00003387514,0.7912356,0.000137626,0.00013714339,0.0000032268847,0.000006895188,0.000018110553,0.00010546738],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99679077,0.00010514062,0.0005276981,0.0011974053,0.0008548561,0.0005241366],"domain_scores_gemma":[0.99765486,0.0003600957,0.00038397664,0.0010660529,0.0003068036,0.00022819058],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002103942,0.0003580646,0.0004399889,0.0015669815,0.00033410627,0.00071752555,0.0014157892,0.00028299715,0.000005227313],"category_scores_gemma":[0.00015226373,0.00033918468,0.00007841437,0.003361161,0.000297496,0.0006477867,0.00082673813,0.00033960957,0.000008299461],"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":[0.000012471573,0.00006875843,0.00023621334,0.00025335053,0.000032300515,0.000024343402,0.0015923318,0.14228608,0.00083229406,0.05496475,0.000009410641,0.7996877],"study_design_scores_gemma":[0.00028132086,0.000024001134,0.00014814388,0.00012309846,0.000015310656,0.00006179277,4.739855e-7,0.9947546,0.0019271416,0.0018675938,0.00039449945,0.0004020262],"about_ca_topic_score_codex":0.000041549803,"about_ca_topic_score_gemma":0.00000939688,"teacher_disagreement_score":0.8524685,"about_ca_system_score_codex":0.00041658102,"about_ca_system_score_gemma":0.00020609493,"threshold_uncertainty_score":0.999906},"labels":[],"label_agreement":null},{"id":"W61148521","doi":"10.1007/978-0-387-71041-9_19","title":"Biometric System Security","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":100,"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":"Biometrics; Spoofing attack; Computer security; Vulnerability (computing); Computer science; Identity theft; Identity (music); Cryptography; Internet privacy","score_opus":0.04525673960993507,"score_gpt":0.2586219694493827,"score_spread":0.21336522983944764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W61148521","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010191872,0.0008599089,0.2759171,0.00008382555,0.0012009924,0.00017262208,0.000013602662,0.000570977,0.72117996],"genre_scores_gemma":[0.012148443,0.00012906044,0.0110647045,0.00032497215,0.00025654264,0.000004079126,0.00003985081,0.000034079396,0.9759983],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.997599,0.000016432172,0.00052989565,0.00072828395,0.00082323863,0.00030314643],"domain_scores_gemma":[0.99780875,0.000114592796,0.00028123826,0.0012785606,0.0002986148,0.00021825366],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008437785,0.00030471687,0.00036109635,0.004395834,0.000118110846,0.0003121977,0.0015772866,0.0005482083,0.00036523864],"category_scores_gemma":[0.000026690333,0.00028170767,0.00022307917,0.0016400802,0.000065965585,0.00020129545,0.00039439465,0.0003595982,0.0033133135],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.3400324e-7,0.000012941253,0.0000014181578,0.00007235352,0.000024509956,0.00002712062,0.000033326804,1.559368e-8,0.0000012389883,0.95882654,0.011229731,0.029770153],"study_design_scores_gemma":[0.00012598642,0.000023550027,0.000028453263,0.000038682243,0.000013992366,0.000051381623,0.000006252754,0.0015059457,0.00006911799,0.009237602,0.9884741,0.00042495245],"about_ca_topic_score_codex":0.00004022166,"about_ca_topic_score_gemma":0.00000968904,"teacher_disagreement_score":0.9772444,"about_ca_system_score_codex":0.00027773474,"about_ca_system_score_gemma":0.00009697462,"threshold_uncertainty_score":0.9999635},"labels":[],"label_agreement":null},{"id":"W6911976265","doi":"10.5281/zenodo.14991187","title":"TSA-WF: Exploring the Effectiveness of Time Series Analysis for Website Fingerprinting","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Table (database); Code (set theory); Directory; TRACE (psycholinguistics); Random forest; Computation; Row; Raw data","score_opus":0.0363562941143157,"score_gpt":0.25053975390529837,"score_spread":0.21418345979098266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6911976265","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":[0.03579794,0.000066352084,0.9468001,0.0007063679,0.00015838258,0.00048250493,0.000044665925,0.00042231657,0.015521357],"genre_scores_gemma":[0.996746,0.0000292343,0.0019137631,0.000038804414,0.00002349747,2.9789535e-7,0.00014533171,0.000119929144,0.0009831662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986323,0.00047038394,0.00020662144,0.00030192934,0.00021045495,0.0001782737],"domain_scores_gemma":[0.99852806,0.00021772871,0.00009060332,0.0005416901,0.00058401184,0.00003787754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002333058,0.000075760894,0.00014401665,0.0006199978,0.001256072,0.0006672296,0.0013680969,0.00002628262,0.0002657318],"category_scores_gemma":[0.0010408404,0.000066990484,0.00009913074,0.003923389,0.00009023261,0.00037903673,0.0010113919,0.00009837292,0.0002938734],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031740003,0.0004577939,0.00040648677,0.0010528599,0.0016274069,0.0000042028078,0.005022241,0.0013984831,0.051500663,0.5017889,0.024193795,0.41222978],"study_design_scores_gemma":[0.00057244103,0.00012340792,0.046560653,0.00007752165,0.0001251283,0.000007543132,0.0002028287,0.02050093,0.022879545,0.0021767125,0.90653276,0.00024053991],"about_ca_topic_score_codex":0.000011204724,"about_ca_topic_score_gemma":1.9818324e-7,"teacher_disagreement_score":0.96094805,"about_ca_system_score_codex":0.00005894859,"about_ca_system_score_gemma":0.000004809883,"threshold_uncertainty_score":0.9660817},"labels":[],"label_agreement":null},{"id":"W6925186628","doi":"10.17182/hepdata.44234.v1/t2","title":"Table 2","year":2000,"lang":"en","type":"dataset","venue":"HEPData","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; McGill University","funders":"","keywords":"Meson; Table (database); Luminosity; Photon; Momentum (technical analysis); Sample (material)","score_opus":0.026486240398135186,"score_gpt":0.2661506052207253,"score_spread":0.23966436482259013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925186628","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.5367339e-7,0.00041311176,0.0028574911,0.00027519642,0.00074985257,0.000085176835,0.9950251,0.000079568475,0.0005141427],"genre_scores_gemma":[0.0000012866782,0.0008122827,0.0013769261,0.00068282697,0.00011223936,0.000008088361,0.99503154,0.0000037788423,0.0019710348],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986766,0.00004593688,0.00020785315,0.000511208,0.00033736366,0.00022105926],"domain_scores_gemma":[0.99715686,0.000032184016,0.00008736761,0.0025950943,0.000035871057,0.00009262667],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002204136,0.00014214765,0.00016923927,0.00034153103,0.00009295683,0.0004332037,0.0032570802,0.00018409971,0.0021695732],"category_scores_gemma":[0.000042900047,0.00013763263,0.000039947216,0.0012987086,0.000029447147,0.00032791536,0.000487997,0.00024954058,0.006823702],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.5191533e-7,0.000044285247,2.560871e-7,0.000017643719,0.00000603531,0.000011454461,0.0000025809002,5.7176543e-8,3.9864494e-7,0.00027852747,0.9958296,0.00380875],"study_design_scores_gemma":[0.000061072584,0.000006643149,0.000014521372,0.000007995332,0.000006005243,0.000011489149,4.0691825e-7,0.00014951623,0.000009632513,0.000118753866,0.99944973,0.00016422667],"about_ca_topic_score_codex":0.00043030028,"about_ca_topic_score_gemma":0.00005772665,"teacher_disagreement_score":0.004654128,"about_ca_system_score_codex":0.0000184748,"about_ca_system_score_gemma":0.00013868193,"threshold_uncertainty_score":0.9987426},"labels":[],"label_agreement":null},{"id":"W6925671879","doi":"10.17895/ices.pub.25244293","title":"Towards