{"meta":{"query_hash":"e8d90fe089b8","filters":{"venue":"Artificial Intelligence Advances"},"cohort_total":6,"direct_labels_cover":0,"predictions_cover":6,"exported":6,"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/e8d90fe089b8","api":"https://metacan.xera.ac/api/v1/cohort?venue=Artificial+Intelligence+Advances"},"results":[{"id":"W3188162669","doi":"10.30564/aia.v3i2.3219","title":"A New Approach of Intelligent Data Retrieval Paradigm","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Advances","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Institute of Standards and Technology; University of Ottawa","keywords":"Computer science; Ranking (information retrieval); Rank (graph theory); Information retrieval; Focus (optics); Learning to rank; Scheme (mathematics); Raw data; Resource (disambiguation); Data mining","score_opus":0.12242496522354752,"score_gpt":0.3434368944768644,"score_spread":0.22101192925331686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188162669","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002631637,0.0026794947,0.9758646,0.0030996304,0.00039540228,0.00021668943,0.00017678882,0.0006270589,0.014308652],"genre_scores_gemma":[0.07939687,0.00397773,0.89330953,0.0026619083,0.0015089037,0.0006523815,0.0005794151,0.00018316902,0.017730072],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946996,0.0012682962,0.0004717483,0.0014461208,0.0019143241,0.00019994348],"domain_scores_gemma":[0.9977703,0.00070342596,0.00014658901,0.0006144103,0.00063894293,0.00012639764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033175445,0.00081633776,0.0013713189,0.0045955232,0.001457841,0.0073911697,0.0032034898,0.0022516672,0.0035244918],"category_scores_gemma":[0.005574176,0.00050326,0.0017326626,0.004419574,0.0024533055,0.010477749,0.0038015477,0.0027613752,0.003047396],"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":[0.00016214984,0.00021278531,0.0012702193,0.00080004684,0.00023374976,0.00043108035,0.0014632862,0.007624173,0.009558939,0.5779471,0.026002401,0.37429428],"study_design_scores_gemma":[0.00008554555,0.00019382789,0.0006120126,0.00017662492,0.00014155214,0.0011891337,0.00059059483,0.15794697,0.006067645,0.58419406,0.24868257,0.00011948806],"about_ca_topic_score_codex":0.0016798377,"about_ca_topic_score_gemma":0.001329336,"teacher_disagreement_score":0.0073911697,"about_ca_system_score_codex":0.0018053211,"about_ca_system_score_gemma":0.0020031112,"threshold_uncertainty_score":0.017545104},"labels":[],"label_agreement":null},{"id":"W4377141943","doi":"10.5121/csit.2023.130701","title":"Efficient Implementation of Tanh: A Comparative Study of New Results","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Advances","topic":"Neural Networks and 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":"University of Windsor","funders":"","keywords":"Hyperbolic function; CORDIC; Activation function; Computer science; Field-programmable gate array; Exponential function; Artificial neural network; Function (biology); Division (mathematics); Tangent; Algorithm; Computational science; Arithmetic; Mathematics; Artificial intelligence; Computer hardware; Mathematical analysis","score_opus":0.13442696053128572,"score_gpt":0.42798047802995975,"score_spread":0.29355351749867403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377141943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1143923,0.029764049,0.81480783,0.0005340514,0.00071737444,0.00011191785,0.000114031776,0.001673057,0.03788533],"genre_scores_gemma":[0.65307486,0.017597849,0.3144975,0.0001683869,0.00037210848,0.000105815714,0.00030001724,0.00044365902,0.013439825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987508,0.00030368648,0.000105976214,0.00012273366,0.0006275749,0.00008922084],"domain_scores_gemma":[0.9979679,0.00087623805,0.000118533295,0.00044072818,0.00054959883,0.000046997462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013109203,0.0005685878,0.00058779307,0.0010371647,0.00019945603,0.0011893027,0.0010661412,0.0004376159,0.006290647],"category_scores_gemma":[0.004611025,0.0002525007,0.0004062653,0.0013248723,0.0003902886,0.0024118791,0.0005204014,0.0005678997,0.0010664032],"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":[0.00074892945,0.00017847268,0.0017668888,0.00093930843,0.00012570924,0.00027756003,0.00022308592,0.066389754,0.019827016,0.061877176,0.0031000446,0.84454614],"study_design_scores_gemma":[0.00015055075,0.00216487,0.0033345018,0.0003190532,0.00022161905,0.0019105712,0.00036659552,0.81313705,0.09103025,0.0181941,0.069074795,0.00009606923],"about_ca_topic_score_codex":0.00061495,"about_ca_topic_score_gemma":0.0005142059,"teacher_disagreement_score":0.006290647,"about_ca_system_score_codex":0.0005407742,"about_ca_system_score_gemma":0.00048724393,"threshold_uncertainty_score":0.021044314},"labels":[],"label_agreement":null},{"id":"W4405790736","doi":"10.30564/aia.v6i1.8128","title":"A