{"meta":{"query_hash":"39227d257eba","filters":{"venue":"International Journal of Advanced Computer Science and Applications"},"cohort_total":44,"direct_labels_cover":0,"predictions_cover":44,"exported":44,"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/39227d257eba","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Advanced+Computer+Science+and+Applications"},"results":[{"id":"W1994036294","doi":"10.14569/ijacsa.2014.050533","title":"Solving for the RC4 stream cipher state register using a genetic algorithm","year":2014,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Coding theory and cryptography","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 Guelph","funders":"","keywords":"Computer science; RC4; Stream cipher; Keystream; Algorithm; Cryptanalysis; Permutation (music); Transposition cipher; Cipher; Encryption; Genetic algorithm; Running key cipher; Cryptography; Theoretical computer science; Computer security; Machine learning","score_opus":0.013266659436945338,"score_gpt":0.2829219701691825,"score_spread":0.2696553107322372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994036294","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":[0.0053735976,0.00012461489,0.9928699,0.00094416976,0.0004839017,0.00014827448,0.0000022137438,0.000015739157,0.00003760379],"genre_scores_gemma":[0.18064155,0.000047355228,0.8181448,0.00067135814,0.0004582556,0.000020230671,1.9478e-7,0.0000049214586,0.000011318146],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889404,0.000019644347,0.00026785047,0.00022282834,0.00043224613,0.00016340864],"domain_scores_gemma":[0.99820954,0.00026968957,0.0002761767,0.0002680832,0.0008974978,0.00007903127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007063069,0.0000853264,0.0000945301,0.00017138514,0.00034162952,0.00041605378,0.001686568,0.000013002079,9.313086e-7],"category_scores_gemma":[0.000027043625,0.0000614168,0.000067078974,0.00035637835,0.00023900793,0.0006705704,0.00023651287,0.00008495509,7.2565297e-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":[0.0000044744224,0.00002356181,0.000031016833,0.0000014858208,0.000017630813,6.9117885e-7,0.00016896192,0.009650282,0.00086040265,0.025679642,0.00002328559,0.9635386],"study_design_scores_gemma":[0.00047110094,0.00014013251,0.0014557966,0.000040794566,0.000012465513,0.00019680521,0.000027471873,0.8622278,0.0008620091,0.09651961,0.037904575,0.00014146697],"about_ca_topic_score_codex":0.0000018238397,"about_ca_topic_score_gemma":5.3693407e-7,"teacher_disagreement_score":0.9633971,"about_ca_system_score_codex":0.00003698816,"about_ca_system_score_gemma":0.00009879781,"threshold_uncertainty_score":0.4012016},"labels":[],"label_agreement":null},{"id":"W2022622562","doi":"10.14569/ijacsa.2014.050324","title":"Mobile Web Services: State of the Art and Challenges","year":2014,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Service-Oriented Architecture and Web Services","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":"Queen's University","funders":"","keywords":"Computer science; Provisioning; Mobile Web; Mobile device; Mobile computing; Enabling; Mobile technology; World Wide Web; Mobile business development; Service provider; Service (business); Telecommunications; Business","score_opus":0.005975147603096581,"score_gpt":0.23916228173677145,"score_spread":0.23318713413367487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022622562","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":[0.21327129,0.0019941507,0.7759567,0.006600857,0.0008595876,0.00031033883,0.0000063977413,0.000033577388,0.00096710096],"genre_scores_gemma":[0.94622993,0.0014257219,0.05090724,0.0012299884,0.00017859443,0.0000141586015,3.1386435e-7,0.0000039127935,0.000010154221],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99882776,0.000023597255,0.00026519946,0.00021162831,0.0005568267,0.000114985385],"domain_scores_gemma":[0.99847186,0.00008387005,0.00031966573,0.00028175424,0.00076118624,0.00008164479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003896118,0.000082622115,0.00011471139,0.00015742754,0.00011963625,0.00011647669,0.0018437158,0.000013351974,4.9407396e-7],"category_scores_gemma":[0.0000034304394,0.000055478464,0.000033285436,0.00033329547,0.00018077824,0.0006483915,0.00051964785,0.00009312111,0.0000014182087],"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":[0.000005367727,0.000052534422,0.00015275822,0.00002768299,0.000018308368,5.613644e-7,0.0016133513,0.0022190441,0.0062140124,0.031916827,0.0000071206496,0.95777243],"study_design_scores_gemma":[0.0018082253,0.0007717328,0.020260073,0.00048001658,0.000026876678,0.0005793599,0.0004211067,0.21191292,0.021000527,0.10879869,0.63346547,0.00047497384],"about_ca_topic_score_codex":0.0000020537254,"about_ca_topic_score_gemma":0.000011108696,"teacher_disagreement_score":0.95729744,"about_ca_system_score_codex":0.000014396455,"about_ca_system_score_gemma":0.000080140984,"threshold_uncertainty_score":0.34261134},"labels":[],"label_agreement":null},{"id":"W2076869920","doi":"10.14569/ijacsa.2014.050919","title":"Privacy Preserving Data Publishing: A Classification Perspective","year":2014,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Privacy-Preserving Technologies in Data","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 Guelph","funders":"","keywords":"Computer science; Data publishing; USable; Differential privacy; Data anonymization; Usability; Information privacy; Data mining; Privacy software; Information retrieval; Information sensitivity; Publishing; Computer security; World Wide Web; Human–computer interaction","score_opus":0.05109513218844717,"score_gpt":0.33885435349562887,"score_spread":0.2877592213071817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076869920","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":[0.0016498799,0.00010748698,0.9247138,0.072068684,0.00046024783,0.00013661335,0.000009252171,0.000102418235,0.0007516565],"genre_scores_gemma":[0.32972828,0.00009975263,0.66928667,0.00048057683,0.00037702546,0.0000122067,0.0000054528996,0.0000051794354,0.000004890015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975762,0.000038350245,0.00041286176,0.0006197184,0.0011303786,0.0002224955],"domain_scores_gemma":[0.9916535,0.0002664327,0.0005554074,0.0046176757,0.002770719,0.00013628151],"candidate_categories":["metaresearch","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001785822,0.00011872918,0.00014503843,0.00048299687,0.00023213435,0.0016924137,0.058774307,0.000045540764,0.0000017856958],"category_scores_gemma":[0.0103959255,0.000106992,0.000028075123,0.00092139223,0.00034993124,0.011097621,0.05020191,0.00029869308,0.0000042832835],"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":[0.000006090238,0.00009637412,0.0002156524,0.0000039326565,0.000027962966,0.0000026491068,0.00016894155,0.00014869557,0.0028682034,0.24076436,0.011972739,0.7437244],"study_design_scores_gemma":[0.0003648117,0.00006534115,0.0036040018,0.000043714746,0.000005327277,0.00014395047,0.00009067425,0.5375731,0.0006468465,0.3955354,0.06177589,0.00015093037],"about_ca_topic_score_codex":0.000008490991,"about_ca_topic_score_gemma":0.000001612824,"teacher_disagreement_score":0.7435735,"about_ca_system_score_codex":0.00020450867,"about_ca_system_score_gemma":0.0003013002,"threshold_uncertainty_score":0.99934393},"labels":[],"label_agreement":null},{"id":"W2095907032","doi":"10.14569/ijacsa.2013.041117","title":"Using Learning Analytics to Understand the Design of an Intelligent Language Tutor – Chatbot Lucy","year":2013,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"AI in Service Interactions","field":"Computer Science","cited_by":90,"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":"Chatbot; Computer science; TUTOR; Language acquisition; Learning analytics; Focus (optics); Analytics; Human–computer interaction; Component (thermodynamics); Natural language processing; Artificial intelligence; Multimedia; Data science; Mathematics education; Programming language","score_opus":0.04128697764260936,"score_gpt":0.3464646060408418,"score_spread":0.3051776283982324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095907032","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":[0.0652323,0.000040203387,0.93246424,0.001726527,0.000272047,0.00021464568,8.7901543e-7,0.0000137806965,0.000035366167],"genre_scores_gemma":[0.6101268,0.000019068837,0.3892399,0.00044085886,0.00015144753,0.000006990346,2.3755894e-7,0.0000039942197,0.0000107141395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985924,0.000040999385,0.0003741657,0.00020739078,0.0006360096,0.00014907077],"domain_scores_gemma":[0.9974853,0.0001926682,0.00035073064,0.0002789485,0.0015546589,0.00013770102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005210164,0.00009010304,0.00011553741,0.0003079015,0.0002040023,0.0003725203,0.0019544188,0.000017409551,0.000007032068],"category_scores_gemma":[0.000049501312,0.000066853296,0.000036460744,0.0006638362,0.00016054412,0.0016627626,0.00032782357,0.00015890911,0.0000062982726],"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":[0.0000084588455,0.00011483308,0.000049465765,0.0000034002726,0.000041980456,0.0000039632946,0.0045707715,0.5485591,0.042446,0.020774696,0.000036947957,0.38339034],"study_design_scores_gemma":[0.00016980477,0.00021574109,0.00079950603,0.000056675533,0.000010332209,0.00022622269,0.0022235774,0.97757024,0.010215453,0.0070315003,0.001342993,0.00013794143],"about_ca_topic_score_codex":0.000025281917,"about_ca_topic_score_gemma":0.0000021648295,"teacher_disagreement_score":0.54489446,"about_ca_system_score_codex":0.00013526392,"about_ca_system_score_gemma":0.00018776435,"threshold_uncertainty_score":0.36318293},"labels":[],"label_agreement":null},{"id":"W2112469872","doi":"10.14569/ijacsa.2013.041206","title":"Anonymous Broadcast Messages","year":2013,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Privacy-Preserving Technologies in Data","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 Guelph","funders":"","keywords":"Computer science; Protocol (science); Encryption; Computer security; Overhead (engineering); Net (polyhedron); Computer network; Operating system","score_opus":0.012492425412570313,"score_gpt":0.28487343484532024,"score_spread":0.2723810094327499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112469872","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":[0.0114111435,0.00019369533,0.9621955,0.025173476,0.0004517295,0.00014991971,0.000002596762,0.00008431393,0.00033762993],"genre_scores_gemma":[0.33422542,0.00018650565,0.6648212,0.0005539583,0.00016920676,0.000027016407,5.9693093e-7,0.0000037317031,0.000012322692],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99838614,0.000012261394,0.00032771018,0.0003054583,0.0007686984,0.00019972907],"domain_scores_gemma":[0.9965924,0.00009947702,0.0002916153,0.001407574,0.0014939057,0.000115029245],"candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00037618473,0.000100442165,0.00012061001,0.0003644202,0.00015612012,0.0005931811,0.02079642,0.000029607934,0.0000075769],"category_scores_gemma":[0.00068887376,0.00008504271,0.000034158158,0.00061484735,0.00036146713,0.003203426,0.014806782,0.00016802677,0.000024291438],"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":[0.0000015665732,0.000046889,0.00010337152,0.0000015992734,0.000013125549,0.000005751415,0.00003653446,0.0001494457,0.0064336513,0.010181783,0.007067889,0.9759584],"study_design_scores_gemma":[0.00097166054,0.0002615482,0.011961075,0.000097619624,0.000008449021,0.0012378312,0.000103261445,0.17742433,0.015473743,0.71423334,0.07774677,0.00048037252],"about_ca_topic_score_codex":0.000005215529,"about_ca_topic_score_gemma":2.8944447e-7,"teacher_disagreement_score":0.975478,"about_ca_system_score_codex":0.000085499516,"about_ca_system_score_gemma":0.00015476167,"threshold_uncertainty_score":0.99316126},"labels":[],"label_agreement":null},{"id":"W2126597536","doi":"10.14569/ijacsa.2013.040922","title":"Improving the Security of the Medical Images","year":2013,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":31,"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":"Computer science; Digital watermarking; Watermark; Encryption; Image (mathematics); Lossless compression; Cryptography; Region of interest; Computer security; Confidentiality; Computer vision; Pixel; Artificial intelligence; Data