interoperability and cooperation for the sustainable management of the St. Lawrence ecosystem","year":2008,"lang":"en","type":"other","venue":"International Council for the Exploration of the Sea (ICES)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Interoperability; Multidisciplinary approach; Resource management (computing); Data sharing; Sustainability; Resource (disambiguation); Workflow; Information sharing; Service (business)","score_opus":0.11182877645438864,"score_gpt":0.27665519755527235,"score_spread":0.1648264211008837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925671879","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":[0.00038488378,0.0022137945,0.9348101,0.027195977,0.0070350505,0.006505625,0.0010291662,0.00009610636,0.020729318],"genre_scores_gemma":[0.6358695,0.002272079,0.0028092603,0.0006230093,0.0005286849,0.0014353796,0.00008491162,0.000118072705,0.35625908],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977978,0.0001351969,0.00045234832,0.00033353813,0.0011344316,0.00014671021],"domain_scores_gemma":[0.9962152,0.00027736445,0.00066108204,0.00095046614,0.00187462,0.000021218459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019538568,0.00017759336,0.00018434245,0.00008386021,0.0003856762,0.00020195152,0.00265611,0.00009294617,0.00003542808],"category_scores_gemma":[0.00032687673,0.00008531016,0.00018722391,0.00046655312,0.00018782688,0.00038711602,0.00050140417,0.00011908302,0.000002888124],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012949173,0.000230722,0.000050192753,0.0009125016,0.00084891735,3.9279695e-7,0.0035108954,0.0011591924,0.000051029525,0.48334432,0.49657896,0.013183383],"study_design_scores_gemma":[0.000483569,0.00003962787,0.00017164243,0.0001342591,0.000063359505,0.0000026706743,0.00057897385,0.05208404,0.00050819694,0.002689441,0.9431162,0.00012803708],"about_ca_topic_score_codex":0.00039255127,"about_ca_topic_score_gemma":0.00074837875,"teacher_disagreement_score":0.9320008,"about_ca_system_score_codex":0.00036079483,"about_ca_system_score_gemma":0.00033399477,"threshold_uncertainty_score":0.49357578},"labels":[],"label_agreement":null},{"id":"W6930125318","doi":"10.5281/zenodo.11883283","title":"the monk who sold his ferrari free pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Thursday; Download; Publishing; Legend; Patron saint; Bazaar","score_opus":0.03459443078093429,"score_gpt":0.24331741448838773,"score_spread":0.20872298370745343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930125318","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011239746,0.0015409628,0.028200518,0.0041635954,0.0007724247,0.00036226673,0.00015698449,0.0018003959,0.9630017],"genre_scores_gemma":[0.00033936178,0.0007035284,0.000575305,0.00019658625,0.00030904845,7.865578e-8,0.00031220692,0.0031827334,0.9943811],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99772596,0.00029136753,0.00026625584,0.00067615404,0.0006518726,0.00038838832],"domain_scores_gemma":[0.9976075,0.00002706654,0.0001568187,0.0017782152,0.00026181364,0.00016856434],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00080931553,0.00020586012,0.00017147824,0.0006880095,0.0013939899,0.0041616503,0.0053394977,0.00017297293,0.025482776],"category_scores_gemma":[0.0005057958,0.00017204425,0.00009591352,0.0018183253,0.00020133829,0.00016354007,0.003622138,0.0004874888,0.14580713],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019341242,0.00002788156,3.826506e-8,0.000047728754,0.000042601987,0.00000852557,0.0002541146,1.785352e-7,0.00001903718,0.0632586,0.88182825,0.054511078],"study_design_scores_gemma":[0.00013534048,0.00003862811,0.000018397819,0.00004881477,0.000010538501,0.0000307997,0.000034151264,0.0006748437,0.00002111793,0.001578791,0.9972111,0.00019748203],"about_ca_topic_score_codex":0.000056587185,"about_ca_topic_score_gemma":0.0000026626824,"teacher_disagreement_score":0.12032436,"about_ca_system_score_codex":0.00013408168,"about_ca_system_score_gemma":0.000008421984,"threshold_uncertainty_score":0.99990606},"labels":[],"label_agreement":null},{"id":"W6931838917","doi":"10.5683/sp3/zewryk","title":"Looking West 2001 [Canada]","year":2001,"lang":"en","type":"dataset","venue":"Borealis","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary; Queen's University","funders":"","keywords":"Feeling; Alienation; Democracy; Public opinion","score_opus":0.021631439939785897,"score_gpt":0.2537976332566069,"score_spread":0.23216619331682103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931838917","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.9544918e-7,0.00022557097,0.0040089507,0.0009668051,0.0007212639,0.000103411934,0.99238986,0.00006332747,0.0015205353],"genre_scores_gemma":[0.0000038534417,0.00047135845,0.00053585833,0.0012901995,0.00022132011,0.0000105947065,0.99695885,0.0000068846716,0.0005010859],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981178,0.00006398085,0.00030577485,0.0004901273,0.0006506393,0.00037168668],"domain_scores_gemma":[0.9978944,0.00006651468,0.0002067028,0.0015227628,0.00012281786,0.00018676689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002913131,0.00020755101,0.00023432853,0.0003681293,0.00013556879,0.00031866928,0.00218169,0.00020143359,0.00025987852],"category_scores_gemma":[0.00012472151,0.00020671438,0.00006478014,0.0011222609,0.000033311902,0.00015088845,0.00028896125,0.00028022943,0.00005841412],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.6007744e-7,0.000030656596,0.0000051426996,0.00001818096,0.000012736586,0.000081104045,0.0000055141886,3.2271655e-7,1.9525541e-7,0.00031521308,0.99566615,0.0038643393],"study_design_scores_gemma":[0.00007959615,0.0000066701673,0.00052957435,0.000014858218,0.000010952715,0.00002727313,0.000002792472,0.0001269418,0.00000484243,0.00008566497,0.9988684,0.0002424214],"about_ca_topic_score_codex":0.9667981,"about_ca_topic_score_gemma":0.97015697,"teacher_disagreement_score":0.0045690057,"about_ca_system_score_codex":0.00020500823,"about_ca_system_score_gemma":0.00094908115,"threshold_uncertainty_score":0.8429568},"labels":[],"label_agreement":null},{"id":"W6983701701","doi":"","title":"Navigating Domestic Violence Service Provision for Diasporic South Asian Communities in Canada: From the Perspectives of South Asian Service Providers Using a Multi-level Systems Approach and Culture Circles","year":2024,"lang":"en","type":"article","venue":"Scholars Commons (Wilfrid Laurier University)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Service provider; Domestic violence; Diaspora; Qualitative research; Service (business); Ethnography; Narrative; Space (punctuation)","score_opus":0.03954732890093244,"score_gpt":0.24643770778975646,"score_spread":0.20689037888882403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6983701701","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91530615,0.0039144726,0.07745315,0.0012157856,0.00026284708,0.0010385836,0.0006568571,0.000099053555,0.000053122494],"genre_scores_gemma":[0.98780084,0.000030738298,0.011993169,0.00007047951,0.00001764454,0.000005951705,0.000047462672,0.000015120528,0.00001857478],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9981658,0.0004223137,0.00028903427,0.00047724717,0.000342913,0.00030269625],"domain_scores_gemma":[0.99843854,0.00025796852,0.00019339746,0.0006450983,0.00034414622,0.00012085238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049047946,0.00022526743,0.00029246017,0.00030214895,0.00053247676,0.0004340899,0.0013421807,0.00011032331,0.0000010034825],"category_scores_gemma":[0.00007852626,0.00020151926,0.0000596557,0.0033469559,0.00011694763,0.0009947458,0.0004667662,0.0007156444,6.095461e-7],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079919606,0.00029990342,0.03699154,0.0018026892,0.00034319834,0.000085468644,0.90708846,0.0011386038,0.00041753007,0.013784564,0.000046169716,0.037921965],"study_design_scores_gemma":[0.00084549433,0.00002585971,0.028141214,0.0014333493,0.000091874695,0.000016657756,0.69703776,0.2712152,0.00003518538,0.00020981896,0.00053794397,0.0004096644],"about_ca_topic_score_codex":0.451545,"about_ca_topic_score_gemma":0.37976357,"teacher_disagreement_score":0.2700766,"about_ca_system_score_codex":0.00048003113,"about_ca_system_score_gemma":0.0010377936,"threshold_uncertainty_score":0.8217717},"labels":[],"label_agreement":null},{"id":"W6992003917","doi":"","title":"Intra-articular