{"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014830107,0.0028851063,0.99159294,0.0007715041,0.0006953506,0.00013070674,0.000016863962,0.00008534943,0.0036739036],"genre_scores_gemma":[0.09784024,0.003762218,0.89603657,0.00030241738,0.00047269225,0.000005197692,0.00027250184,0.00002312179,0.0012850587],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977603,0.000066219196,0.00054856634,0.0008457772,0.00045210004,0.00032703253],"domain_scores_gemma":[0.9975632,0.00013593677,0.00017224156,0.0019143076,0.000091020935,0.00012332934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047134762,0.00016995423,0.0002541017,0.0000973027,0.000088690096,0.00023049657,0.0030483895,0.000042742635,0.00013217104],"category_scores_gemma":[0.00023368775,0.00016231768,0.000064116095,0.0011929118,0.00009473382,0.0023543662,0.0014751997,0.00013318125,0.00022079334],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000101840715,0.00010458849,0.000020756152,0.000020022628,0.000020158126,0.000017101856,0.00017411342,0.00026869914,0.00018471037,0.37833512,0.0005419062,0.6203026],"study_design_scores_gemma":[0.00004228066,0.00009043505,0.000024330242,0.000053458527,0.000032367167,0.000014431772,0.00089680613,0.19129317,0.2600226,0.42604747,0.1210048,0.0004778519],"about_ca_topic_score_codex":0.000043895518,"about_ca_topic_score_gemma":0.00003122329,"teacher_disagreement_score":0.61982477,"about_ca_system_score_codex":0.000016554855,"about_ca_system_score_gemma":0.00016499346,"threshold_uncertainty_score":0.66191226},"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":"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":[0.87270683,0.00006262992,0.12612636,0.00030890448,0.0001599968,0.00042834695,0.000007730277,0.00006635083,0.00013284024],"genre_scores_gemma":[0.9977749,0.00002272047,0.0021093802,0.000009083196,0.00003467023,0.000020591773,0.0000029812477,0.0000026082134,0.000023065038],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99877274,0.000036545953,0.0005310863,0.00026547033,0.00023908103,0.00015509404],"domain_scores_gemma":[0.99914974,0.00017582635,0.0002492751,0.00027899962,0.00010267662,0.000043490283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018994365,0.00008030209,0.00016012286,0.0000936194,0.00007689669,0.000020488835,0.00042458574,0.000012729028,0.000007708209],"category_scores_gemma":[0.00001274718,0.00007066418,0.000033951914,0.0013862671,0.000047842754,0.00012957444,0.0001039308,0.000046171073,0.0000390101],"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":[0.00005395156,0.00050510356,0.0004180047,0.000010657332,0.000024827648,0.0000028849677,0.026646338,0.38142103,0.007871114,0.102576025,0.00047065495,0.4799994],"study_design_scores_gemma":[0.00021101774,0.0014490364,0.004032094,0.000039458286,0.000018501622,0.0000011339995,0.056095764,0.29312122,0.5821627,0.061821193,0.0007458508,0.00030208088],"about_ca_topic_score_codex":0.00023044014,"about_ca_topic_score_gemma":0.0005159616,"teacher_disagreement_score":0.5742915,"about_ca_system_score_codex":0.000008058841,"about_ca_system_score_gemma":0.000034660738,"threshold_uncertainty_score":0.28816018},"labels":[],"label_agreement":null},{"id":"W4405790736","doi":"10.30564/aia.v6i1.8128","title":"A Novel Fingerprint Recognition Framework with Attention Mechanism Based on Domain Adaptation for Improving Applicability in Overpressured Situations","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Advances","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Systems, Applications & Products in Data Processing (Canada)","funders":"","keywords":"Computer science; Generalizability theory; Robustness (evolution); Fingerprint (computing); Domain adaptation; Artificial intelligence; Adaptation (eye); Domain (mathematical analysis); Biometrics; Feature (linguistics); Fingerprint recognition; Machine learning; Word error rate; Reliability (semiconductor); Data mining; Pattern recognition (psychology)","score_opus":0.05617945385564249,"score_gpt":0.3135405589163647,"score_spread":0.2573611050607222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405790736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007032948,0.000106217485,0.9903081,0.0010364444,0.0005445789,0.00072636124,0.000026655865,0.00017373769,0.000044978686],"genre_scores_gemma":[0.7298016,0.000008471249,0.26973668,0.00008757999,0.000051982803,0.00027988505,0.000022463117,0.000007255075,0.0000040661444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984065,0.000051001087,0.0003815847,0.00063442875,0.00030935757,0.00021710891],"domain_scores_gemma":[0.9987171,0.00066712475,0.00010470156,0.0003065003,0.00015558332,0.000048987207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007811057,0.00013741378,0.00012721213,0.00041652087,0.00015934191,0.00033225198,0.0002784823,0.00008889367,0.000009408873],"category_scores_gemma":[0.00028908826,0.00012743221,0.00006642069,0.0015686357,0.00005287657,0.0005982633,0.00002807193,0.00017761659,0.000032892225],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005496386,0.00020192914,0.00000882292,0.00007068024,0.000004208833,0.0000011258564,0.0006583466,0