compression","score_opus":0.004838601738786312,"score_gpt":0.2553693031584482,"score_spread":0.2505307014196619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126597536","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":[0.02060939,0.00010830527,0.97283894,0.005819962,0.00033043342,0.00016399723,0.0000010771678,0.00001835566,0.00010954183],"genre_scores_gemma":[0.91861653,0.00007645668,0.0804928,0.00064440747,0.00014374676,0.000020115413,7.6208586e-8,0.0000020402792,0.0000038355333],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984878,0.000025537276,0.0003030068,0.00015096468,0.000914034,0.00011868409],"domain_scores_gemma":[0.9980243,0.00011771561,0.00035719905,0.00031114058,0.0011203932,0.00006925411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005806709,0.00006820584,0.000086255495,0.0001113365,0.00020869685,0.00017508143,0.003466972,0.00002076074,0.000002185739],"category_scores_gemma":[0.00006332749,0.00003610953,0.00006191043,0.00046953608,0.0005967066,0.0011205254,0.00069695857,0.00017814407,6.542635e-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":[0.000002254054,0.000056902532,0.0003893,0.0000043100436,0.0000151294325,0.0000017867119,0.0003069172,0.00014775291,0.009047047,0.059149917,0.00019940537,0.93067926],"study_design_scores_gemma":[0.0011399214,0.0002944098,0.03737684,0.00033946836,0.000021400498,0.0015340504,0.00019759646,0.109468505,0.20087901,0.62265784,0.025590006,0.00050098164],"about_ca_topic_score_codex":0.000008215538,"about_ca_topic_score_gemma":5.9107725e-7,"teacher_disagreement_score":0.9301783,"about_ca_system_score_codex":0.000023561335,"about_ca_system_score_gemma":0.00015516605,"threshold_uncertainty_score":0.6442554},"labels":[],"label_agreement":null},{"id":"W2132052677","doi":"10.14569/ijacsa.2015.060121","title":"A Survey of Topic Modeling in Text Mining","year":2015,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":368,"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":"Latent Dirichlet allocation; Topic model; Computer science; Probabilistic latent semantic analysis; Natural language processing; Artificial intelligence; Latent semantic analysis; Field (mathematics); Probabilistic logic; Information retrieval","score_opus":0.0669373198898449,"score_gpt":0.32942733013116254,"score_spread":0.26249001024131763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132052677","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":[0.21262115,0.00011561913,0.78649503,0.0003925282,0.00023309188,0.000046753416,6.395013e-7,0.000005181421,0.000089991365],"genre_scores_gemma":[0.803445,0.000017644621,0.19637519,0.00009017181,0.00006426326,0.0000031081252,2.2330323e-7,0.0000015341524,0.0000028467261],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987232,0.000021205076,0.00039202455,0.00017993324,0.0005748692,0.00010874447],"domain_scores_gemma":[0.99790466,0.00007228962,0.00020287574,0.00017876927,0.0015495223,0.00009186235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011018439,0.00005664514,0.00011938165,0.00032322123,0.000030158926,0.00008745848,0.001292682,0.000015946334,3.2630683e-7],"category_scores_gemma":[0.00009499086,0.000051959098,0.000017947325,0.000521121,0.00007077682,0.00088498794,0.00026625852,0.00007877204,5.6626817e-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":[0.0000075159855,0.00006513439,0.003986649,0.0000025862348,0.0000074567374,0.0000049412283,0.0009720907,0.2302879,0.00059258426,0.01371821,0.0000112977705,0.7503436],"study_design_scores_gemma":[0.00037207213,0.00004209414,0.004615111,0.00003659407,8.4518757e-7,0.00004163398,0.000051916773,0.98965025,0.00020881498,0.004713719,0.0002066746,0.000060296738],"about_ca_topic_score_codex":0.000036996178,"about_ca_topic_score_gemma":0.000013364578,"teacher_disagreement_score":0.75936234,"about_ca_system_score_codex":0.00007608223,"about_ca_system_score_gemma":0.00034906046,"threshold_uncertainty_score":0.24021463},"labels":[],"label_agreement":null},{"id":"W2138745689","doi":"10.14569/ijacsa.2013.040719","title":"Performance Evaluation of Two-Hop Wireless Link under Nakagami-m Fading","year":2013,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Cooperative Communication and Network Coding","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 Alberta","funders":"Bangladesh University of Engineering and Technology; University Grants Commission; University of Engineering and Technology, Lahore","keywords":"Fading; Nakagami distribution; Relay; Computer science; Hop (telecommunications); Space–time block code; Wireless; Phase-shift keying; Maximal-ratio combining; Bit error rate; Telecommunications; Computer network; Decoding methods; Physics","score_opus":0.03789014024459944,"score_gpt":0.33760787992960145,"score_spread":0.29971773968500204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138745689","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":[0.30892572,0.00019461034,0.6880354,0.0019337852,0.00028437946,0.00020033651,4.7333614e-7,0.000014197913,0.00041105712],"genre_scores_gemma":[0.9413639,0.000466741,0.057626024,0.00030838526,0.00018895649,0.000033299755,7.511716e-7,0.0000035917433,0.000008363079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807304,0.000051975792,0.00044262013,0.00021261393,0.0010783676,0.00014139559],"domain_scores_gemma":[0.99387944,0.00010495869,0.00042342293,0.00033532624,0.0051597115,0.00009714116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011617123,0.00009509442,0.00013687811,0.00031476674,0.00020048703,0.00022562218,0.0018806006,0.000019265475,0.000011558492],"category_scores_gemma":[0.00002763104,0.00008355553,0.00003992911,0.0006780208,0.00022468477,0.002007276,0.0004133217,0.00013950559,0.000007766111],"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":[0.0000013530317,0.000033504148,0.00016470185,0.0000015626343,0.000013420386,1.3101531e-7,0.00021065031,0.018672174,0.014117062,0.024765927,0.000023893266,0.9419956],"study_design_scores_gemma":[0.00076578785,0.0000758525,0.012826014,0.00009258782,0.000009569424,0.000058888556,0.000042940246,0.97256285,0.006572202,0.005818653,0.001037003,0.00013764523],"about_ca_topic_score_codex":0.0000019943238,"about_ca_topic_score_gemma":0.0000011710856,"teacher_disagreement_score":0.9538907,"about_ca_system_score_codex":0.00013186378,"about_ca_system_score_gemma":0.00031145802,"threshold_uncertainty_score":0.3494655},"labels":[],"label_agreement":null},{"id":"W2151124070","doi":"10.14569/ijacsa.2015.060120","title":"Android Platform Malware Analysis","year":2015,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Advanced Malware Detection Techniques","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":"Concordia University","funders":"Albaha University","keywords":"Malware; Android (operating system); Computer science; Computer security; Mobile malware; Cryptovirology; Authorization; Mobile device; Ransomware; Mobile phone; Software; Confidentiality; Operating system","score_opus":0.016090561655745773,"score_gpt":0.3028820432265874,"score_spread":0.2867914815708416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151124070","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":[0.0052621835,0.00010959369,0.9928117,0.00090752955,0.00036204528,0.000106324296,0.0000025078361,0.00008215056,0.0003559962],"genre_scores_gemma":[0.5249704,0.000054558102,0.47437245,0.00037365788,0.00019128944,0.0000161514,8.798995e-7,0.000003589481,0.000017001492],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99815893,0.00001071164,0.00038076242,0.00029650074,0.0009849152,0.00016815671],"domain_scores_gemma":[0.99627286,0.00005564596,0.00037239504,0.00032416097,0.0027263851,0.00024857203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000621786,0.00010690976,0.00017557543,0.0008903278,0.00012002577,0.00030044932,0.0019348916,0.00002742574,0.0000014383295],"category_scores_gemma":[0.000059068643,0.00009484709,0.0000742419,0.0018091166,0.00019712141,0.0025236655,0.0004170126,0.00013775528,0.000004320919],"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":[0.000014448354,0.00007136512,0.0002989241,0.0000015798943,0.00009730185,0.000017505248,0.00030696215,0.010033375,0.001218938,0.04266035,0.00018352098,0.9450957],"study_design_scores_gemma":[0.003722999,0.0015165078,0.01201886,0.00010013166,0.0002011629,0.0030394723,0.0005081437,0.29308897,0.06500747,0.3166662,0.30272037,0.0014096997],"about_ca_topic_score_codex":0.0000023254402,"about_ca_topic_score_gemma":0.0000021951173,"teacher_disagreement_score":0.943686,"about_ca_system_score_codex":0.00018105115,"about_ca_system_score_gemma":0.0002668664,"threshold_uncertainty_score":0.38677523},"labels":[],"label_agreement":null},{"id":"W2160940988","doi":"10.14569/ijacsa.2013.040920","title":"The Impact of Cognitive Tools on the Development of the Inquiry Skills of High School Students in Physics","year":2013,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Science Education and Pedagogy","field":"Social Sciences","cited_by":20,"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 Ottawa","funders":"","keywords":"Mathematics education; Class (philosophy); Cognition; Cognitive skill; Computer science; Test (biology); Artificial intelligence; Psychology","score_opus":0.045456179191764326,"score_gpt":0.4343196153522579,"score_spread":0.38886343616049357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160940988","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":[0.9905068,0.000015054229,0.007188888,0.0015260518,0.00027503172,0.0002687337,0.0000025685067,0.0000010405652,0.00021581702],"genre_scores_gemma":[0.99868953,0.000043666434,0.0009692929,0.000113505106,0.00014224571,0.000020923902,1.96251e-7,0.000001244634,0.00001938924],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984919,0.000064122134,0.00034352439,0.00008668875,0.00091328134,0.00010047287],"domain_scores_gemma":[0.9971635,0.0007789015,0.00048724414,0.00010590725,0.0014145059,0.00004996373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001154433,0.000047248195,0.00008264961,0.000074011165,0.00030195466,0.00009547932,0.0013332064,0.000012891322,0.00001374462],"category_scores_gemma":[0.00038716802,0.000023375453,0.000042712032,0.00055523874,0.0010140928,0.00039577324,0.00011048724,0.00010379426,0.0000020666087],"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":[0.00002275036,0.00065819913,0.041058414,0.0000020751959,0.000060522852,9.6650936e-8,0.043047383,0.0029824192,0.0029452273,0.04896469,0.00017539352,0.8600828],"study_design_scores_gemma":[0.00027910114,0.000062946456,0.97400665,0.00012645121,0.0000031249485,0.0000013814712,0.008411373,0.00011474649,0.004746854,0.011813597,0.0003803178,0.000053465024],"about_ca_topic_score_codex":0.00008685913,"about_ca_topic_score_gemma":0.000030453717,"teacher_disagreement_score":0.93294823,"about_ca_system_score_codex":0.00009807796,"about_ca_system_score_gemma":0.0010193631,"threshold_uncertainty_score":0.37364674},"labels":[],"label_agreement":null},{"id":"W2162048096","doi":"10.14569/ijacsa.2013.040711","title":"Contribution of the Computer Technologies in the Teaching of Physics: Critical Review and Conception of an Interactive Simulation Software","year":2013,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Knowledge Societies in the 21st Century","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dominican University College; University of Ottawa; Université du Québec à Montréal","funders":"","keywords":"Computer science; Software; Presentation (obstetrics); Human–computer interaction; User modeling; Representation (politics); Multimedia; User interface; Programming language","score_opus":0.01311335114460036,"score_gpt":0.36204777413212064,"score_spread":0.34893442298752025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162048096","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":[0.29451415,0.001366127,0.69718546,0.005608556,0.000406951,0.0008042769,0.000010592573,0.000014196581,0.00008970822],"genre_scores_gemma":[0.985702,0.00032512643,0.013739907,0.00012295284,0.00009708487,0.000010254464,5.4189127e-7,0.0000014092021,7.300205e-7],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989942,0.000112712325,0.00030890855,0.00009115727,0.00041967037,0.000073380834],"domain_scores_gemma":[0.997289,0.00050267595,0.00039217636,0.00010893548,0.0016896515,0.000017534787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085743365,0.000046682828,0.0001287971,0.000055876146,0.00013611112,0.000033013104,0.00065331435,0.000024744246,0.0000015718671],"category_scores_gemma":[0.00049272476,0.000030610114,0.00003512633,0.00024549908,0.0013523211,0.00078404945,0.00012078601,0.0001491654,1.5601468e-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":[0.000008223234,0.00019816645,0.0016064051,0.000047406484,0.000018480614,1.38029e-7,0.008247506,0.002881629,0.0016518546,0.08235266,0.00003567148,0.90295184],"study_design_scores_gemma":[0.0033519666,0.001303724,0.13840939,0.007498062,0.0002967887,0.00008480406,0.050295968,0.11962183,0.0131335305,0.655056,0.010123854,0.0008240737],"about_ca_topic_score_codex":0.000014336086,"about_ca_topic_score_gemma":0.0000028189672,"teacher_disagreement_score":0.9021278,"about_ca_system_score_codex":0.000057916714,"about_ca_system_score_gemma":0.00007570927,"threshold_uncertainty_score":0.49826837},"labels":[],"label_agreement":null},{"id":"W2345690959","doi":"10.14569/ijacsa.2016.070454","title":"Spatiotemporal