ozone or hyaluronic acid injection: Which one is superior in patients with knee osteoarthritis? A 6-month randomized clinical trial","year":2018,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Shahid; Randomized controlled trial; Osteoarthritis; Rehabilitation; Visual analogue scale; Hyaluronic acid; Clinical trial; Ozone therapy","score_opus":0.17071246164400172,"score_gpt":0.4909016420199772,"score_spread":0.3201891803759755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6992003917","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9805987,0.0019223288,0.012100932,0.00040931755,0.002053653,0.0023693724,0.000017630166,0.00006429238,0.0004637706],"genre_scores_gemma":[0.9963698,0.0007045064,0.0018068232,0.00047604597,0.0003205521,0.00014199199,0.000010661758,0.000029933135,0.00013971025],"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","domain_scores_codex":[0.99356097,0.0016471555,0.0020905326,0.00088649377,0.001298792,0.0005160546],"domain_scores_gemma":[0.995658,0.00064299186,0.0010768465,0.0010527469,0.0012121851,0.0003572783],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005669297,0.00034522277,0.0015154403,0.0014212657,0.000303737,0.0023224747,0.0030114106,0.00022592636,0.004354217],"category_scores_gemma":[0.0022470825,0.00026792396,0.00026226617,0.005458777,0.00043551953,0.0033939583,0.00092897506,0.00060678547,0.000063992775],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.47415453,0.006726777,0.200283,0.00009779386,0.00083774945,0.000056348617,0.0020632122,0.000009505195,0.00069059443,0.000429029,0.0082311025,0.30642036],"study_design_scores_gemma":[0.6198964,0.001646623,0.36540973,0.0005076583,0.00019658644,0.00001930603,0.000057791345,0.0015240659,0.00333274,0.0015195642,0.0048487335,0.0010407829],"about_ca_topic_score_codex":0.00030356724,"about_ca_topic_score_gemma":0.00043800403,"teacher_disagreement_score":0.30537957,"about_ca_system_score_codex":0.00013144188,"about_ca_system_score_gemma":0.00050778466,"threshold_uncertainty_score":0.9999773},"labels":[],"label_agreement":null},{"id":"W6995657895","doi":"","title":"Personal identification by the iris of the eye","year":2012,"lang":"en","type":"dissertation","venue":"Electronic Sumy State University Institutional Repository (Sumy State University)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Iris recognition; Biometrics; Identification (biology); IRIS (biosensor); Pattern recognition (psychology); Image processing","score_opus":0.006365738324394642,"score_gpt":0.19286008649035566,"score_spread":0.18649434816596103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6995657895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87883645,0.004857033,0.06372719,0.0013000197,0.0071625747,0.0022025034,0.0010828307,0.00048219753,0.040349223],"genre_scores_gemma":[0.76964194,0.00046232442,0.00006996806,0.00003350196,0.000043401036,7.465607e-7,0.0004092978,0.000015485368,0.22932334],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965408,0.00049127225,0.00041730763,0.00075445836,0.001128316,0.0006678442],"domain_scores_gemma":[0.997142,0.00014619577,0.0009403932,0.00087848346,0.0007156072,0.0001773293],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00047777893,0.00040079022,0.0003330279,0.0008165762,0.0022545008,0.00015185733,0.0030012797,0.00026332584,0.000035961653],"category_scores_gemma":[0.000043510132,0.0003640833,0.0004187267,0.0028616872,0.0005798014,0.0012067147,0.00031292363,0.0009179688,0.000040448762],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032367876,0.004838755,0.018575672,0.0014609067,0.00688561,0.0006100138,0.05508332,0.0022713942,0.073532484,0.67760295,0.08561391,0.070288174],"study_design_scores_gemma":[0.0010112108,0.00007278898,0.040672053,0.000078624296,0.00032866868,0.000038448135,0.0024470047,0.0021005885,0.007215979,0.00026106986,0.944979,0.0007945689],"about_ca_topic_score_codex":0.0009832657,"about_ca_topic_score_gemma":0.0003539953,"teacher_disagreement_score":0.8593651,"about_ca_system_score_codex":0.002042198,"about_ca_system_score_gemma":0.002532257,"threshold_uncertainty_score":0.9998811},"labels":[],"label_agreement":null},{"id":"W7029263684","doi":"","title":"Iris Energy to Release Second Quarter and Half Year FY24 Results and Host Conference Call on February 15, 2024","year":2024,"lang":"en","type":"other","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); IRIS (biosensor); Host (biology); Energy (signal processing)","score_opus":0.016035837527864204,"score_gpt":0.2404133831338009,"score_spread":0.2243775456059367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7029263684","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000102227685,0.0016969808,0.016081493,0.0066939415,0.0015834479,0.00025996607,0.00029370977,0.00034980688,0.9729384],"genre_scores_gemma":[0.01133586,0.00022127997,0.0025957082,0.0015212663,0.00013793384,0.0000121278745,0.000018995295,0.0000665309,0.98409027],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99828637,0.00005626944,0.00024214496,0.00093372463,0.00027037325,0.00021109571],"domain_scores_gemma":[0.99890107,0.000052285628,0.00007316986,0.0006665707,0.000046649973,0.00026026717],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00017952948,0.00024296509,0.00023920512,0.00086749595,0.000034400626,0.00057382986,0.00039255808,0.0002541188,0.0011441095],"category_scores_gemma":[0.00003305771,0.00020626427,0.000039516093,0.00063180533,0.0000644431,0.000073726245,0.00025421786,0.00020965026,0.0011923152],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007731191,0.000023256165,0.0000026593952,0.000055258817,0.000021429913,0.000023631832,0.00024270131,1.1112826e-8,0.000018221432,0.028502423,0.9670446,0.004058102],"study_design_scores_gemma":[0.00023549159,0.00010086548,0.0004528928,0.00011409392,0.000009152594,0.000009475998,0.00003301665,0.00048389754,0.000059264577,0.00036116815,0.9978394,0.00030130695],"about_ca_topic_score_codex":0.0021303163,"about_ca_topic_score_gemma":0.001993278,"teacher_disagreement_score":0.030794801,"about_ca_system_score_codex":0.000022630256,"about_ca_system_score_gemma":0.000059299862,"threshold_uncertainty_score":0.999769},"labels":[],"label_agreement":null},{"id":"W7037903159","doi":"","title":"A família e a gestão da doença crónica: Avaliação e Intervenção do Enfermeiro Especialista em Enfermagem de Saúde Familiar: Projeto de desenvolvimento de competências clínicas especializadas em enfermagem comunitária na área de enfermagem de saúde família","year":2024,"lang":"pt","type":"dissertation","venue":"Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nursing care; Nursing staff; Nursing process; Order (exchange)","score_opus":0.13963950503420522,"score_gpt":0.43194311973047606,"score_spread":0.29230361469627086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7037903159","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8534751,0.011271944,0.089353174,0.004029694,0.004247978,0.012694033,0.004143589,0.002574112,0.01821038],"genre_scores_gemma":[0.9518815,0.0047599683,0.012631588,0.00035626205,0.00061237114,0.0028631513,0.0017827165,0.00026799765,0.024844434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97929627,0.0018818489,0.0028548501,0.0045105214,0.005461589,0.005994946],"domain_scores_gemma":[0.98742115,0.0019926047,0.0012834619,0.0022327693,0.005361202,0.0017088169],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","research_integrity"],"category_scores_codex":[0.029533077,0.0017352612,0.0017175252,0.015548414,0.004491531,0.0060229753,0.008858677,0.0024544157,0.00020535584],"category_scores_gemma":[0.011882755,0.0018531934,0.0007929716,0.02149626,0.0037610224,0.002079733,0.0030607523,0.00506351,0.00015910545],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0014104384,0.00590207,0.012865402,0.005301869,0.0016398694,0.0019247998,0.06751657,0.000024456462,0.10114228,0.5094049,0.09089068,0.20197664],"study_design_scores_gemma":[0.011171583,0.006962825,0.045423906,0.006962376,0.0014496091,0.0044204663,0.062977634,0.32289147,0.06329961,0.26582408,0.19674014,0.011876305],"about_ca_topic_score_codex":0.0014504257,"about_ca_topic_score_gemma":0.0013118904,"teacher_disagreement_score":0.322867,"about_ca_system_score_codex":0.008995225,"about_ca_system_score_gemma":0.023008374,"threshold_uncertainty_score":0.9995393},"labels":[],"label_agreement":null},{"id":"W7043451955","doi":"","title":"Soluciones match-on-device y processing-on-device en sistemas móviles","year":2015,"lang":"es","type":"other","venue":"e-Archivo (Carlos III University of Madrid)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Persona; Catechesis; Nova scotia; Alliance","score_opus":0.028187651767352682,"score_gpt":0.24361144957667763,"score_spread":0.21542379780932494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043451955","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005070437,0.0018299808,0.016561957,0.005525289,0.0024320853,0.0018746748,0.0008166147,0.00078678544,0.9651022],"genre_scores_gemma":[0.19337362,0.0014174574,0.014326743,0.0009696844,0.001068202,0.0000052681085,0.00038707512,0.00036303297,0.7880889],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99437267,0.00069776014,0.0006395148,0.001673452,0.0018532904,0.0007633251],"domain_scores_gemma":[0.99528277,0.00043532767,0.0011914661,0.0018487393,0.0006935708,0.0005481314],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010649775,0.00083496247,0.0012086079,0.0021759688,0.000602314,0.00033624243,0.004064265,0.0006393047,0.0011417741],"category_scores_gemma":[0.00016607864,0.0009196069,0.00044586783,0.0022947844,0.0007660013,0.00048859854,0.0011559788,0.0009248131,0.0026524097],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007863677,0.003282676,0.0003861812,0.0051887464,0.0009880105,0.00037686634,0.025940292,0.000063806874,0.00043713357,0.034383196,0.83260775,0.09555897],"study_design_scores_gemma":[0.001841621,0.00040195722,0.0026272116,0.0020704619,0.0002176687,0.000020919033,0.0020045564,0.0020318269,0.0001571694,0.00055890763,0.9869119,0.0011558027],"about_ca_topic_score_codex":0.0008618537,"about_ca_topic_score_gemma":0.00043934298,"teacher_disagreement_score":0.18830319,"about_ca_system_score_codex":0.00031548768,"about_ca_system_score_gemma":0.00090159796,"threshold_uncertainty_score":0.9997713},"labels":[],"label_agreement":null},{"id":"W7111649772","doi":"","title":"Video-Based Face and Facial Landmark Tracking for Neonatal Vital Sign Monitoring","year":2023,"lang":"en","type":"article","venue":"Monash University Research Portal (Monash University)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; McGill University","funders":"Cerebral Palsy Alliance; Commonwealth Scientific and Industrial Research Organisation; Veski","keywords":"Landmark; Forehead; Sign (mathematics); Face (sociological concept); Tracking (education); Nose; Identification (biology)","score_opus":0.08085555736905556,"score_gpt":0.3008733649557589,"score_spread":0.22001780758670333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7111649772","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83305234,0.00009545961,0.1548702,0.0020805371,0.00067189086,0.0013206935,0.00037960277,0.0010053174,0.006523957],"genre_scores_gemma":[0.98760253,0.00008691551,0.0017186307,0.000011857618,0.00006608195,8.1813647e-7,0.00007378917,0.000014952975,0.0104244305],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971403,0.00023967035,0.00015567496,0.0008340602,0.0008599843,0.0007702992],"domain_scores_gemma":[0.9979948,0.0005854751,0.00008441324,0.00045571683,0.00047440262,0.00040520183],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009774888,0.00020672091,0.00023690016,0.002587138,0.0012543737,0.00027776833,0.0014075587,0.00019510367,0.000024860405],"category_scores_gemma":[0.00016336725,0.00026601247,0.00016241624,0.005170824,0.00032231194,0.0012503586,0.00081520143,0.00043111853,0.00005143114],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002174911,0.0013504586,0.17746767,0.00092200236,0.00083437073,0.017017366,0.013843172,0.0011898401,0.01999149,0.22067301,0.05440882,0.4901269],"study_design_scores_gemma":[0.0060616294,0.0005564338,0.10478907,0.00010773069,0.000059712835,0.000026716805,0.017169649,0.04007972,0.00628823,0.00096306193,0.8224882,0.0014098771],"about_ca_topic_score_codex":0.0002528584,"about_ca_topic_score_gemma":0.00010951175,"teacher_disagreement_score":0.76807934,"about_ca_system_score_codex":0.00021563336,"about_ca_system_score_gemma":0.00030849822,"threshold_uncertainty_score":0.9999792},"labels":[],"label_agreement":null},{"id":"W7116079622","doi":"10.48550/arxiv.2512.15543","title":"Nine Years of Pediatric Iris Recognition: Evidence for Biometric Permanence","year":2025,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Biometrics; Iris recognition; Longitudinal study; Pupillary response; Confounding; IRIS (biosensor)","score_opus":0.2617817531361712,"score_gpt":0.2584119878660572,"score_spread":0.0033697652701140113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116079622","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":[0.17269543,0.002555904,0.81898206,0.00020816963,0.002971218,0.0014049,0.0005563243,0.00014201786,0.00048395613],"genre_scores_gemma":[0.96972764,0.014684707,0.006979935,0.000087897926,0.00025225503,0.000007043926,0.00007856389,0.00001637148,0.0081655625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953909,0.00034871712,0.0009191692,0.0023592757,0.0003541701,0.00062777085],"domain_scores_gemma":[0.99242723,0.0020706444,0.0012633057,0.0020366563,0.0018828934,0.00031927746],"candidate_categories":["metaepi_narrow","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0015381791,0.0004963215,0.00077600795,0.0064081787,0.00030318685,0.00022113584,0.0039184126,0.0006428773,0.00029516823],"category_scores_gemma":[0.0015692234,0.0006974014,0.0007206906,0.024631593,0.00035483792,0.00093084696,0.0020901435,0.0006146358,0.00020446876],"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":[0.002436713,0.008417405,0.07044671,0.02426787,0.002810529,0.0008314656,0.007546378,0.024870822,0.0008554598,0.13979238,0.04242677,0.6752975],"study_design_scores_gemma":[0.005346732,0.0011270851,0.09381485,0.0018352838,0.0034013619,0.000025054438,0.0004456436,0.84230924,0.0015437993,0.025966063,0.01994287,0.004242047],"about_ca_topic_score_codex":0.00032306468,"about_ca_topic_score_gemma":0.000010802056,"teacher_disagreement_score":0.81743836,"about_ca_system_score_codex":0.0003960568,"about_ca_system_score_gemma":0.0011227645,"threshold_uncertainty_score":0.9995477},"labels":[],"label_agreement":null},{"id":"W7116210485","doi":"","title":"Nine