Novel Fingerprint Recognition Framework with Attention Mechanism Based on Domain Adaptation for Improving Applicability in Overpressured Situations","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Advances","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Computer science; Generalizability theory; Robustness (evolution); Fingerprint (computing); Domain adaptation; Artificial intelligence; Adaptation (eye); Domain (mathematical analysis); Biometrics; Feature (linguistics); Fingerprint recognition; Machine learning; Word error rate; Reliability (semiconductor); Data mining; Pattern recognition (psychology)","score_opus":0.05617945385564249,"score_gpt":0.3135405589163647,"score_spread":0.2573611050607222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405790736","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03501696,0.0011574107,0.95783085,0.00024104994,0.00014916802,0.000083219275,0.000100621124,0.002355515,0.003065216],"genre_scores_gemma":[0.8087591,0.0010375623,0.18205687,0.0004285859,0.00015258101,0.00013313569,0.0002801637,0.00011259184,0.007039437],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969625,0.00004763135,0.0000128531265,0.000119643155,0.000067970344,0.000055734563],"domain_scores_gemma":[0.9997149,0.00008083824,0.000030047668,0.00005057859,0.000096446965,0.000027220103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056010234,0.0007647838,0.0007493304,0.0007622194,0.00027037345,0.00064917025,0.0014664752,0.0008397524,0.0016879597],"category_scores_gemma":[0.0010993404,0.00021263947,0.00071837544,0.00057240523,0.00045245345,0.001194973,0.0010373242,0.00094382296,0.00070902513],"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":[0.00027303258,0.0003102483,0.0031382206,0.00014396824,0.0001223252,0.00044128997,0.00015003844,0.2810378,0.06261164,0.007172605,0.0056314454,0.6389674],"study_design_scores_gemma":[0.0000063209413,0.0000568207,0.00072244345,0.000007658704,0.00002659871,0.00013484372,0.000013867526,0.9903916,0.005440147,0.0018590307,0.0013265698,0.000014218362],"about_ca_topic_score_codex":0.005923096,"about_ca_topic_score_gemma":0.004426289,"teacher_disagreement_score":0.005923096,"about_ca_system_score_codex":0.0004987507,"about_ca_system_score_gemma":0.00070842274,"threshold_uncertainty_score":0.011777222},"labels":[],"label_agreement":null},{"id":"W4408343575","doi":"10.30564/aia.v5i1.8691","title":"A Novel Domain Adaptation-based Framework for Face Recognition Under Darkened and Overexposed Situations","year":2023,"lang":"en","type":"article","venue":"Artificial Intelligence Advances","topic":"Face recognition and analysis","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":"PQ Corporation (Canada)","funders":"","keywords":"Adaptation (eye); Face (sociological concept); Domain (mathematical analysis); Domain adaptation; Computer science; Facial recognition system; Artificial intelligence; Pattern recognition (psychology); Optics; Physics; Mathematics; Sociology","score_opus":0.11658118771332833,"score_gpt":0.34102741885284277,"score_spread":0.22444623113951445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408343575","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009311403,0.00023058396,0.9882682,0.00009516869,0.00005339014,0.0000375184,0.000060843868,0.00066129846,0.0012816897],"genre_scores_gemma":[0.44342974,0.0007307715,0.5439448,0.0004937179,0.00022043861,0.00021940506,0.00072972756,0.00028716185,0.009944204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996761,0.00006741747,0.00001078591,0.000111470865,0.00008573095,0.00004843632],"domain_scores_gemma":[0.99979705,0.00004290876,0.000023053557,0.0000464829,0.00007230444,0.000018175731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059677596,0.00079653866,0.00073157105,0.00056871056,0.00032418573,0.00057977415,0.0014602559,0.0007990238,0.0020674672],"category_scores_gemma":[0.00096587645,0.0002840488,0.000992964,0.0005199557,0.0005315416,0.0008691529,0.0011555789,0.0012010038,0.0011788068],"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":[0.00021926424,0.00016737553,0.0014228201,0.00008804255,0.00012265313,0.00024375257,0.00012433551,0.47664517,0.038790658,0.011617925,0.0063894396,0.46416855],"study_design_scores_gemma":[0.000003184357,0.000022712831,0.00018745846,0.0000035850862,0.000008781732,0.0000641449,0.0000103203665,0.9935614,0.0029380878,0.0022708187,0.00092151697,0.000007973965],"about_ca_topic_score_codex":0.004200538,"about_ca_topic_score_gemma":0.0044339756,"teacher_disagreement_score":0.004200538,"about_ca_system_score_codex":0.00041907426,"about_ca_system_score_gemma":0.0007268102,"threshold_uncertainty_score":0.00835222},"labels":[],"label_agreement":null},{"id":"W4408367943","doi":"10.30564/aia.v7i1.8704","title":"Inception