Context Modelling in Pervasive Context-Aware Computing Environment: A Logic Perspective","year":2016,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Context-Aware Activity Recognition Systems","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 Montréal","funders":"","keywords":"Computer science; Context (archaeology); Perspective (graphical); Ubiquitous computing; Context model; Representation (politics); Human–computer interaction; Task (project management); Context awareness; Data science; Artificial intelligence; Cognitive science; Object (grammar); Systems engineering","score_opus":0.02459000223109405,"score_gpt":0.2789716502976982,"score_spread":0.2543816480666042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345690959","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":[0.024559347,0.000222835,0.9684074,0.006080539,0.00033412816,0.00026867862,0.000007751698,0.000031197353,0.00008815329],"genre_scores_gemma":[0.9684493,0.000099055746,0.030601624,0.0005658343,0.00024473498,0.00001849865,4.890433e-7,0.0000066916105,0.000013782663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977601,0.00005886397,0.0005981278,0.00050109945,0.00082883507,0.0002529281],"domain_scores_gemma":[0.9971227,0.00034847562,0.0005976855,0.0002656108,0.0015131293,0.00015236625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000721424,0.0001660677,0.0002576556,0.0005186075,0.00016642096,0.0002526241,0.0014630802,0.000042287567,0.0000051504517],"category_scores_gemma":[0.00004724785,0.00012986058,0.000078037345,0.0004029091,0.00033443896,0.002240439,0.00039424546,0.00017198225,0.000016812743],"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":[0.000018758174,0.0001267228,0.0007235656,0.0000028487048,0.000029054385,0.000024460896,0.0013707232,0.0038205502,0.0013381573,0.05308507,0.000017788467,0.9394423],"study_design_scores_gemma":[0.01055545,0.0010676984,0.012961598,0.0017144012,0.000033362827,0.0025385905,0.0058477228,0.7737557,0.011131131,0.15494908,0.023618756,0.0018264836],"about_ca_topic_score_codex":0.000036127956,"about_ca_topic_score_gemma":0.000014596902,"teacher_disagreement_score":0.9438899,"about_ca_system_score_codex":0.0005340449,"about_ca_system_score_gemma":0.00030875424,"threshold_uncertainty_score":0.5295561},"labels":[],"label_agreement":null},{"id":"W2729475049","doi":"10.14569/ijacsa.2017.080638","title":"Identifying Top-k Most Influential Nodes by using the Topological Diffusion Models in the Complex Networks","year":2017,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","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":"Carleton University","funders":"","keywords":"Centrality; Betweenness centrality; Computer science; Closeness; Heat kernel; TOPSIS; Network science; Identification (biology); Complex network; Graph; Katz centrality; Social network (sociolinguistics); Topology (electrical circuits); Data mining; Theoretical computer science; Mathematics; Operations research","score_opus":0.03366455175935686,"score_gpt":0.355277146131457,"score_spread":0.32161259437210016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729475049","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":[0.21933858,0.00003951349,0.7794844,0.0008214316,0.00007018724,0.000091302245,0.0000024503663,0.0000032418154,0.00014892129],"genre_scores_gemma":[0.9893325,0.000025471265,0.009918439,0.00024244754,0.0004631071,0.000010822199,0.000002200237,0.000002445088,0.000002553379],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990571,0.00002579487,0.00026251085,0.00014222889,0.00038798567,0.0001243366],"domain_scores_gemma":[0.99896926,0.000072022405,0.0003592812,0.0002444351,0.0003226516,0.000032325384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045949343,0.000073303774,0.00010126464,0.00006853578,0.0007108264,0.0006196297,0.0016382417,0.000012204385,0.000006794432],"category_scores_gemma":[0.000004765007,0.000044228396,0.000048516515,0.00015004953,0.000382131,0.0005913006,0.00033403773,0.00016712515,1.912813e-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":[0.000021673975,0.00025023965,0.017196732,0.0000013612304,0.000061544146,0.000003420169,0.00056809036,0.14873399,0.008988966,0.12963387,0.0004100185,0.6941301],"study_design_scores_gemma":[0.0003593788,0.000021645214,0.014561177,0.000037073794,0.000018916035,0.000024702375,0.0002718939,0.9037029,0.00023812476,0.07794794,0.002696286,0.00011995703],"about_ca_topic_score_codex":0.00006394449,"about_ca_topic_score_gemma":0.0000041032504,"teacher_disagreement_score":0.76999396,"about_ca_system_score_codex":0.000030169465,"about_ca_system_score_gemma":0.000034006025,"threshold_uncertainty_score":0.59751034},"labels":[],"label_agreement":null},{"id":"W2732551346","doi":"10.14569/ijacsa.2017.080640","title":"Modeling and FPGA Implementation of a Thermal Peak Detection Unit for Complex System Design","year":2017,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Silicon Carbide Semiconductor Technologies","field":"Engineering","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":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Field-programmable gate array; VHDL; Overheating (electricity); MATLAB; Ring oscillator; Thermal; Hardware description language; Embedded system; Computer hardware; Electronic engineering; Electrical engineering","score_opus":0.04513528633274761,"score_gpt":0.325222438333172,"score_spread":0.2800871520004244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2732551346","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":[0.45274806,0.000041714964,0.5469421,0.000040934054,0.00009185355,0.000105658546,0.000003902749,0.000016557213,0.00000921975],"genre_scores_gemma":[0.9676424,0.000029305013,0.03221917,0.0000070617175,0.0000752816,0.000021569853,4.3327896e-7,0.0000044313992,3.042096e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994692,0.000003413222,0.00020515487,0.00008476226,0.00016709375,0.000070375354],"domain_scores_gemma":[0.99916434,0.00003257147,0.00015509073,0.00011321289,0.000507771,0.000026986661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020661093,0.000053379343,0.00008290851,0.00014648454,0.0001402322,0.00009712556,0.00040635603,0.000016604006,3.4193255e-7],"category_scores_gemma":[0.000011326423,0.00005032116,0.000018302295,0.00004886075,0.00011345441,0.00049412105,0.000056867746,0.000046393052,1.0189523e-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":[0.00000481138,0.0000037649233,0.000043933553,0.000013060102,0.000012532106,2.0925864e-7,0.00007205324,0.08333431,0.5822638,0.0005182368,0.0000016255292,0.33373165],"study_design_scores_gemma":[0.00043007242,0.000054795906,0.001994182,0.000025927571,0.000008770491,0.00003775996,0.0004842086,0.7612478,0.23496357,0.0006352635,0.00005830339,0.000059324484],"about_ca_topic_score_codex":0.0000050563594,"about_ca_topic_score_gemma":0.0000023055266,"teacher_disagreement_score":0.6779135,"about_ca_system_score_codex":0.000048535014,"about_ca_system_score_gemma":0.00002891953,"threshold_uncertainty_score":0.20520373},"labels":[],"label_agreement":null},{"id":"W2799728937","doi":"10.14569/ijacsa.2018.090430","title":"Robust Modeling and Linearization of MIMO RF Power Amplifiers for 4G and 5G Applications","year":2018,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"MIMO; Linearization; Computer science; Amplifier; Robustness (evolution); Polynomial and rational function modeling; Radio frequency; Baseband; Polynomial; RF power amplifier; Crossover; Control theory (sociology); Electronic engineering; Predistortion; Mathematical optimization; Telecommunications; Nonlinear system; Mathematics; Bandwidth (computing); Beamforming","score_opus":0.01752470859160266,"score_gpt":0.26977815446173614,"score_spread":0.2522534458701335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799728937","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":[0.015110173,0.0002573879,0.98394,0.00013080954,0.00014744171,0.00026950813,0.00001529629,0.000020864503,0.00010848978],"genre_scores_gemma":[0.76213944,0.0002403574,0.23724857,0.000075433396,0.000234921,0.00004325383,0.0000025149495,0.000010463689,0.000005047022],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991373,0.0000035348012,0.0003210302,0.00017977727,0.0002381895,0.0001202151],"domain_scores_gemma":[0.99842995,0.00008966199,0.00013132641,0.00011820239,0.0011344531,0.000096417876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021271392,0.00009212354,0.00012405502,0.00022073915,0.00012742462,0.00007365516,0.00028074885,0.0000286649,0.0000013031573],"category_scores_gemma":[0.000024105097,0.00009100659,0.000021282864,0.00023666701,0.00030544272,0.00051838305,0.000048084486,0.000071276496,4.5939075e-7],"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":[0.00005090368,0.000077665325,0.00023302804,0.00005673823,0.00009015157,5.887742e-7,0.00091729144,0.53858143,0.073124304,0.044110876,0.00011119036,0.34264582],"study_design_scores_gemma":[0.000997497,0.00021589288,0.00047800536,0.00010168528,0.000030376721,0.00012161019,0.00020528586,0.9444174,0.009008081,0.028965771,0.015173487,0.00028491596],"about_ca_topic_score_codex":7.5908406e-7,"about_ca_topic_score_gemma":0.0000010329394,"teacher_disagreement_score":0.74702924,"about_ca_system_score_codex":0.000038581413,"about_ca_system_score_gemma":0.000055532182,"threshold_uncertainty_score":0.3711141},"labels":[],"label_agreement":null},{"id":"W2805872881","doi":"10.14569/ijacsa.2018.090529","title":"Effect of Service Broker Policies and Load Balancing Algorithms on the Performance of Large Scale Internet Applications in Cloud Datacenters","year":2018,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Cloud Computing and Resource Management","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":"Innovation Cluster (Canada)","funders":"King Saud University","keywords":"Computer science; Cloud computing; Load balancing (electrical power); The Internet; Distributed computing; Response time; Service (business); Algorithm; Operating system","score_opus":0.005504294446758639,"score_gpt":0.2685050116352376,"score_spread":0.26300071718847895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805872881","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":[0.74177545,0.000051062183,0.2565063,0.0012125452,0.00015878965,0.00018793944,0.0000035297364,0.0000077015275,0.000096646734],"genre_scores_gemma":[0.99158806,0.000028331711,0.007720871,0.0004545353,0.00018065858,0.000013933019,5.0763265e-7,0.0000033741112,0.0000097141465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859947,0.000037466078,0.0003677439,0.00023495732,0.00059113896,0.00016920785],"domain_scores_gemma":[0.9983967,0.00023132784,0.00034934664,0.00029937903,0.00066684384,0.000056424637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001228273,0.00010237641,0.00016089855,0.00023533237,0.00012665993,0.000086048756,0.0015086473,0.000017973127,8.245739e-7],"category_scores_gemma":[0.00001865047,0.00006909901,0.000030321577,0.00065958727,0.00030341503,0.00015783617,0.0006989761,0.00011913165,0.0000013575884],"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":[0.00016552581,0.0006759713,0.0182625,0.0001825969,0.00014581945,0.0000033783706,0.011097984,0.021081187,0.010449795,0.06325627,0.00036571152,0.87431324],"study_design_scores_gemma":[0.0017227486,0.0014143789,0.024318608,0.000656616,0.000021528693,0.00009572459,0.0004015524,0.91418076,0.043658394,0.00073680683,0.012512675,0.00028022352],"about_ca_topic_score_codex":0.000019387351,"about_ca_topic_score_gemma":0.000008305163,"teacher_disagreement_score":0.89309955,"about_ca_system_score_codex":0.00006442321,"about_ca_system_score_gemma":0.00006328056,"threshold_uncertainty_score":0.2817776},"labels":[],"label_agreement":null},{"id":"W2810161871","doi":"10.14569/ijacsa.2018.090661","title":"Introducing