Years of Pediatric Iris Recognition: Evidence for Biometric Permanence","year":2025,"lang":"","type":"article","venue":"ArXiv.org","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Biometrics; Iris recognition; Longitudinal study; Pupillary response; Confounding; IRIS (biosensor)","score_opus":0.17381499401248643,"score_gpt":0.3514280906818504,"score_spread":0.17761309666936395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116210485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79354286,0.01983006,0.17906812,0.0022107405,0.0039330604,0.000956811,0.000100049,0.000103073624,0.00025524982],"genre_scores_gemma":[0.98273516,0.006778209,0.0060592485,0.00043684838,0.00035423823,0.00006993834,0.000023032519,0.000015435262,0.0035278895],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9963361,0.00019045184,0.0011588536,0.0011213974,0.00062270294,0.000570484],"domain_scores_gemma":[0.9950939,0.0016478865,0.00063339114,0.0012264443,0.0012092015,0.00018921026],"candidate_categories":["metaepi_narrow","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0016300211,0.00029727016,0.0004965144,0.00423141,0.00027473806,0.00020598868,0.0018654518,0.00028393196,0.00029427174],"category_scores_gemma":[0.0037751577,0.0003546643,0.0003407275,0.026455533,0.00025542497,0.0009169241,0.0005107541,0.00029118476,0.00046450656],"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":[0.000118157535,0.0010445276,0.43546394,0.0016745279,0.00017975135,0.000016376593,0.001143343,0.0000046170585,0.0024990686,0.0010363596,0.027066536,0.5297528],"study_design_scores_gemma":[0.0011374276,0.00033225483,0.9697154,0.00028664217,0.0003246419,0.000006750412,0.00007035224,0.0053815017,0.00459409,0.00049079733,0.017082047,0.00057808927],"about_ca_topic_score_codex":0.00011443859,"about_ca_topic_score_gemma":0.0000040503755,"teacher_disagreement_score":0.53425145,"about_ca_system_score_codex":0.00014204442,"about_ca_system_score_gemma":0.0005954485,"threshold_uncertainty_score":0.9998905},"labels":[],"label_agreement":null},{"id":"W7116851324","doi":"10.1109/etncc66224.2025.11299795","title":"Hybrid Fingerprint Classification Using Deep Learning and Sobel Feature Fusion","year":2025,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"University of Winnipeg","keywords":"Pattern recognition (psychology); Preprocessor; Robustness (evolution); Deep learning; Feature extraction; Classifier (UML); Biometrics; Confusion matrix","score_opus":0.021194007780338246,"score_gpt":0.2806344602635245,"score_spread":0.25944045248318626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116851324","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":[0.089527585,0.00035845023,0.9056931,0.0015910194,0.00022478298,0.000075257194,1.7338952e-7,0.00012656326,0.002403058],"genre_scores_gemma":[0.95729285,0.000075186756,0.038862575,0.00022427578,0.00001109213,0.0000022184072,0.0000023397138,0.0000022016459,0.0035272576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993224,0.00004959348,0.00011225768,0.0002822894,0.00012280897,0.000110622095],"domain_scores_gemma":[0.999581,0.00004659476,0.00005174276,0.00020267884,0.000079838515,0.000038194994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025604985,0.0000650782,0.00007076029,0.0003115467,0.00023315499,0.00025947465,0.00021525382,0.000041069226,0.000010878684],"category_scores_gemma":[0.00007752061,0.000059959515,0.000023107792,0.00072063575,0.000024958938,0.00018275333,0.00019921953,0.0001437379,0.000009980871],"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":[0.000003499793,0.000059354654,0.004682655,0.000036248024,0.000012262361,0.000002264212,0.00037451778,0.00003511439,0.024312284,0.088151164,0.0009275487,0.8814031],"study_design_scores_gemma":[0.00014763213,0.0000071612862,0.045998473,0.000014906828,0.0000042848365,0.000007894155,0.000049757473,0.9176169,0.003928409,0.0020960544,0.030017816,0.000110714216],"about_ca_topic_score_codex":0.0000218225,"about_ca_topic_score_gemma":0.0000048309967,"teacher_disagreement_score":0.9175818,"about_ca_system_score_codex":0.00004141932,"about_ca_system_score_gemma":0.000031459647,"threshold_uncertainty_score":0.25021198},"labels":[],"label_agreement":null},{"id":"W7123756568","doi":"10.1109/aixmhc65380.2025.00026","title":"Enhancing Human Authentication with a Hybrid Deep Learning-Based Palm Fusion Model Utilizing RGB Images","year":2025,"lang":"","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Science and Technology Council","keywords":"RGB color model; Biometrics; Palm print; Palm; Authentication (law); Fusion; Feature (linguistics); Sensor fusion","score_opus":0.018217461554005363,"score_gpt":0.27490776100096653,"score_spread":0.25669029944696115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7123756568","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":[0.03851584,0.00054453773,0.9527166,0.0015052343,0.00032508466,0.00049983186,0.0000029043492,0.00035121277,0.005538742],"genre_scores_gemma":[0.9635427,0.000047784044,0.024585513,0.00046396762,0.000028183507,0.000034738714,0.00003481984,0.000019602458,0.01124268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618214,0.00030163783,0.00084221706,0.0012882049,0.0007550798,0.0006307008],"domain_scores_gemma":[0.99737215,0.00017316357,0.00041018164,0.0011537434,0.0006931793,0.00019760018],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013394299,0.0004022241,0.00038529315,0.0015138196,0.0015071828,0.0012652252,0.0011247871,0.00014873566,0.00021086005],"category_scores_gemma":[0.00020580225,0.00037780142,0.00016539886,0.0035579705,0.0002387464,0.0006507767,0.0003917857,0.0005584097,0.000097477125],"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":[0.00025278138,0.0040009064,0.0061471453,0.002169015,0.00039320145,0.000059579335,0.010600742,0.038468827,0.2681476,0.20165162,0.002338519,0.46577007],"study_design_scores_gemma":[0.0007417305,0.000100400015,0.0028114594,0.00021119704,0.00007249283,0.0000029919186,0.00020556345,0.8996522,0.09390977,0.0013342805,0.00056512136,0.00039279918],"about_ca_topic_score_codex":0.00016870907,"about_ca_topic_score_gemma":0.000098143,"teacher_disagreement_score":0.9281311,"about_ca_system_score_codex":0.00022249258,"about_ca_system_score_gemma":0.00056361884,"threshold_uncertainty_score":0.9998674},"labels":[],"label_agreement":null},{"id":"W7124178004","doi":"10.37665/leifnko27953","title":"“Optoelectronic Fingerprint Sensor for Mobile Phones”","year":2002,"lang":"","type":"article","venue":"Specialized and Legacy Electronics Manufacturing Conferences","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Native Health Society","funders":"","keywords":"Fingerprint (computing); Microprocessor; Fingerprint recognition; Mobile phone; Biometrics; Mobile device; Chip; Cursor (databases)","score_opus":0.030640109716644232,"score_gpt":0.261544606042661,"score_spread":0.23090449632601676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124178004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57606745,0.10745528,0.27607355,0.008653098,0.008416747,0.0069909156,0.0001532665,0.0009872287,0.015202444],"genre_scores_gemma":[0.97116613,0.020157862,0.0019486422,0.00027918926,0.00085252046,0.0002028907,0.000026670681,0.000045318804,0.005320772],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9943005,0.00026146983,0.0010611925,0.001761674,0.00069882296,0.001916376],"domain_scores_gemma":[0.9972907,0.00046522997,0.00060114893,0.0009646059,0.0002749992,0.00040333855],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008841059,0.00078553375,0.0009413419,0.0007610285,0.0011850789,0.0034308657,0.0014011596,0.00041745979,0.0017486525],"category_scores_gemma":[0.00012612695,0.0007906317,0.000389423,0.0007753223,0.00037129718,0.0010646046,0.00031612653,0.0008699657,0.00014380064],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012362153,0.0005808426,0.000040678246,0.00032986343,0.00033952872,0.000010382318,0.004793877,0.000028257531,0.0009009944,0.12482118,0.0025142024,0.86551654],"study_design_scores_gemma":[0.002710639,0.0010648607,0.00032146357,0.000081981125,0.00014598726,0.000052183754,0.00044021712,0.050349727,0.057261914,0.012029906,0.8741292,0.0014119366],"about_ca_topic_score_codex":0.00017782286,"about_ca_topic_score_gemma":0.00018381761,"teacher_disagreement_score":0.871615,"about_ca_system_score_codex":0.00033170107,"about_ca_system_score_gemma":0.00058310275,"threshold_uncertainty_score":0.99945444},"labels":[],"label_agreement":null},{"id":"W7125472283","doi":"10.52436/1.jutif.2025.6.6.5299","title":"Early Fusion of CNN Features for Multimodal Biometric Authentication from ECG and Fingerprint Using MLP, LSTM, GCN, and GAT","year":2025,"lang":"","type":"article","venue":"Jurnal Teknik Informatika (Jutif)","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Biometrics; Pattern recognition (psychology); Convolutional neural network; Deep learning; Spoofing attack; Fingerprint (computing); Feature (linguistics); Feature extraction; Fingerprint recognition","score_opus":0.020771749851549472,"score_gpt":0.2863057210579277,"score_spread":0.2655339712063782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125472283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6979711,0.0037692923,0.29522818,0.00059374823,0.0012066665,0.00082884997,0.00016793038,0.000049198363,0.00018500592],"genre_scores_gemma":[0.9553118,0.0008617507,0.04315633,0.00023759727,0.000076360906,0.000017129809,0.00003931198,0.000013358809,0.00028639787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640507,0.00011757655,0.0015761603,0.00059457624,0.0007732947,0.0005333052],"domain_scores_gemma":[0.9964307,0.0006048157,0.0011263489,0.0007532126,0.00080360944,0.00028131893],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013377009,0.0004182625,0.0006221832,0.0041481415,0.0007115487,0.00131803,0.00086642813,0.00043612986,0.000024421888],"category_scores_gemma":[0.0007589735,0.00040266907,0.0002051282,0.0053996188,0.00037116715,0.0018610416,0.00087440334,0.00043434778,0.000010733601],"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":[0.00021511779,0.00035278578,0.0051376945,0.0010595318,0.0002734304,0.000001736857,0.013488053,0.000026644138,0.01819047,0.009196939,0.0006643446,0.95139325],"study_design_scores_gemma":[0.0022212416,0.00019955762,0.34826264,0.0005340945,0.00020387334,0.000019844494,0.00047104523,0.6239132,0.016183956,0.002610369,0.0048448057,0.0005353389],"about_ca_topic_score_codex":0.000719583,"about_ca_topic_score_gemma":0.000012377399,"teacher_disagreement_score":0.95085794,"about_ca_system_score_codex":0.00018202802,"about_ca_system_score_gemma":0.00033819987,"threshold_uncertainty_score":0.9998425},"labels":[],"label_agreement":null},{"id":"W7126202468","doi":"10.18280/isi.301214","title":"A Deep Learning-Based Multimodal Biometric Authentication Framework Using Fingerprint and Iris with Score-Level Fusion","year":2025,"lang":"","type":"article","venue":"Ingénierie des systèmes d information","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Biometrics; IRIS (biosensor); Fingerprint (computing); Iris recognition; Fusion; Authentication (law); Pattern recognition (psychology)","score_opus":0.026985354438590476,"score_gpt":0.2619617826791562,"score_spread":0.23497642824056572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7126202468","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":[0.21115361,0.00072841655,0.78616005,0.00020130152,0.0006634405,0.0007067264,0.000013150031,0.00017186091,0.00020142987],"genre_scores_gemma":[0.90609163,0.00011731286,0.09331887,0.00024926494,0.00003478058,0.000042168216,0.00007044742,0.000014502199,0.0000610178],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964126,0.00029408134,0.0012940984,0.0005827599,0.00081429933,0.00060218683],"domain_scores_gemma":[0.99632317,0.00040273293,0.0010745937,0.00076059863,0.0012208633,0.00021805188],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014373704,0.00046198306,0.00046161306,0.005270351,0.0014147421,0.0024739695,0.0006835397,0.00049444905,0.0000475746],"category_scores_gemma":[0.0018349519,0.00045511266,0.000118258155,0.012784285,0.0005400199,0.0034919204,0.00038814807,0.0006006831,0.00005771421],"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":[0.00017198284,0.00027358218,0.018385476,0.0020217781,0.00014988639,0.0000035486526,0.018223586,0.010536763,0.00039500816,0.011638403,0.000028992414,0.93817097],"study_design_scores_gemma":[0.00082152395,0.00016031128,0.1117763,0.0010475755,0.000088343244,0.000013470012,0.00057357486,0.882002,0.00072156,0.0013386119,0.0010040294,0.00045273214],"about_ca_topic_score_codex":0.00040276872,"about_ca_topic_score_gemma":0.000012365439,"teacher_disagreement_score":0.9377183,"about_ca_system_score_codex":0.00082584686,"about_ca_system_score_gemma":0.00056881557,"threshold_uncertainty_score":0.99988526},"labels":[],"label_agreement":null},{"id":"W7127071286","doi":"10.18280/ijsse.151106","title":"A Visual Cryptography Framework with Tuned Cipher Block Chaining and Quantum Key Distribution–Assisted Encryption for Securing Thermal Facial Biometrics in Anti-Doping Applications","year":2025,"lang":"","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chaining; Encryption; Key (lock); Block (permutation group theory); Block cipher; Cryptography; Biometrics; Triple DES","score_opus":0.008594902103490768,"score_gpt":0.2625007822315411,"score_spread":0.2539058801280503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127071286","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":[0.24022388,0.0023453166,0.7549772,0.0010704596,0.00090058876,0.00034008987,0.00010299367,0.000026213776,0.00001319872],"genre_scores_gemma":[0.9892069,0.0017157508,0.008664407,0.00006833745,0.00026830888,0.000021213024,0.000037603746,0.000014454432,0.0000029888333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99731827,0.000070124435,0.0011546621,0.00045930446,0.0006232439,0.0003744178],"domain_scores_gemma":[0.9976607,0.0005658124,0.0005665545,0.00016669412,0.00086972746,0.00017051336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011966196,0.00031565715,0.0004903798,0.0024879205,0.00026864192,0.00059976004,0.00058958307,0.0002705349,0.000005612661],"category_scores_gemma":[0.000400622,0.0003255524,0.00016569656,0.0034544251,0.0001321508,0.0007177389,0.00022015009,0.0006744862,4.0615836e-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":[0.0022925364,0.0021450548,0.041063797,0.0017814261,0.00281338,0.00012671489,0.016824845,0.014839123,0.0071624196,0.67239237,0.00003713359,0.2385212],"study_design_scores_gemma":[0.0054800697,0.00037315473,0.1997365,0.0027140877,0.0001743469,0.00024138184,0.00086035964,0.7670443,0.00088605605,0.003928691,0.017636718,0.0009243034],"about_ca_topic_score_codex":0.000022452332,"about_ca_topic_score_gemma":0.0000058435603,"teacher_disagreement_score":0.7522052,"about_ca_system_score_codex":0.00024542905,"about_ca_system_score_gemma":0.00019186726,"threshold_uncertainty_score":0.99991965},"labels":[],"label_agreement":null},{"id":"W7129485185","doi":"10.1145/3733820.3764683","title":"Information-Theoretically