{"meta":{"query_hash":"8241406cb7fe","filters":{"venue":"Journal of Artificial Intelligence Practice"},"cohort_total":238,"direct_labels_cover":0,"predictions_cover":238,"exported":238,"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/8241406cb7fe","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Artificial+Intelligence+Practice"},"results":[{"id":"W2587306239","doi":"10.23977/jaip.2016.11003","title":"New Distance Measures on Dual Hesitant Fuzzy Sets and Their Application in Pattern Recognition","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Vagueness; Fuzzy logic; Dual (grammatical number); Ambiguity; Distance measures; Fuzzy set; Extension (predicate logic); Computer science; Measure (data warehouse); Artificial intelligence; Data mining; Mathematics; Pattern recognition (psychology)","score_opus":0.25541220896012995,"score_gpt":0.44057954882375866,"score_spread":0.1851673398636287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587306239","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":[0.025078965,0.0012671964,0.9699121,0.00018405273,0.00009723087,0.000059911457,0.00005287117,0.00007238206,0.0032752578],"genre_scores_gemma":[0.5262332,0.0011335777,0.46985587,0.00012426589,0.00017548466,0.00022700458,0.00015033019,0.000036416735,0.0020639033],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995174,0.0015044572,0.0007028438,0.0008560489,0.0015917269,0.00017101745],"domain_scores_gemma":[0.9949309,0.0025166648,0.00071565364,0.0004018517,0.001206893,0.00022791311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041826232,0.00094341516,0.0011263062,0.0044958116,0.0008135263,0.0027348513,0.0013908889,0.0012526417,0.0011627321],"category_scores_gemma":[0.013422065,0.00035687725,0.001223887,0.0031532415,0.002459497,0.0050116917,0.0024483826,0.0016338978,0.00023573585],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027359815,0.00010636293,0.0028309927,0.00052497984,0.00028865153,0.00043855215,0.0011080793,0.131876,0.009612674,0.6269367,0.0014422208,0.22456115],"study_design_scores_gemma":[0.000033899374,0.00024722784,0.0014240702,0.000115015704,0.00007261494,0.0005994479,0.0003953109,0.66490495,0.0074727037,0.31614944,0.008435998,0.00014926356],"about_ca_topic_score_codex":0.00071970135,"about_ca_topic_score_gemma":0.0003455138,"teacher_disagreement_score":0.0044958116,"about_ca_system_score_codex":0.0014668313,"about_ca_system_score_gemma":0.0007233687,"threshold_uncertainty_score":0.022120059},"labels":[],"label_agreement":null},{"id":"W2606766152","doi":"10.23977/jaip.2016.11005","title":"BLSTM Recurrent Neural Network for Object Recognition","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Object (grammar); Representation (politics); Recurrent neural network; Fuse (electrical); Context (archaeology); Sequence (biology); Pattern recognition (psychology); Image (mathematics); Artificial neural network; Tree (set theory); Cognitive neuroscience of visual object recognition; Computer vision","score_opus":0.09685038027984123,"score_gpt":0.36577832241034786,"score_spread":0.26892794213050664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606766152","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":[0.02793443,0.0030215231,0.9578524,0.00030691986,0.00021087959,0.00004942606,0.0006014408,0.006303148,0.003719808],"genre_scores_gemma":[0.67893726,0.0022075942,0.30239198,0.0003362698,0.00014288732,0.00013650043,0.003279469,0.0003084246,0.012259683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996574,0.00004192336,0.000025079076,0.00013031598,0.000098996694,0.000046284265],"domain_scores_gemma":[0.99978,0.000043427808,0.000033946053,0.00004361862,0.00008682775,0.000012077595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051202334,0.0009718984,0.00067636603,0.00060936826,0.00021725755,0.0005892824,0.0013272176,0.00090779265,0.0027529188],"category_scores_gemma":[0.0010247686,0.00035968347,0.00065835164,0.00081219984,0.00029024883,0.0012749285,0.00062990445,0.001132494,0.001690495],"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":[0.00020818067,0.00014313767,0.0012296485,0.00028596196,0.00017773554,0.00021918252,0.000064416745,0.20730738,0.05652143,0.009461008,0.011611752,0.71277016],"study_design_scores_gemma":[0.000005164615,0.00003935957,0.00039647982,0.000010271799,0.000022400716,0.000036982303,0.0000061169167,0.9870869,0.00668032,0.003885371,0.0018203744,0.000010180234],"about_ca_topic_score_codex":0.008024844,"about_ca_topic_score_gemma":0.011214371,"teacher_disagreement_score":0.008024844,"about_ca_system_score_codex":0.00075648044,"about_ca_system_score_gemma":0.00078088004,"threshold_uncertainty_score":0.015956283},"labels":[],"label_agreement":null},{"id":"W2607117265","doi":"10.23977/jaip.2016.11002","title":"Urban Road Congestion Recognition Using Multi-Feature Fusion of Traffic Images","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Histogram; Artificial intelligence; Feature (linguistics); Traffic congestion; Computer vision; Scale-invariant feature transform; Gray level; Pattern recognition (psychology); Feature extraction; Data mining; Image (mathematics); Transport engineering; Engineering","score_opus":0.12180464282793979,"score_gpt":0.3817927935406495,"score_spread":0.2599881507127097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607117265","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":[0.3799766,0.0005267568,0.6146683,0.000115647425,0.00011482682,0.00009493945,0.00029426307,0.0014272034,0.0027814484],"genre_scores_gemma":[0.95147973,0.00020264651,0.04744501,0.000027830129,0.000032984135,0.000026906855,0.00025046687,0.000022432752,0.0005120053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964523,0.00003449762,0.000020064872,0.00009960403,0.00013457584,0.00006611631],"domain_scores_gemma":[0.9997441,0.000034265693,0.000045490146,0.000025954703,0.00012786675,0.000022194263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035995996,0.0006529012,0.0006353338,0.0023019197,0.00024630094,0.0005506599,0.00042855614,0.00048566316,0.0005596739],"category_scores_gemma":[0.00091433397,0.00024550382,0.000706782,0.0012313378,0.00020335328,0.0012803747,0.0005878822,0.00043915038,0.0002082112],"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":[0.0006392027,0.0003936617,0.024661137,0.00026693585,0.00026499399,0.00059556676,0.00022307645,0.10155125,0.15122576,0.0012875233,0.0024698754,0.716421],"study_design_scores_gemma":[0.000018227576,0.00018580197,0.030034354,0.000017134904,0.00012746513,0.0003678355,0.00013411511,0.93251765,0.034630783,0.0009873484,0.00093301694,0.0000463584],"about_ca_topic_score_codex":0.0029650158,"about_ca_topic_score_gemma":0.001926853,"teacher_disagreement_score":0.0029650158,"about_ca_system_score_codex":0.0003145489,"about_ca_system_score_gemma":0.00028474344,"threshold_uncertainty_score":0.0058954954},"labels":[],"label_agreement":null},{"id":"W2607121690","doi":"10.23977/jaip.2016.11007","title":"Human Thermal Comfort Study Based on Average Skin Temperature","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Thermal comfort; Skin temperature; Linear discriminant analysis; Human skin; Homogeneous; Thermal; Computer science; Discriminant; Environmental science; Mathematics; Artificial intelligence; Engineering; Meteorology; Geography","score_opus":0.03502331444999864,"score_gpt":0.35956648279762415,"score_spread":0.32454316834762553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607121690","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":[0.9382071,0.0007231323,0.057565767,0.000055476765,0.000047777736,0.000028483986,0.00017147664,0.000078572,0.0031222606],"genre_scores_gemma":[0.9975643,0.00014801393,0.0018712998,0.000008678935,0.0000085264555,0.000010275408,0.000050734627,0.0000048779084,0.00033324838],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.999777,0.0000683384,0.000009992807,0.000062949744,0.000060782077,0.000020917525],"domain_scores_gemma":[0.9997209,0.000116980664,0.000028720158,0.000026736707,0.000089428606,0.000017315211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035142386,0.00027280097,0.00021243216,0.00038886527,0.00013110378,0.0002891355,0.00015285051,0.00018052771,0.00111415],"category_scores_gemma":[0.0008492254,0.00007433424,0.0002965005,0.0003377599,0.00018758641,0.0004025961,0.00020340816,0.00017237979,0.00015933126],"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":[0.002846837,0.00045281506,0.14491571,0.0013985236,0.00039779348,0.00079558656,0.0024175243,0.13451801,0.41741654,0.003932421,0.0029643017,0.28794396],"study_design_scores_gemma":[0.000045933582,0.0025689031,0.33929494,0.000083804465,0.00029641017,0.0011805802,0.0017086713,0.54038984,0.10830455,0.00230359,0.0036755682,0.00014717008],"about_ca_topic_score_codex":0.0010024847,"about_ca_topic_score_gemma":0.0007405151,"teacher_disagreement_score":0.00111415,"about_ca_system_score_codex":0.00012902693,"about_ca_system_score_gemma":0.00008927439,"threshold_uncertainty_score":0.0037272573},"labels":[],"label_agreement":null},{"id":"W2612743994","doi":"10.23977/jaip.2016.11001","title":"An automatic people counting method of hotel dining with occlusion","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Merge (version control); Computer science; Artificial intelligence; Computer vision; Support vector machine; Segmentation; Information retrieval","score_opus":0.045557689948299965,"score_gpt":0.38526740156041445,"score_spread":0.3397097116121145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612743994","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":[0.05625017,0.0005838457,0.9369772,0.000102262275,0.000249755,0.00016345269,0.00021648283,0.0021202306,0.003336612],"genre_scores_gemma":[0.42440298,0.00088596245,0.5608194,0.00016631985,0.000271479,0.00027893588,0.0012115614,0.0002417575,0.011721629],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925655,0.00006556309,0.000037449823,0.00026547152,0.00028347856,0.000091492766],"domain_scores_gemma":[0.99967325,0.000045922923,0.000041913507,0.00003864845,0.00016949154,0.000030755808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040941424,0.0006806033,0.0011162582,0.0019020613,0.00064906443,0.00063850224,0.0010974277,0.0005519912,0.0015038635],"category_scores_gemma":[0.0007504842,0.00043557203,0.0008595953,0.0013629568,0.00027763285,0.0009054323,0.000694373,0.0005725851,0.00075424643],"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":[0.00031336787,0.00015999758,0.0067990148,0.00021918495,0.000092949325,0.00024234207,0.0002370603,0.006966893,0.0520531,0.0018076639,0.0066063832,0.9245021],"study_design_scores_gemma":[0.00009344416,0.0003197511,0.028482059,0.00005452271,0.0002174663,0.0019197238,0.00028165375,0.8811576,0.069252424,0.002377781,0.015699845,0.00014364245],"about_ca_topic_score_codex":0.0044651995,"about_ca_topic_score_gemma":0.004670648,"teacher_disagreement_score":0.0044651995,"about_ca_system_score_codex":0.00037697103,"about_ca_system_score_gemma":0.0006210441,"threshold_uncertainty_score":0.00887841},"labels":[],"label_agreement":null},{"id":"W2745034076","doi":"10.23977/jaip.2017.21001","title":"Comparison of Three Evolutionary Algorithms: PSOA, ACOA and BCOA on Recognition Arabic Characters Problem","year":2017,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ant colony optimization algorithms; Particle swarm optimization; Metaheuristic; Swarm intelligence; Artificial bee colony algorithm; Meta-optimization; Computer science; Genetic algorithm; Multi-swarm optimization; Parallel metaheuristic; Mathematical optimization; Evolutionary computation; Optimization problem; Algorithm; Artificial intelligence; Mathematics; Machine learning","score_opus":0.18043772774683559,"score_gpt":0.4225814047868751,"score_spread":0.24214367704003953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745034076","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":[0.20223896,0.0101150805,0.74754024,0.0015563745,0.0005712198,0.00022597858,0.00017367207,0.0012042345,0.036374237],"genre_scores_gemma":[0.6675021,0.0040118545,0.3195486,0.0002906628,0.00012905466,0.00020745078,0.00033627308,0.00014951827,0.007824383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994836,0.00013117128,0.00004853231,0.000074056035,0.00021459132,0.000048043443],"domain_scores_gemma":[0.9991062,0.00040213167,0.00006090951,0.000057716938,0.00033544062,0.00003768845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080191245,0.00071331393,0.00084420835,0.0012272624,0.00059147883,0.0009869556,0.00062133215,0.00093626225,0.0015862022],"category_scores_gemma":[0.0027263474,0.00017359,0.0006081055,0.0013004483,0.00033081693,0.00092528085,0.00038269555,0.0004965755,0.0003081215],"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":[0.00039794116,0.00027860657,0.0054664756,0.00054576143,0.00018770645,0.00013578121,0.00014242444,0.29348892,0.0062062405,0.008660797,0.005051695,0.6794377],"study_design_scores_gemma":[0.000052688327,0.00022696059,0.0031222221,0.00005241219,0.00007192512,0.00018755982,0.00011894533,0.9836271,0.0052848887,0.0028184433,0.0044133114,0.0000235286],"about_ca_topic_score_codex":0.006862344,"about_ca_topic_score_gemma":0.004759862,"teacher_disagreement_score":0.006862344,"about_ca_system_score_codex":0.0004110362,"about_ca_system_score_gemma":0.00097340695,"threshold_uncertainty_score":0.0136448145},"labels":[],"label_agreement":null},{"id":"W2766069184","doi":"10.23977/jaip.2017.21002","title":"Research on Dynamic Game Model of Enterprise Green Technology Innovation Driving Force","year":2017,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Environmental Sustainability in Business","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Business; Evolutionary game theory; Sequential game; Industrial organization; Technology innovation; Innovation diffusion; Environmental pollution; Green innovation; Stochastic game; Game theory; Marketing; Environmental economics; Economics; Microeconomics","score_opus":0.0855003657754788,"score_gpt":0.39193936946966706,"score_spread":0.30643900369418825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766069184","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":[0.1828852,0.000989245,0.7071974,0.0034776374,0.00018801936,0.0002027421,0.00028727032,0.00009367073,0.10467882],"genre_scores_gemma":[0.97169477,0.0006489121,0.013205924,0.00012828228,0.000034161512,0.00015690485,0.00007224616,0.0000100459665,0.014048687],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993468,0.00029588293,0.00001837637,0.000110447654,0.00010695423,0.000121560806],"domain_scores_gemma":[0.99935514,0.0003856529,0.000068644775,0.0000207172,0.00010646603,0.00006331541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082460744,0.00055154867,0.000537345,0.00051025965,0.0006733375,0.0016624723,0.0011086705,0.0012039101,0.0055519072],"category_scores_gemma":[0.002364272,0.0002459173,0.00068175467,0.0005525637,0.0009615115,0.0023872433,0.00076139224,0.001211318,0.00033010144],"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":[0.000053022188,0.00007173984,0.0020047757,0.00011142821,0.000046785848,0.00039186544,0.00044730675,0.25069815,0.0016479547,0.73328024,0.0020920837,0.009154567],"study_design_scores_gemma":[0.000026684611,0.000055446093,0.00071104965,0.00001894342,0.00002717627,0.000081426435,0.00017653733,0.8621864,0.0002159596,0.13378903,0.0026898212,0.000021511263],"about_ca_topic_score_codex":0.009499733,"about_ca_topic_score_gemma":0.005533469,"teacher_disagreement_score":0.009499733,"about_ca_system_score_codex":0.001912927,"about_ca_system_score_gemma":0.0015212615,"threshold_uncertainty_score":0.01888889},"labels":[],"label_agreement":null},{"id":"W2946262508","doi":"10.23977/jaip.2017.21003","title":"Research on Fault Self-healing Method of Smart Distribution Network based on Binary Hybrid Algorithm","year":2017,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Smart Grid and Power Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Binary number; Algorithm; Particle swarm optimization; Fault (geology); Convergence (economics); Node (physics); Optimization algorithm; Computer science; Meta-optimization; Multi-swarm optimization; Process (computing); Mathematical optimization; Mathematics; Engineering","score_opus":0.087162111240173,"score_gpt":0.4083150591533643,"score_spread":0.32115294791319127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946262508","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":[0.028735526,0.00084280147,0.96370137,0.00026390448,0.00009154497,0.000043561744,0.000016193804,0.0002266689,0.006078416],"genre_scores_gemma":[0.8741636,0.0010163493,0.11862897,0.00011336816,0.00005378697,0.000091235066,0.00005246789,0.000058173668,0.0058220155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997824,0.00004163851,0.000012929897,0.00004753317,0.00009419892,0.000021465494],"domain_scores_gemma":[0.9998023,0.000068030284,0.00002666992,0.000018092469,0.000072908755,0.000012007302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033816232,0.00039155324,0.0005094585,0.000522736,0.00039083368,0.00074140274,0.00081974966,0.0005124843,0.0017433355],"category_scores_gemma":[0.00071896304,0.00017659453,0.00036617546,0.0004852393,0.00044337733,0.0015725543,0.00039106177,0.0004522079,0.00020313778],"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":[0.0001877677,0.00007675899,0.001747853,0.00029492518,0.000082267405,0.0000956229,0.00017969143,0.6968169,0.015449752,0.0793701,0.0023192884,0.20337908],"study_design_scores_gemma":[0.000013149016,0.000022808688,0.0001346463,0.000005859426,0.0000066219886,0.00003365076,0.000012992639,0.99433964,0.0010323882,0.003619459,0.00077343284,0.0000054446423],"about_ca_topic_score_codex":0.0027314085,"about_ca_topic_score_gemma":0.0012276968,"teacher_disagreement_score":0.0027314085,"about_ca_system_score_codex":0.0006180654,"about_ca_system_score_gemma":0.0004934387,"threshold_uncertainty_score":0.005832076},"labels":[],"label_agreement":null},{"id":"W2947178377","doi":"10.23977/jaip.2019.31001","title":"Traffic sign recognition of Syria and Istanbul using CSA and Curvelet coefficients transform with image processing methods","year":2019,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Traffic sign; YCbCr; Artificial intelligence; Computer vision; Computer science; Traffic sign recognition; RGB color model; Image processing; Curvelet; Noise (video); Image (mathematics); Sign (mathematics); Color image; Mathematics; Wavelet transform","score_opus":0.04195797143626098,"score_gpt":0.334465773801657,"score_spread":0.292507802365396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947178377","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":[0.902412,0.00041680486,0.08037578,0.00028856142,0.00020103592,0.000091323374,0.00071118074,0.0009562192,0.014547004],"genre_scores_gemma":[0.95680815,0.00030875625,0.035777062,0.000034324377,0.000037988626,0.00003139525,0.001068763,0.00004374115,0.0058897333],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981314,0.000022536578,0.000010263109,0.00003066834,0.000091206406,0.000032251202],"domain_scores_gemma":[0.9997874,0.000021323533,0.000021662698,0.000011735894,0.00014375738,0.000014174428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016745336,0.00040152104,0.00026355224,0.0024822648,0.000199189,0.000573624,0.00021974629,0.00031678675,0.0019211503],"category_scores_gemma":[0.00049174554,0.00011337495,0.00028681278,0.0012353603,0.0002238763,0.0003803498,0.00023961352,0.00027718244,0.0008025277],"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":[0.0007555921,0.00032834493,0.042653088,0.0003410319,0.00007656446,0.0012433773,0.00045542073,0.033147853,0.14001621,0.004312791,0.012157335,0.76451236],"study_design_scores_gemma":[0.000049504182,0.0004516971,0.23070697,0.00010185371,0.00012608443,0.0023260943,0.0017082609,0.6375442,0.10707389,0.001547816,0.018254792,0.0001087878],"about_ca_topic_score_codex":0.0074160527,"about_ca_topic_score_gemma":0.007214035,"teacher_disagreement_score":0.0074160527,"about_ca_system_score_codex":0.00027707385,"about_ca_system_score_gemma":0.0004632774,"threshold_uncertainty_score":0.014745772},"labels":[],"label_agreement":null},{"id":"W3009003561","doi":"10.23977/jaip.2020.030101","title":"Ac Steady State Analysis of Transmission Line","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"High-Voltage Power Transmission Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transmission line; Electrical impedance; Spice; Equivalent circuit; Characteristic impedance; Electric power transmission; Line (geometry); Steady state (chemistry); Electrical engineering; Acoustics; Telegrapher's equations; Network analysis; Transmission (telecommunications); Electronic engineering; Physics; Engineering; Mathematics; Voltage","score_opus":0.05605714168273505,"score_gpt":0.3223677578448515,"score_spread":0.26631061616211643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009003561","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":[0.18391414,0.00048711806,0.7380969,0.00023017413,0.0000721878,0.000105823405,0.00024171735,0.0018554839,0.074996464],"genre_scores_gemma":[0.9813316,0.00019858539,0.010164964,0.000035992736,0.000018438986,0.000040760697,0.00013478556,0.00011300744,0.007961755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998908,0.000022161856,0.000004035782,0.000022660402,0.000047098074,0.000013188282],"domain_scores_gemma":[0.99977714,0.00007836046,0.000019806848,0.000028754059,0.00009097041,0.000005082266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018115432,0.00026277243,0.00021276479,0.00047261378,0.00029137393,0.00062413083,0.00038783406,0.00033530602,0.008653172],"category_scores_gemma":[0.00067745434,0.00010393535,0.00031735748,0.00036428813,0.0003401453,0.0009434844,0.00018367,0.00034945176,0.0009613444],"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":[0.00024570554,0.00015180589,0.002983408,0.00053679355,0.0000963417,0.0009435771,0.0008118709,0.64043826,0.11491184,0.12397474,0.0037757324,0.111129865],"study_design_scores_gemma":[0.0000071886534,0.0000810613,0.0005798941,0.000018467563,0.000013418189,0.00010773354,0.000060411778,0.98366445,0.0060326257,0.0066348286,0.002792359,0.000007520228],"about_ca_topic_score_codex":0.0012076476,"about_ca_topic_score_gemma":0.00086492836,"teacher_disagreement_score":0.008653172,"about_ca_system_score_codex":0.00031860953,"about_ca_system_score_gemma":0.00018800364,"threshold_uncertainty_score":0.028947711},"labels":[],"label_agreement":null},{"id":"W3022311949","doi":"10.23977/jaip.2020.030103","title":"Study on the Method and Application of Big Data Mining of Mobile Trajectory Based on MapReduce","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Big data; Computer science; Smart city; Data science; Trajectory; Robustness (evolution); Urban computing; Public transport; Government (linguistics); Data mining; Computer security; Internet of Things; Transport engineering; Engineering; Machine learning","score_opus":0.14497016891668688,"score_gpt":0.37030440479641524,"score_spread":0.22533423587972837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022311949","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":[0.025713548,0.0028359557,0.96165234,0.0014393115,0.00042693288,0.0001804197,0.00038445985,0.0010374909,0.0063294936],"genre_scores_gemma":[0.6142171,0.008388752,0.3662828,0.0005978613,0.0006112989,0.0003687464,0.0017163459,0.00029009144,0.007526951],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813986,0.00028760696,0.00014159661,0.0004709912,0.00079571916,0.00016431732],"domain_scores_gemma":[0.9987392,0.00041126012,0.000058286827,0.00016000647,0.0005675476,0.00006363805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001439548,0.00066539436,0.0007490862,0.0017348455,0.0010167632,0.0018957693,0.001640665,0.0006299747,0.0012215826],"category_scores_gemma":[0.0036293955,0.0004088041,0.0014116425,0.003707253,0.00064583786,0.003821284,0.0010005177,0.0010858182,0.00044791854],"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":[0.00034938016,0.00030231636,0.026484363,0.0014430954,0.00043817327,0.00081195217,0.0015178004,0.11830825,0.01334103,0.08486948,0.020568188,0.73156595],"study_design_scores_gemma":[0.000039046303,0.0001283395,0.007868048,0.000088818684,0.00013152401,0.001117386,0.0010385773,0.8760822,0.014460181,0.050637648,0.048316233,0.00009209744],"about_ca_topic_score_codex":0.010111514,"about_ca_topic_score_gemma":0.005884122,"teacher_disagreement_score":0.010111514,"about_ca_system_score_codex":0.0010106079,"about_ca_system_score_gemma":0.0021116752,"threshold_uncertainty_score":0.020105302},"labels":[],"label_agreement":null},{"id":"W3046197218","doi":"10.23977/jaip.2020.030104","title":"Tongue Localization Method Based on Cascade Classifier","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tongue; Artificial intelligence; Computer science; Classifier (UML); Pattern recognition (psychology); Feature extraction; Cascade; Computer