a Cybersecurity Mindset into Software Engineering Undergraduate Courses","year":2018,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Information and Cyber Security","field":"Computer Science","cited_by":9,"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":"York University; National Science Foundation","keywords":"Mindset; Computer science; Computer security; Software; Workforce; Order (exchange); Software engineering; Engineering management; Artificial intelligence; Operating system; Engineering","score_opus":0.004905091907713245,"score_gpt":0.2572423873282004,"score_spread":0.2523372954204871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810161871","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":[0.022087857,0.00005118722,0.9737709,0.0029251606,0.00093335635,0.00008795755,0.0000019387219,0.000046311237,0.00009529508],"genre_scores_gemma":[0.5873622,0.000031735537,0.41132975,0.00066475745,0.00059523847,0.0000070288843,9.628536e-7,0.0000032679657,0.0000051159477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849933,0.000010152349,0.0003613739,0.00023262964,0.00071435014,0.0001821877],"domain_scores_gemma":[0.99721205,0.000066205066,0.0002822916,0.000256092,0.0020379238,0.0001454478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005148786,0.000106613916,0.000119359,0.000357807,0.00025160215,0.00040852404,0.0015641965,0.000024371611,0.0000027502674],"category_scores_gemma":[0.00009709432,0.00009775459,0.000042117706,0.0005790665,0.0002649296,0.0022437812,0.0003643768,0.00014483088,0.000013717248],"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":[0.000012921358,0.000109885936,0.00029576686,0.000010765637,0.000048459602,0.000008841511,0.003933567,0.002674223,0.00214253,0.2928986,0.00076867774,0.69709575],"study_design_scores_gemma":[0.0029125544,0.00085453066,0.0102224685,0.00034398737,0.000034452383,0.0019226525,0.00044134652,0.48583618,0.035386685,0.08984885,0.3709376,0.0012587059],"about_ca_topic_score_codex":0.0000045861993,"about_ca_topic_score_gemma":0.0000035785863,"teacher_disagreement_score":0.6958371,"about_ca_system_score_codex":0.00011509589,"about_ca_system_score_gemma":0.00024681454,"threshold_uncertainty_score":0.39863166},"labels":[],"label_agreement":null},{"id":"W2811102277","doi":"10.14569/ijacsa.2018.090620","title":"Generating Relational Database using Ontology Review","year":2018,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Relational database; Ontology; Database schema; USable; Database model; Relational model; Inference; Database; Knowledge extraction; Database design; Schema (genetic algorithms); Information retrieval; Data mining; World Wide Web; Artificial intelligence","score_opus":0.03421802743340294,"score_gpt":0.3473999532232925,"score_spread":0.31318192578988957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811102277","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":[0.0025336083,0.0008638059,0.9931855,0.0026856815,0.0004423829,0.00012726184,0.000010811092,0.000021382726,0.00012956254],"genre_scores_gemma":[0.012403353,0.00062754,0.9835641,0.0024407748,0.0009297352,0.000015272826,0.000005183913,0.000004375899,0.000009707877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984241,0.000019047919,0.00043937654,0.0003187995,0.0006361029,0.00016254249],"domain_scores_gemma":[0.99692583,0.00007396216,0.0003975695,0.0003492543,0.0021254355,0.00012792667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073582696,0.00009464556,0.00013788158,0.00021833563,0.00033599988,0.00017724639,0.0016358156,0.000019170606,0.0000065253635],"category_scores_gemma":[0.00007129198,0.00008474262,0.00003437747,0.000656127,0.00037141144,0.0016996734,0.00045918778,0.000120263925,0.00000932053],"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":[0.0000023120729,0.00006813384,0.000048881153,0.00001045965,0.000021100599,0.0000052240975,0.00009296654,0.00046245346,0.010258579,0.10655076,0.0007189375,0.8817602],"study_design_scores_gemma":[0.0005694848,0.00018030548,0.000674455,0.00058472087,0.000025841004,0.00175767,0.000022293583,0.8284536,0.0020862005,0.0063641258,0.15895635,0.0003249522],"about_ca_topic_score_codex":0.0000038784674,"about_ca_topic_score_gemma":0.0000013010135,"teacher_disagreement_score":0.8814352,"about_ca_system_score_codex":0.00008039322,"about_ca_system_score_gemma":0.00036559073,"threshold_uncertainty_score":0.34557036},"labels":[],"label_agreement":null},{"id":"W2895130854","doi":"10.14569/ijacsa.2018.090970","title":"Interface of an Automatic Recognition System for Dysarthric Speech","year":2018,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Mel-frequency cepstrum; Speech recognition; Hidden Markov model; Intelligibility (philosophy); Interface (matter); Cepstrum; Voice activity detection; Feature extraction; Speech processing; Artificial intelligence","score_opus":0.01633857515785491,"score_gpt":0.340155047416418,"score_spread":0.3238164722585631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895130854","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":[0.6377295,0.000045221306,0.36105397,0.0005082603,0.00024676355,0.00023547257,0.000006870023,0.000013571893,0.00016031523],"genre_scores_gemma":[0.85679555,0.000018379971,0.14265649,0.00013727325,0.0003655692,0.000012355387,0.0000027188948,0.0000041821354,0.00000749839],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909866,0.0000063398816,0.00031028307,0.00012964674,0.00036836404,0.00008668573],"domain_scores_gemma":[0.9972626,0.00003921626,0.0002623149,0.00011018601,0.0022423246,0.00008338564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003279902,0.000056826797,0.0001317337,0.000233993,0.000070435475,0.000035041863,0.0002588934,0.000018764666,0.000004433319],"category_scores_gemma":[0.00003540971,0.000047424197,0.00003813746,0.00023074936,0.00022851922,0.000426222,0.000032307762,0.000050471466,0.0000036947474],"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":[0.000053819866,0.00011260836,0.00006167543,0.000029721641,0.000025552,0.0000015628455,0.00017071227,0.000036398334,0.025529234,0.00040680348,0.000037869573,0.97353405],"study_design_scores_gemma":[0.01026546,0.008802691,0.013121255,0.0022618135,0.00027564916,0.0048923865,0.0037362427,0.16914293,0.7477544,0.015472801,0.023673154,0.0006012118],"about_ca_topic_score_codex":0.0000016771027,"about_ca_topic_score_gemma":0.0000017556131,"teacher_disagreement_score":0.9729328,"about_ca_system_score_codex":0.00005788415,"about_ca_system_score_gemma":0.00014663683,"threshold_uncertainty_score":0.19339027},"labels":[],"label_agreement":null},{"id":"W2902668122","doi":"10.14569/ijacsa.2018.091101","title":"Exploring Identifiers of Research Articles Related to Food and Disease using Artificial Intelligence","year":2018,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","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":"Sheridan College; Agriculture and Agri-Food Canada","funders":"","keywords":"Computer science; Identifier; Classifier (UML); Artificial intelligence; Data science; Construct (python library); Machine learning","score_opus":0.164140878476352,"score_gpt":0.4175046342948042,"score_spread":0.2533637558184522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902668122","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":[0.73414534,0.000095718,0.26500487,0.0005774513,0.00012432036,0.000041700696,0.000001992934,0.0000017431729,0.0000068902127],"genre_scores_gemma":[0.9653346,0.00006813669,0.03435881,0.000052088322,0.00017755703,0.0000044138155,3.447271e-7,0.0000019624194,0.0000020608536],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992508,0.000013556328,0.000193201,0.00014353728,0.00030589104,0.00009297778],"domain_scores_gemma":[0.9987676,0.000026450709,0.00007236052,0.00008588861,0.00090975594,0.00013790549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048276945,0.00003581149,0.000050661136,0.00016654607,0.00010498785,0.00004204837,0.00027926575,0.000013594728,7.923269e-7],"category_scores_gemma":[0.00021877149,0.00003123633,0.000014282412,0.00027769624,0.0007771903,0.000025355952,0.00018345061,0.000050778068,5.8601915e-7],"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":[0.000059570935,0.000043713953,0.00023050966,0.0000037470206,0.000018235,0.0000013967236,0.00027258982,0.00035866108,0.2768027,0.007928638,0.000013370506,0.7142669],"study_design_scores_gemma":[0.00037062453,0.0022545373,0.011369947,0.00031587525,0.000022579165,0.00015616305,0.0023679766,0.011643859,0.82756704,0.13591933,0.0077061295,0.0003059177],"about_ca_topic_score_codex":0.0000013234718,"about_ca_topic_score_gemma":0.0000012497535,"teacher_disagreement_score":0.71396095,"about_ca_system_score_codex":0.000012259461,"about_ca_system_score_gemma":0.00010545066,"threshold_uncertainty_score":0.286359},"labels":[],"label_agreement":null},{"id":"W2947296117","doi":"10.14569/ijacsa.2019.0100542","title":"Comparison of Reducing the Speckle Noise in Ultrasound Medical Images using Discrete Wavelet Transform","year":2019,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Image and Signal Denoising Methods","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Speckle noise; Noise (video); Speckle pattern; Computer vision; Discrete wavelet transform; Pattern recognition (psychology); Filter (signal processing); Median filter; Wavelet; Peak signal-to-noise ratio; Wavelet transform; Wiener filter; Image quality; Image processing; Image (mathematics)","score_opus":0.01738715892421896,"score_gpt":0.3568985919563118,"score_spread":0.33951143303209286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947296117","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":[0.14402571,0.00013854408,0.853843,0.0012804123,0.00032992198,0.00013033197,0.0000014319578,0.0000053481112,0.0002453007],"genre_scores_gemma":[0.7843096,0.000048574824,0.21536057,0.0001466425,0.00012097923,0.0000023608604,2.9359202e-7,0.0000028959591,0.000008083114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998014,0.00005228157,0.0005157803,0.00021463507,0.0010442623,0.00015907097],"domain_scores_gemma":[0.998423,0.00039019276,0.00029583432,0.00023182361,0.0005774036,0.00008173932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016533317,0.00008654292,0.00019574286,0.00025285126,0.00009535788,0.00017519864,0.0018501536,0.000025368712,0.00000595309],"category_scores_gemma":[0.0000818067,0.00006094614,0.000053784388,0.00058313465,0.0002880075,0.0010568051,0.00016799402,0.00021768286,0.0000014356741],"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":[0.000055855184,0.00021945129,0.0022718618,0.00001744592,0.00003406406,0.000014873566,0.002108829,0.029406134,0.19894785,0.02679034,0.000045626028,0.7400877],"study_design_scores_gemma":[0.0028528117,0.00037416175,0.021873105,0.00059941056,0.00002079234,0.0012865927,0.00042977085,0.7524643,0.18140046,0.0343271,0.003917816,0.0004536532],"about_ca_topic_score_codex":0.000015237492,"about_ca_topic_score_gemma":0.0000012406243,"teacher_disagreement_score":0.73963404,"about_ca_system_score_codex":0.00007005225,"about_ca_system_score_gemma":0.0002923343,"threshold_uncertainty_score":0.34380764},"labels":[],"label_agreement":null},{"id":"W3009579997","doi":"10.14569/ijacsa.2020.0110279","title":"An Investigation of a Convolution Neural Network Architecture for Detecting Distracted Pedestrians","year":2020,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sheridan College","funders":"","keywords":"Computer science; SAFER; Pedestrian; Convolutional neural network; Architecture; Artificial intelligence; Deep learning; Distracted driving; Machine learning; Human–computer interaction; Computer security; Poison control; Transport