{"meta":{"query_hash":"3f17b19c19c4","filters":{"venue":"International Journal on Recent and Innovation Trends in Computing and Communication"},"cohort_total":11,"direct_labels_cover":0,"predictions_cover":11,"exported":11,"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/3f17b19c19c4","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+on+Recent+and+Innovation+Trends+in+Computing+and+Communication"},"results":[{"id":"W2333408188","doi":"10.17762/ijritcc2321-8169.150205","title":"�Empathy Scaling and Its Impact on Employee�s Eustress� - A Study With Special Reference to Autonomous Colleges in Mangalore","year":2015,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Emotional Intelligence and Performance","field":"Psychology","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":"Empathy; Scaling; Psychology; Veterinary medicine; Medicine; Mathematics; Social psychology","score_opus":0.13200233517776616,"score_gpt":0.44139026746592785,"score_spread":0.3093879322881617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333408188","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":[0.99147815,0.00011943149,0.000030412466,0.0018684906,0.00021937194,0.000104581115,0.0000056417907,0.000010959502,0.00616299],"genre_scores_gemma":[0.9989662,0.0001971611,0.00015528566,0.0003194754,0.0001858633,0.000006434016,0.000030210345,0.0000079223655,0.00013142117],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99884415,0.00015275233,0.0004174954,0.00019149085,0.00026387363,0.0001302391],"domain_scores_gemma":[0.999164,0.00011275764,0.000186325,0.000119326665,0.00035874566,0.000058883736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007861362,0.00013029673,0.00014609567,0.0011389405,0.00010031324,0.00012670853,0.00020301816,0.00005438778,0.00006275618],"category_scores_gemma":[0.00004685114,0.000104613115,0.000009040861,0.00071765715,0.000027268086,0.000147467,0.000070887465,0.00042760264,0.0000060578163],"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":[0.0020977522,0.00079587963,0.4321131,0.0000028837626,0.00008284145,0.000030435063,0.01579518,0.0036963401,0.0000107193355,0.023768539,0.0006269209,0.5209794],"study_design_scores_gemma":[0.0019063659,0.0016784852,0.9860388,0.00030985769,0.0000053091608,0.00009613486,0.004574664,0.0027681978,0.00003490117,0.00066790776,0.0017247674,0.00019462808],"about_ca_topic_score_codex":0.00006096377,"about_ca_topic_score_gemma":0.00008670284,"teacher_disagreement_score":0.5539257,"about_ca_system_score_codex":0.000140468,"about_ca_system_score_gemma":0.000032076594,"threshold_uncertainty_score":0.4265999},"labels":[],"label_agreement":null},{"id":"W3043510852","doi":"10.17762/ijritcc.v7i8.5348","title":"Face Liveness Detection using Feature Fusion Using Block Truncation Code Technique","year":2019,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Compute Canada","funders":"","keywords":"Spoofing attack; Computer science; Liveness; Biometrics; Facial recognition system; Password; Artificial intelligence; Face (sociological concept); Feature (linguistics); Block (permutation group theory); Three-dimensional face recognition; Computer vision; Pattern recognition (psychology); AdaBoost; Face detection; Computer security; Support vector machine","score_opus":0.051209439150043924,"score_gpt":0.34776656516203386,"score_spread":0.2965571260119899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043510852","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7040598,0.00018443025,0.29262885,0.002017435,0.0006736496,0.00012359822,0.0000020056993,0.00004437528,0.00026590357],"genre_scores_gemma":[0.977817,0.0005339503,0.021266954,0.0002305525,0.000059563896,0.0000020941595,0.000028602963,0.000007860916,0.000053416592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985429,0.00021069887,0.00046857764,0.00026663992,0.00038168178,0.00012947165],"domain_scores_gemma":[0.9984641,0.00008957475,0.0005044059,0.00028777213,0.00061931903,0.000034850385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011265391,0.00013458406,0.00014070023,0.0022316808,0.00025950556,0.0004400356,0.0005089374,0.00012878317,0.000011422874],"category_scores_gemma":[0.00006292773,0.00013487307,0.000