vision; Medicine; Pathology; Engineering","score_opus":0.12587849725692885,"score_gpt":0.4134713990423943,"score_spread":0.2875929017854655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046197218","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":[0.06817074,0.001075866,0.92011124,0.00024250531,0.00048019714,0.00025699646,0.00016020125,0.00260057,0.0069016097],"genre_scores_gemma":[0.71282774,0.0014872458,0.26638615,0.00022849759,0.0003120164,0.00024369113,0.00060124905,0.0001761713,0.017737258],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993567,0.000035897905,0.000036435333,0.00019349324,0.0002892884,0.00008815892],"domain_scores_gemma":[0.99953604,0.00006522154,0.000027405804,0.000033684584,0.00029961247,0.000038073806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005089738,0.0008949874,0.001325913,0.0021071415,0.00093874364,0.0007524506,0.0012869193,0.0011731738,0.004008534],"category_scores_gemma":[0.0008793297,0.00053482666,0.0011186252,0.0009061697,0.0002657558,0.0013242604,0.00069548195,0.0007424566,0.0019866168],"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":[0.0005645989,0.00025379995,0.0048085945,0.00020601386,0.00009215602,0.0005395873,0.00015646916,0.019930875,0.11831706,0.0024066428,0.008287444,0.8444367],"study_design_scores_gemma":[0.000050498238,0.00029612964,0.0050117113,0.000027062284,0.000120923476,0.00066823734,0.000069232876,0.9538362,0.034697607,0.0010377725,0.0041346224,0.000050033108],"about_ca_topic_score_codex":0.006490181,"about_ca_topic_score_gemma":0.0052329553,"teacher_disagreement_score":0.006490181,"about_ca_system_score_codex":0.0005594477,"about_ca_system_score_gemma":0.0009711781,"threshold_uncertainty_score":0.013409853},"labels":[],"label_agreement":null},{"id":"W3046556377","doi":"10.23977/jaip.2020.030106","title":"A Co-word Analysis of the Applications of Machine Learning in China","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Realization (probability); Word (group theory); Computer science; Natural language processing; Statistical analysis; Machine learning; Moment (physics); Linguistics; Statistics; Mathematics","score_opus":0.03615764190301387,"score_gpt":0.3415110604561321,"score_spread":0.30535341855311826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046556377","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":[0.96316195,0.011558494,0.0038044213,0.0015005344,0.000188124,0.00013476286,0.0055824895,0.00012323256,0.013945987],"genre_scores_gemma":[0.9852997,0.003630871,0.0033570062,0.00012416973,0.00010949521,0.000068162015,0.0035846932,0.000038378414,0.003787523],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9962637,0.00042296678,0.0008072825,0.0003918685,0.0017603234,0.00035389187],"domain_scores_gemma":[0.98668575,0.004154246,0.0028117036,0.00046721252,0.005209998,0.0006711231],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001899324,0.0002785834,0.000381503,0.016493086,0.0012388286,0.0015909827,0.00036524373,0.0002934492,0.0017955842],"category_scores_gemma":[0.0083689615,0.000116829404,0.0003710717,0.03171243,0.0007445783,0.0019944771,0.001066096,0.00035406317,0.0004996741],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030510433,0.00011347169,0.6226406,0.0025888328,0.00019212873,0.0026911926,0.014560639,0.0009161567,0.009128391,0.007976573,0.016224451,0.32266253],"study_design_scores_gemma":[0.000013008633,0.000116086965,0.9036382,0.0003871655,0.00018724833,0.0014622303,0.010508053,0.005023324,0.0050383294,0.0021238027,0.07142929,0.00007316962],"about_ca_topic_score_codex":0.021087643,"about_ca_topic_score_gemma":0.02214094,"teacher_disagreement_score":0.9835069,"about_ca_system_score_codex":0.0023742602,"about_ca_system_score_gemma":0.0040622903,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3046771691","doi":"10.23977/jaip.2020.030105","title":"Complexion Classification Based on Convolutional Neural Network","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Deep learning; Computer science; Face (sociological concept); Field (mathematics); Artificial neural network; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.20193604509447627,"score_gpt":0.38771246828534806,"score_spread":0.1857764231908718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046771691","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":[0.29019776,0.0019538575,0.69486964,0.0007300454,0.0002858103,0.00013470474,0.0005304416,0.0018572366,0.009440451],"genre_scores_gemma":[0.9433306,0.0007572803,0.047683157,0.00015638409,0.00006321599,0.000056188153,0.0006348816,0.00003691698,0.0072813323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998466,0.0000136632625,0.000008157189,0.000046862835,0.00004447628,0.000040260293],"domain_scores_gemma":[0.9998424,0.000041997948,0.000020822758,0.000012856477,0.000070091206,0.000011826062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026449672,0.00061708625,0.0004353852,0.0007031665,0.00026780996,0.0005137008,0.0006195362,0.00040393896,0.00151347],"category_scores_gemma":[0.000561955,0.00017426396,0.00052052963,0.00045684408,0.0002867425,0.0006070565,0.00039184582,0.0005246856,0.0003208267],"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":[0.00045087954,0.00022596681,0.0116366055,0.00011293725,0.00015480342,0.00024673736,0.000105309955,0.283932,0.031196108,0.006277727,0.0071750083,0.6584859],"study_design_scores_gemma":[0.0000031384582,0.000019802817,0.0012443507,0.000005494871,0.000014185148,0.000026439473,0.000006982331,0.9950588,0.0025044368,0.0007371957,0.000373417,0.000005780369],"about_ca_topic_score_codex":0.01824532,"about_ca_topic_score_gemma":0.012961141,"teacher_disagreement_score":0.01824532,"about_ca_system_score_codex":0.000880866,"about_ca_system_score_gemma":0.0006012643,"threshold_uncertainty_score":0.036278248},"labels":[],"label_agreement":null},{"id":"W3095274557","doi":"10.23977/jaip.2020.030108","title":"Artificial Intelligence and Depression: How AI powered chatbots in virtual reality games may reduce anxiety and depression levels","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chatbot; Anxiety; Depression (economics); Psychology; Virtual reality; Applied psychology; Clinical psychology; Psychotherapist; Computer science; Psychiatry; Human–computer interaction; Artificial intelligence","score_opus":0.1720330997908542,"score_gpt":0.4483340292736015,"score_spread":0.2763009294827473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095274557","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":[0.93688124,0.018516108,0.0037367379,0.008758105,0.00061516796,0.0004819591,0.00015477213,0.0001723091,0.030683586],"genre_scores_gemma":[0.9800433,0.0074469955,0.0052772244,0.0010730639,0.00012612404,0.00028435208,0.000082963335,0.000013298296,0.0056525473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994667,0.00032445346,0.000026902228,0.00004639678,0.0000820933,0.000053460764],"domain_scores_gemma":[0.9991579,0.0005059037,0.00007898261,0.000024569666,0.00005629709,0.00017637789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008007792,0.0002656856,0.00031130778,0.00036955014,0.00046530887,0.0012443566,0.00035357694,0.0005340447,0.004752612],"category_scores_gemma":[0.0032122,0.00012132986,0.00059035886,0.00019636455,0.00030631723,0.0005868339,0.0005003287,0.0007553782,0.0004582048],"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":[0.0037345786,0.017537404,0.026149508,0.004433112,0.0007579912,0.00051099143,0.006764848,0.0010686205,0.007104122,0.004466789,0.009492206,0.91797996],"study_design_scores_gemma":[0.0069403546,0.07373769,0.58709335,0.010670993,0.007513106,0.003829483,0.027450483,0.025590464,0.0194016,0.034284703,0.20303671,0.00045102154],"about_ca_topic_score_codex":0.0015100769,"about_ca_topic_score_gemma":0.0034016196,"teacher_disagreement_score":0.004752612,"about_ca_system_score_codex":0.00042979355,"about_ca_system_score_gemma":0.0005685304,"threshold_uncertainty_score":0.015899062},"labels":[],"label_agreement":null},{"id":"W3097657226","doi":"10.23977/jaip.2020.030107","title":"The Progess That Natural Language Processing Has Made Towards Human-level AI","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing; Human language; Context (archaeology); Natural language understanding; Natural (archaeology); Language technology; Natural language; Linguistics; History; Comprehension approach; Philosophy","score_opus":0.20598678695038364,"score_gpt":0.39334098531349554,"score_spread":0.1873541983631119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097657226","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":[0.018777767,0.13394791,0.3548681,0.225081,0.005006437,0.00014282606,0.00048751288,0.0013448081,0.2603437],"genre_scores_gemma":[0.36834553,0.15543458,0.3810594,0.03417202,0.01381455,0.00026751735,0.00069972966,0.00085722946,0.04534936],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9941479,0.0029526511,0.0002209982,0.00076082186,0.0017197763,0.00019793457],"domain_scores_gemma":[0.9705244,0.019440677,0.001204744,0.0047062174,0.0033113672,0.0008126832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011626828,0.00074869726,0.0005775201,0.0023855474,0.002112043,0.010822037,0.0013010622,0.0029367707,0.010896986],"category_scores_gemma":[0.019923914,0.0005110043,0.00068574195,0.002669128,0.014995903,0.019217702,0.004596985,0.006248216,0.004409213],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007660234,0.00007690522,0.002295748,0.0014554706,0.000062116036,0.000103453545,0.0021222704,0.002283836,0.0018602266,0.8497337,0.015136573,0.12479313],"study_design_scores_gemma":[0.0000106021125,0.00010959486,0.0015640236,0.00075097475,0.000027180546,0.0003607313,0.0010159006,0.0043462734,0.0016662423,0.49870878,0.49137115,0.00006849618],"about_ca_topic_score_codex":0.0034279402,"about_ca_topic_score_gemma":0.0029291513,"teacher_disagreement_score":0.011626828,"about_ca_system_score_codex":0.0022712962,"about_ca_system_score_gemma":0.002092015,"threshold_uncertainty_score":0.061489284},"labels":[],"label_agreement":null},{"id":"W3108335167","doi":"10.23977/jaip.2020.030109","title":"Research on Airport Taxi Dispatching based on Probability Model","year":2020,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Taxis; Computer science; Operations research; Revenue; Scheduling (production processes); Order (exchange); Transport engineering; Engineering; Operations management","score_opus":0.2441847588478845,"score_gpt":0.41276744050097663,"score_spread":0.16858268165309215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108335167","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":[0.022097256,0.0016558467,0.96685386,0.0007556448,0.00015590253,0.000053940115,0.00012104379,0.00022589085,0.008080646],"genre_scores_gemma":[0.9065251,0.0067657204,0.07608549,0.0001563232,0.0003765315,0.000176379,0.000377327,0.000102868304,0.0094343005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861073,0.00030907925,0.00008650088,0.00040884598,0.00038806396,0.00019673909],"domain_scores_gemma":[0.99899524,0.0005665086,0.00010624444,0.000052339474,0.00021423299,0.00006542965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011739961,0.0008311571,0.0011761199,0.0011144207,0.00082185795,0.002572161,0.001607436,0.00092456397,0.002962333],"category_scores_gemma":[0.003565896,0.00069888047,0.0014856367,0.0021773684,0.0005846574,0.0043788403,0.0007465446,0.0012809541,0.00039456415],"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":[0.000042842647,0.00005050245,0.0025479181,0.00016634964,0.000060965296,0.00013685609,0.000120314784,0.894822,0.0007852044,0.062269498,0.0018786974,0.037118856],"study_design_scores_gemma":[0.000003523944,0.000013437462,0.00032830396,0.000007638116,0.000013162587,0.00003290564,0.000030510302,0.9884878,0.00017761072,0.010009406,0.0008843056,0.000011453622],"about_ca_topic_score_codex":0.021252347,"about_ca_topic_score_gemma":0.0060026767,"teacher_disagreement_score":0.021252347,"about_ca_system_score_codex":0.0022943518,"about_ca_system_score_gemma":0.002661429,"threshold_uncertainty_score":0.04225725},"labels":[],"label_agreement":null},{"id":"W3146905089","doi":"10.23977/jaip.2020.040101","title":"The Recognition of Tibetan Handwritten Numbers Based on Federated Learning","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Numeral system; Computer science; Identification (biology); Process (computing); Artificial intelligence; Pattern recognition (psychology); Speech recognition","score_opus":0.04480459474651922,"score_gpt":0.32261531270985916,"score_spread":0.27781071796333995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146905089","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":[0.3021484,0.00082188525,0.6786416,0.00021290846,0.00024062407,0.00015228626,0.0004710068,0.0069791456,0.010332151],"genre_scores_gemma":[0.84253544,0.00032736378,0.14920922,0.00014095634,0.00003666533,0.00006288838,0.0007878831,0.000039972307,0.0068595987],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973875,0.000030081388,0.000022134198,0.00008856914,0.00008022744,0.00004019837],"domain_scores_gemma":[0.9997248,0.000042442047,0.000041111765,0.00006603278,0.0001086537,0.000016859223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039060388,0.0003791289,0.00042054756,0.0007902578,0.00023935504,0.0005672949,0.00058992277,0.00030290784,0.0013170538],"category_scores_gemma":[0.0008369026,0.00009720268,0.00029027928,0.0006723019,0.00023649406,0.00079548464,0.00041393892,0.00028239947,0.0005684556],"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":[0.0003489035,0.00015428361,0.006737511,0.00010533144,0.000059026006,0.00027593144,0.00009426384,0.025362352,0.04618254,0.0019800896,0.002945942,0.9157538],"study_design_scores_gemma":[0.000028570756,0.0002908697,0.012864304,0.000049015125,0.00007298024,0.0007229442,0.00012788402,0.86586237,0.107130796,0.0045948424,0.00820631,0.00004907261],"about_ca_topic_score_codex":0.0031798268,"about_ca_topic_score_gemma":0.0039636274,"teacher_disagreement_score":0.0031798268,"about_ca_system_score_codex":0.00043738133,"about_ca_system_score_gemma":0.0004732623,"threshold_uncertainty_score":0.0063226223},"labels":[],"label_agreement":null},{"id":"W3159623677","doi":"10.23977/jaip.2020.040102","title":"Motor Group Aggregation of Refinery and Chemical Enterprises Based on Hierarchical Clustering Algorithm","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Hierarchical clustering; Refinery; Group (periodic table); Computer science; Induction motor; Algorithm; Feature (linguistics); Data mining; Artificial intelligence; Engineering; Voltage; Chemistry","score_opus":0.02011552760928694,"score_gpt":0.28180730148734395,"score_spread":0.26169177387805703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159623677","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":[0.05889307,0.00021758286,0.9369456,0.0001570253,0.000045841443,0.0001232541,0.00017830142,0.0006175354,0.0028218161],"genre_scores_gemma":[0.6744552,0.00022720327,0.31843665,0.00007867579,0.00006763552,0.00019081523,0.0010828404,0.000116230054,0.005344844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903286,0.00013272243,0.000067668305,0.00027719725,0.0003384485,0.00015112733],"domain_scores_gemma":[0.99894017,0.00026511375,0.00013640289,0.0001172165,0.0004761701,0.0000649154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008866345,0.00068745145,0.0010193522,0.0020874138,0.0010157867,0.000951556,0.0013473852,0.00064321497,0.001694534],"category_scores_gemma":[0.0025918174,0.00034285584,0.001077614,0.001977474,0.00040099642,0.00138901,0.00089394255,0.00046057382,0.0004848773],"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":[0.00014576313,0.00009299607,0.006858448,0.00008916459,0.00014075036,0.00013216499,0.00029194262,0.7857793,0.0037499208,0.009470274,0.004163931,0.18908536],"study_design_scores_gemma":[0.0000064404157,0.000011981302,0.0007750188,0.0000031019913,0.000013729087,0.0000128766615,0.000029048711,0.99585223,0.0005466567,0.0022622037,0.00047915228,0.000007564026],"about_ca_topic_score_codex":0.038495373,"about_ca_topic_score_gemma":0.028705051,"teacher_disagreement_score":0.038495373,"about_ca_system_score_codex":0.0015099419,"about_ca_system_score_gemma":0.0018002138,"threshold_uncertainty_score":0.076542616},"labels":[],"label_agreement":null},{"id":"W3195266546","doi":"10.23977/jaip.2020.040103","title":"Hair counting method based on image processing technology","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Textile materials and evaluations","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Scalp; Computer vision; Hair growth; Artificial intelligence; Computer science; Noise (video); Cabello; Image processing; Image (mathematics); Mathematics; Pattern recognition (psychology); Anatomy; Biology","score_opus":0.06554976808159496,"score_gpt":0.40957450315490457,"score_spread":0.3440247350733096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195266546","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":[0.017524144,0.00047874,0.97768366,0.00008660804,0.00009590622,0.000096311174,0.00006900545,0.0012586846,0.0027069377],"genre_scores_gemma":[0.24549457,0.0015194102,0.7462414,0.00015655386,0.00014891107,0.00022584727,0.00026050725,0.00021428801,0.005738516],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99907184,0.00009159903,0.000039692295,0.00020517965,0.00053158833,0.000060133905],"domain_scores_gemma":[0.99939656,0.00013760295,0.000058084177,0.00006950957,0.00031616326,0.000022027141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046162348,0.00064885785,0.0004974695,0.0017540833,0.0003336482,0.0007083258,0.00089909125,0.00057350897,0.0021431586],"category_scores_gemma":[0.0010327878,0.0003350424,0.00065233046,0.0010563944,0.00042293483,0.0011396332,0.00048940093,0.00053400325,0.001146983],"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":[0.00015091013,0.00006315347,0.0017314814,0.00045053786,0.000048990307,0.00020974905,0.00023008972,0.005581663,0.3441863,0.004935289,0.0035021298,0.6389097],"study_design_scores_gemma":[0.00005884369,0.0005614173,0.010993966,0.00010438906,0.0002072754,0.0025506692,0.0002474878,0.39757746,0.5500068,0.0058365194,0.031673335,0.00018185921],"about_ca_topic_score_codex":0.0010492312,"about_ca_topic_score_gemma":0.0008020607,"teacher_disagreement_score":0.0021431586,"about_ca_system_score_codex":0.00031544623,"about_ca_system_score_gemma":0.00043517133,"threshold_uncertainty_score":0.0071695447},"labels":[],"label_agreement":null},{"id":"W3199268650","doi":"10.23977/jaip.2020.040104","title":"Progressive Sampling-Based Joint Automatic Model Selection of Machine Learning and Feature Selection","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Machine learning; Artificial intelligence; Computer science; Feature selection; Hyperparameter; Model selection; Selection (genetic algorithm); Data pre-processing; Bayesian optimization; Preprocessor; Feature (linguistics); Bayesian inference; Bayesian probability","score_opus":0.055212396438347966,"score_gpt":0.35274937334783774,"score_spread":0.29753697690948977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199268650","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":[0.0065228897,0.00013609113,0.9924164,0.000050184924,0.000015679305,0.00007280627,0.000016826109,0.00039979996,0.00036941137],"genre_scores_gemma":[0.46475515,0.00034637647,0.5314207,0.00027466877,0.0001401269,0.0007284497,0.00055142684,0.00028625524,0.0014968265],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99588543,0.001928926,0.00021704847,0.000490844,0.0012152863,0.00026240482],"domain_scores_gemma":[0.9956617,0.0025676538,0.00027620074,0.0005747197,0.0007981743,0.000121496734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004452738,0.0017904659,0.002603387,0.0016984119,0.0008306357,0.001147624,0.0024738265,0.0011159478,0.0013922203],"category_scores_gemma":[0.010925635,0.0008923652,0.0019864268,0.001810848,0.0010454745,0.0017421055,0.0021070864,0.0015883197,0.00053802587],"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":[0.00029529168,0.00027276413,0.0023615833,0.00019242264,0.0002632728,0.00025339762,0.00024426237,0.6793012,0.007487074,0.018566588,0.0029923746,0.28776976],"study_design_scores_gemma":[0.000017105209,0.00002977483,0.00013677456,0.0000036311792,0.000013475882,0.000029193501,0.0000060450348,0.9954568,0.00075274735,0.0032841081,0.00026183054,0.00000848725],"about_ca_topic_score_codex":0.00716654,"about_ca_topic_score_gemma":0.0065671606,"teacher_disagreement_score":0.00716654,"about_ca_system_score_codex":0.00087264023,"about_ca_system_score_gemma":0.0021622058,"threshold_uncertainty_score":0.023548603},"labels":[],"label_agreement":null},{"id":"W3209668695","doi":"10.23977/jaip.2020.040105","title":"Research on Entity Recognition and Knowledge Graph Construction Based on Tcm Medical Records","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Knowledge extraction; Ambiguity; Information retrieval; Visualization; Graph; Conditional random field; Artificial intelligence; Data science; Data mining","score_opus":0.1629362907974108,"score_gpt":0.4410426488735801,"score_spread":0.2781063580761693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209668695","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":[0.029765598,0.0014931012,0.9613385,0.00077929214,0.000101839556,0.00016615633,0.0010699022,0.0012662738,0.0040193065],"genre_scores_gemma":[0.4444378,0.0046755574,0.5359733,0.0003174694,0.0001324491,0.000279611,0.0068498137,0.00016625327,0.007167697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987301,0.00022043745,0.00014374909,0.0005060499,0.0003237848,0.00007582807],"domain_scores_gemma":[0.9986823,0.0005426259,0.0001552313,0.00018910311,0.00038040357,0.00005037138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009484158,0.000563537,0.00059434766,0.003769376,0.00085000263,0.0014332646,0.0014001916,0.0006658911,0.0022700238],"category_scores_gemma":[0.0041025546,0.00037879765,0.0014636242,0.0050204806,0.00061520125,0.005702992,0.0010516429,0.0007521481,0.0005260191],"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":[0.000102628524,0.0001541917,0.014889164,0.00095914176,0.00025161062,0.0005505711,0.0008539269,0.092781164,0.006634037,0.087778114,0.00979711,0.78524846],"study_design_scores_gemma":[0.000021686043,0.00006847229,0.007246079,0.00014214724,0.000252377,0.00061775785,0.00040661913,0.8984666,0.010140923,0.05830303,0.024242297,0.00009195484],"about_ca_topic_score_codex":0.015946036,"about_ca_topic_score_gemma":0.012213337,"teacher_disagreement_score":0.015946036,"about_ca_system_score_codex":0.0010688168,"about_ca_system_score_gemma":0.0018327127,"threshold_uncertainty_score":0.031706452},"labels":[],"label_agreement":null},{"id":"W3213331957","doi":"10.23977/jaip.2020.040106","title":"Research on the Rock-paper-scissors Game and Cooperation","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Participant observation; MATLAB; Computer science; Software; Basis (linear algebra); Artificial intelligence; Mathematics","score_opus":0.12663347789601279,"score_gpt":0.43193384088752557,"score_spread":0.30530036299151275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213331957","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":[0.3010226,0.0013864647,0.5789446,0.005656803,0.00028467205,0.00051272544,0.00019508117,0.000118429954,0.11187856],"genre_scores_gemma":[0.9609281,0.0005077891,0.031405803,0.00019245087,0.000044739405,0.00027489843,0.00005469138,0.000028449922,0.00656306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9902683,0.0069352477,0.00026446817,0.0012071243,0.0006389376,0.0006860142],"domain_scores_gemma":[0.97164774,0.022288347,0.002191431,0.0015034195,0.00095785834,0.0014112663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0082100425,0.0009208956,0.0008671266,0.0009213182,0.0016041603,0.0035779593,0.002082487,0.0020194943,0.008126853],"category_scores_gemma":[0.03508421,0.0004740104,0.0011437187,0.0012375896,0.004716558,0.009406349,0.0023978038,0.002456496,0.0006740935],"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":[0.00014697082,0.0001757986,0.004084198,0.00018709952,0.000070174035,0.00018460874,0.0028678305,0.030238263,0.0004944154,0.9424632,0.0011621811,0.017925138],"study_design_scores_gemma":[0.0000624997,0.00033958515,0.0037537783,0.00010773345,0.00005922411,0.00029709586,0.002786172,0.2597531,0.000457369,0.72222686,0.01008442,0.00007210375],"about_ca_topic_score_codex":0.0062752375,"about_ca_topic_score_gemma":0.004013824,"teacher_disagreement_score":0.0082100425,"about_ca_system_score_codex":0.0035741364,"about_ca_system_score_gemma":0.002641663,"threshold_uncertainty_score":0.04341942},"labels":[],"label_agreement":null},{"id":"W3215177466","doi":"10.23977/jaip.2020.040108","title":"Ultra-wideband (UWB) precise location problem under signal interference based on Shark optimization algorithm","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ultra-wideband; Multipath propagation; Interference (communication); Computer science; Algorithm; Optimization problem; Electronic engineering; Engineering; Telecommunications","score_opus":0.02770249681916532,"score_gpt":0.2786256198608083,"score_spread":0.250923123041643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215177466","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":[0.011135437,0.00015108663,0.98724735,0.00011925294,0.000017628316,0.0000144279065,0.00001726997,0.00007422172,0.0012233098],"genre_scores_gemma":[0.65299654,0.0007511934,0.33759898,0.00014616142,0.00004965527,0.00024887035,0.00021182642,0.00011189776,0.007884796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996045,0.00012109617,0.000023550818,0.00010776003,0.00010341135,0.00003958905],"domain_scores_gemma":[0.99957925,0.00022540388,0.00005408197,0.000017080863,0.00010921514,0.0000149366615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007546239,0.00066923565,0.0011677308,0.0004522626,0.00043306968,0.000855017,0.000624876,0.0008352789,0.001146474],"category_scores_gemma":[0.0011945075,0.00043971612,0.0006280668,0.0007718494,0.0006838474,0.0010658457,0.00078961236,0.0007428876,0.00022344725],"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":[0.000036244845,0.000017278697,0.00048466722,0.000054467375,0.00002629611,0.000045700563,0.000043853797,0.9690148,0.0011133465,0.006136121,0.0006255859,0.022401663],"study_design_scores_gemma":[0.0000032232474,0.000012952058,0.00006960172,0.0000024157082,0.0000032599917,0.000008798042,0.000009141914,0.9982919,0.0001729057,0.0012835134,0.00013902057,0.0000033405354],"about_ca_topic_score_codex":0.006094826,"about_ca_topic_score_gemma":0.0023459261,"teacher_disagreement_score":0.006094826,"about_ca_system_score_codex":0.00060230703,"about_ca_system_score_gemma":0.001032438,"threshold_uncertainty_score":0.012118697},"labels":[],"label_agreement":null},{"id":"W4291136122","doi":"10.23977/jaip.2022.050116","title":"Overview