Secure, Robust, and Reusable Fuzzy Extractors for Structured Sources","year":2025,"lang":"","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Randomness; Fuzzy logic; Extractor; Robustness (evolution); Cryptography; Key (lock); Fuzzy control system","score_opus":0.011450693186588626,"score_gpt":0.24410508328220223,"score_spread":0.23265439009561362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7129485185","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":[0.005894394,0.00076747884,0.965582,0.005909573,0.0019565881,0.000925619,0.000064385335,0.00013166557,0.018768301],"genre_scores_gemma":[0.90238756,0.0002300456,0.08612116,0.002464285,0.00013225865,0.00004854376,0.00005284195,0.000010705783,0.008552617],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977218,0.00008945787,0.0008687464,0.00044877434,0.00038486475,0.00048636334],"domain_scores_gemma":[0.9976721,0.00046046134,0.00025580384,0.0007413351,0.000653228,0.00021706302],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00091857096,0.0002940226,0.00033486515,0.0009598852,0.0005745511,0.0021520494,0.0009941612,0.0003384857,0.00026123284],"category_scores_gemma":[0.0007425055,0.00026717284,0.00013062816,0.0024383601,0.00036411037,0.0017547468,0.00038764675,0.00024571904,0.000028858094],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029852537,0.00005113772,0.0002925783,0.00023593158,0.000058264195,2.1975298e-7,0.0015533388,0.000036542566,0.000030677606,0.9098473,0.020870391,0.066993766],"study_design_scores_gemma":[0.0019533474,0.0001629145,0.009289151,0.000085793334,0.000111744455,0.000011672548,0.0010384348,0.2390501,0.0023711673,0.22595555,0.51921487,0.0007552698],"about_ca_topic_score_codex":0.00004875489,"about_ca_topic_score_gemma":0.000015022884,"teacher_disagreement_score":0.89649314,"about_ca_system_score_codex":0.00006248252,"about_ca_system_score_gemma":0.00033476998,"threshold_uncertainty_score":0.99997807},"labels":[],"label_agreement":null},{"id":"W7131233680","doi":"10.1109/icoiics67115.2025.11390439","title":"Smart Handlebar with Integrated Auto Finger Sensor for Biometric Authentication and Rider Safety","year":2025,"lang":"","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Biometrics; Authentication (law); Fingerprint recognition; Fingerprint (computing); Identification (biology); Password; Merge (version control)","score_opus":0.016531361306789245,"score_gpt":0.260352138254132,"score_spread":0.24382077694734275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131233680","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":[0.011267524,0.0010907393,0.97501796,0.008502754,0.001138588,0.0012436229,0.000069225476,0.00017655402,0.0014930047],"genre_scores_gemma":[0.86668,0.00029183066,0.07663951,0.0010157562,0.000044238554,0.000080633654,0.00007612825,0.000021054173,0.05515081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969746,0.00015543806,0.0007816227,0.0011549435,0.0004133083,0.00052008167],"domain_scores_gemma":[0.99699485,0.0005784723,0.00025834265,0.0009056843,0.0010516206,0.00021100345],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012740762,0.00036454955,0.0004325091,0.00351677,0.00064616115,0.001164915,0.0006274688,0.00026757675,0.0001993084],"category_scores_gemma":[0.00060454704,0.00029170056,0.00011660948,0.015540502,0.00031907947,0.0005248412,0.00023456107,0.00022137606,0.00006297633],"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":[0.0004995625,0.0014073987,0.0052042813,0.0008507634,0.00075546367,0.0000054844663,0.0030457429,0.000007308174,0.0034771531,0.20784096,0.022086782,0.7548191],"study_design_scores_gemma":[0.0031745064,0.00035657044,0.07652408,0.00020280761,0.00027544954,0.000017176877,0.0003727522,0.61771166,0.007444054,0.0013703961,0.29177052,0.0007800119],"about_ca_topic_score_codex":0.00024847774,"about_ca_topic_score_gemma":0.00006241855,"teacher_disagreement_score":0.8983785,"about_ca_system_score_codex":0.0001855544,"about_ca_system_score_gemma":0.00046851396,"threshold_uncertainty_score":0.9999535},"labels":[],"label_agreement":null},{"id":"W7133340357","doi":"10.1109/ijcb65343.2025.11410879","title":"2nd Latent in the Wild Fingerprint Recognition Competition","year":2025,"lang":"","type":"article","venue":"","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"National Natural Science Foundation of China","keywords":"Fingerprint (computing); Competition (biology); Quality (philosophy); Fingerprint recognition; Pattern recognition (psychology); Latent class model","score_opus":0.04006497407302008,"score_gpt":0.27117395127770866,"score_spread":0.2311089772046886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133340357","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":[0.045423135,0.00067002664,0.8418988,0.051737897,0.0029225901,0.0008857222,0.000011131137,0.000095102085,0.056355577],"genre_scores_gemma":[0.9918725,0.00036520034,0.001819521,0.0042631677,0.000037173675,0.000028401744,0.00002015143,0.0000025238248,0.0015913746],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99796766,0.00039401493,0.00052041037,0.0004894641,0.000364656,0.00026380763],"domain_scores_gemma":[0.9988367,0.00021932983,0.00010974683,0.0006255443,0.00016963064,0.00003905749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016355007,0.00014512565,0.00015246584,0.0007775322,0.000232032,0.00072230195,0.0009033498,0.00012755407,0.00045552407],"category_scores_gemma":[0.000120334036,0.000114521434,0.000094725605,0.004502646,0.00009433614,0.0003165533,0.00020635412,0.00032922643,0.0003729334],"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":[0.000011871457,0.00075535936,0.0012604494,0.00007486809,0.000018938508,0.000008291161,0.002298625,0.00001042074,0.00009332918,0.33833233,0.005114378,0.6520211],"study_design_scores_gemma":[0.0018902048,0.00014826522,0.41791165,0.0005789834,0.000057816775,0.000022929067,0.0010550573,0.33908075,0.0021289855,0.090185605,0.14618458,0.0007551681],"about_ca_topic_score_codex":0.0004612022,"about_ca_topic_score_gemma":0.00028986848,"teacher_disagreement_score":0.94644934,"about_ca_system_score_codex":0.00011665706,"about_ca_system_score_gemma":0.00011596626,"threshold_uncertainty_score":0.69651747},"labels":[],"label_agreement":null},{"id":"W7143893681","doi":"10.71465/ajainn474","title":"Exploring Neural Networks for Biometric Authentication Systems","year":2022,"lang":"","type":"article","venue":"American Journal of Artificial Intelligence and Neural Networks","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Biometrics; Authentication (law); Artificial neural network; Identification (biology); Focus (optics); Fingerprint (computing); Scalability; Fingerprint