engineering","score_opus":0.019752454113086102,"score_gpt":0.2885755945388652,"score_spread":0.2688231404257791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009579997","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":[0.08549529,0.00005705397,0.9096624,0.004212852,0.00018173733,0.0003363411,0.00000825575,0.000040957562,0.0000050809213],"genre_scores_gemma":[0.65169734,0.000013464007,0.34722453,0.00049257686,0.0005339446,0.000029293295,0.0000036211543,0.0000048367096,3.8485558e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984633,0.00002693241,0.00050153735,0.00033769596,0.0004793861,0.00019118276],"domain_scores_gemma":[0.9972923,0.00021118997,0.0006758204,0.00022465193,0.0013701177,0.00022594705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031280174,0.00011632688,0.00016937449,0.00012577248,0.00022118738,0.00014189497,0.0015020982,0.00003074295,4.2851934e-7],"category_scores_gemma":[0.000059803875,0.0001094902,0.000057885143,0.00085653184,0.0002589123,0.0013191402,0.00014268594,0.00016454973,3.707781e-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":[0.000048471455,0.000041730607,0.00043111274,0.000011192796,0.000019864334,0.0000015186728,0.000588156,0.3367873,0.087776996,0.028484326,0.000048013462,0.5457613],"study_design_scores_gemma":[0.0010463209,0.0007339034,0.006688244,0.000052030275,0.00002086975,0.00016097647,0.00006669082,0.9295548,0.014037253,0.04439334,0.0029686475,0.00027692885],"about_ca_topic_score_codex":0.0000012206335,"about_ca_topic_score_gemma":0.0000015872796,"teacher_disagreement_score":0.5927675,"about_ca_system_score_codex":0.00004819894,"about_ca_system_score_gemma":0.00017981463,"threshold_uncertainty_score":0.44648808},"labels":[],"label_agreement":null},{"id":"W3134435486","doi":"10.14569/ijacsa.2021.0120253","title":"The Enrichment of Texture Information to Improve Optical Flow for Silhouette Image","year":2021,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Advanced Vision and Imaging","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":"Lembaga Pengelola Dana Pendidikan; Institute of Genetics; University of Tokyo; Institute of Medical Science, University of Tokyo; Research Organization of Information and Systems","keywords":"Silhouette; Computer science; Optical flow; Computer vision; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Texture (cosmology); Tracking (education); Computation; Flow (mathematics); Image (mathematics); Algorithm; Mathematics","score_opus":0.0043619867870823646,"score_gpt":0.29000598968622354,"score_spread":0.28564400289914116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134435486","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":[0.0004053755,0.000101684294,0.9890371,0.009644988,0.00052107044,0.0001731972,0.000006005272,0.000008900973,0.0001016773],"genre_scores_gemma":[0.039039288,0.00010109027,0.9591373,0.0014948876,0.00017768625,0.000029626884,0.0000012394448,0.0000024566336,0.000016378739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987196,0.000007973068,0.00039275753,0.00015904542,0.00057660573,0.00014401929],"domain_scores_gemma":[0.9961873,0.00017836234,0.0002529596,0.00023381434,0.0030362024,0.00011131433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041896032,0.000071206436,0.000099871955,0.00013067301,0.00020725625,0.00035519165,0.0011057594,0.0000131626075,6.248273e-7],"category_scores_gemma":[0.00015553003,0.000051667215,0.00005159265,0.0004159057,0.000132141,0.0017823635,0.00035030526,0.000085575164,0.0000022746729],"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":[0.000008332779,0.000024906496,0.000003222544,0.0000026647263,0.000008148385,8.235946e-7,0.00015634876,0.0016179278,0.027648862,0.044299804,0.00019151781,0.92603743],"study_design_scores_gemma":[0.0013885234,0.00038962904,0.0009977584,0.00009242631,0.000011801603,0.0002379705,0.00028963728,0.504342,0.13717934,0.038724184,0.3160788,0.00026789797],"about_ca_topic_score_codex":1.9751862e-7,"about_ca_topic_score_gemma":2.7120046e-7,"teacher_disagreement_score":0.92576957,"about_ca_system_score_codex":0.000058511545,"about_ca_system_score_gemma":0.00023703137,"threshold_uncertainty_score":0.34251213},"labels":[],"label_agreement":null},{"id":"W3188801641","doi":"10.14569/ijacsa.2021.0120701","title":"Edge-based Video Analytic for Smart Cities","year":2021,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Video Surveillance and Tracking Methods","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Video tracking; Cloud computing; Convolutional neural network; Enhanced Data Rates for GSM Evolution; Edge device; Artificial intelligence; Real-time computing; Analytics; Edge computing; Bandwidth (computing); Video processing; Computer vision; Data mining; Computer network","score_opus":0.020364381339458924,"score_gpt":0.32723712658793913,"score_spread":0.3068727452484802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188801641","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":[0.0044678473,0.0002319981,0.99011374,0.004170877,0.0007473768,0.000095117815,0.0000046505197,0.000023138988,0.00014526647],"genre_scores_gemma":[0.29411966,0.000054981898,0.70394033,0.0014493003,0.00037294035,0.000025128944,0.0000018812493,0.000004628924,0.000031137984],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858433,0.000026650067,0.00034103703,0.00029815495,0.00057525834,0.00017454407],"domain_scores_gemma":[0.9961358,0.0003434587,0.00024843158,0.000278833,0.002882738,0.000110753914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086521596,0.000092229944,0.00015980663,0.000261798,0.00019033684,0.00041752172,0.001224062,0.000021610731,0.000002273328],"category_scores_gemma":[0.00013258758,0.000084405314,0.00008744841,0.0006167736,0.00017820502,0.00088465464,0.00016398105,0.00009231998,0.0000017254247],"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":[0.000012562329,0.00009756173,0.000705152,0.000010761849,0.0000405244,0.000016940116,0.00011459882,0.006387737,0.0049394225,0.07356159,0.00024765832,0.9138655],"study_design_scores_gemma":[0.004059061,0.000534773,0.028253306,0.00028016168,0.000046161214,0.0010365035,0.00013865501,0.37206927,0.09113664,0.2020498,0.29955974,0.00083591166],"about_ca_topic_score_codex":8.9918547e-7,"about_ca_topic_score_gemma":0.000001943034,"teacher_disagreement_score":0.91302955,"about_ca_system_score_codex":0.00006989637,"about_ca_system_score_gemma":0.0006497786,"threshold_uncertainty_score":0.40261716},"labels":[],"label_agreement":null},{"id":"W3201550731","doi":"10.14569/ijacsa.2021.0120902","title":"Monitoring Indoor Activity of Daily Living using Thermal Imaging: A Case Study","year":2021,"lang":"en","type":"preprint","venue":"International Journal of Advanced Computer Science and Applications","topic":"Video Surveillance and Tracking Methods","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 Sherbrooke","funders":"","keywords":"Activities of daily living; Computer science; Identification (biology); Assisted living; Internet of Things; Real-time computing; Dependency (UML); Field (mathematics); Stability (learning theory); Artificial intelligence; Environmental science; Human–computer interaction; Computer vision; Machine learning; Internet privacy; Psychology; Ecology; Mathematics; Gerontology; Medicine","score_opus":0.04028310823059859,"score_gpt":0.376168591546849,"score_spread":0.3358854833162504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201550731","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":[0.50324404,0.00019081353,0.4955138,0.00007717744,0.0008244232,0.00012826252,0.0000014639545,0.000011971676,0.000008036923],"genre_scores_gemma":[0.7332946,0.000041179843,0.26623154,0.000023473769,0.00039014313,0.00001154734,1.121965e-7,0.0000066366524,7.752738e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997468,0.00013260367,0.0005946755,0.00054027664,0.0010540916,0.00021038186],"domain_scores_gemma":[0.9952448,0.00031401016,0.00105845,0.00056028314,0.002695622,0.00012685846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017437062,0.00020074566,0.00036814253,0.00049122865,0.0002122784,0.00070851605,0.0021440194,0.000043536667,7.824006e-7],"category_scores_gemma":[0.00009400656,0.00019197281,0.00011742936,0.00057478127,0.00020347653,0.0014973832,0.002314225,0.00047753862,1.5578684e-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":[0.000007137018,0.00048162628,0.024911504,0.000022775632,0.0001166252,0.0007213871,0.0035452843,0.025138097,0.020834042,0.00010826219,6.7193145e-7,0.9241126],"study_design_scores_gemma":[0.0027515495,0.000575912,0.2694685,0.0032387197,0.0002037171,0.026106862,0.006181743,0.6330815,0.05316477,0.0031586173,0.00018784769,0.0018802832],"about_ca_topic_score_codex":0.000078915014,"about_ca_topic_score_gemma":0.0000036453953,"teacher_disagreement_score":0.92223233,"about_ca_system_score_codex":0.00015863533,"about_ca_system_score_gemma":0.00083832163,"threshold_uncertainty_score":0.7828424},"labels":[],"label_agreement":null},{"id":"W3204750940","doi":"10.14569/ijacsa.2021.0120975","title":"Applying Grey Clustering and Shannon’s Entropy to Assess Sediment Quality from a Watershed","year":2021,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Water quality; Watershed; Sediment; Environmental science; Computer science; Pollution; Environmental quality; Cluster analysis; Mercury (programming language); Water resource management; Hydrology (agriculture); Environmental resource management; Geology; Ecology; Artificial intelligence","score_opus":0.036288455511809384,"score_gpt":0.3374588148727334,"score_spread":0.301170359360924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204750940","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":[0.38579935,0.000034066616,0.609971,0.0036434722,0.000254825,0.0001635115,0.000013632637,0.000008812892,0.00011133206],"genre_scores_gemma":[0.85830545,0.000057454607,0.13931188,0.0020416928,0.00019617082,0.000045994904,0.0000045004113,0.0000037590985,0.00003309957],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985445,0.00003772758,0.00033240663,0.00028049367,0.000664059,0.00014079546],"domain_scores_gemma":[0.9992604,0.00005745837,0.00016191733,0.00013707909,0.00019630032,0.00018684506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005049817,0.000083313236,0.00012046098,0.00006347429,0.0001593723,0.00023508866,0.0003744598,0.000017405813,0.00006954164],"category_scores_gemma":[0.000019993377,0.000074185475,0.000026946254,0.0002046724,0.00014248485,0.00056183187,0.0005081254,0.00008648019,0.000012719834],"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":[0.00005114098,0.00021748169,0.007482948,0.0000075829416,0.000056394092,0.000021582906,0.0018682183,0.017349955,0.43703,0.003001333,0.00028646336,0.5326269],"study_design_scores_gemma":[0.003159957,0.00029189818,0.35675287,0.00021668446,0.000051518236,0.00043289023,0.0013925567,0.02655673,0.15484741,0.020549301,0.43479654,0.0009516595],"about_ca_topic_score_codex":0.000047613546,"about_ca_topic_score_gemma":0.00002010036,"teacher_disagreement_score":0.5316752,"about_ca_system_score_codex":0.00018724125,"about_ca_system_score_gemma":0.000050436236,"threshold_uncertainty_score":0.3025196},"labels":[],"label_agreement":null},{"id":"W4210768133","doi":"10.14569/ijacsa.2022.0130175","title":"Balanced Schedule on Storm for Performance Enhancement","year":2022,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Cloud Computing and Resource Management","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":"Academy of Scientific Research and Technology","keywords":"Computer science; Workload; Scheduling (production processes); Distributed computing; Storm; Big data; Schedule; Latency (audio); Network topology; Real-time computing; Computer network; Operating system; Telecommunications; Mathematical optimization","score_opus":0.00971491653426371,"score_gpt":0.2671384462725287,"score_spread":0.257423529738265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210768133","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":[0.18986116,0.000053242406,0.8061883,0.0026014254,0.00085303,0.00022283038,0.0000018754142,0.000022688788,0.00019544193],"genre_scores_gemma":[0.8775118,0.00001598035,0.12079565,0.001210542,0.00028943195,0.0001038834,7.774096e-7,0.0000042431648,0.00006768221],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998298,0.000014177129,0.00028748804,0.00029089875,0.0009252381,0.00018418898],"domain_scores_gemma":[0.99872416,0.000078564066,0.00029367665,0.00024802834,0.000572826,0.00008272957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007460184,0.0000906677,0.00011030257,0.00029269324,0.0005660777,0.00015784474,0.0020181662,0.0000084075255,0.0000032039802],"category_scores_gemma":[0.00001300341,0.000082301915,0.000050616545,0.0004180078,0.00008971689,0.00014604154,0.00066831097,0.00013594254,0.0000021477106],"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":[0.000025594283,0.00017625325,0.00005499744,0.0000045472766,0.000021761454,0.0000020523828,0.00021541408,0.098318025,0.0014921542,0.039578974,0.00028846212,0.85982174],"study_design_scores_gemma":[0.0016187344,0.0015475723,0.0017568173,0.000047482536,0.000008191581,0.00013758661,0.000111460125,0.69832516,0.005938406,0.0041823583,0.2860357,0.0002905185],"about_ca_topic_score_codex":5.532614e-7,"about_ca_topic_score_gemma":9.852207e-8,"teacher_disagreement_score":0.8595312,"about_ca_system_score_codex":0.00021456607,"about_ca_system_score_gemma":0.00013973043,"threshold_uncertainty_score":0.43538696},"labels":[],"label_agreement":null},{"id":"W4285233275","doi":"10.14569/ijacsa.2022.01306108","title":"Identifying