of Sensorless Zero-Low Speed Range Control Technology based on High-Frequency Signal Injection for SPMSM","year":2022,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"China Postdoctoral Science Foundation","keywords":"Rotor (electric); Position (finance); Control theory (sociology); Zero (linguistics); SIGNAL (programming language); Range (aeronautics); Control (management); Process (computing); Computer science; Control engineering; Permanent magnet synchronous motor; Engineering; Artificial intelligence; Electrical engineering","score_opus":0.037658568928499725,"score_gpt":0.31810385255186113,"score_spread":0.2804452836233614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291136122","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":[0.0101077305,0.053972084,0.90549326,0.00042533752,0.00048308322,0.00020550353,0.000099007215,0.0013827836,0.027831236],"genre_scores_gemma":[0.37264374,0.074138425,0.52834123,0.00075783866,0.0012784783,0.00044554053,0.0005963259,0.00015561706,0.021642724],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996101,0.00004350847,0.00003816478,0.00010304974,0.00018265784,0.000022588974],"domain_scores_gemma":[0.9998555,0.00002715167,0.000018790703,0.000019858788,0.00006827107,0.000010273122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036489172,0.00060157897,0.0004258249,0.0011632517,0.0002891228,0.00080943183,0.00075771805,0.00076834165,0.0020771883],"category_scores_gemma":[0.00021579527,0.00047764892,0.00048508047,0.00081081945,0.0003413331,0.001572343,0.00043855887,0.00080983306,0.0008417993],"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":[0.00020993921,0.0001590171,0.0012504515,0.0034478167,0.00010240113,0.0003460032,0.0002943475,0.012925657,0.19992843,0.060750823,0.005652626,0.7149325],"study_design_scores_gemma":[0.00008172115,0.0023831641,0.0069061937,0.0007949419,0.00028131716,0.0041098874,0.0001892139,0.29819775,0.2050876,0.024390208,0.45724583,0.00033219162],"about_ca_topic_score_codex":0.00053065945,"about_ca_topic_score_gemma":0.00036943005,"teacher_disagreement_score":0.0020771883,"about_ca_system_score_codex":0.00040176013,"about_ca_system_score_gemma":0.0005540898,"threshold_uncertainty_score":0.0069488883},"labels":[],"label_agreement":null},{"id":"W4292702225","doi":"10.23977/jaip.2022.050301","title":"The Development and Application of Computer Vision Technology in The Era of Artificial Intelligence","year":2022,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and Big Data Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Popularity; Field (mathematics); Mainstream; Computer science; Computer technology; Artificial intelligence; The Internet; Marketing and artificial intelligence; Data science; Multimedia; Intelligent decision support system; World Wide Web; Political science","score_opus":0.04505437267913227,"score_gpt":0.34581626575343916,"score_spread":0.3007618930743069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292702225","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":[0.012330646,0.13349794,0.7242927,0.022401828,0.002766637,0.00019134063,0.00020267793,0.0007638627,0.1035524],"genre_scores_gemma":[0.32002607,0.11305938,0.5416919,0.005262938,0.0049028634,0.00024554422,0.00031383397,0.00019075553,0.014306632],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984469,0.00052132783,0.00007923531,0.00028094143,0.0005864404,0.0000851239],"domain_scores_gemma":[0.9981578,0.00090500433,0.00010040013,0.00017261524,0.00055062684,0.000113464754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020269125,0.0005005467,0.0005538822,0.0025853517,0.00070305896,0.003563076,0.0011884125,0.0022766504,0.0019103923],"category_scores_gemma":[0.003989323,0.00038596496,0.0005937916,0.0023957675,0.0031067377,0.0042839935,0.0016057846,0.0034146348,0.0010463872],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057631227,0.000102682345,0.0018082128,0.00072550995,0.00006843369,0.00033936196,0.00072053535,0.0057928674,0.0069428813,0.52840126,0.018837737,0.4362029],"study_design_scores_gemma":[0.000020418409,0.0001500058,0.0030869818,0.00085375743,0.00004896198,0.0013631507,0.0006252504,0.07105752,0.007421401,0.6159116,0.2993087,0.00015226782],"about_ca_topic_score_codex":0.0017406952,"about_ca_topic_score_gemma":0.0008664535,"teacher_disagreement_score":0.003563076,"about_ca_system_score_codex":0.0012473257,"about_ca_system_score_gemma":0.0011892607,"threshold_uncertainty_score":0.010719478},"labels":[],"label_agreement":null},{"id":"W4292707179","doi":"10.23977/jaip.2022.050210","title":"A Deep Reinforcement Learning Based Emotional State Analysis Method for Online Learning","year":2022,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Reinforcement learning; Unsupervised learning; Machine learning; Feature extraction; Deep learning; Pattern recognition (psychology); Facial recognition system; Set (abstract data type); Feature (linguistics)","score_opus":0.04846748785835515,"score_gpt":0.37768015068739136,"score_spread":0.3292126628290362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292707179","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":[0.010318608,0.00014241085,0.9873877,0.00007886838,0.00004191653,0.0000440912,0.000021327296,0.0006022359,0.0013628307],"genre_scores_gemma":[0.78774285,0.0001738587,0.20650516,0.00015083527,0.000039738454,0.00022684273,0.00009993486,0.00007808003,0.0049827886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996722,0.000068866706,0.000022898163,0.000096138065,0.00009640192,0.00004359807],"domain_scores_gemma":[0.9996493,0.00012511283,0.00004324274,0.000031381584,0.00012503208,0.000025954145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007286067,0.00052765413,0.0005698321,0.0003073695,0.00027969078,0.00043776445,0.0008145568,0.0004195625,0.0024079827],"category_scores_gemma":[0.0012254239,0.00023209058,0.0004853057,0.00022872204,0.00030157485,0.0005925504,0.0006315843,0.0010073209,0.00034614437],"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":[0.00017883712,0.000270615,0.0022123163,0.000107452455,0.00010791509,0.00009981684,0.00015211836,0.34937352,0.016103731,0.008333499,0.0025580248,0.6205021],"study_design_scores_gemma":[0.0000047645053,0.000031230436,0.00016845702,0.000002549123,0.0000052035048,0.000010177955,0.000003879353,0.9974318,0.0011627931,0.0008676537,0.00030779216,0.0000037638654],"about_ca_topic_score_codex":0.00490032,"about_ca_topic_score_gemma":0.004083037,"teacher_disagreement_score":0.00490032,"about_ca_system_score_codex":0.0005835854,"about_ca_system_score_gemma":0.00071299245,"threshold_uncertainty_score":0.009743571},"labels":[],"label_agreement":null},{"id":"W4292714195","doi":"10.23977/jaip.2022.050208","title":"On Data Analysis and Design and Implementation of Data Preprocessing Scheme Based on Low-quality Rock Datasets","year":2022,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Preprocessor; Computer science; Deep learning; Data pre-processing; Generalization; Quality (philosophy); Artificial intelligence; Scheme (mathematics); Machine learning; Data mining","score_opus":0.14908133755257572,"score_gpt":0.4408978987709113,"score_spread":0.29181656121833555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292714195","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":[0.019179398,0.00018116344,0.97268987,0.00048523388,0.00009912047,0.0007335588,0.0014956965,0.003947624,0.0011883805],"genre_scores_gemma":[0.09671421,0.00039564786,0.8897916,0.00028113433,0.00007391841,0.0016458388,0.009041291,0.0004185,0.0016378588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971734,0.00057995797,0.00031641175,0.00062229286,0.001109167,0.00019867692],"domain_scores_gemma":[0.99571735,0.0010093768,0.0003163465,0.0010370456,0.0017986958,0.00012127281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036026342,0.0010675371,0.000743004,0.0026516442,0.0010809766,0.0021084945,0.002305272,0.0008547634,0.0033638794],"category_scores_gemma":[0.011379945,0.00054007565,0.0012020603,0.0025890071,0.0008395883,0.0029324775,0.0018282215,0.0017325709,0.0021429954],"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":[0.00083579734,0.0006047231,0.021940328,0.00088988285,0.00018951019,0.00046345347,0.00045622917,0.054936454,0.086562105,0.028303063,0.031516533,0.77330196],"study_design_scores_gemma":[0.00015207594,0.0004403704,0.017425532,0.00017383724,0.000093750175,0.00047893907,0.00048604814,0.760954,0.15677391,0.023384944,0.03950391,0.00013270602],"about_ca_topic_score_codex":0.002912577,"about_ca_topic_score_gemma":0.0033889764,"teacher_disagreement_score":0.0036026342,"about_ca_system_score_codex":0.00088471215,"about_ca_system_score_gemma":0.0022071041,"threshold_uncertainty_score":0.019052804},"labels":[],"label_agreement":null},{"id":"W4312810405","doi":"10.23977/jaip.2022.050402","title":"Ordering Problem of Vascular Robot Based on Time Series Prediction","year":2022,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robot; Operator (biology); Computer science; Container (type theory); Integer programming; Multivariate statistics; Mathematical optimization; Linear programming; Work (physics); Integer (computer science); Series (stratigraphy); Artificial intelligence; Mathematics; Algorithm; Engineering; Machine learning; Mechanical engineering","score_opus":0.01817982829150998,"score_gpt":0.2531892982542393,"score_spread":0.2350094699627293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312810405","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":[0.22410382,0.00084214367,0.76683956,0.00147267,0.0001535963,0.00016431289,0.0005761133,0.00042030032,0.0054276134],"genre_scores_gemma":[0.95342654,0.0003896456,0.03907083,0.00010702826,0.00006380487,0.00012093116,0.0005256085,0.000054900203,0.0062406594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990103,0.00023247868,0.000060400787,0.00029011548,0.0001627602,0.0002438799],"domain_scores_gemma":[0.99676573,0.0021065031,0.0004515097,0.00006565819,0.00039565176,0.00021486178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017203548,0.0011489775,0.0021074864,0.00084158516,0.00077069335,0.0015597979,0.0014205127,0.0017731291,0.005378868],"category_scores_gemma":[0.004087431,0.0007409895,0.0009104797,0.0009936094,0.0008089442,0.0016316406,0.00078015256,0.0018355455,0.00034493802],"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":[0.0001634117,0.00007741264,0.0018407957,0.00010314054,0.000034094563,0.00016190314,0.000047269572,0.978234,0.0007597745,0.004610496,0.0010651386,0.012902667],"study_design_scores_gemma":[0.0000044577446,0.000020244377,0.00023436625,0.0000028254494,0.0000054350776,0.0000067766205,0.000012612335,0.99843055,0.000119911085,0.0010842779,0.0000738041,0.000004728751],"about_ca_topic_score_codex":0.01874804,"about_ca_topic_score_gemma":0.010374206,"teacher_disagreement_score":0.01874804,"about_ca_system_score_codex":0.0015941815,"about_ca_system_score_gemma":0.0018139967,"threshold_uncertainty_score":0.037277818},"labels":[],"label_agreement":null},{"id":"W4313652981","doi":"10.23977/jaip.2022.050409","title":"The Promotion Mode of Chinese Martial Arts under the Background of the International Development of Taekwondo Based on Artificial Intelligence","year":2022,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Martial Arts: Techniques, Psychology, and Education","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Martial arts; Promotion (chess); Psychology; Political science; Visual arts; Art; Law","score_opus":0.11930963746050352,"score_gpt":0.433990782127212,"score_spread":0.3146811446667085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313652981","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":[0.8775552,0.00028605925,0.0078525245,0.00077512895,0.00008822761,0.00015049327,0.000040921404,0.00008688297,0.11316461],"genre_scores_gemma":[0.98348665,0.00013068128,0.003166347,0.000045007713,0.000008676882,0.000039360042,0.000012526494,0.000006346545,0.013104433],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998511,0.000039407812,0.0000046703303,0.000018710169,0.000042900974,0.0000432358],"domain_scores_gemma":[0.9999069,0.000018209394,0.000013802509,0.0000050009685,0.000014864497,0.00004121544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025527255,0.00027029793,0.00009042997,0.0004696386,0.0011239573,0.000948723,0.0002704549,0.00024563936,0.0036156166],"category_scores_gemma":[0.00040634855,0.00008753279,0.00015426656,0.00029232947,0.000779336,0.0009628591,0.0006915798,0.00033913247,0.00022409008],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096977956,0.0009797047,0.10106057,0.0010329011,0.00008441958,0.0026201247,0.12317264,0.0018606303,0.08735566,0.26891124,0.013045475,0.3989069],"study_design_scores_gemma":[0.00032977806,0.0021561882,0.41646373,0.0008821026,0.0003265223,0.0033484036,0.17622197,0.044739384,0.04005944,0.054153528,0.26098746,0.00033147662],"about_ca_topic_score_codex":0.0026778663,"about_ca_topic_score_gemma":0.0052904678,"teacher_disagreement_score":0.0036156166,"about_ca_system_score_codex":0.00047025585,"about_ca_system_score_gemma":0.0008532225,"threshold_uncertainty_score":0.012095451},"labels":[],"label_agreement":null},{"id":"W4321505295","doi":"10.23977/jaip.2023.060102","title":"Research and Application of Health Code Recognition Based on Paddle OCR under the Background of Epidemic Prevention and Control","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and Big Data Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Normalization (sociology); Computer science; Code (set theory); Audit; Paddle; Control (management); Source code; Artificial intelligence; Data mining","score_opus":0.31351430565429356,"score_gpt":0.4760308145976832,"score_spread":0.16251650894338965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321505295","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":[0.10161661,0.010269646,0.8577371,0.001842558,0.0008089037,0.00037359406,0.00025014867,0.0038611402,0.023240333],"genre_scores_gemma":[0.63671213,0.007725383,0.34063658,0.0006448136,0.0004564969,0.00013288175,0.0005126721,0.00016834118,0.013010769],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99727964,0.0003402338,0.00017000506,0.00059025944,0.0014645315,0.00015532463],"domain_scores_gemma":[0.9975732,0.000620711,0.00017420246,0.0002476339,0.0012978194,0.000086492204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011932264,0.00063584966,0.0005922861,0.0026685097,0.00039797646,0.0014188071,0.001147001,0.00089943147,0.0019812728],"category_scores_gemma":[0.003954836,0.00031616038,0.0006183771,0.0016675744,0.00085962645,0.0024264464,0.00050290156,0.00070532894,0.0007264075],"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":[0.000153779,0.00014549398,0.00809862,0.0006458181,0.0000554354,0.00030702126,0.00043054865,0.008383223,0.07606983,0.00943131,0.004314774,0.89196414],"study_design_scores_gemma":[0.0000895302,0.0007858236,0.034452077,0.00025606534,0.00030332152,0.003802712,0.00094455533,0.5666598,0.30918127,0.008265985,0.07491115,0.0003477154],"about_ca_topic_score_codex":0.0067793312,"about_ca_topic_score_gemma":0.0022681432,"teacher_disagreement_score":0.0067793312,"about_ca_system_score_codex":0.0007457561,"about_ca_system_score_gemma":0.001584624,"threshold_uncertainty_score":0.013479769},"labels":[],"label_agreement":null},{"id":"W4321505447","doi":"10.23977/jaip.2023.060103","title":"UAV planar passive pure orientation positioning under different conditions","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Triangulation; Planar; Orientation (vector space); Plane (geometry); Computer science; Geometry; Argumentative; Field (mathematics); Computer vision; Artificial intelligence; Mathematics; Computer graphics (images); Pure mathematics","score_opus":0.03572606187996875,"score_gpt":0.31641122837827523,"score_spread":0.2806851664983065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321505447","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":[0.31982827,0.0004050385,0.6710676,0.00023336659,0.00009104323,0.000031318476,0.0001742455,0.00033580407,0.007833362],"genre_scores_gemma":[0.9869239,0.00023674415,0.011438764,0.000020543053,0.000019626486,0.00002157617,0.00010305692,0.000015088846,0.0012206201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943596,0.00008231537,0.000015445492,0.00016072285,0.00022799165,0.00007747813],"domain_scores_gemma":[0.99967086,0.00009466677,0.00008051423,0.000049504375,0.00009093688,0.000013467923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000285325,0.0004679686,0.00046095884,0.00027877497,0.00030701046,0.00061113085,0.00040462034,0.0005619657,0.00040383116],"category_scores_gemma":[0.0013238013,0.00015029544,0.0002261403,0.0002693497,0.00065899256,0.0009692056,0.0008303714,0.00034288852,0.0001571468],"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":[0.00061318267,0.00005276388,0.005544724,0.0003997563,0.00007676463,0.0012949624,0.00036179472,0.6444712,0.22654203,0.034231693,0.0013186734,0.08509234],"study_design_scores_gemma":[0.00004451628,0.00047817762,0.0063684224,0.000019654515,0.000047922927,0.00073491625,0.00044026694,0.91636664,0.06005318,0.01331058,0.0020718782,0.00006384969],"about_ca_topic_score_codex":0.001734231,"about_ca_topic_score_gemma":0.00090541627,"teacher_disagreement_score":0.001734231,"about_ca_system_score_codex":0.00029898543,"about_ca_system_score_gemma":0.00030751928,"threshold_uncertainty_score":0.0034483075},"labels":[],"label_agreement":null},{"id":"W4321639248","doi":"10.23977/jaip.2023.060104","title":"Research on semantic segmentation of unmanned aerial vehicle visual image based on deep learning—take the outdoor environment of Anhui University of Finance & Economics as an example","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Anhui University","keywords":"Segmentation; Aerial image; Artificial intelligence; Computer science; Image segmentation; Set (abstract data type); Computer vision; Data set; Deep learning; Image (mathematics)","score_opus":0.09929820706131509,"score_gpt":0.3731941285813287,"score_spread":0.27389592152001363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321639248","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":[0.2580248,0.0032041855,0.7285645,0.0010632295,0.00019992547,0.00009989664,0.0008457142,0.0023516305,0.005646099],"genre_scores_gemma":[0.83208364,0.0024564648,0.15525015,0.0004162219,0.00011296398,0.00006763565,0.0035837241,0.0001827126,0.00584648],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961096,0.000039412782,0.000024627738,0.00015471426,0.000093950424,0.00007640228],"domain_scores_gemma":[0.9997681,0.00004689752,0.000033595315,0.000032599975,0.00009254246,0.000026324993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039226143,0.0007353657,0.00062602834,0.0016150991,0.00037729318,0.00086328987,0.00082784094,0.0006879327,0.0008462561],"category_scores_gemma":[0.0006829964,0.0002568482,0.00086792064,0.0015052676,0.000490844,0.001972392,0.00047300383,0.00064814993,0.00025792525],"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":[0.00021054683,0.0002713849,0.012834607,0.00025167913,0.00024451362,0.00017762384,0.0001942316,0.18037787,0.03510907,0.0090506375,0.0066843187,0.7545934],"study_design_scores_gemma":[0.0000067252813,0.000049850067,0.004029691,0.000014104816,0.000036044778,0.000052668693,0.000082239516,0.9784188,0.01128245,0.0037601078,0.00225193,0.0000153329],"about_ca_topic_score_codex":0.02250352,"about_ca_topic_score_gemma":0.015058526,"teacher_disagreement_score":0.02250352,"about_ca_system_score_codex":0.0009610595,"about_ca_system_score_gemma":0.0011227166,"threshold_uncertainty_score":0.044745088},"labels":[],"label_agreement":null},{"id":"W4323527314","doi":"10.23977/jaip.2023.060105","title":"A survey of Few-Shot Action Recognition","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Computer science; Shot (pellet); Action (physics); Field (mathematics); Artificial intelligence; Metric (unit); Process (computing); Embedding; Machine learning; One shot; Pattern recognition (psychology); Engineering; Mathematics","score_opus":0.3422828057942334,"score_gpt":0.41993388037641693,"score_spread":0.07765107458218351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323527314","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015737778,0.33759844,0.6193746,0.0012150084,0.0016217818,0.00025484723,0.00088755955,0.0028666132,0.020443419],"genre_scores_gemma":[0.26656568,0.36966494,0.31983158,0.0020698938,0.0036512164,0.0004840751,0.0072676516,0.00049355626,0.029971499],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99819225,0.00022037231,0.00018647971,0.0006796308,0.00063240743,0.000088786124],"domain_scores_gemma":[0.99862754,0.00052055536,0.00008839876,0.00019776689,0.00048304212,0.000082675426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010903805,0.0013740996,0.0023783827,0.0029787377,0.00047716926,0.0016498733,0.002192855,0.0013150809,0.004273024],"category_scores_gemma":[0.002868997,0.00059134385,0.0012137186,0.0036886972,0.00076153985,0.003058074,0.0009580668,0.00103197,0.00320248],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010294715,0.00011093577,0.0014542853,0.0012890584,0.000075158816,0.00009038094,0.00006738419,0.0050554634,0.0036901599,0.0042201485,0.008339713,0.9755044],"study_design_scores_gemma":[0.000051843195,0.0012744743,0.02290187,0.0019482226,0.0005533412,0.0050966092,0.00090190285,0.5029399,0.03627718,0.046327595,0.38122448,0.0005025828],"about_ca_topic_score_codex":0.0059130895,"about_ca_topic_score_gemma":0.0035744775,"teacher_disagreement_score":0.0059130895,"about_ca_system_score_codex":0.00066651334,"about_ca_system_score_gemma":0.0013611335,"threshold_uncertainty_score":0.014294684},"labels":[],"label_agreement":null},{"id":"W4323527357","doi":"10.23977/jaip.2023.060106","title":"Design of Multi-Channel Temperature Acquisition System Based on STM32","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Sensor and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Data acquisition; STM32; Operability; Controller (irrigation); Microcomputer; Reliability (semiconductor); Temperature control; Computer science; Process (computing); Computer hardware; Engineering; Electrical engineering; Chip; Control engineering","score_opus":0.04921695985839919,"score_gpt":0.30156397646918426,"score_spread":0.25234701661078507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323527357","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":[0.15060727,0.0014627739,0.8162166,0.0006304781,0.0006177154,0.0013691845,0.0007379969,0.015557128,0.012800844],"genre_scores_gemma":[0.6647863,0.00038512846,0.32118982,0.00050814543,0.00020177683,0.001251863,0.00074159255,0.00017858934,0.010756927],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913245,0.00008352936,0.00005792292,0.00027622914,0.00035378494,0.00009612703],"domain_scores_gemma":[0.9995229,0.000056145193,0.000042394684,0.000047643174,0.00027471822,0.00005615772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056763424,0.0005847143,0.0008375611,0.00090780435,0.0004522682,0.00052534917,0.0019647202,0.0007380212,0.005574721],"category_scores_gemma":[0.0005707252,0.00049371395,0.00022753682,0.00042834398,0.0001980412,0.000979415,0.00061887206,0.0004824412,0.0015365353],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008064761,0.0002465583,0.006274362,0.0007891984,0.00010579704,0.0006202514,0.00034795605,0.0051977853,0.7503981,0.0042082714,0.010027583,0.2209777],"study_design_scores_gemma":[0.00066956936,0.00438601,0.028375888,0.00014129315,0.00030560093,0.006388832,0.00023525837,0.33930495,0.5430174,0.0017052254,0.07498722,0.0004827621],"about_ca_topic_score_codex":0.0008989525,"about_ca_topic_score_gemma":0.0008403415,"teacher_disagreement_score":0.005574721,"about_ca_system_score_codex":0.00042140455,"about_ca_system_score_gemma":0.00077701715,"threshold_uncertainty_score":0.01864934},"labels":[],"label_agreement":null},{"id":"W4323649860","doi":"10.23977/jaip.2023.060107","title":"Research on Artificial Intelligence Applications Based on Data Mining Algorithms in the Era of Big Data","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Big data; Data science; Computer science; Field (mathematics); Connotation; Scale (ratio); Data mining; Artificial