026439271,0.002409134,0.000031613246,0.0005374599,0.00021366998,0.00045888725,0.0000017357518],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005431346,0.00016999769,0.0056652594,0.000010938302,0.000030479656,0.0000017754206,0.000770478,0.002083541,0.023488455,0.019408789,0.000031076343,0.9482849],"study_design_scores_gemma":[0.0011421489,0.0000931172,0.036011674,0.0002813079,0.000007667352,0.00033180945,0.00019639595,0.94178957,0.0067759557,0.0023278769,0.0107333055,0.00030915445],"about_ca_topic_score_codex":0.000024107321,"about_ca_topic_score_gemma":0.000006085287,"teacher_disagreement_score":0.94797575,"about_ca_system_score_codex":0.00025429795,"about_ca_system_score_gemma":0.00004037427,"threshold_uncertainty_score":0.54999644},"labels":[],"label_agreement":null},{"id":"W3204330663","doi":"10.17762/ijritcc.v9i7.5475","title":"Trends and determinants of raising ECBs in Indian Context","year":2021,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","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":"Market liquidity; Volatility (finance); Interest rate; Context (archaeology); Exchange rate; Capital (architecture); Economics; Quarter (Canadian coin); Econometrics; Business; Monetary economics; Geography","score_opus":0.0665021637147464,"score_gpt":0.33109855430610236,"score_spread":0.264596390591356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204330663","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":[0.9932387,0.001342145,0.00019340217,0.0026205098,0.00022161264,0.00001584773,0.000010647313,0.0000038559683,0.002353305],"genre_scores_gemma":[0.99592376,0.0033667597,0.0004503598,0.000083607505,0.00004521951,9.3176953e-7,0.000037770627,0.000005190748,0.00008641848],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99899334,0.0000338947,0.00069669914,0.00013832322,0.000053939642,0.000083811115],"domain_scores_gemma":[0.99922097,0.000087413064,0.00042678934,0.00010148062,0.00014311855,0.00002020721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005512089,0.00007296692,0.00018552184,0.0015374013,0.00008038775,0.00008889486,0.000100589095,0.00005810184,0.00003925554],"category_scores_gemma":[0.00014063364,0.00008485297,0.000017814824,0.00084156013,0.000050005798,0.0001576181,0.00007177429,0.00020503403,4.7875466e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014804277,0.000047618792,0.33347014,0.0000019455113,0.0000060562024,0.000002663365,0.00071070535,0.000024170771,0.000009831436,0.04625796,0.00001949946,0.6194346],"study_design_scores_gemma":[0.0009775633,0.00003594299,0.97791004,0.00016223769,0.0000012923451,0.000044550543,0.00021603759,0.0035158924,0.00009488102,0.010365877,0.006581025,0.000094636845],"about_ca_topic_score_codex":0.00005203754,"about_ca_topic_score_gemma":0.00016005253,"teacher_disagreement_score":0.64443994,"about_ca_system_score_codex":0.00005637721,"about_ca_system_score_gemma":0.000014581304,"threshold_uncertainty_score":0.34602034},"labels":[],"label_agreement":null},{"id":"W4317105991","doi":"10.17762/ijritcc.v10i2s.5911","title":"Modernized Wildlife Surveillance and Behaviour Detection using a Novel Machine Learning Algorithm","year":2022,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Identification and Quantification in Food","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Python (programming language); Background subtraction; Wildlife; Artificial intelligence; Machine learning; Frame (networking); Data mining; Ecology; Pixel","score_opus":0.04279317530974102,"score_gpt":0.33134088913321313,"score_spread":0.2885477138234721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317105991","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":[0.97569185,0.00037442282,0.021831566,0.0016054423,0.0002851183,0.0000525582,0.000014139401,0.000012843348,0.00013207646],"genre_scores_gemma":[0.9954353,0.0010787398,0.002659248,0.00030721893,0.000066414366,0.000005132357,0.0003206148,0.000010663255,0.00011668295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989678,0.00017942056,0.00036971088,0.0001934403,0.00020469262,0.000084908665],"domain_scores_gemma":[0.9992359,0.000027032671,0.00033241918,0.00011754475,0.00026236387,0.000024740135