.00378672,0.0058680363,0.26656023,6.9008576e-7,0.7227842],"study_design_scores_gemma":[0.000030689014,0.00009672859,0.00011403218,0.000085469015,0.0000053801873,6.393876e-7,0.00024588977,0.5765275,0.013354254,0.4092826,0.00013226234,0.0001246049],"about_ca_topic_score_codex":0.000073652955,"about_ca_topic_score_gemma":0.00037768768,"teacher_disagreement_score":0.72276866,"about_ca_system_score_codex":0.00010874201,"about_ca_system_score_gemma":0.0001007955,"threshold_uncertainty_score":0.5196535},"labels":[],"label_agreement":null},{"id":"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069655115,0.00008598209,0.9880841,0.0040036915,0.00020578259,0.00028422335,0.000046578407,0.0002472134,0.00007696165],"genre_scores_gemma":[0.5986297,0.00012406362,0.400273,0.0006589019,0.000059214377,0.00013480234,0.000081094695,0.000011277064,0.000027929518],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987263,0.000040970583,0.0003190145,0.0004307707,0.00021952282,0.0002634393],"domain_scores_gemma":[0.9983262,0.0010819379,0.0001249535,0.00019772297,0.00018173933,0.00008743771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030885186,0.00013965537,0.00015296669,0.00023010702,0.00034392235,0.00020615137,0.00023362323,0.00006931005,0.000028771514],"category_scores_gemma":[0.00035450034,0.00014058776,0.00008891132,0.0011553728,0.00008727251,0.00063969713,0.000036121724,0.000088706474,0.00017106524],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003473561,0.00013367015,0.000019027513,0.000030595547,0.000032960266,0.0000014599866,0.0011377459,0.04508115,0.008255463,0.20322113,0.000024903558,0.74202716],"study_design_scores_gemma":[0.00004002157,0.000046134024,0.00004304274,0.000038397462,0.000009804512,5.699686e-7,0.002238542,0.40326786,0.017613357,0.57641375,0.00013972785,0.00014882037],"about_ca_topic_score_codex":0.000014544135,"about_ca_topic_score_gemma":0.00016071,"teacher_disagreement_score":0.74187833,"about_ca_system_score_codex":0.000024907564,"about_ca_system_score_gemma":0.00007280574,"threshold_uncertainty_score":0.57330024},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94902885,0.00013433467,0.04393005,0.0012926452,0.000632105,0.00073667575,0.000013026788,0.00005517467,0.0041771093],"genre_scores_gemma":[0.9981601,0.00027939695,0.0003838504,0.00021391372,0.00022184476,0.00018326777,0.000014625052,0.000008917589,0.00053408684],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977894,0.00024660907,0.0005435402,0.00045359266,0.0004782136,0.00048863783],"domain_scores_gemma":[0.99893314,0.0005223169,0.00014926826,0.00024957763,0.000089304725,0.000056373865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016710219,0.00016832231,0.00019414432,0.00014147378,0.0008196291,0.00017386621,0.0004709558,0.00007511318,0.0000938342],"category_scores_gemma":[0.00052923785,0.00013764662,0.00010207575,0.000614695,0.0005462441,0.00065928895,0.000057667283,0.0001736069,0.0000127830535],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033000865,0.00037505146,0.6123602,0.00006252064,0.000021679914,0.000002730908,0.021695806,0.064886786,0.00034240496,0.045391366,0.0005049159,0.25402653],"study_design_scores_gemma":[0.0003261583,0.0004713224,0.0899629,0.0006482584,0.00013941448,5.169664e-7,0.090919085,0.19915012,0.014930948,0.59434366,0.007998622,0.0011089653],"about_ca_topic_score_codex":0.0029693749,"about_ca_topic_score_gemma":0.06954917,"teacher_disagreement_score":0.54895234,"about_ca_system_score_codex":0.00020966501,"about_ca_system_score_gemma":0.000120323195,"threshold_uncertainty_score":0.9474291},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036525195,0.000059160677,0.986382,0.002556411,0.00041642872,0.00026074194,0.00000707419,0.00040488262,0.006260787],"genre_scores_gemma":[0.9313475,0.00014770412,0.066873595,0.00052759866,0.00003781266,0.00011327383,0.000036105448,0.000010734404,0.00090570026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823725,0.00016398108,0.00049944664,0.0005827035,0.00023006435,0.00028652413],"domain_scores_gemma":[0.9988842,0.00022629823,0.00016712189,0.0005245646,0.00013386089,0.0000639767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047375064,0.0002003697,0.00032021778,0.00054711965,0.00022189101,0.00024113391,0.00066206383,0.00007789765,0.00027774318],"category_scores_gemma":[0.000033899178,0.0001824854,0.00021157424,0.0018224423,0.000067738736,0.00066373026,0.00007998552,0.00012428768,0.000106033374],"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":[0.000058224046,0.00042118528,0.0011747627,0.00001674265,0.00009981917,0.0000069101043,0.00037938825,0.0011512464,0.0020940357,0.11839268,0.00083828374,0.87536675],"study_design_scores_gemma":[0.00010927645,0.0005991805,0.00062260305,0.00014524076,0.00024881127,0.0000012927414,0.0005805975,0.70763665,0.1723864,0.05634688,0.0604544,0.0008686751],"about_ca_topic_score_codex":0.00020048238,"about_ca_topic_score_gemma":0.0001468757,"teacher_disagreement_score":0.927695,"about_ca_system_score_codex":0.00009282562,"about_ca_system_score_gemma":0.000060538,"threshold_uncertainty_score":0.74415386},"labels":[],"label_agreement":null}]}