intelligence; Mathematics","score_opus":0.4817331705348353,"score_gpt":0.47119993491537643,"score_spread":0.010533235619458847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323649860","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":[0.024868596,0.22671866,0.6056132,0.028371977,0.0025800203,0.00044442702,0.00029808775,0.00092210714,0.11018293],"genre_scores_gemma":[0.22222792,0.2684317,0.4856686,0.0047239205,0.004521022,0.00039401365,0.00055252115,0.00021562132,0.013264701],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969913,0.0010172413,0.00022551346,0.0004897528,0.0011790819,0.000097169526],"domain_scores_gemma":[0.99305326,0.004927652,0.0002536372,0.0005821871,0.0010666822,0.0001164403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031994707,0.0007531608,0.00083189947,0.0034375174,0.00081527664,0.0048953583,0.0015513084,0.0017622338,0.001871474],"category_scores_gemma":[0.009304257,0.0004907042,0.0009613374,0.007673205,0.0022206276,0.008910184,0.0009906971,0.0028447432,0.0013497338],"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":[0.000071622104,0.00030591871,0.005243909,0.00276941,0.00017919882,0.00035783497,0.00084765925,0.013576042,0.0032071152,0.40567285,0.012222144,0.55554634],"study_design_scores_gemma":[0.000038571954,0.0002861425,0.005855351,0.0026840304,0.00016552146,0.001689095,0.0012912337,0.16227971,0.007981711,0.5277288,0.2898546,0.00014536647],"about_ca_topic_score_codex":0.001133705,"about_ca_topic_score_gemma":0.0007210283,"teacher_disagreement_score":0.0048953583,"about_ca_system_score_codex":0.0011679718,"about_ca_system_score_gemma":0.0015996074,"threshold_uncertainty_score":0.016920626},"labels":[],"label_agreement":null},{"id":"W4323649978","doi":"10.23977/jaip.2023.060108","title":"Development and Application of Campus Sports Competition System","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Competition (biology); Sport management; Process (computing); Advertising; Function (biology); Sports marketing; Physical education; Medical education; Marketing; Multimedia; Mathematics education; Psychology; Computer science; Public relations; Business; Political science; Medicine","score_opus":0.03606161021824911,"score_gpt":0.29819549882504925,"score_spread":0.26213388860680015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323649978","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":[0.17612791,0.0011510979,0.3527194,0.0014618193,0.0018040114,0.011404039,0.011581807,0.29501584,0.14873414],"genre_scores_gemma":[0.5498959,0.0010210118,0.2758756,0.0015545079,0.0004177255,0.005678039,0.03736148,0.0064962655,0.12169956],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986148,0.00017449415,0.00017842546,0.000302773,0.0005577772,0.00017170695],"domain_scores_gemma":[0.9987923,0.00012809123,0.00005643261,0.00017627854,0.0005877751,0.0002590635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011228471,0.00069228635,0.0005438308,0.0015714512,0.000622828,0.0015533593,0.0018531758,0.0006655967,0.021090059],"category_scores_gemma":[0.0027580578,0.0003936921,0.0006330223,0.0007309365,0.00024790558,0.0015574916,0.001684366,0.0008970034,0.010050224],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013308781,0.0024251253,0.028639467,0.0015289299,0.00018130978,0.0020647582,0.0015839983,0.004886262,0.036788363,0.009437632,0.20978592,0.70134735],"study_design_scores_gemma":[0.00058785523,0.0017376429,0.056875747,0.0004655529,0.00030578955,0.0021144722,0.001104701,0.16274811,0.061080214,0.005066585,0.7073716,0.0005418474],"about_ca_topic_score_codex":0.004195955,"about_ca_topic_score_gemma":0.0025505498,"teacher_disagreement_score":0.021090059,"about_ca_system_score_codex":0.00041359733,"about_ca_system_score_gemma":0.0014280275,"threshold_uncertainty_score":0.0705533},"labels":[],"label_agreement":null},{"id":"W4366431549","doi":"10.23977/jaip.2023.060110","title":"AI Application to Generate an Expected Picture Using Keywords with Stable Diffusion","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Noise (video); Image (mathematics); Artificial intelligence; Generator (circuit theory); Field (mathematics); Painting; Creativity; Diffusion; Process (computing); Computer vision; Visual arts; Law; Mathematics; Art; Power (physics); Programming language","score_opus":0.045394645205839315,"score_gpt":0.3835652351684803,"score_spread":0.338170589962641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366431549","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":[0.034509137,0.0006855475,0.9244999,0.0011233648,0.00028989272,0.00049446436,0.0005527133,0.008991453,0.028853469],"genre_scores_gemma":[0.39592054,0.0008767395,0.5664105,0.00025406343,0.000094588315,0.00033812507,0.0010088222,0.00086521555,0.03423137],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995503,0.000091070324,0.000042714993,0.00009856417,0.00019048507,0.000026886419],"domain_scores_gemma":[0.9992918,0.0003143021,0.000047303416,0.00010539254,0.00019685016,0.00004444651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006340397,0.0004597825,0.00033043267,0.0010101752,0.00047791103,0.0013259395,0.0007789559,0.0007626245,0.0131338],"category_scores_gemma":[0.0036274304,0.00017014718,0.0005890247,0.0008434147,0.00038560457,0.0021636146,0.0007463226,0.00043526798,0.0033517056],"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":[0.0005575608,0.00021440031,0.0023064346,0.0013473092,0.00007464955,0.0015809062,0.0015947814,0.03633319,0.113488644,0.11678375,0.028324747,0.6973936],"study_design_scores_gemma":[0.00010483917,0.00027993368,0.0012401546,0.00010019223,0.00006423154,0.0016372616,0.00045593028,0.7584859,0.08290822,0.062566414,0.09207853,0.000078315396],"about_ca_topic_score_codex":0.0012079121,"about_ca_topic_score_gemma":0.0008892489,"teacher_disagreement_score":0.0131338,"about_ca_system_score_codex":0.00049991685,"about_ca_system_score_gemma":0.0004553291,"threshold_uncertainty_score":0.04393691},"labels":[],"label_agreement":null},{"id":"W4366810973","doi":"10.23977/jaip.2023.060204","title":"Improved Method for Pedestrian Recognition Based on Generative Adversarial Networks","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Pedestrian; Benchmark (surveying); Artificial intelligence; Train; Image (mathematics); Class (philosophy); Process (computing); Pedestrian detection; Feature (linguistics); Machine learning; Field (mathematics); Pattern recognition (psychology); Generative grammar; Data mining; Computer vision; Engineering; Mathematics","score_opus":0.12444374033177845,"score_gpt":0.4114869856206453,"score_spread":0.2870432452888668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366810973","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":[0.007104484,0.00026030964,0.98893285,0.00010675589,0.00013307708,0.00004507969,0.00008535235,0.0020033307,0.0013287715],"genre_scores_gemma":[0.4584716,0.0006666287,0.5205955,0.0005632042,0.0002565512,0.00017964892,0.0013069414,0.00048376134,0.017476209],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909544,0.00020697984,0.000033058015,0.0002914936,0.0002520671,0.00012099803],"domain_scores_gemma":[0.9995148,0.00012319416,0.000041028954,0.00012052251,0.00016111492,0.000039439747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010319975,0.0011655954,0.0013233545,0.0010855524,0.00037294618,0.00067745533,0.0016362345,0.0009205329,0.0035873144],"category_scores_gemma":[0.0013204191,0.00055817846,0.0015675833,0.0006605243,0.00046646103,0.00093114167,0.0012610892,0.0016833296,0.001999042],"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":[0.00039139178,0.00014546163,0.0025726093,0.00010650922,0.00021074271,0.00029184154,0.00010384972,0.30595872,0.016879825,0.00826938,0.010912188,0.6541575],"study_design_scores_gemma":[0.000006007079,0.000020797728,0.000261999,0.00000466095,0.000014829062,0.00011023645,0.0000055465343,0.99374795,0.0034370886,0.0013573074,0.0010240537,0.000009484537],"about_ca_topic_score_codex":0.0048000435,"about_ca_topic_score_gemma":0.004344025,"teacher_disagreement_score":0.0048000435,"about_ca_system_score_codex":0.00063507474,"about_ca_system_score_gemma":0.0007824607,"threshold_uncertainty_score":0.01200074},"labels":[],"label_agreement":null},{"id":"W4366810975","doi":"10.23977/jaip.2023.060203","title":"Research on the application of artificial intelligence in computer recognition technology","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and Big Data Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Computer technology; Applications of artificial intelligence; Context (archaeology); Music and artificial intelligence; Marketing and artificial intelligence; Technology development; Engineering; Intelligent decision support system; Multimedia; Manufacturing engineering","score_opus":0.2478859255365832,"score_gpt":0.4459868720762145,"score_spread":0.1981009465396313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366810975","genre_codex":"review","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":[0.024556167,0.39404434,0.24838935,0.023915298,0.0035520478,0.0002709473,0.00012629418,0.00039182452,0.30475384],"genre_scores_gemma":[0.38744166,0.45995957,0.11662392,0.004030064,0.0043219584,0.00028242773,0.0002337096,0.00011303563,0.026993597],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99728715,0.0007826366,0.00021988459,0.0004339344,0.0011257967,0.00015055655],"domain_scores_gemma":[0.99680126,0.0019023139,0.00014501848,0.00023186578,0.00081461814,0.00010506722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026124935,0.0005907152,0.00060056185,0.0023392288,0.0009450539,0.0037928156,0.00121727,0.0015638906,0.0032567687],"category_scores_gemma":[0.00481689,0.0003207799,0.0006699089,0.0038900173,0.0028274795,0.006498889,0.0011578646,0.0023868175,0.0012353931],"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":[0.000042820717,0.00013326362,0.0032121297,0.0019901732,0.00007398432,0.00029724516,0.0012716863,0.0028168024,0.002622194,0.5708796,0.010240837,0.40641928],"study_design_scores_gemma":[0.00002786261,0.00022129125,0.007014888,0.0021056447,0.00012612788,0.0012016083,0.0015900714,0.023704216,0.0062224013,0.48856363,0.46909508,0.00012717363],"about_ca_topic_score_codex":0.0020306963,"about_ca_topic_score_gemma":0.0008445213,"teacher_disagreement_score":0.0037928156,"about_ca_system_score_codex":0.0016744505,"about_ca_system_score_gemma":0.0025400417,"threshold_uncertainty_score":0.013816297},"labels":[],"label_agreement":null},{"id":"W4367665557","doi":"10.23977/jaip.2023.060208","title":"The Analysis of the Application of Computer Virtualization Technology to Modern Sports Training","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Computing and Algorithms","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Trampoline; Virtualization; Computer science; Training (meteorology); Computer technology; Multimedia; Full virtualization; Basketball; Software; Human–computer interaction; Operating system; Cloud computing","score_opus":0.053388543775305566,"score_gpt":0.41134271686397905,"score_spread":0.3579541730886735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367665557","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6891531,0.016900988,0.08772083,0.0031244217,0.00044902175,0.00023114782,0.0006552009,0.00029841092,0.20146686],"genre_scores_gemma":[0.98477155,0.004063893,0.004545319,0.00012527278,0.000078898775,0.000032758115,0.00018598283,0.00002388088,0.006172456],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99913764,0.00021639175,0.000033433927,0.000104907806,0.0002987078,0.00020878477],"domain_scores_gemma":[0.9986966,0.0005289458,0.00019951872,0.00005837585,0.0003867875,0.000129747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057163433,0.00033294436,0.00016712748,0.00155441,0.0004940887,0.0016006608,0.00048598734,0.00046223242,0.0044957283],"category_scores_gemma":[0.0028737655,0.00013609693,0.0003842628,0.0019736146,0.0004566799,0.0013347167,0.0006178424,0.0004540635,0.0006430495],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038339096,0.00048768474,0.19045061,0.001512178,0.00020339129,0.0019764837,0.002317627,0.07962346,0.014335585,0.14273195,0.013431866,0.5525458],"study_design_scores_gemma":[0.000022877977,0.0008204839,0.5355368,0.0011443904,0.0002869682,0.0030430125,0.0069459756,0.24932699,0.011853447,0.051536165,0.13932678,0.00015612744],"about_ca_topic_score_codex":0.0046507022,"about_ca_topic_score_gemma":0.0035692893,"teacher_disagreement_score":0.0046507022,"about_ca_system_score_codex":0.0010291645,"about_ca_system_score_gemma":0.0008919695,"threshold_uncertainty_score":0.015039742},"labels":[],"label_agreement":null},{"id":"W4367666343","doi":"10.23977/jaip.2023.060207","title":"Discussion of Practical Application of Virtualization Technology in Computer System","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Technology and Security Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Virtualization; Computer science; Information technology; Computer technology; Productivity paradox; Engineering management; Information system; Management information systems; Productivity; Information technology management; Knowledge management; Engineering; Cloud computing; Multimedia; Operating system","score_opus":0.03214813485657425,"score_gpt":0.34594099353555763,"score_spread":0.3137928586789834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367666343","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0262799,0.09295887,0.21957757,0.06281486,0.0049989014,0.00024937233,0.00008525146,0.00023485719,0.59280044],"genre_scores_gemma":[0.7676934,0.09577712,0.05044709,0.010155678,0.005428451,0.000448732,0.00012087189,0.00010138809,0.06982725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977635,0.0011433855,0.000108284075,0.00020782642,0.00058784196,0.00018921116],"domain_scores_gemma":[0.999238,0.00042742252,0.000040295923,0.00007505581,0.00017809415,0.00004117927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001832703,0.00050747354,0.0002693579,0.0011986633,0.0020239728,0.0034401482,0.0010581737,0.0029102806,0.0060750945],"category_scores_gemma":[0.0029756578,0.0002508852,0.00074085325,0.0011210953,0.002858364,0.0053869765,0.001828848,0.0022792872,0.00086605485],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014796468,0.000019170875,0.0007282803,0.00037808382,0.000011571472,0.0011289995,0.0014209752,0.0017876806,0.0008618621,0.93546015,0.010150086,0.04803832],"study_design_scores_gemma":[0.000021876269,0.00011335528,0.0013190104,0.0009258757,0.000026047408,0.003880514,0.0025594912,0.011560287,0.0021813824,0.5118639,0.46549487,0.000053433934],"about_ca_topic_score_codex":0.0016748218,"about_ca_topic_score_gemma":0.0009902649,"teacher_disagreement_score":0.0060750945,"about_ca_system_score_codex":0.0016049325,"about_ca_system_score_gemma":0.0015060452,"threshold_uncertainty_score":0.020323157},"labels":[],"label_agreement":null},{"id":"W4376470781","doi":"10.23977/jaip.2023.060209","title":"Neural network and system for attitude and behavior detection based on pressure data","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and Big Data Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Sichuan Province Science and Technology Support Program; Department of Science and Technology of Sichuan Province","keywords":"Wearable computer; Computer science; Process (computing); Convolutional neural network; Artificial intelligence; Trajectory; Artificial neural network; Real-time computing; Point (geometry); Wearable technology; Computer vision; Change detection; Embedded system","score_opus":0.1385312769342542,"score_gpt":0.380654993746815,"score_spread":0.24212371681256079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376470781","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":[0.14370102,0.001872341,0.8366066,0.0005604355,0.00060076034,0.00021347994,0.0008582428,0.0061074905,0.009479596],"genre_scores_gemma":[0.89771223,0.001024867,0.08709076,0.00026755122,0.00013307467,0.00025905677,0.00092235964,0.000064251435,0.012525889],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967015,0.000032806296,0.00002592337,0.00010767341,0.000113896735,0.000049504764],"domain_scores_gemma":[0.9997094,0.000048417707,0.000031437812,0.000026439733,0.00016834134,0.00001582678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004128129,0.00063007465,0.00045229966,0.00065423816,0.00035533682,0.0006089836,0.0007128392,0.00060672214,0.0023465524],"category_scores_gemma":[0.001074423,0.0002612755,0.00041122606,0.0005074721,0.00019349856,0.0006490165,0.0004272639,0.00062800245,0.00087081705],"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":[0.0005618916,0.00044148063,0.017154563,0.00030777123,0.00024438353,0.0003336883,0.000176441,0.09417526,0.050619785,0.0023032224,0.008167311,0.8255142],"study_design_scores_gemma":[0.000023143366,0.00019849665,0.011953449,0.000032894986,0.000090538415,0.00017278497,0.000038911618,0.964366,0.018648038,0.0011408987,0.003295917,0.00003900089],"about_ca_topic_score_codex":0.011377214,"about_ca_topic_score_gemma":0.008759085,"teacher_disagreement_score":0.011377214,"about_ca_system_score_codex":0.00054352795,"about_ca_system_score_gemma":0.0006446958,"threshold_uncertainty_score":0.02262199},"labels":[],"label_agreement":null},{"id":"W4376853386","doi":"10.23977/jaip.2023.060303","title":"A rapid simulation development platform for autonomous driving based on CARLA and ROS","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Simulation and Modeling Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Architecture; Development (topology); Process (computing); Computer science; Systems engineering; Motion planning; Computer architecture; Simulation; Embedded system; Engineering; Artificial intelligence; Robot; Operating system","score_opus":0.09967515555111567,"score_gpt":0.3558853442290614,"score_spread":0.2562101886779457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376853386","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021913107,0.00021005808,0.9164568,0.0002963865,0.00024697697,0.00062478584,0.0006579329,0.035953395,0.023640558],"genre_scores_gemma":[0.26028436,0.00047492768,0.7166164,0.00020368368,0.000060686274,0.0012926561,0.0028976207,0.0037161612,0.014453617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934834,0.000120695775,0.000046703175,0.00009832319,0.000288566,0.000097422475],"domain_scores_gemma":[0.9990447,0.00023466456,0.00006083564,0.00019699322,0.0002976643,0.00016514084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001168793,0.00092751574,0.00046722224,0.0008271084,0.00059977785,0.0010921311,0.0017087425,0.00069844397,0.00861501],"category_scores_gemma":[0.0017828904,0.00057217834,0.0009312308,0.00023629772,0.00050521834,0.0014149514,0.0019404802,0.0015759725,0.00253203],"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":[0.0013545159,0.0007156241,0.009581694,0.001135318,0.00035705647,0.0016417206,0.0015861241,0.30060768,0.17217378,0.1149275,0.047830362,0.34808868],"study_design_scores_gemma":[0.00022171588,0.00041618815,0.0017746348,0.00012752134,0.00008257278,0.00039496532,0.00013011984,0.71924835,0.043141164,0.010054931,0.22423461,0.00017326452],"about_ca_topic_score_codex":0.0038527676,"about_ca_topic_score_gemma":0.0033192888,"teacher_disagreement_score":0.00861501,"about_ca_system_score_codex":0.0005564457,"about_ca_system_score_gemma":0.0025285713,"threshold_uncertainty_score":0.028820097},"labels":[],"label_agreement":null},{"id":"W4376853397","doi":"10.23977/jaip.2023.060304","title":"Design of an intelligent prevention and control platform for major public health emergencies based on a new generation of information technology","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pandemic; Social media; Public health; Business; Health care; Computer security; Civilization; Internet privacy; Computer science; Engineering; Medicine; Coronavirus disease 2019 (COVID-19); Political science; Economic growth; Nursing; World Wide Web; Economics","score_opus":0.37948871531401235,"score_gpt":0.5027440124431911,"score_spread":0.12325529712917876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376853397","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":[0.07935265,0.00050732365,0.89079326,0.0005873204,0.00037767706,0.0016957137,0.00020836909,0.007525492,0.018952318],"genre_scores_gemma":[0.60840416,0.0005367199,0.37487632,0.00058166956,0.00007751948,0.0012506759,0.00049956876,0.00024250649,0.013530945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995828,0.000052518655,0.000041638294,0.00010593965,0.00014225833,0.00007481902],"domain_scores_gemma":[0.9997253,0.00003966269,0.000040827617,0.00003279331,0.000108182685,0.00005319581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004828907,0.00056045695,0.00042230813,0.00045944232,0.00046969767,0.0011821594,0.001390066,0.00075064506,0.002482423],"category_scores_gemma":[0.00060690433,0.00035733174,0.00060594344,0.00019088287,0.00031181422,0.0008899443,0.00083006243,0.0006213875,0.00077844085],"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":[0.0013699154,0.0014040917,0.01818296,0.0016624994,0.0004915393,0.004422749,0.0015810862,0.12106651,0.38144264,0.044596486,0.021175751,0.40260375],"study_design_scores_gemma":[0.00028569697,0.0016010153,0.010316394,0.00015978594,0.0004974806,0.0011835318,0.0004216497,0.79104996,0.09570106,0.0071868272,0.09140985,0.00018675275],"about_ca_topic_score_codex":0.0016793356,"about_ca_topic_score_gemma":0.001525248,"teacher_disagreement_score":0.002482423,"about_ca_system_score_codex":0.000349992,"about_ca_system_score_gemma":0.0010963446,"threshold_uncertainty_score":0.008304477},"labels":[],"label_agreement":null},{"id":"W4378084634","doi":"10.23977/jaip.2023.060305","title":"Optimization of 3D WSN coverage based on equilibrium optimization algorithm","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Initialization; Particle swarm optimization; Computer science; Wireless sensor network; Meta-optimization; Swarm intelligence; Multi-swarm optimization; Optimization problem; Mathematical optimization; Metaheuristic; Node (physics); Algorithm; Engineering; Mathematics; Computer network","score_opus":0.03217502800050251,"score_gpt":0.3007218645917847,"score_spread":0.2685468365912822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378084634","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":[0.03804781,0.00035391763,0.95540786,0.0001336087,0.000024331976,0.000043768792,0.000056003453,0.00022548976,0.0057071466],"genre_scores_gemma":[0.881118,0.00057944795,0.11473259,0.00009410836,0.000021168053,0.00025760903,0.00015947224,0.00010035196,0.0029373185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997435,0.000059724847,0.000012209126,0.000059689373,0.00008207539,0.00004277878],"domain_scores_gemma":[0.9997404,0.00012624505,0.00003673194,0.000012854037,0.00006991402,0.000013842284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046800432,0.0006920673,0.0006645461,0.00069895777,0.00043450686,0.00081899273,0.00068179396,0.00067305,0.0011991903],"category_scores_gemma":[0.0010965415,0.0003340833,0.0007477183,0.00054776925,0.00044874486,0.0006522279,0.0009923597,0.00037990787,0.00017855404],"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":[0.000023685483,0.000013749322,0.00054512004,0.000024853629,0.000013794265,0.000041709496,0.000031806867,0.9818111,0.0020986209,0.004501501,0.0003075036,0.010586508],"study_design_scores_gemma":[0.000003847708,0.000009984765,0.00009679278,0.0000019278093,0.000003482957,0.000008866468,0.000007022239,0.9985134,0.00030805304,0.00084799505,0.0001963372,0.0000023700723],"about_ca_topic_score_codex":0.005609778,"about_ca_topic_score_gemma":0.0024957187,"teacher_disagreement_score":0.005609778,"about_ca_system_score_codex":0.0007327306,"about_ca_system_score_gemma":0.00067292724,"threshold_uncertainty_score":0.011154234},"labels":[],"label_agreement":null},{"id":"W4379229011","doi":"10.23977/jaip.2023.060307","title":"A Method for Eliminating Pig Face Recognition Errors Caused by Too Short Pig Growth Cycle","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Upload; Computer science; Pig breeding; Overhead (engineering); Identification (biology); Facial recognition system; Real-time computing; Artificial intelligence; Pattern recognition (psychology); Biology; Ecology; World Wide Web; Animal science","score_opus":0.2090117660412691,"score_gpt":0.45945996007643863,"score_spread":0.25044819403516955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379229011","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":[0.06866542,0.0004489734,0.9230969,0.00018696944,0.00036523232,0.00018513018,0.00013279718,0.0036144988,0.0033040738],"genre_scores_gemma":[0.46814108,0.00057873735,0.5195129,0.00029266928,0.00012432407,0.00027503213,0.00039703233,0.00025429367,0.010423872],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908745,0.00006929622,0.0000543738,0.00025712582,0.00044355905,0.0000881794],"domain_scores_gemma":[0.998809,0.00018337439,0.00011159085,0.00016863043,0.0006943664,0.00003301609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079427956,0.00079946395,0.0006267954,0.0010482175,0.00057199894,0.0004949402,0.0009996265,0.0007024184,0.0026626938],"category_scores_gemma":[0.0020621626,0.0002954522,0.0005776814,0.00041459664,0.00025440345,0.0007272117,0.0006968394,0.0006398512,0.0012442546],"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":[0.0003834158,0.0001275512,0.0033576023,0.00011554191,0.000050522223,0.00027789825,0.0001287726,0.00511253,0.096613765,0.000890888,0.0035606052,0.88938093],"study_design_scores_gemma":[0.00010035019,0.0006632496,0.022648048,0.000077897654,0.00028263894,0.0028063033,0.00022280676,0.6212267,0.33294514,0.0018682888,0.017024694,0.00013400974],"about_ca_topic_score_codex":0.0029023655,"about_ca_topic_score_gemma":0.002929895,"teacher_disagreement_score":0.0029023655,"about_ca_system_score_codex":0.00032923307,"about_ca_system_score_gemma":0.0006534552,"threshold_uncertainty_score":0.0089075565},"labels":[],"label_agreement":null},{"id":"W4379232314","doi":"10.23977/jaip.2023.060306","title":"Review of Theories Applied in Artificial