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008204232,0.00009250503,0.00008662275,0.00052328984,0.00047279138,0.00012690609,0.00014847654,0.00004256869,0.00002149182],"category_scores_gemma":[0.00006850278,0.00010336991,0.000016346903,0.00036245742,0.000042588195,0.000017538061,0.00017597768,0.00033640992,1.6527757e-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":[0.00023886049,0.0003048473,0.03580105,0.0000042998045,0.00008227808,9.607927e-7,0.00046307404,0.0042389985,0.0621264,0.0027063861,0.00008563294,0.8939472],"study_design_scores_gemma":[0.0050522215,0.0004717066,0.1408169,0.00006196104,0.000020379786,0.0008026174,0.000888887,0.7444036,0.0047866837,0.00040114007,0.10169433,0.0005995862],"about_ca_topic_score_codex":0.0000212254,"about_ca_topic_score_gemma":0.000009232981,"teacher_disagreement_score":0.8933476,"about_ca_system_score_codex":0.000051826984,"about_ca_system_score_gemma":0.00001760522,"threshold_uncertainty_score":0.42153025},"labels":[],"label_agreement":null},{"id":"W4376955857","doi":"10.17762/ijritcc.v11i4s.6315","title":"Secure Digital Information Forward Using Highly Developed AES Techniques in Cloud Computing","year":2023,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Chaos-based Image/Signal Encryption","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Encryption; Communication source; Key (lock); Cryptography; Public-key cryptography; The Internet; Computer network; Computer security; Secure communication; World Wide Web","score_opus":0.04121510443575173,"score_gpt":0.33826648188384434,"score_spread":0.2970513774480926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376955857","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":[0.844295,0.000064224594,0.14599206,0.0069132545,0.00053093996,0.0001222007,0.0000045365273,0.00021809468,0.0018596695],"genre_scores_gemma":[0.9816838,0.00034516372,0.01728958,0.00042068685,0.000095275995,0.0000019602944,0.00013596108,0.000007813815,0.000019764439],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832207,0.000115689494,0.00079670415,0.0001797577,0.00039907452,0.00018673393],"domain_scores_gemma":[0.9986289,0.00018261372,0.00047707034,0.00017835118,0.00049888424,0.000034156998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001242539,0.00014983004,0.00015409573,0.0027601672,0.00019578278,0.0008070964,0.0005341732,0.00007971182,0.000005636389],"category_scores_gemma":[0.000161443,0.00015279015,0.000023309587,0.0026278142,0.00004160461,0.0017938734,0.00037077392,0.00045020337,0.000005708456],"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":[0.000029143657,0.00004220809,0.002901043,0.0000053080603,0.000012108166,0.0000041905787,0.0009817354,0.0023928273,0.00013511775,0.018818138,0.00020328065,0.9744749],"study_design_scores_gemma":[0.0017966368,0.00014227057,0.03702578,0.0007682331,0.000004094159,0.00016800711,0.0003583534,0.92209196,0.0009735735,0.010282739,0.025927752,0.00046061535],"about_ca_topic_score_codex":0.000016779542,"about_ca_topic_score_gemma":0.0000040965697,"teacher_disagreement_score":0.9740143,"about_ca_system_score_codex":0.00024878632,"about_ca_system_score_gemma":0.00005680814,"threshold_uncertainty_score":0.7782849},"labels":[],"label_agreement":null},{"id":"W4383888721","doi":"10.17762/ijritcc.v11i5s.6655","title":"Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review","year":2023,"lang":"en","type":"review","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Glaucoma; Computer science; Support vector machine; Machine learning; Artificial intelligence; Blindness; Cluster analysis; Data