Community-Supported Technologies and Software Developments Concepts by K-means Clustering","year":2022,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Cluster analysis; Software; Software development; Java; Data science; Data mining; Machine learning; Artificial intelligence","score_opus":0.01566211667717065,"score_gpt":0.3197264531457159,"score_spread":0.30406433646854525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285233275","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":[0.03823379,0.00018589951,0.9586276,0.0025580465,0.00023140156,0.0000664512,0.0000062616127,0.00006998747,0.000020610272],"genre_scores_gemma":[0.7005102,0.000090954236,0.29898834,0.00033347192,0.000031420688,0.000012597248,0.0000029948876,0.0000038057453,0.000026208896],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873334,0.000037580186,0.00027973476,0.0001872048,0.00061652437,0.00014559671],"domain_scores_gemma":[0.9989165,0.000098382545,0.00028326604,0.0001837441,0.00045595394,0.0000621446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006307723,0.00008345132,0.0001107846,0.00028324683,0.0008175815,0.00030370223,0.0018192405,0.00001470634,0.000001299892],"category_scores_gemma":[0.000048843445,0.00008240449,0.000022299275,0.0005019028,0.00022715406,0.0007648399,0.0015480405,0.0003852444,6.478602e-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":[0.0000035362393,0.00008511978,0.00094725005,0.0000054704283,0.000027465132,0.000008801892,0.0009832507,0.005535328,0.003417065,0.003530286,0.00017418551,0.98528224],"study_design_scores_gemma":[0.0045973836,0.0012278872,0.013744452,0.00035836274,0.000055041957,0.0047066184,0.014285616,0.5860199,0.007921462,0.10022844,0.26518565,0.0016691956],"about_ca_topic_score_codex":0.0000039844,"about_ca_topic_score_gemma":0.0000011965253,"teacher_disagreement_score":0.9836131,"about_ca_system_score_codex":0.00009587433,"about_ca_system_score_gemma":0.00014330505,"threshold_uncertainty_score":0.62882584},"labels":[],"label_agreement":null},{"id":"W4285275275","doi":"10.14569/ijacsa.2022.0130488","title":"IAGA: Interference Aware Genetic Algorithm based VM Allocation Policy for Cloud Systems","year":2022,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Cloud Computing and Resource Management","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":"Cloud computing; Computer science; Virtual machine; Server; Resource allocation; Distributed computing; Multitenancy; Interference (communication); Resource (disambiguation); Computer network; Operating system; Software as a service","score_opus":0.010631568612156321,"score_gpt":0.27434935328748045,"score_spread":0.26371778467532414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285275275","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":[0.004873365,0.00015812901,0.9891888,0.0039967247,0.0013392618,0.00035005118,0.0000108626455,0.000041647174,0.000041177864],"genre_scores_gemma":[0.7437324,0.000012851363,0.2539008,0.0009958151,0.0011183094,0.00017605776,0.0000029390158,0.000008650201,0.000052174928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980187,0.00004191378,0.0004443865,0.0003775223,0.00089247164,0.00022503463],"domain_scores_gemma":[0.9978208,0.00013728756,0.00044113863,0.00032486944,0.0011514797,0.00012443379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072155724,0.00012266399,0.00015027006,0.000567559,0.0005410148,0.0003835099,0.0026389477,0.000015597596,0.0000012426054],"category_scores_gemma":[0.00002605965,0.000116635325,0.000070701404,0.0007936621,0.00013048135,0.00015055801,0.000779106,0.00015133205,0.0000013310398],"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":[0.000006981996,0.00009800014,0.000022150261,0.0000068954055,0.00002008736,0.0000043400996,0.00015807456,0.35857898,0.00021101735,0.021203443,0.00026862643,0.6194214],"study_design_scores_gemma":[0.0005428802,0.00025692367,0.00039061607,0.000028916915,0.00000639649,0.00016580724,0.000103500184,0.9501423,0.00015260611,0.002924296,0.045143794,0.00014191054],"about_ca_topic_score_codex":0.000011909479,"about_ca_topic_score_gemma":2.9460998e-7,"teacher_disagreement_score":0.73885906,"about_ca_system_score_codex":0.0003201592,"about_ca_system_score_gemma":0.00046548934,"threshold_uncertainty_score":0.49038652},"labels":[],"label_agreement":null},{"id":"W4313331018","doi":"10.14569/ijacsa.2022.0131234","title":"Transfer Learning for Closed Domain Question Answering in COVID-19","year":2022,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Direktorat Riset and Pengembangan, Universitas Indonesia; Universitas Indonesia","keywords":"Computer science; Benchmark (surveying); Labrador Retriever; Cosine similarity; Transfer of learning; Baseline (sea); Question answering; Artificial intelligence; Coronavirus disease 2019 (COVID-19); Domain (mathematical analysis); Similarity (geometry); Open domain; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.016209179709699137,"score_gpt":0.31620118342794273,"score_spread":0.2999920037182436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313331018","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":[0.07457796,0.00007750777,0.92089385,0.003929408,0.00030770275,0.00017307658,0.0000013761663,0.000019775107,0.000019369827],"genre_scores_gemma":[0.76246375,0.000027320822,0.2364157,0.0008799831,0.00012812031,0.0000757895,0.000001010357,0.0000031340728,0.0000051866637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870914,0.000036091045,0.00031081907,0.00025077275,0.0005512415,0.00014194433],"domain_scores_gemma":[0.9992662,0.00011865044,0.000114395996,0.00012130584,0.00027391166,0.00010552148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012169481,0.000064325286,0.00009516094,0.00038234383,0.00031338862,0.00015762355,0.0011951947,0.000011340263,0.0000021394],"category_scores_gemma":[0.000038463342,0.00006713959,0.00003620425,0.00043328796,0.00006496316,0.0011007513,0.00021466392,0.0001798828,2.2570201e-7],"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":[0.000015506705,0.000042287542,0.0001325717,0.0000036457336,0.000004756431,0.0000059896383,0.0007987569,0.53945017,0.006704368,0.11829812,0.000006141164,0.33453766],"study_design_scores_gemma":[0.0023973186,0.0003231078,0.0006991861,0.000040429255,0.000004093217,0.0004577901,0.0005238093,0.7952869,0.0011573563,0.09533571,0.10350769,0.00026662616],"about_ca_topic_score_codex":0.0000051812426,"about_ca_topic_score_gemma":0.0000018783683,"teacher_disagreement_score":0.6878858,"about_ca_system_score_codex":0.00029960767,"about_ca_system_score_gemma":0.00033388744,"threshold_uncertainty_score":0.2737873},"labels":[],"label_agreement":null},{"id":"W4379385814","doi":"10.14569/ijacsa.2023.0140505","title":"An Enhanced SVM Model for Implicit Aspect Identification in Sentiment Analysis","year":2023,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Sentiment Analysis and Opinion Mining","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":"Université de Moncton","funders":"","keywords":"Overfitting; Computer science; Support vector machine; WordNet; Sentiment analysis; Artificial intelligence; Machine learning; Benchmark (surveying); Identification (biology); Kernel (algebra); Task (project management); Artificial neural network","score_opus":0.018695742225354292,"score_gpt":0.3463898390964029,"score_spread":0.3276940968710486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379385814","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":[0.084197104,0.000020602762,0.9146081,0.00079208054,0.00018694518,0.00015082373,0.000004122076,0.00002697843,0.0000132081395],"genre_scores_gemma":[0.89674586,0.000056087,0.102824435,0.00014373595,0.00013688764,0.00004975294,0.000008949445,0.000003686381,0.000030635314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982265,0.000014879017,0.0005264177,0.0003872599,0.0006565987,0.00018833617],"domain_scores_gemma":[0.99817806,0.00007378165,0.00037270016,0.0003020289,0.0009721561,0.00010126409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010354901,0.000091998016,0.00017931701,0.001458759,0.00015053984,0.00039270284,0.001453885,0.000020878522,0.0000013654148],"category_scores_gemma":[0.000017732931,0.00008699919,0.000105338244,0.0026133498,0.000068739755,0.0014407142,0.0001491809,0.000064834516,0.0000038401536],"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":[0.000010098988,0.00013044642,0.00062258466,0.0000026712205,0.00013164092,0.0000017900021,0.000837832,0.5919978,0.056806948,0.03469144,0.000055444332,0.31471127],"study_design_scores_gemma":[0.0003082588,0.000036451867,0.008360171,0.0000086219015,0.000024716537,0.000003799634,0.00005782891,0.9783363,0.0058437213,0.0067125876,0.00021317603,0.000094384486],"about_ca_topic_score_codex":0.000002297013,"about_ca_topic_score_gemma":0.0000051745783,"teacher_disagreement_score":0.81254876,"about_ca_system_score_codex":0.000097399934,"about_ca_system_score_gemma":0.0001207627,"threshold_uncertainty_score":0.37868425},"labels":[],"label_agreement":null},{"id":"W4386389382","doi":"10.14569/ijacsa.2023.0140836","title":"Model Classification of Fire Weather Index using the SVM-FF Method on Forest Fire in North Sumatra, Indonesia","year":2023,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Outlier; Computer science; Index (typography); Environmental science; Meteorology; Machine learning; Artificial intelligence; Geography","score_opus":0.021730149413418373,"score_gpt":0.30258971381473765,"score_spread":0.28085956440131926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386389382","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":[0.7955536,0.000010842323,0.2035462,0.00054277724,0.00009764133,0.00018656209,0.000004497432,0.00000738277,0.000050477323],"genre_scores_gemma":[0.9900915,0.000026447551,0.009682069,0.0000998386,0.00006281402,0.000021598878,0.000001297047,0.000006082315,0.00000833126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860543,0.000031806398,0.0003420457,0.00019964248,0.00068615447,0.00013493166],"domain_scores_gemma":[0.999161,0.00014514299,0.00032860495,0.00018306603,0.0001296836,0.00005248945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006711368,0.00008330773,0.000115368915,0.00014275467,0.000116557654,0.00004839792,0.00075376744,0.00002469286,0.0000030169647],"category_scores_gemma":[0.000022733642,0.00006123531,0.000036240646,0.00090365077,0.00020955663,0.000453412,0.00014126384,0.00013012496,0.0000068109543],"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":[0.000015867165,0.00006518752,0.06147027,0.0000037368975,0.0000071795753,0.0000021000492,0.00033749797,0.71655077,0.006229084,0.000688834,0.00003954138,0.21458991],"study_design_scores_gemma":[0.00016061595,0.000027067741,0.281712,0.000025515486,0.0000022676484,0.000017876964,0.000040196184,0.71699625,0.00015215558,0.0006480357,0.00017207472,0.000045940662],"about_ca_topic_score_codex":0.0000506843,"about_ca_topic_score_gemma":0.00009656904,"teacher_disagreement_score":0.22024171,"about_ca_system_score_codex":0.00017393786,"about_ca_system_score_gemma":0.00005788638,"threshold_uncertainty_score":0.24971035},"labels":[],"label_agreement":null},{"id":"W4388144777","doi":"10.14569/ijacsa.2023.0141080","title":"A Novel Multidimensional Reference Model for Heterogeneous Textual Datasets using Context, Semantic and Syntactic Clues","year":2023,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Universiti Teknologi Petronas","keywords":"Computer science; Context (archaeology); Natural language processing; Information retrieval; Synonym (taxonomy); Dimension (graph theory); Search engine indexing; Semantics (computer science); Artificial