Intelligence Service","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI in Service Interactions","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Connotation; Marketing and artificial intelligence; Service (business); Field (mathematics); Artificial intelligence; Context (archaeology); Computer science; Artificial intelligence, situated approach; Order (exchange); Artificial psychology; Applications of artificial intelligence; Knowledge management; Management science; Artificial Intelligence System; Engineering; Marketing; Business; Intelligent decision support system","score_opus":0.08358526271720736,"score_gpt":0.3854939000808755,"score_spread":0.30190863736366813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379232314","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010201809,0.9619586,0.0038133243,0.0052736336,0.0011516287,0.000022035842,0.00006427818,0.000023108587,0.02667326],"genre_scores_gemma":[0.012942407,0.97935474,0.0022721423,0.0015791585,0.0014159462,0.000039042356,0.00008416421,0.000012867106,0.0022996096],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99821967,0.00060477835,0.00022835571,0.00020828999,0.00062492694,0.00011391221],"domain_scores_gemma":[0.9934964,0.0051026614,0.00028777885,0.00016392829,0.00084191863,0.00010723349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002610432,0.0009489861,0.0010020159,0.008820905,0.0012078994,0.0044776867,0.0016319295,0.0020551053,0.00819441],"category_scores_gemma":[0.0065292297,0.00044125458,0.0009927152,0.012430485,0.0028819893,0.0049892287,0.0013853341,0.0025075353,0.0023557271],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057873596,0.00011551766,0.0012842078,0.017888907,0.00013557039,0.0005012229,0.0016112822,0.001393762,0.0003934232,0.32693386,0.052983664,0.5967007],"study_design_scores_gemma":[0.000010650151,0.000058706137,0.0026017486,0.015652657,0.00011398502,0.0009434427,0.00120453,0.0013822521,0.00032264434,0.106722035,0.8709417,0.000045704084],"about_ca_topic_score_codex":0.0035936402,"about_ca_topic_score_gemma":0.0028177921,"teacher_disagreement_score":0.008820905,"about_ca_system_score_codex":0.0032619052,"about_ca_system_score_gemma":0.00457447,"threshold_uncertainty_score":0.02741307},"labels":[],"label_agreement":null},{"id":"W4379792682","doi":"10.23977/jaip.2023.060310","title":"Design and Implementation of Tour Guide Robot for Red Education Base","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Mobile and Web Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robot; STM32; The Internet; Obstacle; Obstacle avoidance; Motion planning; Control (management); Computer science; Base (topology); Path (computing); Human–computer interaction; Motion (physics); Mobile robot; Artificial intelligence; Simulation; World Wide Web; Political science; Telecommunications; Operating system; Mathematics","score_opus":0.09207457828136535,"score_gpt":0.42265967752694034,"score_spread":0.330585099245575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379792682","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":[0.076534666,0.00044649426,0.8766267,0.00039234466,0.00035073052,0.00074182736,0.0002445747,0.012416874,0.032245908],"genre_scores_gemma":[0.5546912,0.00045207958,0.40159917,0.00031859684,0.000047047386,0.0008573917,0.00048883044,0.00030038666,0.041245405],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970907,0.000028011642,0.000014235847,0.00006617484,0.00012710496,0.000055383825],"domain_scores_gemma":[0.9998739,0.0000071054674,0.0000119784245,0.000012502456,0.000064154396,0.000030503557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021372263,0.0006057927,0.00038220483,0.0004823152,0.00053004175,0.00048265874,0.0010512903,0.00067406264,0.0041658427],"category_scores_gemma":[0.0001981944,0.00035431673,0.00029971404,0.00015859946,0.00024505527,0.0003550876,0.0005674601,0.00040012863,0.0015202392],"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":[0.0005605033,0.0004365229,0.0071695107,0.0012228065,0.00010902025,0.0022851257,0.0015303321,0.040607743,0.3613555,0.014192429,0.028486338,0.54204416],"study_design_scores_gemma":[0.00046790607,0.0029473396,0.017393257,0.00024675127,0.00026019983,0.0037901225,0.0011362416,0.41366595,0.23465122,0.002113238,0.32297894,0.00034881546],"about_ca_topic_score_codex":0.0032631552,"about_ca_topic_score_gemma":0.0023780162,"teacher_disagreement_score":0.0041658427,"about_ca_system_score_codex":0.000227547,"about_ca_system_score_gemma":0.000992406,"threshold_uncertainty_score":0.013936102},"labels":[],"label_agreement":null},{"id":"W4379795519","doi":"10.23977/jaip.2023.060309","title":"Applications and challenges of hybrid artificial intelligence in chip age testing: a comprehensive review","year":2023,"lang":"en","type":"review","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reliability (semiconductor); Computer science; Artificial intelligence; Adaptation (eye); Artificial neural network; Genetic algorithm; Convolutional neural network; Machine learning; Generalization; Deep learning; Stability (learning theory); Evolutionary algorithm; Reliability engineering; Engineering","score_opus":0.3448067366208292,"score_gpt":0.4146184699396857,"score_spread":0.06981173331885648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379795519","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022633708,0.9970356,0.0009752399,0.00032891927,0.00015803044,0.0000073434894,0.000011073672,0.000012242027,0.0012452536],"genre_scores_gemma":[0.0018673444,0.99636793,0.0008408092,0.00019990292,0.00021742984,0.000010518638,0.000024780702,0.0000035375815,0.00046762748],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996606,0.000061158265,0.000049217422,0.000056476933,0.0001438103,0.000028674065],"domain_scores_gemma":[0.99891794,0.0006552172,0.000079524245,0.00003090704,0.00027215696,0.000044274148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011619433,0.0009536031,0.0010858601,0.0023781452,0.00024798975,0.001243539,0.0010618385,0.0013666775,0.002294614],"category_scores_gemma":[0.001541968,0.0004096147,0.00072004023,0.002632569,0.0005365673,0.0020893079,0.00083822856,0.0013579049,0.0012300847],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003322055,0.00008113526,0.00035575227,0.012089926,0.00009430129,0.000113464994,0.00007245765,0.0011453036,0.0009705217,0.0074534365,0.012652433,0.9649379],"study_design_scores_gemma":[0.000018096362,0.00024677365,0.0016445811,0.007579979,0.00022883223,0.0011894377,0.00014809308,0.0018457049,0.001321864,0.008730437,0.97697693,0.00006929269],"about_ca_topic_score_codex":0.0010912415,"about_ca_topic_score_gemma":0.0012624322,"teacher_disagreement_score":0.0023781452,"about_ca_system_score_codex":0.00047553983,"about_ca_system_score_gemma":0.0011908868,"threshold_uncertainty_score":0.007676184},"labels":[],"label_agreement":null},{"id":"W4380088359","doi":"10.23977/jaip.2023.060401","title":"Application and Performance of Space Time Coding in MIMO System—Analysis of Alamouti Space Time Coding Scheme","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Satellite Communication Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Space–time code; MIMO; Coding (social sciences); Antenna diversity; Computer science; Diversity gain; Algorithm; Wireless; Space time; Coding gain; Electronic engineering; Telecommunications; Mathematics; Decoding methods; Engineering; Channel (broadcasting)","score_opus":0.037828036911055074,"score_gpt":0.3048797202751403,"score_spread":0.26705168336408525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380088359","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":[0.080802694,0.0067417314,0.8582181,0.00063736935,0.00018893539,0.000056696565,0.0001202248,0.0002507321,0.052983537],"genre_scores_gemma":[0.9595838,0.0037150828,0.03172882,0.000085986,0.00014614219,0.00003893063,0.000082320075,0.000044586668,0.0045741578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920875,0.00019363829,0.000024093375,0.00007354645,0.00040737528,0.000092653296],"domain_scores_gemma":[0.99880815,0.0005587477,0.00010773005,0.00010071621,0.00039946922,0.000025161267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072828395,0.0004679039,0.00035231648,0.00070534006,0.00041468302,0.0007894548,0.00038687617,0.00066985906,0.0013575074],"category_scores_gemma":[0.0022060154,0.00018417086,0.00035391806,0.0008592637,0.00088116946,0.0009486693,0.00037987749,0.00063637795,0.00031644263],"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":[0.00014906481,0.000040643845,0.002448151,0.00020417811,0.000054889002,0.00046446867,0.00042899133,0.66195595,0.035436314,0.25272793,0.0016587522,0.044430662],"study_design_scores_gemma":[0.000003837435,0.000060561528,0.0008289235,0.000031636984,0.000010341538,0.0002883908,0.000044265023,0.97531825,0.0054858695,0.016272357,0.0016293894,0.000026139287],"about_ca_topic_score_codex":0.004001589,"about_ca_topic_score_gemma":0.0016700283,"teacher_disagreement_score":0.004001589,"about_ca_system_score_codex":0.0008846721,"about_ca_system_score_gemma":0.0006567204,"threshold_uncertainty_score":0.007956564},"labels":[],"label_agreement":null},{"id":"W4380090656","doi":"10.23977/jaip.2023.060402","title":"Research on the Application of Computer Artificial Intelligence Recognition Technology","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Field (mathematics); Computer science; Authentication (law); Attendance; Identity (music); Artificial intelligence; Computer security; Data science; Political science","score_opus":0.22675325410734046,"score_gpt":0.4155275604561471,"score_spread":0.18877430634880663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380090656","genre_codex":"other","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":[0.041948512,0.2673046,0.29937872,0.015276497,0.0034887504,0.00042740355,0.00028368397,0.0008775051,0.37101436],"genre_scores_gemma":[0.47520706,0.31476784,0.14108168,0.0037305546,0.00303168,0.00032155766,0.0005983643,0.00012634018,0.06113485],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982805,0.00035996712,0.000113368806,0.00034633334,0.0007669363,0.00013283869],"domain_scores_gemma":[0.9977355,0.00089778495,0.00011763619,0.00017579636,0.0010039092,0.00006925467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001465274,0.0005264319,0.0004951226,0.002048713,0.0005855472,0.0031146249,0.0011714011,0.0012847774,0.0048444048],"category_scores_gemma":[0.0041191145,0.00024229081,0.00062079023,0.0031614285,0.0011171529,0.0045179133,0.0008078507,0.0015039109,0.0023040266],"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":[0.000055063072,0.0001475091,0.0052816025,0.0012643164,0.00006483457,0.0002477055,0.0005735028,0.0028921992,0.006488195,0.18873933,0.012742465,0.7815033],"study_design_scores_gemma":[0.000034618202,0.0004068398,0.014855855,0.0016975019,0.00019552822,0.001990493,0.0014674775,0.0460004,0.02460768,0.14637129,0.7622041,0.00016830192],"about_ca_topic_score_codex":0.0024559128,"about_ca_topic_score_gemma":0.0010196995,"teacher_disagreement_score":0.0048444048,"about_ca_system_score_codex":0.0011754681,"about_ca_system_score_gemma":0.0019012955,"threshold_uncertainty_score":0.016206145},"labels":[],"label_agreement":null},{"id":"W4380271999","doi":"10.23977/jaip.2023.060403","title":"Research based on computer artificial intelligence recognition technology and its application","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Southwest Minzu University","keywords":"Connotation; Perspective (graphical); Production (economics); Computer science; Artificial intelligence; Marketing and artificial intelligence; Quality (philosophy); Social life; Social intelligence; Sociology; Psychology; Intelligent decision support system; Social science","score_opus":0.14625648516684192,"score_gpt":0.4185817625368847,"score_spread":0.2723252773700428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380271999","genre_codex":"other","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":[0.032157578,0.26876032,0.26775384,0.020281792,0.0036481365,0.0004244954,0.0002653734,0.0006403778,0.40606803],"genre_scores_gemma":[0.43947843,0.34441563,0.14840153,0.00485506,0.0044707186,0.00051406346,0.0004770922,0.00013582158,0.057251703],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979176,0.0005674091,0.00017276323,0.00036310786,0.00084707135,0.00013202819],"domain_scores_gemma":[0.99793005,0.0009745098,0.00013181797,0.00018997527,0.0006971748,0.000076477314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016242216,0.0005316267,0.00046668257,0.003646574,0.00089097646,0.0041376427,0.0010252988,0.0016308296,0.0033104979],"category_scores_gemma":[0.0030390208,0.00023484456,0.00065260124,0.004685995,0.0025830618,0.0053628962,0.00092454895,0.0014563127,0.0013014135],"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":[0.000046532296,0.00012964824,0.005017876,0.0017796644,0.00006889677,0.00045754144,0.0016527501,0.002139968,0.004737365,0.48533693,0.014640761,0.48399207],"study_design_scores_gemma":[0.000022845943,0.00022862825,0.009210166,0.0021016505,0.00012837951,0.0023954918,0.0021709981,0.01417632,0.008768134,0.26178297,0.6988647,0.00014968048],"about_ca_topic_score_codex":0.0022349854,"about_ca_topic_score_gemma":0.0009355431,"teacher_disagreement_score":0.0041376427,"about_ca_system_score_codex":0.0017882464,"about_ca_system_score_gemma":0.0027032515,"threshold_uncertainty_score":0.0129746795},"labels":[],"label_agreement":null},{"id":"W4380681890","doi":"10.23977/jaip.2023.060404","title":"Security Impact of Federated and Transfer Learning on Network Management Systems with Fuzzy DEMATEL Approach","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Situation awareness; Computer security; Cloud computing; Field (mathematics); Fuzzy logic; Security management; Transfer of learning; Artificial intelligence; Machine learning; Knowledge management; Data science; Engineering","score_opus":0.057808064962158595,"score_gpt":0.3298492900116429,"score_spread":0.27204122504948436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380681890","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":[0.6057225,0.0003020116,0.38085565,0.0007757285,0.000045147597,0.00017286868,0.000081354534,0.00046987037,0.011574899],"genre_scores_gemma":[0.991638,0.000026122252,0.00769695,0.000018434435,0.0000028471723,0.000018312761,0.00001103897,0.0000029504833,0.0005853222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987588,0.0005510664,0.000057311012,0.0001841752,0.00028576312,0.00016291517],"domain_scores_gemma":[0.99758744,0.0013467306,0.00023919875,0.00022443873,0.00048408078,0.00011807616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025523796,0.00038600859,0.00047151968,0.0008158995,0.00090551336,0.0014306967,0.00076111295,0.00073400466,0.002083534],"category_scores_gemma":[0.004377442,0.00014562465,0.0005350622,0.00038228332,0.0009028199,0.0017763316,0.0010411434,0.0005946618,0.00013125777],"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":[0.00045128455,0.00026811595,0.0044608098,0.000071051385,0.00007100682,0.0002061567,0.00031479422,0.9126853,0.0022503058,0.018297834,0.0004206906,0.060502723],"study_design_scores_gemma":[0.0000061735946,0.00006502814,0.00042149352,0.000004326864,0.0000076154224,0.000014536,0.00004905769,0.9948715,0.0008980292,0.0035248266,0.00013240096,0.000005017613],"about_ca_topic_score_codex":0.0066265645,"about_ca_topic_score_gemma":0.0037258677,"teacher_disagreement_score":0.0066265645,"about_ca_system_score_codex":0.0028047014,"about_ca_system_score_gemma":0.0010519986,"threshold_uncertainty_score":0.020349622},"labels":[],"label_agreement":null},{"id":"W4380683194","doi":"10.23977/jaip.2023.060405","title":"Privacy Enhancement with Perturbation Method for Multidimensional Grid","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Computer Science and Engineering","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Data mining; Normalization (sociology); Big data; Database normalization; Data processing; Data grid; Algorithm; Database; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.05779818465434757,"score_gpt":0.35809212600046775,"score_spread":0.3002939413461202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380683194","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":[0.006503499,0.00018061932,0.9915386,0.00016959252,0.000049781185,0.00002862059,0.000065118955,0.00030103893,0.0011631327],"genre_scores_gemma":[0.4845843,0.00078097323,0.50814885,0.00024618852,0.00017577478,0.0002175233,0.0005090235,0.00019240369,0.0051448126],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792826,0.00086792704,0.000098441116,0.0003434345,0.000603539,0.0001584461],"domain_scores_gemma":[0.9980671,0.00074630196,0.00012308365,0.0006321313,0.00036030653,0.00007103767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015653878,0.00040339117,0.0007086006,0.0008839121,0.00082397595,0.0013190799,0.0009431375,0.00062714977,0.0027561397],"category_scores_gemma":[0.006273067,0.00021811684,0.00087681843,0.0015200641,0.001113905,0.0019985018,0.0019646904,0.0011880549,0.0007064043],"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":[0.0006910669,0.00009317124,0.0031775439,0.00024216084,0.00014857814,0.00046443983,0.0005396405,0.2917299,0.024701381,0.34798893,0.009764933,0.32045823],"study_design_scores_gemma":[0.000020286907,0.000056718778,0.00034093476,0.00001229499,0.00001427527,0.00020211298,0.00006138461,0.915859,0.005751412,0.07312712,0.0045320163,0.00002251921],"about_ca_topic_score_codex":0.0015895419,"about_ca_topic_score_gemma":0.00089718885,"teacher_disagreement_score":0.0027561397,"about_ca_system_score_codex":0.00065145013,"about_ca_system_score_gemma":0.0009535258,"threshold_uncertainty_score":0.0092202425},"labels":[],"label_agreement":null},{"id":"W4381051785","doi":"10.23977/jaip.2023.060406","title":"The significance and scope of application of the principle of legality in criminal law","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Criminal Law and Evidence","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Principle of legality; Scope (computer science); Law; China; Criminal law; Punishment (psychology); Political science; Socialist market economy; Limiting; Law and economics; Economics; Engineering","score_opus":0.10252876638725533,"score_gpt":0.43744207577878397,"score_spread":0.33491330939152864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381051785","genre_codex":"other","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":[0.08583797,0.028273622,0.103980094,0.10096311,0.0019986308,0.00036698917,0.00014848207,0.000105148676,0.678326],"genre_scores_gemma":[0.96347046,0.0061830864,0.016319007,0.0062495307,0.0015886795,0.0002608161,0.000049950726,0.00003253364,0.005845867],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97807777,0.0105969235,0.0017620556,0.0025693001,0.0055545783,0.0014394054],"domain_scores_gemma":[0.96850944,0.020777589,0.0022328373,0.0026598037,0.005037313,0.000783004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018930264,0.00033459967,0.0009305838,0.0039045692,0.005723375,0.0070703803,0.0019841073,0.0052232966,0.0020367643],"category_scores_gemma":[0.0316059,0.00045679093,0.0010507266,0.0024420086,0.04608992,0.010171174,0.0061016553,0.0071911598,0.00032100384],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060207135,0.000014496898,0.0009240257,0.0000721315,0.0000068452705,0.00010852039,0.0009867249,0.00023943151,0.00012025992,0.98793787,0.00064280967,0.0089408895],"study_design_scores_gemma":[0.000015796288,0.000046143818,0.003043537,0.0002925198,0.000019107605,0.0003271558,0.001002373,0.00090231985,0.00026677726,0.9620365,0.03201409,0.000033687786],"about_ca_topic_score_codex":0.0045847455,"about_ca_topic_score_gemma":0.0029548868,"teacher_disagreement_score":0.018930264,"about_ca_system_score_codex":0.0042033633,"about_ca_system_score_gemma":0.010636266,"threshold_uncertainty_score":0.10011405},"labels":[],"label_agreement":null},{"id":"W4382896137","doi":"10.23977/jaip.2023.060407","title":"An Analysis of the Requirements for Smart Guiding Services in Museums Using the Kano-AHP Method","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Museums and Cultural Heritage","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kano model; Visitor pattern; Analytic hierarchy process; Ranking (information retrieval); Service (business); Categorization; Computer science; Field (mathematics); Knowledge management; Engineering management; Process management; Engineering; Operations research; Service quality; Business; Artificial intelligence; Marketing","score_opus":0.29173804433373396,"score_gpt":0.4354370815911636,"score_spread":0.14369903725742966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382896137","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":[0.5281331,0.00032339172,0.4529488,0.00065225206,0.000037086716,0.0017715404,0.0011175027,0.00018836811,0.014828047],"genre_scores_gemma":[0.7027466,0.0002532527,0.2939512,0.000052928244,0.0000070783917,0.0010226916,0.0007030623,0.00003559064,0.0012276254],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9955741,0.0022310135,0.00035461047,0.00022095653,0.0013119732,0.0003072655],"domain_scores_gemma":[0.993978,0.004387541,0.0003693493,0.00019159057,0.00094257254,0.00013094931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042959577,0.00065207534,0.0005322364,0.002790393,0.0013235115,0.0017236335,0.00097268063,0.00063266995,0.0020373128],"category_scores_gemma":[0.009412323,0.0004895101,0.0012157712,0.00380296,0.00087911315,0.001257201,0.0012818133,0.0007352862,0.00023409475],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00120298,0.0010510796,0.048245713,0.0077085164,0.00053270336,0.006915165,0.08129817,0.34275377,0.070345335,0.07191201,0.007917674,0.3601169],"study_design_scores_gemma":[0.00015514877,0.0010846194,0.041143533,0.0008373756,0.00031638876,0.0016897611,0.156772,0.70567554,0.01964423,0.03998637,0.032373294,0.00032174253],"about_ca_topic_score_codex":0.007158701,"about_ca_topic_score_gemma":0.009935166,"teacher_disagreement_score":0.007158701,"about_ca_system_score_codex":0.0016302544,"about_ca_system_score_gemma":0.0028861293,"threshold_uncertainty_score":0.022719502},"labels":[],"label_agreement":null},{"id":"W4384571004","doi":"10.23977/jaip.2023.060410","title":"Discussion on Key Technologies of Computer Artificial Intelligence Recognition","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Scope (computer science); Key (lock); Process (computing); Computer technology; Artificial intelligence; Marketing and artificial intelligence; Multimedia; Intelligent decision support system; Computer security","score_opus":0.09289606047032123,"score_gpt":0.3560808601860882,"score_spread":0.26318479971576697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384571004","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007005851,0.4202778,0.07961562,0.074231006,0.01475826,0.0003036845,0.00030898637,0.00025475142,0.40324408],"genre_scores_gemma":[0.12963352,0.63381517,0.034573745,0.027451681,0.02314846,0.00053982483,0.0006214034,0.00014984177,0.15006632],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988256,0.00025810496,0.00013398264,0.00021629035,0.0004506821,0.00011533516],"domain_scores_gemma":[0.9988392,0.0005192512,0.000086816064,0.00007969456,0.00041008712,0.0000650378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014105929,0.0005618725,0.0003085801,0.0027621856,0.0013532393,0.003337537,0.0010405866,0.0023946716,0.010778139],"category_scores_gemma":[0.0020768088,0.00022927925,0.0006228514,0.0031838606,0.0018156222,0.0062610283,0.00096403953,0.0024617773,0.003995514],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004255061,0.00006493387,0.0015770011,0.0016507132,0.000020264239,0.00072523055,0.0009133303,0.0010651135,0.0027667887,0.6592318,0.0747229,0.25721946],"study_design_scores_gemma":[0.0000062471145,0.000035730227,0.0011852663,0.0006387471,0.00001293429,0.0011636149,0.00037141942,0.0011138465,0.001291271,0.091913454,0.90223825,0.000029292694],"about_ca_topic_score_codex":0.001408279,"about_ca_topic_score_gemma":0.0007407206,"teacher_disagreement_score":0.010778139,"about_ca_system_score_codex":0.0016640787,"about_ca_system_score_gemma":0.002022113,"threshold_uncertainty_score":0.03605646},"labels":[],"label_agreement":null},{"id":"W4384571074","doi":"10.23977/jaip.2023.060409","title":"Study on Inversion of Damage Incentives of High Pile Wharf in Inland River Based on SEResNet","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chongqing University of Science and Technology; Chongqing Municipal Education Commission; Chongqing University","keywords":"Pile; Wharf; Parameterized complexity; Finite element method; Inversion (geology); Structural engineering; Geotechnical engineering; Engineering; Computer