Residual RNN-LSTM Hybrid Model for Predicting Pension Coverage Trends Among Private-Sector Workers in the USA","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Advances","topic":"Retirement, Disability, and Employment","field":"Social Sciences","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":"Impact","funders":"","keywords":"Residual; Pension; Private sector; Business; Computer science; Artificial intelligence; Actuarial science; Economics; Finance; Economic growth; Algorithm","score_opus":0.20559812930011878,"score_gpt":0.4380832934807722,"score_spread":0.2324851641806534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408367943","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9399022,0.0013796403,0.0487949,0.0011757894,0.00016659913,0.000035831807,0.0030897378,0.0009119652,0.004543401],"genre_scores_gemma":[0.98983216,0.0002230908,0.005774674,0.000089880086,0.000025237883,0.000025238607,0.001426669,0.000017850183,0.0025850707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992967,0.000014980937,0.0000046040773,0.000024447954,0.000009617451,0.000016603968],"domain_scores_gemma":[0.99980634,0.000083855404,0.000021356278,0.000009277792,0.00006573029,0.000013515752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040272129,0.0005484053,0.00029441543,0.00042516438,0.00015843986,0.0003199943,0.00052762823,0.00045567908,0.0015184849],"category_scores_gemma":[0.0009962688,0.0001479005,0.00031834442,0.00036272456,0.000090147565,0.00045489226,0.00033705466,0.00065155135,0.00040252708],"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":[0.00040555288,0.00034205167,0.04372691,0.0001211786,0.00013870225,0.000254099,0.00013145321,0.80461276,0.0027289847,0.0012375196,0.008877642,0.13742307],"study_design_scores_gemma":[0.0000025605164,0.00001383062,0.0020175772,0.0000054030347,0.0000075868275,0.0000048122038,0.000010162042,0.99734265,0.00017273684,0.00028258425,0.00013719895,0.0000029688579],"about_ca_topic_score_codex":0.034475725,"about_ca_topic_score_gemma":0.056240655,"teacher_disagreement_score":0.034475725,"about_ca_system_score_codex":0.0005206555,"about_ca_system_score_gemma":0.000588406,"threshold_uncertainty_score":0.06855011},"labels":[],"label_agreement":null},{"id":"W4411251770","doi":"10.30564/aia.v7i1.9761","title":"Real-Time Personalized Ad Recommendation Based on User Behavioral Analysis","year":2025,"lang":"en","type":"article","venue":"Artificial Intelligence Advances","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PQ Corporation (Canada)","funders":"National Science Foundation","keywords":"Computer science; Behavioral analysis; Human–computer interaction; World Wide Web; Information retrieval; Psychology; Cognitive psychology","score_opus":0.04260030907643951,"score_gpt":0.36229376466677504,"score_spread":0.3196934555903355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411251770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17028753,0.0013338851,0.78660935,0.00059115485,0.00023629275,0.0003612899,0.0023526752,0.02876555,0.009462244],"genre_scores_gemma":[0.8318161,0.00066928385,0.1586858,0.00035633132,0.00015938783,0.0001516991,0.0016794837,0.00020587757,0.0062761134],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999514,0.00008032161,0.000037402428,0.00015716132,0.00017023672,0.00004081673],"domain_scores_gemma":[0.9989672,0.00034868205,0.00010380008,0.00020499251,0.00030559933,0.000069745314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007112652,0.00087264815,0.001087246,0.0015279134,0.00032579282,0.0008777179,0.00092459517,0.0006524359,0.0017414015],"category_scores_gemma":[0.0021807502,0.00041817097,0.00045212577,0.0012554642,0.0001467567,0.0012460728,0.00040055072,0.0008481426,0.0019487038],"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":[0.0010793282,0.0008346888,0.023301529,0.00027315706,0.00041582814,0.00032831734,0.00031016677,0.025668088,0.047353037,0.0017109357,0.017932428,0.8807925],"study_design_scores_gemma":[0.00003449918,0.00013173971,0.009133492,0.00001755814,0.00014610248,0.0002662571,0.000076446915,0.96835774,0.01623875,0.0020094372,0.003531886,0.00005612971],"about_ca_topic_score_codex":0.008626136,"about_ca_topic_score_gemma":0.015361756,"teacher_disagreement_score":0.008626136,"about_ca_system_score_codex":0.000417748,"about_ca_system_score_gemma":0.0005030068,"threshold_uncertainty_score":0.017151833},"labels":[],"label_agreement":null}]}