intelligence","score_opus":0.05753518558212063,"score_gpt":0.34597527862950467,"score_spread":0.28844009304738405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388144777","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":[0.051882815,0.00007885476,0.9458901,0.0017441984,0.000113230235,0.00017976672,0.000038336897,0.00006847025,0.00000421951],"genre_scores_gemma":[0.74128056,0.000085976644,0.2583102,0.0002374185,0.00004598534,0.000023028833,0.000006690617,0.000003829588,0.000006308035],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986671,0.0000065785657,0.00032648304,0.00033713647,0.00048370022,0.00017900424],"domain_scores_gemma":[0.9984992,0.00017892783,0.00028789014,0.0002391043,0.00070204836,0.00009281529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036073642,0.00010677713,0.00013442677,0.0003993894,0.00026251012,0.00024412194,0.00102941,0.00003124169,3.8156557e-7],"category_scores_gemma":[0.00006743947,0.00009382114,0.00003001249,0.00039972787,0.000269912,0.0011945312,0.00048850756,0.00009191151,0.000001972876],"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":[0.000032152533,0.00016042887,0.00009666226,0.00001688043,0.00005706255,0.000008964314,0.00035095518,0.03923295,0.121361636,0.28068632,0.00026805355,0.55772793],"study_design_scores_gemma":[0.00044309488,0.000048801267,0.00031859407,0.000032465472,0.0000067900774,0.0002611785,0.000053762073,0.98605466,0.002560294,0.007854444,0.0022519098,0.00011399031],"about_ca_topic_score_codex":0.0000027097144,"about_ca_topic_score_gemma":0.0000016382529,"teacher_disagreement_score":0.94682175,"about_ca_system_score_codex":0.00006249396,"about_ca_system_score_gemma":0.00018130381,"threshold_uncertainty_score":0.3825915},"labels":[],"label_agreement":null},{"id":"W4391546094","doi":"10.14569/ijacsa.2024.0150132","title":"A Cost-Efficient Approach for Creating Virtual Fitting Room using Generative Adversarial Networks (GANs)","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Face recognition and analysis","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":"Ministry of Communication and Information Technology","keywords":"Computer science; Purchasing; Clothing; Adversarial system; Laptop; Intrusiveness; Process (computing); Quality (philosophy); Multimedia; Artificial intelligence; Marketing","score_opus":0.028464013914448485,"score_gpt":0.32165407871659984,"score_spread":0.2931900648021514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391546094","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":[0.0036683544,0.00021091115,0.9946861,0.00042200598,0.0006382878,0.00025700443,0.0000075517396,0.000036861027,0.00007291203],"genre_scores_gemma":[0.3858997,0.00003702258,0.61284465,0.0002508619,0.0009077896,0.000037327925,0.000003903738,0.000005962868,0.000012792645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844706,0.000021317248,0.0003938684,0.00037468565,0.0005619097,0.00020116255],"domain_scores_gemma":[0.99819344,0.00018944156,0.00021566903,0.00013090475,0.0011445936,0.00012591935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070439227,0.00011720842,0.00015418719,0.00038131457,0.0003483733,0.0008403144,0.0008904733,0.000030155587,0.0000016445564],"category_scores_gemma":[0.000043853575,0.000101162965,0.0001100519,0.0007794409,0.00012364966,0.000904523,0.00020624393,0.00014934028,9.171474e-7],"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":[0.0000048379256,0.000042058793,0.0000057803695,0.000003134658,0.000040310853,0.0000025687862,0.0003110835,0.60432816,0.001510241,0.020136481,0.000019741134,0.37359557],"study_design_scores_gemma":[0.0002954665,0.000054642926,0.0000167278,0.000058374357,0.00001806931,0.000107829146,0.00013590694,0.9959899,0.0007399514,0.00066987105,0.0018000859,0.000113151895],"about_ca_topic_score_codex":0.000001946603,"about_ca_topic_score_gemma":3.059763e-7,"teacher_disagreement_score":0.39166173,"about_ca_system_score_codex":0.00015609551,"about_ca_system_score_gemma":0.00028199155,"threshold_uncertainty_score":0.8103171},"labels":[],"label_agreement":null},{"id":"W4393441017","doi":"10.14569/ijacsa.2024.0150305","title":"Generative Adversarial Neural Networks for Realistic Stock Market Simulations","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Adversarial system; Generative grammar; Artificial neural network; Computer science; Stock market; Artificial intelligence; Generative adversarial network; Stock (firearms); Machine learning; Deep learning; Engineering; History","score_opus":0.0690060025597425,"score_gpt":0.43136765424143786,"score_spread":0.3623616516816954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393441017","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":[0.005351969,0.00014609829,0.98934984,0.0018339243,0.0025425786,0.0002901843,0.00004463246,0.00002168359,0.00041906786],"genre_scores_gemma":[0.76974565,0.000013933213,0.22781041,0.0002513989,0.0019939686,0.00003086449,0.0000025510885,0.000008793299,0.00014242539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971561,0.00007292214,0.00066180003,0.0003999391,0.0015246547,0.00018457958],"domain_scores_gemma":[0.9919066,0.003584218,0.0003240526,0.00021622342,0.0038268736,0.00014204372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034834214,0.000114948176,0.00017931963,0.00058481825,0.0002879569,0.00085561955,0.0012213152,0.00003307323,0.000029350886],"category_scores_gemma":[0.0018683448,0.00008790664,0.00010289906,0.0009835889,0.00031089183,0.00096970564,0.00019565197,0.0001590394,0.0000013078719],"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":[0.00004840685,0.000018916533,0.00008355126,0.0000015500677,0.000023622382,0.0000027896463,0.000098436285,0.2176709,0.00041398103,0.0079653775,0.0018384173,0.771834],"study_design_scores_gemma":[0.00026482955,0.00009199343,0.0015171489,0.000021271611,0.000015005169,0.00010751155,0.000026828078,0.9255503,0.000055371318,0.04460732,0.027652076,0.00009033565],"about_ca_topic_score_codex":0.0000017971927,"about_ca_topic_score_gemma":0.0000034395086,"teacher_disagreement_score":0.7717437,"about_ca_system_score_codex":0.00014780849,"about_ca_system_score_gemma":0.0003497913,"threshold_uncertainty_score":0.8250759},"labels":[],"label_agreement":null},{"id":"W4393619018","doi":"10.14569/ijacsa.2024.0150359","title":"Speech Emotion Recognition in Multimodal Environments with Transformer: Arabic and English Audio Datasets","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Arabic; Transformer; Speech recognition; Computer science; Natural language processing; Linguistics; Engineering; Electrical engineering; Voltage","score_opus":0.0071688534748173645,"score_gpt":0.2539454191585412,"score_spread":0.24677656568372383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393619018","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":[0.16503255,0.00024930452,0.8335334,0.0007662321,0.00022756559,0.00011087583,0.000010779414,0.000018605726,0.000050677965],"genre_scores_gemma":[0.7692596,0.00035203327,0.22997336,0.0001791868,0.000210268,0.000011357378,0.000006551715,0.0000042442416,0.0000034114526],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883384,0.000009969553,0.00023687918,0.00029610217,0.00048800453,0.00013518195],"domain_scores_gemma":[0.99948853,0.000046886766,0.000093690855,0.00010228149,0.00018192104,0.00008670793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035533655,0.00008779441,0.00009111744,0.0003498434,0.00008772811,0.00043354067,0.0005144284,0.000019838362,0.0000013343457],"category_scores_gemma":[0.000012321527,0.000071320865,0.00001625893,0.0004354392,0.00015576756,0.0027284236,0.00006576107,0.0001493001,0.0000024784822],"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":[0.000007487155,0.000041125968,0.00015595776,0.0000069161156,0.000008620507,0.000026137684,0.00040070852,0.00035093923,0.006166213,0.00032816944,0.000010401737,0.9924973],"study_design_scores_gemma":[0.009256774,0.0021291298,0.0779271,0.0038099515,0.000102208534,0.007381664,0.0011222814,0.31381494,0.39420438,0.0823073,0.10563307,0.0023112157],"about_ca_topic_score_codex":0.000001846133,"about_ca_topic_score_gemma":0.0000033785213,"teacher_disagreement_score":0.9901861,"about_ca_system_score_codex":0.000087059685,"about_ca_system_score_gemma":0.000115078976,"threshold_uncertainty_score":0.41806427},"labels":[],"label_agreement":null},{"id":"W4393621326","doi":"10.14569/ijacsa.2024.0150306","title":"Integrating Generative AI for Advancing Agile Software Development and Mitigating Project Management Challenges","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Software Engineering Techniques and Practices","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 Ottawa","funders":"","keywords":"Agile software development; Generative grammar; Computer science; Project management; Software development; Process management; Software engineering; Engineering management; Systems engineering; Software; Engineering; Knowledge management; Artificial intelligence","score_opus":0.018399462642236526,"score_gpt":0.3247596525783265,"score_spread":0.30636018993609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393621326","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":[0.0013332376,0.0018386219,0.9943639,0.001760531,0.00034305616,0.00022225708,0.0000016434304,0.000098214325,0.000038525406],"genre_scores_gemma":[0.027548991,0.0004993147,0.9714093,0.00023683248,0.00019160564,0.000097587734,7.6473196e-7,0.000005593251,0.000009973424],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99899715,0.000008556393,0.00025424088,0.0002748207,0.0003288054,0.00013645341],"domain_scores_gemma":[0.998973,0.00024202804,0.000119813594,0.00010161001,0.0005066049,0.00005690972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061168714,0.00009474692,0.00009276146,0.00028896958,0.00017324477,0.0005049414,0.00060364493,0.000016401056,2.1882622e-7],"category_scores_gemma":[0.000049194652,0.00007983294,0.000024607241,0.00025001442,0.000059128426,0.0014549854,0.0002485152,0.00011442974,3.0299867e-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":[0.0000016144619,0.000011025709,0.0000045004817,0.000033582182,0.00002581413,0.0000061922924,0.0009469168,0.0005050086,0.00034960028,0.067397624,0.00006881332,0.9306493],"study_design_scores_gemma":[0.00053978787,0.00034146543,0.0003467962,0.0012831779,0.000025828485,0.00075462717,0.0006037954,0.3269445,0.016696516,0.052147213,0.599767,0.0005492535],"about_ca_topic_score_codex":6.399694e-7,"about_ca_topic_score_gemma":8.296854e-7,"teacher_disagreement_score":0.9301001,"about_ca_system_score_codex":0.00008440103,"about_ca_system_score_gemma":0.00015450081,"threshold_uncertainty_score":0.48691612},"labels":[],"label_agreement":null},{"id":"W4393621421","doi":"10.14569/ijacsa.2024.01503127","title":"A Machine Learning-based Solution for Monitoring of Converters in Smart Grid Application","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Smart Grid Security and Resilience","field":"Engineering","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":"Université de Moncton","funders":"","keywords":"Converters; Computer science; Grid; Smart grid; Engineering; Electrical engineering; Mathematics","score_opus":0.006440866135040533,"score_gpt":0.258135267660171,"score_spread":0.25169440152513045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393621421","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":[0.04071485,0.0007046111,0.9573621,0.00027509683,0.000756153,0.00013962464,0.000006705756,0.00002511017,0.000015739213],"genre_scores_gemma":[0.9853846,0.00019538114,0.014101594,0.000015803922,0.00025865046,0.000035294823,0.000002631544,0.0000046322557,0.0000014117055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993318,0.0000037223426,0.00023251216,0.000107211185,0.00023969107,0.000085069],"domain_scores_gemma":[0.9995012,0.00008533463,0.000056415938,0.000052506344,0.0002669673,0.00003753827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002589874,0.000054331413,0.00007921883,0.00028576885,0.000040908773,0.00004508564,0.00025682294,0.000018288007,4.841983e-7],"category_scores_gemma":[0.000012303541,0.00005073675,0.00003140702,0.00029875856,0.000101010526,0.0003090515,0.000019161324,0.00010540001,6.3808346e-7],"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":[0.000022855143,0.000049172504,0.002378481,0.00008420376,0.000021721497,0.00000211511,0.00032129633,0.48092368,0.058484774,0.002115812,0.000028971785,0.4555669],"study_design_scores_gemma":[0.00024413392,0.000049921797,0.0020122202,0.00011350739,0.0000043423197,0.000016629134,0.000021258089,0.97466147,0.011367609,0.0006071637,0.010842696,0.00005906655],"about_ca_topic_score_codex":0.000002991905,"about_ca_topic_score_gemma":0.0000017371784,"teacher_disagreement_score":0.9446697,"about_ca_system_score_codex":0.00007929725,"about_ca_system_score_gemma":0.00006389932,"threshold_uncertainty_score":0.20689847},"labels":[],"label_agreement":null},{"id":"W4401287517","doi":"10.14569/ijacsa.2024.0150797","title":"The Low-Cost Transition Towards Smart Grids in