science; Geology; Algorithm; Seismology","score_opus":0.07221632369129635,"score_gpt":0.3830380414101088,"score_spread":0.3108217177188124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384571074","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":[0.58505183,0.0002939699,0.40502548,0.00045468006,0.000078251025,0.000048828388,0.00027404106,0.00077785295,0.007995134],"genre_scores_gemma":[0.9831376,0.000084836975,0.014627786,0.00004557404,0.000013620948,0.000025122139,0.00021730197,0.000031973243,0.0018161603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998772,0.000018093806,0.0000063281914,0.000028407221,0.000039334194,0.000030614407],"domain_scores_gemma":[0.9997917,0.000067853965,0.000024689503,0.000012906399,0.000084032064,0.000018936113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000401122,0.0005127296,0.00043187354,0.0005012519,0.00024465815,0.00045839653,0.00086192257,0.00063302496,0.0014902636],"category_scores_gemma":[0.0008960155,0.00027986363,0.00047882827,0.00034713728,0.00033317396,0.0007611978,0.00037674024,0.0004937558,0.00018958775],"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":[0.00005161398,0.000045562734,0.0060376073,0.000034576984,0.000026489308,0.00010319493,0.000040333864,0.9693973,0.0023899067,0.0011367124,0.0004393263,0.020297358],"study_design_scores_gemma":[0.0000014330683,0.000006862197,0.00054777344,0.0000015414777,0.000002077611,0.000006880985,0.000009188994,0.9987702,0.00037821228,0.0002168612,0.000057038273,0.0000019367237],"about_ca_topic_score_codex":0.023184778,"about_ca_topic_score_gemma":0.01711065,"teacher_disagreement_score":0.023184778,"about_ca_system_score_codex":0.00054117455,"about_ca_system_score_gemma":0.000955031,"threshold_uncertainty_score":0.046099663},"labels":[],"label_agreement":null},{"id":"W4384920015","doi":"10.23977/jaip.2023.060503","title":"Design study of fire risk early warning robot","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Warning system; Manual fire alarm activation; ALARM; Fire detection; Firefighting; Risk analysis (engineering); False alarm; Flexibility (engineering); Fire protection; Computer science; Engineering; Computer security; Forensic engineering; Artificial intelligence; Architectural engineering; Business; Civil engineering; Telecommunications; Geography; Cartography","score_opus":0.07059250718815392,"score_gpt":0.31282653661124893,"score_spread":0.24223402942309502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384920015","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":[0.03198974,0.00050613296,0.95255435,0.0003012793,0.00015879938,0.0005595056,0.000049820457,0.0010332726,0.012847053],"genre_scores_gemma":[0.75821316,0.00068764016,0.22073887,0.00017895432,0.0000660303,0.0014398725,0.00013585541,0.000076138385,0.018463517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992976,0.00014408723,0.00004067812,0.00017460814,0.0002637525,0.000079260564],"domain_scores_gemma":[0.999519,0.0000961966,0.00007127574,0.00004537073,0.0002127231,0.000055520297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008266388,0.00078203203,0.00065229164,0.0004898354,0.00083256286,0.000882709,0.0016219884,0.0011330858,0.0051342864],"category_scores_gemma":[0.0009308144,0.000393185,0.00054338365,0.0001627195,0.0005045944,0.0008259923,0.00075399276,0.00046475098,0.0009746031],"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":[0.0010141834,0.00052688125,0.0081883045,0.0028809214,0.00020765256,0.0024303591,0.001759061,0.3946987,0.23575984,0.04372212,0.0049175876,0.30389446],"study_design_scores_gemma":[0.00023955485,0.0032784871,0.00358111,0.000141241,0.0001994743,0.0011896597,0.00033118494,0.91842467,0.036528267,0.0054455893,0.030525947,0.00011487458],"about_ca_topic_score_codex":0.0022753077,"about_ca_topic_score_gemma":0.0010836249,"teacher_disagreement_score":0.0051342864,"about_ca_system_score_codex":0.00040083495,"about_ca_system_score_gemma":0.0011986244,"threshold_uncertainty_score":0.017175913},"labels":[],"label_agreement":null},{"id":"W4385384759","doi":"10.23977/jaip.2023.060504","title":"Vehicle Driving Intent Recognition Based on Enhanced Bidirectional Long Short-Term Memory Network","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","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":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Context (archaeology); Hyperparameter; Trajectory; Feature (linguistics); Artificial intelligence; Machine learning; Term (time); Long short term memory; Artificial neural network; Recurrent neural network","score_opus":0.04173288790346016,"score_gpt":0.2954710916081289,"score_spread":0.25373820370466876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385384759","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":[0.33771086,0.0012360344,0.6461195,0.0004949622,0.00030687623,0.0001098736,0.0010472605,0.0040470385,0.008927546],"genre_scores_gemma":[0.9575828,0.00027253688,0.036007445,0.00015025087,0.000030964235,0.00009358892,0.0010155266,0.00003986548,0.004807074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999895,0.000011543205,0.000006063555,0.00003447085,0.000024912715,0.00002803893],"domain_scores_gemma":[0.9998584,0.000033372355,0.000017893522,0.000014269865,0.000066731816,0.0000093886565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022693409,0.0006201463,0.00034485862,0.00037858463,0.00017174831,0.00035534054,0.00073873193,0.00035108582,0.0011909067],"category_scores_gemma":[0.00052375,0.00017825192,0.00038936254,0.00032596712,0.00015145008,0.0006351386,0.0005593096,0.00075297424,0.00067038625],"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":[0.0005262382,0.0005162095,0.010211921,0.00016087118,0.0001461248,0.0003032498,0.00019880933,0.19120753,0.05202257,0.002353852,0.006463212,0.73588943],"study_design_scores_gemma":[0.0000070148817,0.00007938959,0.0017469967,0.000009244855,0.000024422916,0.00004241778,0.00002591889,0.9898691,0.0066355327,0.00095346,0.00059586647,0.000010698876],"about_ca_topic_score_codex":0.0073552346,"about_ca_topic_score_gemma":0.010693919,"teacher_disagreement_score":0.0073552346,"about_ca_system_score_codex":0.00031497533,"about_ca_system_score_gemma":0.00046321558,"threshold_uncertainty_score":0.014624834},"labels":[],"label_agreement":null},{"id":"W4385762426","doi":"10.23977/jaip.2023.060506","title":"Optimization and Evaluation of Spoken English CAF Based on Artificial Intelligence and Corpus","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Hidden Markov model; Artificial intelligence; Convolutional neural network; Pronunciation; Speech recognition; Fluency; Natural language processing; Linguistics","score_opus":0.1445020882038076,"score_gpt":0.40355548233989086,"score_spread":0.25905339413608325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385762426","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":[0.71534663,0.0016146977,0.271693,0.00023353439,0.00011841202,0.00020141296,0.00034888965,0.002786018,0.0076573784],"genre_scores_gemma":[0.9205224,0.00035756332,0.07583603,0.000059658938,0.00002069614,0.00014326595,0.00083404937,0.00023394336,0.0019923095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987208,0.00038189586,0.00012083805,0.00033452903,0.00034437972,0.000097438584],"domain_scores_gemma":[0.99737203,0.0012497676,0.0002222942,0.00024560266,0.0008084327,0.000101882266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023353882,0.0012974581,0.0010743288,0.0012926346,0.0003916292,0.0014100275,0.0006835226,0.00059607107,0.001439143],"category_scores_gemma":[0.0067051463,0.00026670436,0.0006913985,0.0007271747,0.0005563309,0.0017985292,0.00079281145,0.0005388915,0.00035745592],"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":[0.0011611383,0.0004871049,0.018101318,0.0008387868,0.00037595132,0.0004970875,0.00052241236,0.40241718,0.08677872,0.002218048,0.002495213,0.48410705],"study_design_scores_gemma":[0.00003115273,0.0004469785,0.008866771,0.000028652887,0.00011931987,0.00010675006,0.0003144065,0.9602264,0.028252402,0.0005259316,0.0010396634,0.000041515686],"about_ca_topic_score_codex":0.008895555,"about_ca_topic_score_gemma":0.006874168,"teacher_disagreement_score":0.008895555,"about_ca_system_score_codex":0.0009223797,"about_ca_system_score_gemma":0.00075466774,"threshold_uncertainty_score":0.01768756},"labels":[],"label_agreement":null},{"id":"W4385762429","doi":"10.23977/jaip.2023.060507","title":"The Aesthetic Ethics of Midjourney under the Development of Artificial Intelligence","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Originality; Field (mathematics); Engineering ethics; Sociology; Artificial intelligence; Computer science; Engineering; Social science; Mathematics; Qualitative research","score_opus":0.19373606308881466,"score_gpt":0.4185542016120218,"score_spread":0.22481813852320712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385762429","genre_codex":"other","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":[0.09058987,0.004248462,0.09776223,0.058965765,0.001195796,0.00009797548,0.000037670172,0.00020627391,0.74689597],"genre_scores_gemma":[0.93663377,0.000722348,0.01563612,0.0039440067,0.00023186636,0.00009274464,0.0000151037175,0.00016626556,0.042557776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9881842,0.0075684446,0.0003297838,0.0007154969,0.0026107037,0.0005912489],"domain_scores_gemma":[0.99453694,0.0023537609,0.00042702898,0.0010470486,0.001176174,0.00045890707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009540898,0.000312217,0.0002491109,0.0008581357,0.005782938,0.011453994,0.00078631716,0.0028953846,0.0026508751],"category_scores_gemma":[0.012047857,0.00030040691,0.0003382526,0.00064605277,0.035584122,0.0073044025,0.0056335554,0.0041641616,0.00049438595],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014939634,0.000008841047,0.0002601872,0.000021015925,0.0000017388096,0.00007416353,0.013511099,0.00013395667,0.00028629357,0.97309476,0.0028300106,0.009763025],"study_design_scores_gemma":[0.00001215486,0.000032934415,0.0008141296,0.00021121679,0.000008271183,0.0004936557,0.012720935,0.0012629356,0.0010310519,0.68562585,0.2977497,0.000037192167],"about_ca_topic_score_codex":0.00164265,"about_ca_topic_score_gemma":0.0016847526,"teacher_disagreement_score":0.011453994,"about_ca_system_score_codex":0.003416857,"about_ca_system_score_gemma":0.0028402319,"threshold_uncertainty_score":0.050457716},"labels":[],"label_agreement":null},{"id":"W4385762522","doi":"10.23977/jaip.2023.060505","title":"Exploration on User Acceptance Behavior of Hotel Artificial Intelligence Technology Based on Experience Quality","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Loyalty; Service quality; Quality (philosophy); Marketing; Customer satisfaction; Business; Order (exchange); Service (business); Work (physics); Perspective (graphical); Knowledge management; Computer science; Engineering; Artificial intelligence","score_opus":0.18813940621950478,"score_gpt":0.4621832391654202,"score_spread":0.2740438329459154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385762522","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":[0.99826163,0.000029295168,0.00055004586,0.000029115077,0.0000017681456,0.000016905755,0.00002034769,0.000006192511,0.0010846944],"genre_scores_gemma":[0.99936503,0.000032767955,0.00021952661,0.000015088785,0.000001706863,0.000014458556,0.000031636337,0.0000023632108,0.00031753266],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99878067,0.0005813799,0.00008034088,0.00009777991,0.00032722528,0.00013266677],"domain_scores_gemma":[0.9937218,0.0036840918,0.000628008,0.0002141411,0.0014587021,0.00029329525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013395126,0.00022537657,0.00021484614,0.0006649253,0.0002506889,0.00083955156,0.00016932387,0.00027871225,0.0016100722],"category_scores_gemma":[0.0064211446,0.00012794901,0.00043495814,0.0004695128,0.00024405424,0.0006051326,0.0003248828,0.00038755703,0.0002255348],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045354033,0.00068165973,0.9323434,0.00016526818,0.00007604196,0.00022076299,0.011688902,0.00040314253,0.008301881,0.00044110997,0.00029974355,0.04492442],"study_design_scores_gemma":[0.000010043235,0.0013609513,0.97680193,0.00003526094,0.00009085685,0.00032151243,0.011187783,0.0061414093,0.0028939154,0.0001862838,0.00093250244,0.000037506124],"about_ca_topic_score_codex":0.001662799,"about_ca_topic_score_gemma":0.0014055389,"teacher_disagreement_score":0.001662799,"about_ca_system_score_codex":0.0002721951,"about_ca_system_score_gemma":0.00023253934,"threshold_uncertainty_score":0.0070840716},"labels":[],"label_agreement":null},{"id":"W4386802532","doi":"10.23977/jaip.2023.060510","title":"Deep learning based face recognition algorithm optimisation and application exploration","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Biometrics; Facial recognition system; Face (sociological concept); Machine learning; Identification (biology); Identity (music); Focus (optics); Authentication (law); Face Recognition Grand Challenge; Algorithm; Pattern recognition (psychology); Face detection; Computer security","score_opus":0.07201738481903794,"score_gpt":0.33320094135479783,"score_spread":0.26118355653575986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802532","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":[0.025809472,0.0028785213,0.9600739,0.0016256948,0.00006457338,0.00006245699,0.000058891874,0.00037603895,0.009050417],"genre_scores_gemma":[0.5195909,0.0038299512,0.4670287,0.0005913122,0.00013682908,0.00027063827,0.00024030583,0.00021484724,0.008096586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943787,0.00021010947,0.000027399088,0.00009077967,0.00015824633,0.00007560846],"domain_scores_gemma":[0.9990345,0.00062687974,0.000038174687,0.000074310425,0.00019261189,0.000033537868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018942838,0.00071300124,0.0006673711,0.00057805184,0.00023439512,0.0012152884,0.0010028854,0.001221635,0.0027367899],"category_scores_gemma":[0.0049188845,0.00037021452,0.0005879332,0.0005160285,0.0006371411,0.0010243498,0.0015336695,0.0014858772,0.0006410775],"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":[0.00010279752,0.00009788411,0.00094514614,0.00030947488,0.000050815717,0.00011239542,0.00012093301,0.73296034,0.0051153013,0.035652135,0.0032230343,0.22130975],"study_design_scores_gemma":[0.000005830353,0.000028226608,0.00008038919,0.00002242411,0.000005886665,0.00002460769,0.000016881211,0.9854058,0.0011685163,0.011378742,0.0018581097,0.0000045558827],"about_ca_topic_score_codex":0.0018505317,"about_ca_topic_score_gemma":0.002059311,"teacher_disagreement_score":0.0027367899,"about_ca_system_score_codex":0.0007338449,"about_ca_system_score_gemma":0.0014542048,"threshold_uncertainty_score":0.010018051},"labels":[],"label_agreement":null},{"id":"W4386802537","doi":"10.23977/jaip.2023.060501","title":"Control System Design of Remote-controlled Floating Garbage Cleaning Robot Suitable for Small Water Area","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Arduino; Remote control; Garbage; Microprocessor; Embedded system; Wireless; Computer science; Robot; Computer hardware; Robotic arm; Control (management); Real-time computing; Engineering; Operating system; Artificial intelligence","score_opus":0.10922860709562975,"score_gpt":0.32011374786507324,"score_spread":0.21088514076944348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802537","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":[0.06623664,0.0009647827,0.89285165,0.0004498287,0.0004435923,0.000575606,0.00017705337,0.0074956287,0.030805377],"genre_scores_gemma":[0.88586247,0.00051064324,0.086713076,0.00026248084,0.00012320558,0.00073085766,0.00028750513,0.00015289783,0.025356878],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971944,0.000020583362,0.000013753227,0.00012035466,0.00009619406,0.000029764878],"domain_scores_gemma":[0.99982953,0.000018252596,0.000019907537,0.000013419235,0.000104816594,0.000014057473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022067242,0.000591039,0.00047529442,0.00036016127,0.00064328086,0.00059634045,0.0010473422,0.0005200486,0.0064199544],"category_scores_gemma":[0.00025128954,0.00024122154,0.00033070432,0.00014613752,0.00025334905,0.00042013582,0.00036016147,0.00031027634,0.0010748595],"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":[0.0008843483,0.00023249432,0.0033529173,0.0014867899,0.00015457558,0.0012305039,0.0010838177,0.112145714,0.43447164,0.00879906,0.013890778,0.42226747],"study_design_scores_gemma":[0.0006673768,0.0032108885,0.010482068,0.00018574046,0.00033378167,0.0019873541,0.00034467276,0.7278837,0.17416658,0.0035917265,0.076945096,0.00020103354],"about_ca_topic_score_codex":0.0030390024,"about_ca_topic_score_gemma":0.0024760799,"teacher_disagreement_score":0.0064199544,"about_ca_system_score_codex":0.00031748516,"about_ca_system_score_gemma":0.0006172127,"threshold_uncertainty_score":0.021476924},"labels":[],"label_agreement":null},{"id":"W4386802583","doi":"10.23977/jaip.2023.060509","title":"Intelligent Following Car Based on Dual Detection Positioning Using Ultrasonic and Camera","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ultrasonic sensor; Computer vision; Computer science; Artificial intelligence; Kalman filter; Positioning system; Object detection; Position (finance); Control unit; Tracking (education); Object (grammar); Video tracking; Real-time computing; Engineering; Pattern recognition (psychology); Acoustics","score_opus":0.0493612012024063,"score_gpt":0.3458651695253216,"score_spread":0.2965039683229153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802583","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":[0.18883964,0.0010140546,0.7756842,0.00033233353,0.00037830527,0.0003124421,0.0001907121,0.012102802,0.021145474],"genre_scores_gemma":[0.8793234,0.00034379226,0.10340473,0.00021707512,0.00005528233,0.00014116999,0.00023693821,0.0000624985,0.01621518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995732,0.000020587673,0.000018627106,0.00014905784,0.00016944969,0.00006918639],"domain_scores_gemma":[0.99977106,0.000020574988,0.000020673804,0.000033125707,0.00012070124,0.000033850964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002056222,0.00062847516,0.0008560976,0.00056842715,0.00066611485,0.00063238427,0.0015231937,0.0008203728,0.0029558532],"category_scores_gemma":[0.00033902438,0.00040542727,0.00036496826,0.0003096171,0.0002630138,0.00069841073,0.00070561096,0.0003050818,0.0012038604],"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":[0.0010204277,0.0003161285,0.007533715,0.00049279595,0.000094459174,0.0009255539,0.00049058924,0.02224494,0.38033804,0.0043661697,0.0104371775,0.5717401],"study_design_scores_gemma":[0.00022290384,0.0019986278,0.012303962,0.00005707483,0.0003466863,0.0025809193,0.0002698486,0.744402,0.20335592,0.0013507075,0.03286128,0.00025007152],"about_ca_topic_score_codex":0.005586303,"about_ca_topic_score_gemma":0.005525116,"teacher_disagreement_score":0.005586303,"about_ca_system_score_codex":0.00030767443,"about_ca_system_score_gemma":0.00088459835,"threshold_uncertainty_score":0.011107564},"labels":[],"label_agreement":null},{"id":"W4386802598","doi":"10.23977/jaip.2023.060508","title":"Abnormal Event Detection and Localization Based on Crowd Analysis in Video Surveillance","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Frame (networking); Event (particle physics); Energy (signal processing); Computer vision; Artificial intelligence; Block (permutation group theory); Point (geometry); Key frame; Tracking (education); Feature (linguistics); Identification (biology); Pattern recognition (psychology); Index (typography); Key (lock); Computer security; Mathematics; Statistics","score_opus":0.026097760587632735,"score_gpt":0.3279151023470592,"score_spread":0.3018173417594264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802598","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":[0.6043874,0.0017247614,0.38732347,0.00019698952,0.00014565782,0.00022305419,0.00080372486,0.0019740288,0.0032208716],"genre_scores_gemma":[0.9527395,0.00048399923,0.044948902,0.00002508687,0.000051347142,0.00004775015,0.0009269356,0.00002980009,0.00074672437],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928206,0.00009523218,0.000057774516,0.0002087355,0.00023888433,0.00011742301],"domain_scores_gemma":[0.9992773,0.00015456132,0.00016512856,0.000071107934,0.0002532069,0.000078708705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006807613,0.000662681,0.00075965934,0.0039228234,0.00038465695,0.00071168906,0.00053869735,0.00037110213,0.0002608454],"category_scores_gemma":[0.0015555138,0.00018199053,0.00047568133,0.0012814038,0.00034498257,0.00086816837,0.00081835454,0.00039514177,0.00017856307],"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":[0.0012981104,0.00035561505,0.09245498,0.00044917446,0.00029895286,0.0016283877,0.0010600584,0.07655993,0.109797,0.0035262043,0.00766044,0.7049112],"study_design_scores_gemma":[0.000021341551,0.0002992907,0.057054162,0.00005957948,0.00011398677,0.00093998935,0.0007186773,0.8953294,0.037712086,0.003227484,0.004461367,0.00006275838],"about_ca_topic_score_codex":0.005568792,"about_ca_topic_score_gemma":0.0049111233,"teacher_disagreement_score":0.005568792,"about_ca_system_score_codex":0.00044274676,"about_ca_system_score_gemma":0.00040408337,"threshold_uncertainty_score":0.011072755},"labels":[],"label_agreement":null},{"id":"W4386802610","doi":"10.23977/jaip.2023.060502","title":"Network Information Platform Construction Based on Computer Data Mining and Processing","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Data processing; Data mining; Information processing; Database","score_opus":0.10655052878903923,"score_gpt":0.32550658772893676,"score_spread":0.21895605893989753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802610","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":[0.06658388,0.00067868386,0.9089632,0.00087078434,0.00020626959,0.00060227053,0.00041561935,0.0017799017,0.019899303],"genre_scores_gemma":[0.5174327,0.0013435209,0.4697847,0.00017619699,0.000114445225,0.00038091917,0.0017254754,0.00016318417,0.008878882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882287,0.00013509688,0.00009965524,0.00023987626,0.0005673335,0.00013509612],"domain_scores_gemma":[0.999203,0.00015966696,0.0000922868,0.00018431318,0.00028410318,0.000076652024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000929377,0.0005947971,0.000494459,0.0027621936,0.0012679604,0.002821828,0.0010778537,0.00051263085,0.0021818578],"category_scores_gemma":[0.0021869652,0.00035220283,0.0007821259,0.002681511,0.00083266164,0.0039024297,0.0022542323,0.000792757,0.00060943654],"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":[0.00027796143,0.00023557388,0.016389083,0.0005207842,0.0000838975,0.0011445191,0.0009867256,0.07370888,0.033722922,0.1571932,0.013723513,0.70201296],"study_design_scores_gemma":[0.000072822484,0.00026356918,0.008462123,0.00019764644,0.00015279707,0.0012172644,0.0009562558,0.71364963,0.060845062,0.11888684,0.09509072,0.0002053172],"about_ca_topic_score_codex":0.0048787664,"about_ca_topic_score_gemma":0.0030107126,"teacher_disagreement_score":0.0048787664,"about_ca_system_score_codex":0.0009646735,"about_ca_system_score_gemma":0.0037251103,"threshold_uncertainty_score":0.009700716},"labels":[],"label_agreement":null},{"id":"W4386802903","doi":"10.23977/jaip.2023.060606","title":"Research on the Dilemma and Paths of Developing Smart Sports Parks in Cold Areas from the Perspective of Big Data","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Environmental Engineering and Cultural Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"People's Government of Jilin Province","keywords":"Dilemma; Perspective (graphical); Plan (archaeology); Big data; Modernization theory; China; Business; Principal (computer security); Marketing; Computer science; Knowledge management; Architectural engineering; Engineering management; Engineering; Computer security; Political science; Economics; Geography; Economic growth; Artificial intelligence","score_opus":0.2306152154165632,"score_gpt":0.38067163426078954,"score_spread":0.15005641884422632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802903","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":[0.38665426,0.021710662,0.17066379,0.1955222,0.0010374887,0.0009955313,0.0009123764,0.000188514,0.2223152],"genre_scores_gemma":[0.94672114,0.009471789,0.036509797,0.002435249,0.00012871683,0.0003587056,0.00016704746,0.000025288287,0.004182317],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99432373,0.0031445154,0.0002830312,0.0005902299,0.0011015962,0.0005568309],"domain_scores_gemma":[0.9865043,0.008089993,0.0015012161,0.0006336068,0.0021853796,0.0010855309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0089388415,0.00046522083,0.0004223132,0.004673941,0.0051618437,0.01235531,0.0015698794,0.0018225941,0.0026270896],"category_scores_gemma":[0.011104588,0.00043711936,0.000588565,0.0076494925,0.008063914,0.022375213,0.00344709,0.0026601928,0.00025115567],"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":[0.000032246975,0.000095688796,0.019691937,0.0013067239,0.00006941798,0.00068556424,0.018211562,0.0026021556,0.0004884908,0.8910745,0.0066532013,0.059088573],"study_design_scores_gemma":[0.00003565829,0.00009132778,0.015416741,0.0019557958,0.000112786285,0.00042951707,0.21699077,0.0152071165,0.001420107,0.63223773,0.1160106,0.00009179854],"about_ca_topic_score_codex":0.008018982,"about_ca_topic_score_gemma":0.010255056,"teacher_disagreement_score":0.01235531,"about_ca_system_score_codex":0.006835431,"about_ca_system_score_gemma":0.018218294,"threshold_uncertainty_score":0.04959476},"labels":[],"label_agreement":null},{"id":"W4386802927","doi":"10.23977/jaip.2023.060605","title":"DeeTune: Design and Application of an eBPF-based Network Framework for Baidu","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Baidu","keywords":"Microservices; Cloud computing; Computer science; Function (biology); Quality (philosophy); Service (business); Network topology; Session (web analytics); Computer security; Computer network; World Wide Web; Business; Operating