recognition","score_opus":0.14843692469609732,"score_gpt":0.30856947599447215,"score_spread":0.16013255129837484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143893681","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":[0.1393571,0.0073090456,0.8407018,0.0014445587,0.010586675,0.0005471172,0.000016456206,0.00003350914,0.000003684917],"genre_scores_gemma":[0.99477243,0.0024369094,0.0008196043,0.00033621214,0.001475025,0.00007732795,0.000010127977,0.000038277813,0.00003406715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942418,0.0008179267,0.0023120171,0.00075922394,0.0009466834,0.0009223922],"domain_scores_gemma":[0.9943977,0.001134238,0.0026450858,0.00056546973,0.0007419073,0.0005155776],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0030294445,0.00043789056,0.000897957,0.0021692717,0.001394995,0.0011160995,0.0016212406,0.000080111786,0.00003755012],"category_scores_gemma":[0.00024420265,0.0004418869,0.0004501793,0.010336393,0.0006437882,0.0012859841,0.0005147983,0.0011546348,0.0000018333086],"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":[0.00024834194,0.00021989133,0.00011362656,0.000017289807,0.00007953639,0.000019366313,0.00095345505,0.31320113,0.000033971497,0.003837123,0.00012600332,0.68115026],"study_design_scores_gemma":[0.00009495351,0.0029014607,0.00029530868,0.000029655243,0.00012649618,0.0003402552,0.004019789,0.9900831,0.000061746294,0.00024713017,0.0013578791,0.0004422056],"about_ca_topic_score_codex":0.00014885057,"about_ca_topic_score_gemma":0.000004028225,"teacher_disagreement_score":0.85541534,"about_ca_system_score_codex":0.00020303963,"about_ca_system_score_gemma":0.00010188088,"threshold_uncertainty_score":0.99992085},"labels":[],"label_agreement":null},{"id":"W7144119657","doi":"10.71465/ajainn244","title":"Exploring Neural Networks for Biometric Authentication Systems","year":2022,"lang":"","type":"article","venue":"American Journal of Artificial Intelligence and Neural Networks","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Biometrics; Authentication (law); Artificial neural network; Identification (biology); Focus (optics); Fingerprint (computing); Scalability; Fingerprint recognition","score_opus":0.14843692469609732,"score_gpt":0.30856947599447215,"score_spread":0.16013255129837484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7144119657","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":[0.1393571,0.0073090456,0.8407018,0.0014445587,0.010586675,0.0005471172,0.000016456206,0.00003350914,0.000003684917],"genre_scores_gemma":[0.99477243,0.0024369094,0.0008196043,0.00033621214,0.001475025,0.00007732795,0.000010127977,0.000038277813,0.00003406715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942418,0.0008179267,0.0023120171,0.00075922394,0.0009466834,0.0009223922],"domain_scores_gemma":[0.9943977,0.001134238,0.0026450858,0.00056546973,0.0007419073,0.0005155776],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0030294445,0.00043789056,0.000897957,0.0021692717,0.001394995,0.0011160995,0.0016212406,0.000080111786,0.00003755012],"category_scores_gemma":[0.00024420265,0.0004418869,0.0004501793,0.010336393,0.0006437882,0.0012859841,0.0005147983,0.0011546348,0.0000018333086],"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":[0.00024834194,0.00021989133,0.00011362656,0.000017289807,0.00007953639,0.000019366313,0.00095345505,0.31320113,0.000033971497,0.003837123,0.00012600332,0.68115026],"study_design_scores_gemma":[0.00009495351,0.0029014607,0.00029530868,0.000029655243,0.00012649618,0.0003402552,0.004019789,0.9900831,0.000061746294,0.00024713017,0.0013578791,0.0004422056],"about_ca_topic_score_codex":0.00014885057,"about_ca_topic_score_gemma":0.000004028225,"teacher_disagreement_score":0.85541534,"about_ca_system_score_codex":0.00020303963,"about_ca_system_score_gemma":0.00010188088,"threshold_uncertainty_score":0.99992085},"labels":[],"label_agreement":null},{"id":"W7164184173","doi":"10.2196/88657","title":"Fingerprint-Based Patient Identification Technology: A Simulated Pilot Study on Technical Performance and Usability (Preprint)","year":2025,"lang":"en","type":"article","venue":"JMIR Biomedical Engineering","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Usability; Identification (biology); Data collection; Usability goals; Key (lock)","score_opus":0.010657210069061887,"score_gpt":0.25965559527535587,"score_spread":0.24899838520629397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7164184173","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7501329,0.000018869861,0.24733748,0.0010663394,0.00023949855,0.0006258769,0.0000015767652,0.0005516707,0.000025741203],"genre_scores_gemma":[0.99767476,0.0000022161748,0.0020888317,0.00007694252,0.0000082994875,0.00012526984,0.0000035329163,0.000005822973,0.000014330304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983154,0.000034326535,0.0004518806,0.00060299324,0.00036388246,0.0002315358],"domain_scores_gemma":[0.99879354,0.0001461198,0.00006842103,0.00080443255,0.00008347251,0.00010399924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056565093,0.0001556876,0.00018180182,0.0010696657,0.00010877582,0.000112326874,0.00058421365,0.00011309682,0.0000058426613],"category_scores_gemma":[0.0004593729,0.000147019,0.000028370374,0.0031015973,0.00011184518,0.00011317625,0.0003189351,0.0003374701,0.000019496818],"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":[0.00022429814,0.020167533,0.041992765,0.00096237165,0.00019168796,0.000045934437,0.0013770206,0.00883425,0.16388217,0.023615073,0.0007755337,0.7379314],"study_design_scores_gemma":[0.00083216256,0.0010989682,0.32830548,0.00010691672,0.000008015495,0.0000030375602,0.00003496043,0.6605158,0.0043175244,0.00011931586,0.00439119,0.00026661356],"about_ca_topic_score_codex":0.0000064489986,"about_ca_topic_score_gemma":6.0102786e-7,"teacher_disagreement_score":0.73766476,"about_ca_system_score_codex":0.00013454036,"about_ca_system_score_gemma":0.00005865741,"threshold_uncertainty_score":0.59952605},"labels":[],"label_agreement":null},{"id":"W96825578","doi":"10.5220/0002851404580463","title":"ROBUST MULTIMODAL BIOMETRIC SYSTEM USING MARKOV CHAIN BASED RANK LEVEL FUSION","year":2010,"lang":"en","type":"article","venue":"","topic":"Biometric Identification and Security","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 Calgary","funders":"","keywords":"Biometrics; Computer science; Markov chain; Artificial intelligence; Rank (graph theory); Hidden Markov model; Markov process; Pattern recognition (psychology); Machine learning; Mathematics; Statistics","score_opus":0.06960151745609633,"score_gpt":0.26106681486288225,"score_spread":0.19146529740678592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W96825578","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":[0.076475136,0.000025048656,0.9196264,0.00033451343,0.0018704599,0.00021318295,0.000009928327,0.00031480193,0.001130506],"genre_scores_gemma":[0.7082564,7.2960415e-7,0.29120636,0.000108905755,0.00005869149,0.0000040498267,0.0000058655787,0.0000068198583,0.00035215396],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829036,0.000090877824,0.00032571531,0.0004934527,0.00049922423,0.00030038107],"domain_scores_gemma":[0.99856067,0.00013470337,0.00012846717,0.0007867459,0.00021813202,0.00017126442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011403194,0.00015207013,0.00017406773,0.0022165684,0.00025861769,0.00033708685,0.00092688994,0.00016489852,0.00013984508],"category_scores_gemma":[0.00014928273,0.00013135489,0.000094338204,0.006824738,0.000048304442,0.00032241148,0.00019719687,0.00020857737,0.000116077084],"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":[0.000043381242,0.0012577951,0.0065117525,0.0004940757,0.00006546892,0.00007071485,0.00054356264,0.0005069672,0.40664363,0.1063035,0.0049198302,0.47263932],"study_design_scores_gemma":[0.00054975954,0.0000114768,0.009831011,0.000008928888,0.0000045074153,0.000016933756,0.000024663135,0.9818714,0.0058752913,0.000009239195,0.0016009795,0.00019576335],"about_ca_topic_score_codex":0.000564346,"about_ca_topic_score_gemma":0.00003896625,"teacher_disagreement_score":0.9813645,"about_ca_system_score_codex":0.00008289195,"about_ca_system_score_gemma":0.000112389476,"threshold_uncertainty_score":0.53564966},"labels":[],"label_agreement":null}]}