mining; Optometry; Medicine; Ophthalmology","score_opus":0.06822258148505944,"score_gpt":0.3997225103073322,"score_spread":0.33149992882227275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383888721","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030948274,0.997929,0.0002760193,0.0007743118,0.00008758081,0.00026062602,0.0000065455565,0.000053450352,0.00030303095],"genre_scores_gemma":[0.0025028372,0.9964825,0.00039556288,0.000100863675,0.00006115406,0.000014506523,0.0002659083,0.000020356849,0.00015633718],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9977166,0.00036166556,0.0013295903,0.00018678635,0.0003186329,0.00008669192],"domain_scores_gemma":[0.9978221,0.00022032768,0.0012997019,0.00017268692,0.00045207745,0.000033094122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017205172,0.00018958953,0.00094727165,0.0021170874,0.00010918071,0.0000731441,0.00013161654,0.00010742143,0.000006200729],"category_scores_gemma":[0.00046648047,0.00014946409,0.00008807641,0.001376281,0.00004987539,0.00007919689,0.000096561605,0.0007983061,6.7116895e-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":[0.0000128329075,0.000042137206,0.00027227285,0.046197426,0.00020817728,0.000003357554,0.000037053545,2.610591e-7,0.0000040009,0.00023714786,0.00001583291,0.9529695],"study_design_scores_gemma":[0.0005981533,0.0002975452,0.00059524743,0.88537455,0.0015002957,0.0013055191,0.00005483869,0.0060973484,0.000010820344,0.00017006593,0.1037595,0.00023611297],"about_ca_topic_score_codex":0.000013928222,"about_ca_topic_score_gemma":0.0000016941532,"teacher_disagreement_score":0.9527334,"about_ca_system_score_codex":0.00011691318,"about_ca_system_score_gemma":0.00002846331,"threshold_uncertainty_score":0.6094969},"labels":[],"label_agreement":null},{"id":"W4386970185","doi":"10.17762/ijritcc.v11i8s.7202","title":"Congestion Detection and Mitigation Technique for Multi-Hop Communication in WSN","year":2023,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Energy Efficient Wireless Sensor Networks","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":"","keywords":"Computer network; Hop (telecommunications); Computer science; Network packet; Node (physics); Network congestion; Wireless sensor network; Transmission (telecommunications); Real-time computing; Telecommunications; Engineering","score_opus":0.05082698988786589,"score_gpt":0.3481712521050269,"score_spread":0.29734426221716104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386970185","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":[0.6627899,0.0003514157,0.32519272,0.010378931,0.0004635181,0.00029201192,0.0000025895313,0.00014403599,0.0003848742],"genre_scores_gemma":[0.97140044,0.0031704595,0.025025675,0.0002038018,0.000039218758,0.000031478834,0.0000858298,0.000009981341,0.000033110464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861836,0.00022979677,0.0005523848,0.0002366247,0.0002109163,0.00015192774],"domain_scores_gemma":[0.99863595,0.00033292483,0.0003525602,0.0002318526,0.00041646493,0.000030240053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015937468,0.00012586164,0.00013375012,0.0018952636,0.00024213154,0.00027572218,0.0003747553,0.00011186893,0.0000013944419],"category_scores_gemma":[0.00016857563,0.00013417158,0.000017072492,0.0015969926,0.000061392435,0.00040951214,0.00019481573,0.00038040962,5.3551094e-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":[0.000042284635,0.00009088345,0.00355507,0.0000067541264,0.000013192001,0.0000013986214,0.0006521562,0.007386894,0.0020753932,0.047905188,0.00004950126,0.9382213],"study_design_scores_gemma":[0.001676056,0.00010099856,0.113105156,0.00036011392,0.000003180055,0.000052840405,0.00014023007,0.87644625,0.0015537372,0.004318929,0.0020420002,0.00020052919],"about_ca_topic_score_codex":0.000017843802,"about_ca_topic_score_gemma":0.000069813505,"teacher_disagreement_score":0.93802077,"about_ca_system_score_codex":0.00012843574,"about_ca_system_score_gemma":0.00001517832,"threshold_uncertainty_score":0.54713583},"labels":[],"label_agreement":null},{"id":"W4388002426","doi":"10.17762/ijritcc.v11i10s.7653","title":"Detection and Predicting Air Pollution Level in a Specific City using Deep Learning","year":2023,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Air Quality Monitoring and Forecasting","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Air quality index; Air pollution; Deep learning; Computer science; Artificial neural network; Metropolitan area; Pollution; Machine learning; Backpropagation; Meteorology; Artificial intelligence; Environmental