system","score_opus":0.056177275158378226,"score_gpt":0.3649095761066239,"score_spread":0.30873230094824566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802927","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026220892,0.0002603416,0.9468545,0.000302844,0.00008698971,0.00062473136,0.00018394203,0.005620462,0.01984538],"genre_scores_gemma":[0.30826202,0.00046868945,0.6753546,0.00017774971,0.00003142949,0.0006319206,0.0007523763,0.0006645269,0.013656708],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99954945,0.0000824141,0.000028956738,0.00009357848,0.00017034376,0.00007513378],"domain_scores_gemma":[0.99967766,0.00007328717,0.000030255042,0.000040864703,0.00011892846,0.000058984853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011961882,0.0004267223,0.00026655538,0.0006924018,0.00060262973,0.001400701,0.0017838717,0.0005970818,0.0033397628],"category_scores_gemma":[0.0015076488,0.00028262733,0.00029688067,0.00033157817,0.00054077234,0.001672817,0.0013016658,0.00081653387,0.0005899733],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004747564,0.00047793266,0.006256296,0.00072454073,0.00007144199,0.001789649,0.0024208177,0.23517707,0.051276322,0.26279694,0.018923871,0.41961032],"study_design_scores_gemma":[0.000060793882,0.00022688576,0.0012410488,0.00012501795,0.00003636135,0.0006544532,0.00030223143,0.81564385,0.017186848,0.0174267,0.14702395,0.00007189687],"about_ca_topic_score_codex":0.009560785,"about_ca_topic_score_gemma":0.011368396,"teacher_disagreement_score":0.009560785,"about_ca_system_score_codex":0.0014011586,"about_ca_system_score_gemma":0.0012316228,"threshold_uncertainty_score":0.019010305},"labels":[],"label_agreement":null},{"id":"W4386802946","doi":"10.23977/jaip.2023.060602","title":"Research on the Application of Artificial Intelligence Empowered Education Management","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and Big Data Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Empowerment; Construct (python library); Knowledge management; Engineering ethics; Artificial intelligence; Field (mathematics); Engineering; Quality (philosophy); Engineering management; Sociology; Computer science; Political science","score_opus":0.21679455194414854,"score_gpt":0.4696133214355091,"score_spread":0.25281876949136056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802946","genre_codex":"other","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":[0.088649645,0.028447948,0.112769805,0.04655255,0.0008819697,0.000284552,0.000083530256,0.00021569306,0.72211426],"genre_scores_gemma":[0.9313521,0.021590004,0.02977608,0.0023210659,0.00037816787,0.0001499939,0.000055518016,0.000027886676,0.0143492],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961437,0.0018350607,0.00020279721,0.00045046967,0.0010439941,0.00032390465],"domain_scores_gemma":[0.9936593,0.0042318907,0.0005685493,0.000606467,0.00061946595,0.000314217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036784338,0.00037456487,0.0002659388,0.0021672556,0.0014809368,0.007121364,0.0010461136,0.0017388246,0.004334728],"category_scores_gemma":[0.00699389,0.00021870554,0.00046280646,0.0030624967,0.0053631742,0.007827253,0.0025542355,0.001949185,0.0006043484],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012525314,0.0001268578,0.0025674258,0.00041085985,0.000026253345,0.00015099632,0.0035218108,0.0017573212,0.00045045654,0.89460164,0.0020579179,0.09431588],"study_design_scores_gemma":[0.000027645738,0.00012798299,0.006359517,0.0014816951,0.00004693567,0.0003467405,0.00741547,0.01337687,0.0024257589,0.6473937,0.32094282,0.0000548587],"about_ca_topic_score_codex":0.0018984824,"about_ca_topic_score_gemma":0.0014770207,"teacher_disagreement_score":0.007121364,"about_ca_system_score_codex":0.0030844721,"about_ca_system_score_gemma":0.0046686,"threshold_uncertainty_score":0.022379458},"labels":[],"label_agreement":null},{"id":"W4386802952","doi":"10.23977/jaip.2023.060604","title":"Research on the Application of Artificial Intelligence Technology in the Field of Network Security","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","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":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer security; Security information and event management; Network security; Security service; Computer science; Cloud computing security; Network Access Control; Network security policy; Security through obscurity; Cyberspace; Asset (computer security); Field (mathematics); Information security; The Internet; Cloud computing; World Wide Web","score_opus":0.09994750075180181,"score_gpt":0.4194085936900688,"score_spread":0.31946109293826697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802952","genre_codex":"other","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":[0.033420358,0.3481609,0.17405115,0.042021714,0.002452765,0.00029586893,0.00013256335,0.00026073042,0.39920402],"genre_scores_gemma":[0.40054846,0.4562976,0.11044062,0.006404689,0.0024776026,0.0003540927,0.00018236067,0.00008606677,0.023208441],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973437,0.0010369106,0.00015863805,0.00040066824,0.00092726893,0.00013283154],"domain_scores_gemma":[0.9906385,0.006981124,0.00033277264,0.00039465327,0.0014044949,0.00024847066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030798072,0.0006326863,0.00053687376,0.00283592,0.0009792779,0.0044956645,0.0010286322,0.0020639256,0.0034660283],"category_scores_gemma":[0.0074020503,0.00032545696,0.0007556579,0.004310284,0.004300595,0.006285007,0.0010873693,0.0030872915,0.001055287],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049344806,0.00022401613,0.0056241974,0.0024353075,0.00012216684,0.00036104547,0.0017124263,0.005855414,0.0019221321,0.65232134,0.00908481,0.32028776],"study_design_scores_gemma":[0.00003634977,0.00032030127,0.011204099,0.0037217448,0.00011536059,0.0010374446,0.0020072164,0.022134125,0.0033613895,0.60659367,0.3493467,0.00012156719],"about_ca_topic_score_codex":0.003414672,"about_ca_topic_score_gemma":0.0015668016,"teacher_disagreement_score":0.0044956645,"about_ca_system_score_codex":0.0027528808,"about_ca_system_score_gemma":0.0038880943,"threshold_uncertainty_score":0.019973695},"labels":[],"label_agreement":null},{"id":"W4386802969","doi":"10.23977/jaip.2023.060603","title":"Selection of Development Mode of Police Unmanned Aerial Vehicle from the Perspective of Intelligent Policing","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"SWOT analysis; China; Mode (computer interface); Perspective (graphical); Business; Computer security; Computer science; Political science; Artificial intelligence; Marketing; Law","score_opus":0.05941155239020289,"score_gpt":0.3433339764919173,"score_spread":0.2839224241017144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802969","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":[0.6124428,0.0011480349,0.31026325,0.0011373902,0.0001976265,0.00068171334,0.00050080975,0.00041596917,0.07321245],"genre_scores_gemma":[0.95760286,0.0005421832,0.036179524,0.000027689512,0.000010675329,0.0001585731,0.00015138453,0.000021609121,0.005305436],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993303,0.000208073,0.00003840559,0.00008947128,0.00019001045,0.00014386963],"domain_scores_gemma":[0.99951816,0.00006815405,0.00008440514,0.000024581996,0.00024449444,0.00006029092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006960821,0.00057834445,0.00025285484,0.0016940049,0.0006091273,0.0012776222,0.00045233587,0.00036213978,0.003321989],"category_scores_gemma":[0.0015537189,0.0002134304,0.0003357396,0.00088971335,0.0004932863,0.001445192,0.000786988,0.00030293674,0.00043592489],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080312055,0.00023418726,0.11274731,0.000862147,0.00010420469,0.0020161434,0.0029183528,0.2208761,0.04081291,0.093053006,0.012209393,0.51336306],"study_design_scores_gemma":[0.00020474805,0.0012859654,0.07119968,0.00041050982,0.00023061554,0.0011158191,0.019506833,0.7681855,0.039134335,0.044897106,0.053553954,0.00027491915],"about_ca_topic_score_codex":0.006819196,"about_ca_topic_score_gemma":0.00612931,"teacher_disagreement_score":0.006819196,"about_ca_system_score_codex":0.0008947913,"about_ca_system_score_gemma":0.001819781,"threshold_uncertainty_score":0.013558984},"labels":[],"label_agreement":null},{"id":"W4386802971","doi":"10.23977/jaip.2023.060601","title":"Application of Artificial Intelligence Graphics and Intraoral Scanning in Medical Scenes","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Scanner; Computer science; 3d scanning; Dentition; Artificial intelligence; Laser scanning; Process (computing); Computer vision; Dentistry; Medicine","score_opus":0.05175106971491168,"score_gpt":0.3810294845166353,"score_spread":0.32927841480172365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386802971","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":[0.02085666,0.00432867,0.9520365,0.001037938,0.00019862209,0.00009995273,0.00010908417,0.002047272,0.019285357],"genre_scores_gemma":[0.30215377,0.0046235407,0.68864554,0.0003617766,0.00015939909,0.00011308564,0.0001835526,0.00029938106,0.00346002],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99822766,0.0007058458,0.00010257545,0.00023377826,0.0006594436,0.0000707072],"domain_scores_gemma":[0.99858534,0.00084115326,0.000078210054,0.0002375405,0.00022315823,0.000034629524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001029645,0.0006015237,0.00042128423,0.002926103,0.00042889887,0.002626846,0.0009253946,0.0009195747,0.0029275739],"category_scores_gemma":[0.0043617007,0.00043816253,0.00081155886,0.0026632482,0.0016037952,0.001521646,0.0014554871,0.0008159516,0.00078985735],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000101313424,0.000044391098,0.00439636,0.00048043168,0.000110800975,0.00046901833,0.001399476,0.025064003,0.020828357,0.054364208,0.0044015646,0.88834],"study_design_scores_gemma":[0.000059818576,0.00043101635,0.021236578,0.00052146666,0.00026627484,0.00367701,0.00162786,0.5236892,0.050235692,0.17329559,0.2247552,0.00020423459],"about_ca_topic_score_codex":0.0013736102,"about_ca_topic_score_gemma":0.0012485429,"teacher_disagreement_score":0.0029275739,"about_ca_system_score_codex":0.0005926608,"about_ca_system_score_gemma":0.0005056136,"threshold_uncertainty_score":0.009793699},"labels":[],"label_agreement":null},{"id":"W4387531957","doi":"10.23977/jaip.2023.060608","title":"Research on the Emotional Impact of AI Care Robots on Elderly Living Alone","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Dementia; Gerontology; Vulnerability (computing); Population; Successful aging; Depression (economics); Elderly people; Elderly care; Psychology; Population ageing; Mental health; Health care; Medicine; Psychiatry; Computer science; Disease; Nursing; Computer security; Environmental health; Political science","score_opus":0.17130147358647052,"score_gpt":0.49173615357357947,"score_spread":0.32043467998710895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387531957","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":[0.8667818,0.025899794,0.0024257859,0.0064213728,0.0005614773,0.00011817212,0.00015398419,0.000035556197,0.09760212],"genre_scores_gemma":[0.9693477,0.017458567,0.0011935538,0.0015455681,0.00017246869,0.000119831166,0.000114164555,0.00001171135,0.010036311],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994586,0.00021428615,0.000024222789,0.00006239361,0.00016916177,0.000071343726],"domain_scores_gemma":[0.99692434,0.00150504,0.0004341966,0.00008487649,0.000704598,0.0003468075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013111477,0.00041145016,0.00023784768,0.00066617964,0.0010250439,0.0017343377,0.0004693611,0.000561925,0.0060569653],"category_scores_gemma":[0.0060137543,0.000112187234,0.00044955875,0.0004985532,0.0010325797,0.0013768971,0.00078362843,0.0006555401,0.00078581535],"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":[0.001160156,0.0019823718,0.1678043,0.004135244,0.00037072768,0.0016859344,0.12077992,0.001239899,0.0075031994,0.017937312,0.02394538,0.6514556],"study_design_scores_gemma":[0.0000946999,0.0023682944,0.648221,0.0028571938,0.00068411365,0.0017117178,0.16882358,0.0018790347,0.0031414086,0.010467488,0.15962997,0.000121507575],"about_ca_topic_score_codex":0.002190982,"about_ca_topic_score_gemma":0.0032466317,"teacher_disagreement_score":0.0060569653,"about_ca_system_score_codex":0.0011272067,"about_ca_system_score_gemma":0.00065861834,"threshold_uncertainty_score":0.02026254},"labels":[],"label_agreement":null},{"id":"W4387532056","doi":"10.23977/jaip.2023.060607","title":"Design of intelligent human resource management system based on cloud computing platform","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cloud computing; Computer science; Login; Management information systems; Information system; Information management; Structure of Management Information; Human resource management system; Redundancy (engineering); Human resource management; Knowledge management; Computer security; Engineering; Operating system","score_opus":0.11061903343972729,"score_gpt":0.32464197487393837,"score_spread":0.21402294143421108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387532056","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":[0.15899162,0.0015849484,0.77306837,0.0011482111,0.0005691977,0.0014471716,0.00022971079,0.004179612,0.058781125],"genre_scores_gemma":[0.8728748,0.00077635783,0.11379639,0.00027441044,0.000089997986,0.00050954014,0.0002260802,0.000077469114,0.011374897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995592,0.00007230917,0.000028074086,0.00009900491,0.0001457282,0.00009563544],"domain_scores_gemma":[0.9998708,0.000015395522,0.000014150554,0.000012718658,0.0000560583,0.00003084537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029531278,0.0003095085,0.0004622247,0.00046998257,0.00080459146,0.0012775036,0.0010457818,0.0005298215,0.0026461568],"category_scores_gemma":[0.00037668904,0.00024283823,0.0004620012,0.00033419958,0.00026432125,0.0008974727,0.0005724972,0.0003112793,0.00053195085],"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":[0.0013468011,0.000886868,0.012112254,0.0015098496,0.00035860788,0.003910693,0.0017131377,0.20700444,0.25269103,0.1024286,0.027908001,0.38812983],"study_design_scores_gemma":[0.0002917227,0.00057268055,0.006928769,0.00010668184,0.0002502477,0.0009703919,0.00042580478,0.87522763,0.056132287,0.0076897047,0.051275652,0.00012848384],"about_ca_topic_score_codex":0.004350909,"about_ca_topic_score_gemma":0.002263987,"teacher_disagreement_score":0.004350909,"about_ca_system_score_codex":0.00051624444,"about_ca_system_score_gemma":0.0014402864,"threshold_uncertainty_score":0.008852243},"labels":[],"label_agreement":null},{"id":"W4387716957","doi":"10.23977/jaip.2023.060609","title":"Enhancing Trust in Supply Chain Management with a Blockchain Approach","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Blockchain; Supply chain; Traceability; Database transaction; Interoperability; Computer security; Computer science; Intermediary; Business; Supply chain management; Transparency (behavior); Standardization; Database; Finance; World Wide Web; Marketing","score_opus":0.02588835861794914,"score_gpt":0.29151316998093574,"score_spread":0.2656248113629866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387716957","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":[0.054607134,0.0020140568,0.8995836,0.0045109782,0.00019807064,0.0003558359,0.00009017651,0.00042334368,0.038216766],"genre_scores_gemma":[0.90392596,0.0015272875,0.08520541,0.00019845794,0.00011405082,0.00019214157,0.00008921687,0.000056362573,0.008691245],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954182,0.002154809,0.00026166587,0.0003783742,0.0013703813,0.00041649322],"domain_scores_gemma":[0.9932689,0.0028686526,0.00077636726,0.001268602,0.001273739,0.00054372294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044921422,0.00045106912,0.00061603205,0.001011337,0.0014882273,0.0030899548,0.0012937248,0.0015647091,0.0039174743],"category_scores_gemma":[0.009265159,0.00038501376,0.0004960778,0.0012425032,0.0018280902,0.0072935303,0.004879072,0.0015548888,0.0008866053],"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":[0.0002892032,0.00022058764,0.0026913737,0.00045989166,0.00014699034,0.0007452437,0.0019234674,0.19409706,0.0076932684,0.6284839,0.004254291,0.15899469],"study_design_scores_gemma":[0.000099301644,0.00019815727,0.0006015128,0.0001655287,0.00008226054,0.00025796218,0.0004405404,0.51585066,0.0037561278,0.44594175,0.032540254,0.00006590597],"about_ca_topic_score_codex":0.0035901705,"about_ca_topic_score_gemma":0.0032147923,"teacher_disagreement_score":0.0044921422,"about_ca_system_score_codex":0.002423768,"about_ca_system_score_gemma":0.003971768,"threshold_uncertainty_score":0.02375704},"labels":[],"label_agreement":null},{"id":"W4387788855","doi":"10.23977/jaip.2023.060610","title":"Exploration and Application of Artificial Intelligence—The Case of Oral English and English Writing","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Mathematics education; College English; English studies; Psychology; Linguistics","score_opus":0.10178901075104564,"score_gpt":0.3929878416356731,"score_spread":0.29119883088462745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387788855","genre_codex":"other","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":[0.34035695,0.0052432814,0.018647624,0.013300579,0.00025617995,0.00009079558,0.000020534335,0.00005211008,0.622032],"genre_scores_gemma":[0.97194463,0.0014888686,0.008307134,0.0004569091,0.00006737884,0.000053564596,0.0000120764425,0.000014296723,0.017655177],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974069,0.0018393713,0.00006315734,0.00012230649,0.0003429727,0.0002252128],"domain_scores_gemma":[0.9967269,0.0025371967,0.00019254597,0.00018466069,0.00016249971,0.00019623784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002127688,0.00031840726,0.0003904531,0.0011036005,0.0044341357,0.006070444,0.0008435843,0.0023658301,0.0026718853],"category_scores_gemma":[0.005575665,0.00022281923,0.000580571,0.001247261,0.010906019,0.004004869,0.0032265256,0.0019762837,0.00028486075],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005824609,0.00012986678,0.0038164798,0.00021391368,0.000019973651,0.017786795,0.08928943,0.00155727,0.0008583846,0.8558463,0.0025206094,0.02790277],"study_design_scores_gemma":[0.00008464768,0.00014802447,0.008095188,0.0005759568,0.00005362753,0.012732515,0.12653397,0.022391332,0.0015983586,0.5504597,0.27723852,0.000088116845],"about_ca_topic_score_codex":0.005864293,"about_ca_topic_score_gemma":0.006137539,"teacher_disagreement_score":0.006070444,"about_ca_system_score_codex":0.0020769096,"about_ca_system_score_gemma":0.002094257,"threshold_uncertainty_score":0.015069127},"labels":[],"label_agreement":null},{"id":"W4388265538","doi":"10.23977/jaip.2023.060702","title":"The Development Trend of Digital Art in the Age of Artificial Intelligence","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Digital art; Computer science; Connotation; Field (mathematics); Artificial intelligence; Promotion (chess); Process (computing); Digital transformation; Multimedia; Data science; World Wide Web; Art; Political science","score_opus":0.11824210073876544,"score_gpt":0.37708748181318064,"score_spread":0.2588453810744152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388265538","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047599662,0.054904673,0.026588846,0.043736868,0.0015444488,0.0000971116,0.00012641316,0.00028022166,0.82512176],"genre_scores_gemma":[0.7761401,0.068943836,0.03812978,0.0067635663,0.0021917394,0.00016736459,0.00014917307,0.00019907515,0.10731531],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99857795,0.00042935702,0.000080395,0.00019623176,0.0006094252,0.00010661053],"domain_scores_gemma":[0.9987331,0.00055805553,0.0001123063,0.00018169702,0.00025354564,0.00016132502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011625732,0.00032393332,0.0002773643,0.0025926558,0.003128472,0.009696678,0.00077400863,0.0018376487,0.007825572],"category_scores_gemma":[0.0024833442,0.00017612454,0.00038466722,0.0035082062,0.0075487266,0.0109334085,0.0027522605,0.0024158785,0.0015310126],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022115933,0.00002631562,0.0012946845,0.00032326672,0.0000067315614,0.0002596674,0.007369954,0.00023390188,0.0007073786,0.87756574,0.009914099,0.10227614],"study_design_scores_gemma":[0.000009374796,0.000038692448,0.0036161232,0.0005714827,0.000017496026,0.0010161252,0.009499118,0.0014816108,0.0008017878,0.2712926,0.71162385,0.000031771557],"about_ca_topic_score_codex":0.001834914,"about_ca_topic_score_gemma":0.0024899915,"teacher_disagreement_score":0.009696678,"about_ca_system_score_codex":0.0026438825,"about_ca_system_score_gemma":0.0023387591,"threshold_uncertainty_score":0.026179194},"labels":[],"label_agreement":null},{"id":"W4388266842","doi":"10.23977/jaip.2023.060701","title":"Research on the Application of Artificial Intelligence Technology in Electrical Automation Control","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Engineering and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Automation; Control (management); Computer science; Artificial intelligence; Engineering; Manufacturing engineering; Systems engineering; Mechanical engineering","score_opus":0.10263012472005698,"score_gpt":0.3841190712298041,"score_spread":0.2814889465097471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388266842","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":[0.039956555,0.27363998,0.37296036,0.01309143,0.0020802324,0.0001618915,0.000062792475,0.00032384676,0.29772294],"genre_scores_gemma":[0.584516,0.3006981,0.085582994,0.002146154,0.0023657943,0.00017442019,0.00011198686,0.00006357876,0.02434099],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985958,0.00034353725,0.000103773564,0.0002041429,0.0006671397,0.00008563903],"domain_scores_gemma":[0.99850345,0.00090110046,0.00007806876,0.000084236344,0.00039116442,0.000042043263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010881496,0.0004352315,0.00042091132,0.0013303662,0.000609829,0.0021708214,0.0007678717,0.0012007436,0.002030218],"category_scores_gemma":[0.0026269248,0.00021048711,0.0005227632,0.0020847844,0.0015693831,0.0033514872,0.0006931369,0.0014306811,0.0005265102],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006254426,0.00014744069,0.0024760962,0.0017618025,0.00008533237,0.00039199417,0.0009741588,0.011926879,0.0055706217,0.5509452,0.0049447124,0.42071316],"study_design_scores_gemma":[0.00005302594,0.00055996137,0.007582103,0.0023571039,0.00019463577,0.0014834924,0.001428355,0.08107488,0.015009613,0.4023003,0.48779404,0.00016250263],"about_ca_topic_score_codex":0.0014961461,"about_ca_topic_score_gemma":0.0006382953,"teacher_disagreement_score":0.0021708214,"about_ca_system_score_codex":0.0010419119,"about_ca_system_score_gemma":0.0014882606,"threshold_uncertainty_score":0.007559657},"labels":[],"label_agreement":null},{"id":"W4388411857","doi":"10.23977/jaip.2023.060703","title":"The Effectiveness of Brain-computer Interface Technology in the Metaverse","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Metaverse; Computer science; Human–computer interaction; Interface (matter); Virtual reality; Face (sociological concept); Sociology","score_opus":0.05564606833494112,"score_gpt":0.3687437836710681,"score_spread":0.31309771533612696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388411857","genre_codex":"other","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":[0.19521567,0.23713247,0.1538589,0.007897276,0.001053799,0.00015550354,0.00020535973,0.00083853875,0.40364245],"genre_scores_gemma":[0.9044341,0.04974138,0.032481093,0.0009882066,0.00030671817,0.00010142992,0.00009881203,0.00012369268,0.011724583],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988181,0.00047395664,0.00009517725,0.00017462074,0.00036345495,0.000074772484],"domain_scores_gemma":[0.9986303,0.0007742103,0.0001639831,0.00016368844,0.00019856474,0.000069380265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013083109,0.0004466154,0.00040651925,0.0017735629,0.00074845436,0.004960823,0.0009650431,0.0013061471,0.0056876945],"category_scores_gemma":[0.0030652375,0.00016971941,0.0005737011,0.0015194779,0.0020271654,0.009318808,0.0028272294,0.0009679754,0.0010446392],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029080128,0.00011141457,0.0053221574,0.0047749,0.00024050818,0.001818578,0.0070568617,0.0016459734,0.021177104,0.3867638,0.0047037737,0.56609416],"study_design_scores_gemma":[0.00007415913,0.0011291801,0.022658939,0.005391601,0.00060284534,0.011461722,0.011336498,0.009929486,0.040822662,0.29442564,0.6019966,0.00017067653],"about_ca_topic_score_codex":0.0004028305,"about_ca_topic_score_gemma":0.0004615218,"teacher_disagreement_score":0.0056876945,"about_ca_system_score_codex":0.0006870028,"about_ca_system_score_gemma":0.00089641346,"threshold_uncertainty_score":0.019027174},"labels":[],"label_agreement":null},{"id":"W4388543398","doi":"10.23977/jaip.2023.060704","title":"The Impact of Autonomous Robot Design and Programming on Student Creativity","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Teaching and Learning Programming","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Creativity; GRASP; Field (mathematics); Robot; Computer science; Engineering ethics; Autonomous robot; Mathematics education; Management science; Knowledge management; Human–computer interaction; Artificial