Low-Income Countries: The Case Study of Togo","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Energy and Environment Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Transition (genetics)","score_opus":0.006005752936852342,"score_gpt":0.266557851456946,"score_spread":0.26055209852009364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401287517","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":[0.9641912,0.00007989891,0.033115614,0.0020406414,0.00026305113,0.00019531186,0.000003727376,0.0000048915945,0.0001056667],"genre_scores_gemma":[0.9991426,0.00019589518,0.00036572013,0.00017218808,0.00008533249,0.00002350138,3.0968056e-7,0.0000030959195,0.000011351365],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989119,0.000023484914,0.00028790932,0.00014357254,0.00051713455,0.00011596015],"domain_scores_gemma":[0.99953747,0.00012878425,0.00009588367,0.0001348504,0.000049406335,0.000053589476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068676926,0.00006841932,0.00007024598,0.00007047899,0.00020669057,0.00013268106,0.00052411,0.000013982132,0.000013514163],"category_scores_gemma":[0.00001004028,0.000039978506,0.000026701358,0.00034902437,0.00042957597,0.0005689148,0.00011024729,0.00012548431,0.0000051188704],"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":[0.000053674117,0.00038582485,0.002338967,0.000011690367,0.00004606135,0.00034492242,0.0063795554,0.42438915,0.0013755705,0.0023423522,0.00009293927,0.5622393],"study_design_scores_gemma":[0.006517878,0.002485001,0.5517178,0.0014326094,0.0001781888,0.015319515,0.021362735,0.24040432,0.011474034,0.024812376,0.12286884,0.0014266755],"about_ca_topic_score_codex":0.00009043458,"about_ca_topic_score_gemma":0.00013838244,"teacher_disagreement_score":0.5608126,"about_ca_system_score_codex":0.000142552,"about_ca_system_score_gemma":0.000043995395,"threshold_uncertainty_score":0.16302761},"labels":[],"label_agreement":null},{"id":"W4406039185","doi":"10.14569/ijacsa.2024.0151288","title":"Multi-Label Decision-Making for Aerobics Platform Selection with Enhanced BERT-Residual Network","year":2024,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Selection (genetic algorithm); Residual; Artificial intelligence; Machine learning; Algorithm","score_opus":0.00814691319867866,"score_gpt":0.2787814304765091,"score_spread":0.27063451727783044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406039185","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":[0.06413481,0.00025882368,0.9348234,0.00014181946,0.00036984778,0.0001393069,0.0000038407957,0.000094739415,0.00003342497],"genre_scores_gemma":[0.5887549,0.00006605657,0.41083533,0.000042542928,0.00026699775,0.00002278829,7.292658e-7,0.000006543612,0.000004121898],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992738,0.0000015486338,0.00022252179,0.00014559779,0.00021434316,0.00014218621],"domain_scores_gemma":[0.9992984,0.00014467885,0.000059947783,0.000063374304,0.00039479343,0.00003878911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021051193,0.00008103398,0.00009071815,0.00016933237,0.00015001028,0.00010508488,0.00033139013,0.00003726003,0.00000153596],"category_scores_gemma":[0.000009949687,0.000066902154,0.000021241327,0.0003441437,0.000113911,0.000536181,0.00003849701,0.00015314043,0.0000016803053],"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":[0.000022326858,0.000016507007,0.00001616222,0.0000056805757,0.0000407472,0.0000018159682,0.00007029585,0.15683185,0.0042627356,0.011265998,0.00007138673,0.8273945],"study_design_scores_gemma":[0.0005495249,0.00015564487,0.00056739524,0.00024865192,0.000017391476,0.00024103494,0.000028692915,0.97178024,0.0038104716,0.012721771,0.009721243,0.00015793406],"about_ca_topic_score_codex":1.6957753e-7,"about_ca_topic_score_gemma":0.0000064090195,"teacher_disagreement_score":0.82723653,"about_ca_system_score_codex":0.00010961467,"about_ca_system_score_gemma":0.00009710502,"threshold_uncertainty_score":0.27281904},"labels":[],"label_agreement":null},{"id":"W4407144483","doi":"10.14569/ijacsa.2025.0160103","title":"Detection of DDoS Cyberattack Using a Hybrid Trust-Based Technique for Smart Home Networks","year":2025,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":2,"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":"Trent University; Nottingham Trent University","keywords":"Computer science; Denial-of-service attack; Computer security; Application layer DDoS attack; Trinoo; Internet of Things; World Wide Web; The Internet","score_opus":0.008991689951532955,"score_gpt":0.2819835518330352,"score_spread":0.27299186188150226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407144483","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":[0.01526985,0.000116290175,0.9830276,0.00039399348,0.00080642954,0.00034087826,0.0000031682034,0.000019088298,0.000022735048],"genre_scores_gemma":[0.79809475,0.000036939637,0.20132512,0.00030539848,0.00018092379,0.000048874674,6.5198026e-7,0.0000031677082,0.000004159268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879736,0.000020144496,0.00043195876,0.00024388258,0.00036235197,0.00014430896],"domain_scores_gemma":[0.99759334,0.00012360317,0.00038762,0.0002084098,0.0016255329,0.000061514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060774555,0.00009443294,0.00015437331,0.00051763916,0.00022568747,0.0001562817,0.0009845435,0.000034465545,6.1287324e-7],"category_scores_gemma":[0.000027935639,0.00009007468,0.0000777182,0.00079655444,0.000179737,0.0007842912,0.00015545108,0.00013314614,1.9325594e-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":[0.00008144024,0.00015707925,0.000080522834,0.000019432073,0.00003972732,0.0000021065514,0.000043736123,0.09987803,0.14558838,0.025875099,0.000052991716,0.7281814],"study_design_scores_gemma":[0.00051389326,0.00016173846,0.00027114202,0.000101669044,0.0000095195555,0.000074335876,0.0000052626906,0.84020823,0.14108133,0.012177403,0.0052966564,0.00009882815],"about_ca_topic_score_codex":0.000003578685,"about_ca_topic_score_gemma":0.0000015062866,"teacher_disagreement_score":0.78282493,"about_ca_system_score_codex":0.00011815728,"about_ca_system_score_gemma":0.00025273074,"threshold_uncertainty_score":0.3673139},"labels":[],"label_agreement":null},{"id":"W4409171446","doi":"10.14569/ijacsa.2025.0160389","title":"Performance Evaluation of Machine Learning-Based Cyber Attack Detection in Electric Vehicles Charging Stations","year":2025,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Computer security; Artificial intelligence; Real-time computing; Machine learning","score_opus":0.008866415330195481,"score_gpt":0.27872965163684454,"score_spread":0.26986323630664905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409171446","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":[0.8049697,0.00026143884,0.19425738,0.00011458846,0.00018283325,0.000102735066,0.0000011159443,0.000010976072,0.000099202545],"genre_scores_gemma":[0.9980203,0.0001885725,0.0016910544,0.00002898506,0.000047920337,0.000017255563,0.0000011268556,0.0000024739493,0.000002347742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990965,0.000014850399,0.0002570335,0.000093364884,0.00045260656,0.00008562084],"domain_scores_gemma":[0.9989381,0.00006621864,0.00009796651,0.000059686055,0.0008126144,0.000025418896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058922975,0.00005412558,0.0000778765,0.00055167,0.00007977209,0.000030699495,0.00024360968,0.00001758033,0.0000016819527],"category_scores_gemma":[0.00003811581,0.000052158262,0.000019324149,0.00075805647,0.00007259926,0.0003588007,0.000020404083,0.00013939869,6.0652627e-7],"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":[0.000005538167,0.000021947484,0.001298573,0.000006591247,0.0000057555826,1.5425427e-7,0.00006511774,0.7111206,0.019987758,0.00009075868,0.0000022764068,0.2673949],"study_design_scores_gemma":[0.0003768032,0.000029219818,0.03630514,0.00006120106,0.0000070250935,0.0000053252747,0.000018019353,0.92444766,0.03786342,0.00017200234,0.00067009666,0.000044108572],"about_ca_topic_score_codex":0.000002543022,"about_ca_topic_score_gemma":0.0000110786195,"teacher_disagreement_score":0.2673508,"about_ca_system_score_codex":0.00013993232,"about_ca_system_score_gemma":0.00013927727,"threshold_uncertainty_score":0.21269523},"labels":[],"label_agreement":null},{"id":"W4412996470","doi":"10.14569/ijacsa.2025.0160726","title":"Speech Emotion Recognition from Audio Data Using LSTM Model","year":2025,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Speech recognition; Emotion recognition; Artificial intelligence; Natural language processing","score_opus":0.04248749141003563,"score_gpt":0.3368987048711981,"score_spread":0.2944112134611625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412996470","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":[0.052679356,0.0001655496,0.94478,0.0015923977,0.0005002999,0.0000833861,0.000014596662,0.000025982601,0.00015842299],"genre_scores_gemma":[0.19322866,0.00009384896,0.80556464,0.000817451,0.00027375395,0.000002635799,0.0000069873586,0.0000028506488,0.000009185625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852365,0.000012568181,0.000350752,0.0003857899,0.00058309874,0.0001441246],"domain_scores_gemma":[0.9978739,0.00006156667,0.0003057704,0.0004059803,0.0012726324,0.00008015387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046903308,0.00009315459,0.00012121874,0.00036394398,0.00022689885,0.00050260953,0.0026179394,0.000027682676,0.0000014300867],"category_scores_gemma":[0.000052004376,0.00008755192,0.000027604592,0.0006611098,0.00013614236,0.0030889728,0.00074697984,0.0001334341,0.0000029246219],"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":[0.000005208638,0.000046968446,0.00006437172,0.0000022970642,0.000017173585,0.0000034330424,0.000057117482,0.0062296228,0.022767045,0.001136843,0.00008952594,0.9695804],"study_design_scores_gemma":[0.00043692708,0.00002073385,0.000510751,0.00015119687,0.00001399014,0.00006582614,0.000030104007,0.86824566,0.03420162,0.09443097,0.0017601207,0.00013210777],"about_ca_topic_score_codex":0.0000055293053,"about_ca_topic_score_gemma":0.0000018981898,"teacher_disagreement_score":0.96944827,"about_ca_system_score_codex":0.00011423876,"about_ca_system_score_gemma":0.0005094586,"threshold_uncertainty_score":0.48648265},"labels":[],"label_agreement":null},{"id":"W7117982317","doi":"10.14569/ijacsa.2025.0161208","title":"Latent-Topology Graph State-Space Model (LT-GSSM) for Robust Traffic Fore-Casting","year":2025,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais; Cégep de l'Outaouais","funders":"","keywords":"Probabilistic logic; Robustness (evolution); Adaptability; Graph; Adjacency matrix; Adjacency list; Uncertain data; Synthetic data; Sensor fusion","score_opus":0.011356297014001312,"score_gpt":0.26313207850654396,"score_spread":0.25177578149254265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117982317","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":[0.011395117,0.00009788164,0.98672795,0.0007495079,0.0003742337,0.00018507669,0.000006887683,0.000205878,0.00025745668],"genre_scores_gemma":[0.7901014,0.00023916569,0.20931987,0.00018675697,0.000072501294,0.000043968554,0.00000171934,0.0000050217823,0.000029589002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993421,0.0000027334236,0.00023090003,0.00012393712,0.00017805399,0.0001222989],"domain_scores_gemma":[0.9993566,0.00003812509,0.00007454963,0.00007885182,0.00040481891,0.000047027013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018926548,0.00007154783,0.00008952879,0.00036396759,0.00009746464,0.00007406815,0.00040438792,0.000018875717,4.2527296e-7],"category_scores_gemma":[0.0000091979455,0.000068941554,0.00003796894,0.00024645266,0.00011381001,0.00032968988,0.000055470275,0.00007951777,3.0737198e-7],"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":[0.000005136417,0.000015541496,0.000006830231,0.0000065632316,0.000019953513,4.6718745e-7,0.00003671568,0.72748524,0.0008603684,0.013143313,0.0011823376,0.25723755],"study_design_scores_gemma":[0.000313551,0.000028122926,0.00014019081,0.000031103053,0.000008417647,0.0000128492775,0.00001900179,0.9862254,0.0005879662,0.005018692,0.0075530377,0.000061635925],"about_ca_topic_score_codex":2.5215417e-7,"about_ca_topic_score_gemma":0.0000021773592,"teacher_disagreement_score":0.7787063,"about_ca_system_score_codex":0.00006199039,"about_ca_system_score_gemma":0.000047692683,"threshold_uncertainty_score":0.2811355},"labels":[],"label_agreement":null}]}