science; Geography","score_opus":0.10781392245355485,"score_gpt":0.34650413919307393,"score_spread":0.23869021673951907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388002426","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":[0.99526685,0.000060204806,0.0028262055,0.0011088963,0.00021079119,0.00003088912,6.2055506e-7,0.000031660595,0.00046388715],"genre_scores_gemma":[0.9978916,0.0007757618,0.001132948,0.00006309552,0.00008385991,0.0000010850215,0.000012701324,0.000006432859,0.00003252134],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989551,0.00014852849,0.00038717547,0.00015792572,0.00022458694,0.00012670935],"domain_scores_gemma":[0.9995321,0.00010062298,0.00023633402,0.00006261793,0.00004399831,0.00002433665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013433169,0.00008476725,0.00008631716,0.0006957516,0.0003024701,0.00008985064,0.000098031465,0.00005479532,0.000015206808],"category_scores_gemma":[0.00012303318,0.00008915687,0.0000102814865,0.001083448,0.000058245467,0.00023047229,0.0001627808,0.00043324174,0.0000011382324],"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":[0.000024838915,0.000020917982,0.20842989,0.0000014081627,0.0000043993614,0.0000014371537,0.00093873125,0.016693905,0.00077369687,0.00018631943,0.0000051568095,0.7729193],"study_design_scores_gemma":[0.00045589477,0.000035236917,0.7178359,0.00011892216,0.0000015399336,0.000038984213,0.00051800784,0.27892557,0.00012671547,0.0006069968,0.0012489988,0.00008724399],"about_ca_topic_score_codex":0.00008649065,"about_ca_topic_score_gemma":0.000036659887,"teacher_disagreement_score":0.77283204,"about_ca_system_score_codex":0.0002208977,"about_ca_system_score_gemma":0.0000033029203,"threshold_uncertainty_score":0.36357117},"labels":[],"label_agreement":null},{"id":"W4389341525","doi":"10.17762/ijritcc.v11i10.8525","title":"Convolutional Neural Network – Based Algorithm for Currency Exchange Rate Prediction","year":2023,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","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":"","keywords":"Liberian dollar; Currency; Foreign exchange market; Computer science; Random forest; Convolutional neural network; Artificial intelligence; Feature selection; Artificial neural network; Exchange rate; Machine learning; Economics; Finance; Monetary economics","score_opus":0.19294788612911962,"score_gpt":0.4532814949866392,"score_spread":0.2603336088575196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389341525","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":[0.45193008,0.0011175632,0.48588222,0.045619342,0.0100730825,0.0005266465,0.00014202822,0.00028865077,0.004420409],"genre_scores_gemma":[0.91154736,0.0014169179,0.082168445,0.0016065787,0.0014287318,0.000043773867,0.0007807509,0.000032908294,0.0009745211],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973499,0.0006192247,0.00090366,0.0002778772,0.00064687897,0.0002024475],"domain_scores_gemma":[0.99522114,0.0027328518,0.0006023804,0.00020007452,0.0011981392,0.000045398418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009483985,0.00013275575,0.0001773053,0.0019955914,0.0003956652,0.00036037376,0.00045869563,0.00007551707,0.000080829086],"category_scores_gemma":[0.0015335509,0.00011797871,0.000044845285,0.0027056928,0.000062977255,0.00025162444,0.00015505266,0.00033874827,0.0000029956861],"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":[0.00007324833,0.00003396431,0.008213349,0.0000012976146,0.000013773608,8.7609453e-7,0.00009472296,0.0030484977,0.0000092585815,0.0034273688,0.006730285,0.9783534],"study_design_scores_gemma":[0.0010804287,0.00009749095,0.11895766,0.000077697325,0.0000040663335,0.000018077484,0.000053891603,0.8171571,0.000008574504,0.021453526,0.04099279,0.00009871083],"about_ca_topic_score_codex":0.000003845849,"about_ca_topic_score_gemma":0.0000040434825,"teacher_disagreement_score":0.9782547,"about_ca_system_score_codex":0.00011569436,"about_ca_system_score_gemma":0.000042904732,"threshold_uncertainty_score":0.4811032},"labels":[],"label_agreement":null},{"id":"W4389349623","doi":"10.17762/ijritcc.v11i10.8760","title":"COVID-19 Regional Safety Assessment Using Evaluation Based on Distance from Average Solution (EDAS) Method","year":2023,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"COVID-19 epidemiological studies","field":"Mathematics","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":"Order (exchange); Risk analysis (engineering); Pandemic; Computer science; Health care; Quality (philosophy); Public health; Coronavirus disease 2019 (COVID-19); Business; Operations research; Medicine; Engineering; Economic growth; Economics; Nursing","score_opus":0.45451667612729924,"score_gpt":0.5570780098773841,"score_spread":0.10256133375008486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389349623","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":[0.21668296,0.00016944224,0.7024768,0.07785107,0.0006167643,0.00028190177,0.000036803933,0.00014934527,0.0017348933],"genre_scores_gemma":[0.95787597,0.0008958297,0.036661252,0.003983653,0.00014820378,0.000010438938,0.0003858082,0.000012807779,0.000026032376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969402,0.0010301247,0.00083439215,0.00030263464,0.0007253178,0.00016735603],"domain_scores_gemma":[0.99464387,0.0038731047,0.0006890445,0.00023234563,0.00049376,0.000067870744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059600086,0.000172049,0.00024556846,0.0009650154,0.00050046976,0.00011544573,0.00026460568,0.00009497443,0.00011448358],"category_scores_gemma":[0.0032302157,0.00015378608,0.00004402928,0.001112325,0.000064105516,0.00013411709,0.00014991924,0.0004853187,0.0000012756863],"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":[0.00050914806,0.000351525,0.019786812,0.000019537798,0.00012915547,0.0000065424756,0.00073892495,0.09211827,0.00021781547,0.14676256,0.0033241971,0.7360355],"study_design_scores_gemma":[0.0015097215,0.000063268664,0.061108015,0.00019846359,0.000016083935,0.0000053204535,0.00009828414,0.8349574,0.000008309765,0.09067908,0.0112030795,0.00015296663],"about_ca_topic_score_codex":0.00007086516,"about_ca_topic_score_gemma":0.00003172542,"teacher_disagreement_score":0.74283916,"about_ca_system_score_codex":0.0011625502,"about_ca_system_score_gemma":0.00012976716,"threshold_uncertainty_score":0.62712145},"labels":[],"label_agreement":null},{"id":"W4391661949","doi":"10.17762/ijritcc.v11i11.9969","title":"The Use of Technology as the Indonesia's Strategy to Counter China's Gray Zone Operations in the North Natuna Sea","year":2023,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Coastal Management and Development","field":"Environmental 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":"Gray (unit); China; Operations research; Geography; Mathematics; Medicine; Archaeology","score_opus":0.03583573131020356,"score_gpt":0.31087435224032695,"score_spread":0.2750386209301234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391661949","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":[0.9617287,0.000022711896,0.00022916563,0.03685659,0.00009653473,0.00009457867,0.000001581418,0.000008960237,0.0009611694],"genre_scores_gemma":[0.9979277,0.00094412116,0.00019118522,0.00076138234,0.000014229213,0.000008368703,0.000023904222,0.0000031965958,0.0001259047],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990864,0.00010258801,0.00033689837,0.00009309187,0.00028652293,0.000094527866],"domain_scores_gemma":[0.99953103,0.00012945099,0.00011071826,0.00015638626,0.000062475905,0.0000099298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008108213,0.0000678161,0.000057866757,0.00044778516,0.0002998644,0.00020070518,0.00042274993,0.000024139677,0.000019865922],"category_scores_gemma":[0.000062900515,0.000038812814,0.000009930572,0.0019858747,0.000089821086,0.00013051131,0.0002968313,0.00026183843,0.0000045776383],"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":[0.00003911444,0.000057642406,0.10631778,6.5035584e-7,0.000013912571,0.0000023903985,0.0011215921,0.013405261,0.000025101675,0.017172467,0.0011075189,0.86073655],"study_design_scores_gemma":[0.00029655846,0.000071300215,0.9408713,0.000035548048,0.0000020213758,0.000015322756,0.00052183407,0.016116096,0.000013803165,0.0009814742,0.04101182,0.00006293027],"about_ca_topic_score_codex":0.00010049793,"about_ca_topic_score_gemma":0.0009381234,"teacher_disagreement_score":0.86067367,"about_ca_system_score_codex":0.00005706746,"about_ca_system_score_gemma":0.000008480283,"threshold_uncertainty_score":0.23063447},"labels":[],"label_agreement":null}]}