intelligence; Psychology; Engineering; Mobile robot; Software engineering","score_opus":0.09607528582378523,"score_gpt":0.41635728894707064,"score_spread":0.3202820031232854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388543398","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":[0.9884654,0.00024993042,0.0010059391,0.0008582085,0.000021346706,0.00003442876,0.000030956955,0.00001185859,0.009321884],"genre_scores_gemma":[0.99860543,0.000105204206,0.0005215839,0.000055419954,0.0000070087553,0.000038627903,0.000013195192,0.000006149362,0.000647428],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9881505,0.0055927215,0.0006461536,0.0007657204,0.0038484796,0.0009964419],"domain_scores_gemma":[0.9163553,0.061281797,0.006968979,0.003165972,0.0066283,0.0055996063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008639504,0.00024048849,0.00037175824,0.0010622041,0.0012275886,0.006884948,0.0009246472,0.0008749198,0.0044757975],"category_scores_gemma":[0.060518816,0.0002605372,0.0006634437,0.0009963737,0.0020159925,0.0022025148,0.0035863924,0.0020362476,0.0005141498],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014456909,0.0047422154,0.5669442,0.00078566076,0.00022636945,0.00095201127,0.07377915,0.0021811498,0.0032572232,0.015201409,0.0028538234,0.32763103],"study_design_scores_gemma":[0.0003067817,0.004826575,0.828094,0.0008284916,0.00030927217,0.001400612,0.09655309,0.0105686085,0.009527976,0.028484827,0.018868186,0.00023153344],"about_ca_topic_score_codex":0.00036978855,"about_ca_topic_score_gemma":0.00056573225,"teacher_disagreement_score":0.008639504,"about_ca_system_score_codex":0.0009863973,"about_ca_system_score_gemma":0.0019059434,"threshold_uncertainty_score":0.045690596},"labels":[],"label_agreement":null},{"id":"W4388709194","doi":"10.23977/jaip.2023.060706","title":"Exploration of Computer-Assisted Translation Technology in Translating Technical Terms in Traditional Chinese Medicine under the Perspective of AI Vision","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Translation (biology); Computer science; Traditional Chinese medicine; Artificial intelligence; Computer-aided; Computer technology; Natural language processing; Medicine; Multimedia; Pathology; Alternative medicine; Programming language","score_opus":0.15275306208595577,"score_gpt":0.4303788987783743,"score_spread":0.2776258366924186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388709194","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":[0.110845484,0.019521097,0.7285464,0.008672427,0.0006049767,0.00046866422,0.00027528423,0.0010797039,0.129986],"genre_scores_gemma":[0.58451504,0.010690879,0.39349738,0.00080203934,0.00031949335,0.00031773507,0.00038143434,0.00012367906,0.009352341],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998696,0.0007225999,0.00009100334,0.00019441168,0.00023491579,0.000061152416],"domain_scores_gemma":[0.99831593,0.0010617549,0.000101121506,0.00017453112,0.00030134481,0.000045267618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019702222,0.0004702545,0.00036732596,0.0018723694,0.00085473235,0.0028508948,0.0007266322,0.00073356903,0.0030702807],"category_scores_gemma":[0.00399057,0.00023637325,0.00060782477,0.0024959834,0.0016613093,0.004786796,0.0011096352,0.0007246607,0.0007630003],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016786273,0.00015552016,0.0064539746,0.0016142478,0.000106110274,0.0010306343,0.005871274,0.0077100303,0.018146792,0.42405158,0.0066253464,0.52806664],"study_design_scores_gemma":[0.00010772964,0.00068264076,0.012538476,0.0011591254,0.00040246453,0.003689443,0.007662382,0.24656901,0.0314429,0.49283397,0.2027408,0.00017101488],"about_ca_topic_score_codex":0.0021262213,"about_ca_topic_score_gemma":0.0018854415,"teacher_disagreement_score":0.0030702807,"about_ca_system_score_codex":0.0010142139,"about_ca_system_score_gemma":0.0023153983,"threshold_uncertainty_score":0.010419607},"labels":[],"label_agreement":null},{"id":"W4388709207","doi":"10.23977/jaip.2023.060705","title":"Research on Robot Path Planning Based on Simulated Annealing Algorithm","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Shortest path problem; Motion planning; Simulated annealing; Constrained Shortest Path First; Crossover; Computer science; K shortest path routing; Mathematical optimization; Correctness; Shortest Path Faster Algorithm; Any-angle path planning; Yen's algorithm; MATLAB; Path (computing); Algorithm; Robot; Mathematics; Dijkstra's algorithm; Artificial intelligence; Theoretical computer science; Graph","score_opus":0.2319650491379905,"score_gpt":0.46551648775397564,"score_spread":0.23355143861598515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388709207","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":[0.0069677676,0.0014563869,0.9862579,0.00017315504,0.00006263167,0.000041712537,0.000023205832,0.0003334275,0.0046838443],"genre_scores_gemma":[0.4287197,0.0067835134,0.55615,0.00014341195,0.000103350336,0.0003633643,0.00018936895,0.00015337905,0.0073940307],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931765,0.00016728094,0.000040522194,0.00018413157,0.000237453,0.00005302989],"domain_scores_gemma":[0.9996263,0.00017559735,0.000039042796,0.00003638696,0.00010523786,0.000017464748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005891538,0.00070579397,0.00092770095,0.00078276935,0.0005811365,0.00088636513,0.00094998156,0.0008078345,0.0019040711],"category_scores_gemma":[0.0012982702,0.0005448361,0.00096684176,0.0012899652,0.0006577799,0.0015363364,0.00059340696,0.00095375435,0.000312558],"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":[0.00007122775,0.000045181852,0.001231716,0.0005633125,0.0001020157,0.0001773385,0.00024504337,0.75969064,0.013404413,0.058353096,0.0020492522,0.16406679],"study_design_scores_gemma":[0.0000192586,0.000074982614,0.0003900724,0.00002817719,0.00003135725,0.00011565942,0.000036537687,0.978962,0.0033867655,0.010473022,0.0064580035,0.000024113],"about_ca_topic_score_codex":0.0057346355,"about_ca_topic_score_gemma":0.002384559,"teacher_disagreement_score":0.0057346355,"about_ca_system_score_codex":0.00084533077,"about_ca_system_score_gemma":0.0017762826,"threshold_uncertainty_score":0.011402488},"labels":[],"label_agreement":null},{"id":"W4388998246","doi":"10.23977/jaip.2023.060707","title":"Artificial intelligence for satellite communications and geophysics: current and future trends","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Big data; Communications satellite; Process (computing); Boom; Field (mathematics); Telecommunications; Computer science; Data science; Artificial intelligence; Engineering; Satellite","score_opus":0.42613064045003063,"score_gpt":0.4966793961804116,"score_spread":0.070548755730381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388998246","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024757504,0.9102715,0.0076243533,0.04877664,0.0019865865,0.000029107283,0.000050926785,0.000111885645,0.028673273],"genre_scores_gemma":[0.029217103,0.94366455,0.012253438,0.005776366,0.0044153724,0.000045666195,0.00010413117,0.00004258567,0.004480737],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979006,0.00085440744,0.00014567771,0.00018904217,0.0007694982,0.00014065191],"domain_scores_gemma":[0.99078965,0.005708148,0.00053295697,0.00033392108,0.0019427736,0.0006924821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051288186,0.0006178142,0.0007868284,0.0031426672,0.0008303484,0.004717901,0.0014373412,0.0035993867,0.0040067066],"category_scores_gemma":[0.004478233,0.00028736214,0.0005143875,0.0064508617,0.0038032965,0.00985966,0.0018831688,0.0039308583,0.0016401283],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008492485,0.00017958527,0.0026315358,0.0035916574,0.00004982662,0.00013474541,0.000696418,0.0010193647,0.000951788,0.16936503,0.05364354,0.7676517],"study_design_scores_gemma":[0.00001899444,0.00014088162,0.0031867654,0.0037564095,0.00004704017,0.00041744078,0.001676367,0.004259916,0.0005479356,0.12303155,0.86285454,0.00006211085],"about_ca_topic_score_codex":0.0015720213,"about_ca_topic_score_gemma":0.0017290934,"teacher_disagreement_score":0.0051288186,"about_ca_system_score_codex":0.0019331475,"about_ca_system_score_gemma":0.003569812,"threshold_uncertainty_score":0.027124107},"labels":[],"label_agreement":null},{"id":"W4388998271","doi":"10.23977/jaip.2023.060708","title":"The Effectiveness of Artificial Intelligence Teaching Methods in Art Subject Classrooms","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subject (documents); Mathematics education; Teaching method; Computer science; Artificial intelligence; Control (management); Process (computing); Psychology","score_opus":0.09775885757806732,"score_gpt":0.4480274105924718,"score_spread":0.3502685530144045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388998271","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":[0.9935329,0.00037277752,0.0013438715,0.00006492214,0.000020611471,0.00009327948,0.0000091541215,0.000024552432,0.0045380183],"genre_scores_gemma":[0.99528235,0.00031983928,0.0033602982,0.000026552438,0.000021025871,0.00006593217,0.000016101734,0.000007916884,0.0008999419],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99524575,0.0022344845,0.00028382585,0.00032257705,0.0016566258,0.0002567912],"domain_scores_gemma":[0.9910319,0.0054083783,0.0011679936,0.00056202203,0.0007844414,0.0010452792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032748561,0.00035573356,0.00046665076,0.00081147574,0.0005701657,0.0014392835,0.0005683394,0.00033187988,0.0016266489],"category_scores_gemma":[0.009665592,0.00016437155,0.0004526769,0.0004162841,0.0005410755,0.0008457917,0.0008284982,0.0004858361,0.00025890436],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033180632,0.028413696,0.09292862,0.001457407,0.00025629628,0.0003255615,0.012510524,0.0015927865,0.04100658,0.0013209702,0.000800562,0.8160689],"study_design_scores_gemma":[0.0014837362,0.076572694,0.7724858,0.0008359371,0.0011865345,0.0012763018,0.020083481,0.009045185,0.092800096,0.0019353459,0.022103062,0.00019189696],"about_ca_topic_score_codex":0.00048055095,"about_ca_topic_score_gemma":0.0007748448,"teacher_disagreement_score":0.0032748561,"about_ca_system_score_codex":0.00047172597,"about_ca_system_score_gemma":0.0007297109,"threshold_uncertainty_score":0.017319322},"labels":[],"label_agreement":null},{"id":"W4388998285","doi":"10.23977/jaip.2023.060709","title":"Research on Classroom Teaching Innovation Promoted by Artificial Intelligence from the Perspective of High-Quality Development","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Quality (philosophy); Computer science; Knowledge management; Engineering ethics; Mathematics education; Engineering; Artificial intelligence; Psychology","score_opus":0.20825649560163167,"score_gpt":0.48049835645852407,"score_spread":0.2722418608568924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388998285","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":[0.7676773,0.0066709416,0.036984313,0.0060193664,0.00016832145,0.00011670901,0.000032990407,0.00008812195,0.18224192],"genre_scores_gemma":[0.9936807,0.0011973125,0.002993447,0.000120172066,0.000024300589,0.000022243978,0.000007887585,0.000008938571,0.0019450265],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956163,0.0024908748,0.00013241456,0.00045245828,0.00092696946,0.00038096445],"domain_scores_gemma":[0.9841171,0.010852633,0.0020802922,0.0009818946,0.0009948208,0.0009732663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036666077,0.0001894403,0.0002818545,0.0012255587,0.001073724,0.005769383,0.0008323701,0.0008144695,0.0022369216],"category_scores_gemma":[0.009216996,0.00012702099,0.00028183463,0.0015120519,0.004734039,0.0045498204,0.0022996399,0.0012338596,0.00022404651],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013738715,0.0007876546,0.04823528,0.0013087369,0.0000836827,0.00049128814,0.09865072,0.0022065977,0.0051310784,0.5304471,0.0017290211,0.3107915],"study_design_scores_gemma":[0.00016100793,0.001399883,0.1872417,0.0025842001,0.0003025427,0.0018259368,0.21035197,0.013892801,0.025397647,0.2752989,0.28139278,0.00015071689],"about_ca_topic_score_codex":0.0010083641,"about_ca_topic_score_gemma":0.001275554,"teacher_disagreement_score":0.005769383,"about_ca_system_score_codex":0.003249208,"about_ca_system_score_gemma":0.0027928518,"threshold_uncertainty_score":0.02357483},"labels":[],"label_agreement":null},{"id":"W4389351758","doi":"10.23977/jaip.2023.060710","title":"Exploration and application of mixed reality technology in modern home design","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Design technology; Virtual reality; Computer science; Personalization; Process (computing); Key (lock); Engineering design process; Mixed reality; Field (mathematics); Augmented reality; Design process; Visualization; Human–computer interaction; Systems engineering; Engineering management; Engineering; Work in process; Operations management; World Wide Web","score_opus":0.11773004418014829,"score_gpt":0.37286907595052116,"score_spread":0.2551390317703729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389351758","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":[0.08526593,0.019887604,0.8022246,0.0021778985,0.00021312397,0.00012829735,0.000048790964,0.00067393936,0.0893798],"genre_scores_gemma":[0.61456937,0.009944568,0.3641649,0.00038384582,0.0001369122,0.00012027667,0.000035736604,0.00009124861,0.010553158],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981248,0.0011971449,0.00006425168,0.000113072776,0.00042877454,0.00007188721],"domain_scores_gemma":[0.9992681,0.0004053107,0.000053677355,0.00011666848,0.00011957275,0.000036574347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014637051,0.0004565726,0.00033656834,0.0013747587,0.00047459817,0.0022272673,0.000518428,0.00088357995,0.0027068688],"category_scores_gemma":[0.0019290918,0.0004288357,0.00056104246,0.00073555845,0.0014698502,0.002324122,0.0017812448,0.00056858646,0.00048236467],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022547123,0.00014751554,0.0033697754,0.0014117493,0.00012027909,0.0015199677,0.012788786,0.01313983,0.062341202,0.18950915,0.00464716,0.71077925],"study_design_scores_gemma":[0.00010505629,0.002152534,0.013300016,0.0020919547,0.00032789793,0.014398122,0.012431035,0.13123165,0.06811568,0.1708337,0.5845263,0.0004860421],"about_ca_topic_score_codex":0.00036808944,"about_ca_topic_score_gemma":0.00048645635,"teacher_disagreement_score":0.0027068688,"about_ca_system_score_codex":0.00050196145,"about_ca_system_score_gemma":0.00038468244,"threshold_uncertainty_score":0.009055316},"labels":[],"label_agreement":null},{"id":"W4389510863","doi":"10.23977/jaip.2023.060803","title":"Research on the Communication Opportunities of Intangible Cultural Heritage under the Background of Big Data and AI","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Cultural Heritage Management and Preservation","field":"Arts and Humanities","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Big data; Intangible cultural heritage; Context (archaeology); Personalization; Cultural heritage; Inheritance (genetic algorithm); Knowledge management; Business; Data science; Computer science; Political science; Marketing; History","score_opus":0.8262724159310398,"score_gpt":0.4740173188879564,"score_spread":0.35225509704308344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389510863","genre_codex":"other","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":[0.19815291,0.038133,0.11001422,0.074778825,0.0015140106,0.0001638204,0.0003635121,0.00015677896,0.576723],"genre_scores_gemma":[0.96332246,0.014087127,0.013148502,0.0016640552,0.00062278786,0.00010423142,0.000095155185,0.000039001887,0.006916705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.995845,0.002585513,0.00013627898,0.00033975166,0.0007323524,0.00036107528],"domain_scores_gemma":[0.9814337,0.014609036,0.001333701,0.0010802236,0.0008902591,0.0006531394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045200605,0.00045535216,0.00030813558,0.0039857044,0.0038714793,0.012068261,0.0009998393,0.0019000001,0.0043333364],"category_scores_gemma":[0.009774017,0.0002942681,0.0004705691,0.005468538,0.011645807,0.021541836,0.004772705,0.0026882398,0.00034073164],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038935093,0.00003722341,0.004800274,0.00056558836,0.000030219277,0.0006131153,0.042300317,0.0004461041,0.00056018087,0.88340425,0.0036489177,0.06355492],"study_design_scores_gemma":[0.000013658064,0.00006702511,0.009156841,0.0017246848,0.000059122955,0.0016898108,0.11204673,0.0036323185,0.0017265938,0.6408331,0.22897501,0.00007513361],"about_ca_topic_score_codex":0.0018022663,"about_ca_topic_score_gemma":0.001771119,"teacher_disagreement_score":0.012068261,"about_ca_system_score_codex":0.002555787,"about_ca_system_score_gemma":0.002730084,"threshold_uncertainty_score":0.023904622},"labels":[],"label_agreement":null},{"id":"W4389510882","doi":"10.23977/jaip.2023.060801","title":"Patentability Analysis of Artificial Intelligence and Big Data Patent Applications","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Patentability; Patent law; Big data; Flourishing; Legislation; Business; Computer science; Engineering; Artificial intelligence; Intellectual property; Law; Political science; Data mining; Psychology","score_opus":0.347622916190066,"score_gpt":0.37417553550604693,"score_spread":0.026552619315980908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389510882","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":[0.47657752,0.0105322525,0.090020895,0.008266698,0.0003440529,0.0018468968,0.0043699266,0.00028735708,0.4077544],"genre_scores_gemma":[0.98147285,0.0014972044,0.008536324,0.0003995158,0.0003070434,0.0005358273,0.001209677,0.000051830877,0.005989706],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9425704,0.010116739,0.005085018,0.0037159203,0.035302557,0.0032093017],"domain_scores_gemma":[0.68480206,0.22721395,0.040306374,0.012912192,0.031804554,0.0029608987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.032284256,0.0004540105,0.0011020892,0.032179676,0.0028226506,0.010920689,0.0018991657,0.0026626748,0.009100337],"category_scores_gemma":[0.17972936,0.000312387,0.002148434,0.023778705,0.0070339553,0.010020909,0.0030533224,0.002982683,0.00087479275],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022026815,0.0002400898,0.049584534,0.0006052683,0.00024225457,0.0007359205,0.0012512909,0.0045217928,0.001092546,0.8643292,0.0052293283,0.07194748],"study_design_scores_gemma":[0.00014885061,0.0005896646,0.21729597,0.0009557524,0.0006477354,0.0018984436,0.0031121767,0.044007357,0.0049687214,0.6523717,0.07378108,0.00022263973],"about_ca_topic_score_codex":0.0038465173,"about_ca_topic_score_gemma":0.0016108213,"teacher_disagreement_score":0.032284256,"about_ca_system_score_codex":0.005109057,"about_ca_system_score_gemma":0.0051100357,"threshold_uncertainty_score":0.1707375},"labels":[],"label_agreement":null},{"id":"W4389510892","doi":"10.23977/jaip.2023.060802","title":"Analysis of Evaluation in Artificial Intelligence Music","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Music and artificial intelligence; Computer science; Rhythm; Style (visual arts); Music industry; Field (mathematics); Artificial intelligence; Musical composition; Music technology; Quality (philosophy); Popular music; Music education; Visual arts; Aesthetics; Art; Mathematics","score_opus":0.14009079718043396,"score_gpt":0.39216499726153675,"score_spread":0.2520742000811028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389510892","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":[0.3913102,0.017828083,0.30494496,0.005500556,0.0005570928,0.0031483504,0.001912146,0.00048821096,0.27431038],"genre_scores_gemma":[0.9558225,0.0009968773,0.03707802,0.00021664963,0.00011989167,0.0010110689,0.00048081522,0.000080007696,0.004194091],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.91473496,0.05266691,0.004742713,0.0028159174,0.023553116,0.0014864181],"domain_scores_gemma":[0.7257784,0.22060661,0.013401825,0.0069951545,0.031435777,0.0017823019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04522286,0.0007794326,0.0009870665,0.008015792,0.0011319676,0.004967529,0.00095684436,0.0007915602,0.006516449],"category_scores_gemma":[0.21508162,0.0002192028,0.0010420275,0.0074259965,0.0037032375,0.0031857481,0.0020220724,0.0011936778,0.0004968456],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011745497,0.00045999096,0.08521338,0.0025026991,0.00073840923,0.00035744652,0.008243225,0.016149055,0.0012098488,0.45840204,0.009648346,0.41590098],"study_design_scores_gemma":[0.00026902114,0.0017778982,0.23230043,0.0026717884,0.000704948,0.0005981766,0.013889016,0.16755615,0.004390657,0.4984832,0.077121854,0.00023689172],"about_ca_topic_score_codex":0.003242434,"about_ca_topic_score_gemma":0.0019278143,"teacher_disagreement_score":0.04522286,"about_ca_system_score_codex":0.0070998934,"about_ca_system_score_gemma":0.003370916,"threshold_uncertainty_score":0.23916423},"labels":[],"label_agreement":null},{"id":"W4389633105","doi":"10.23977/jaip.2023.060805","title":"Evaluation of the Influence of Artificial Intelligence on College Students' Learning Based on Group Decision-making Method","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Operability; Realm; Computer science; Rationality; Psychology; Mathematics education","score_opus":0.07819486097998976,"score_gpt":0.44997778595933713,"score_spread":0.3717829249793474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389633105","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":[0.9740027,0.00005680396,0.02290385,0.00006741264,0.000024684954,0.0005902195,0.000044145156,0.000043792927,0.0022663316],"genre_scores_gemma":[0.9720657,0.000045238194,0.027162604,0.00001619139,0.0000089580035,0.0004231849,0.000042761927,0.0000059192976,0.00022954051],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9817625,0.013313313,0.0009175416,0.0010163125,0.0023498666,0.000640463],"domain_scores_gemma":[0.89212334,0.09754231,0.0032102163,0.0015798444,0.004058868,0.0014854461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.023058986,0.0009823317,0.0013245569,0.0028016008,0.0008196813,0.0018827763,0.0010054182,0.00093925686,0.0017733789],"category_scores_gemma":[0.047506653,0.00026057198,0.0010839233,0.0024190145,0.0009504723,0.0014084184,0.0012965205,0.0009808238,0.0001537426],"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":[0.010491151,0.010893524,0.2730956,0.0014178306,0.0013367023,0.0005238779,0.009496353,0.22571774,0.00905565,0.009632783,0.0011034717,0.4472353],"study_design_scores_gemma":[0.0004966206,0.010630568,0.066298075,0.00011519569,0.00047830847,0.00008149449,0.006560799,0.8929634,0.014429108,0.006675067,0.0011018272,0.00016955115],"about_ca_topic_score_codex":0.0015022247,"about_ca_topic_score_gemma":0.0018952407,"teacher_disagreement_score":0.023058986,"about_ca_system_score_codex":0.0016790433,"about_ca_system_score_gemma":0.0016825292,"threshold_uncertainty_score":0.12194902},"labels":[],"label_agreement":null},{"id":"W4389633337","doi":"10.23977/jaip.2023.060804","title":"Utilization of Artificial Intelligence Technology in Higher Education Management: Teaching Theory and Practical Skills of Landscape Architecture Construction Technology","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Flexibility (engineering); Computer science; Artificial intelligence; Construction management; Virtual reality; Information technology; Knowledge management; Architecture; Engineering management; Engineering; Civil engineering; Management","score_opus":0.0401336381329237,"score_gpt":0.3692833889134328,"score_spread":0.3291497507805091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389633337","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":[0.21178038,0.017013662,0.46904537,0.018185629,0.0006087035,0.00046632256,0.00008476223,0.00089664967,0.28191844],"genre_scores_gemma":[0.8555285,0.00920407,0.119068004,0.0011686176,0.00021143937,0.00017809855,0.000062087405,0.000043971468,0.014535328],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990171,0.00036144216,0.00007630041,0.000120635756,0.00032930906,0.00009520538],"domain_scores_gemma":[0.99913186,0.00040414697,0.00009420454,0.00008952264,0.00017374041,0.00010652003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001176802,0.00036417303,0.0002013652,0.0011313214,0.0007374767,0.002805951,0.0005889991,0.0009795295,0.0022547718],"category_scores_gemma":[0.0018366439,0.00019305517,0.00033418078,0.001390505,0.0014903878,0.002392234,0.0011819775,0.00088768953,0.00053764676],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041394705,0.0005460556,0.015498439,0.0010649344,0.00003517216,0.00042487003,0.0037197582,0.006778991,0.010373167,0.09332655,0.007305677,0.8608849],"study_design_scores_gemma":[0.00008320274,0.0009880123,0.07754833,0.0028508576,0.00014089806,0.0021680675,0.009707012,0.09189459,0.031727858,0.28360534,0.49907064,0.00021513828],"about_ca_topic_score_codex":0.001355383,"about_ca_topic_score_gemma":0.0015627831,"teacher_disagreement_score":0.002805951,"about_ca_system_score_codex":0.0014748219,"about_ca_system_score_gemma":0.0028990998,"threshold_uncertainty_score":0.010700643},"labels":[],"label_agreement":null},{"id":"W4389684056","doi":"10.23977/jaip.2023.060806","title":"Research on Improving Education Quality and Efficiency through Artificial Intelligence and Big Data Analysis","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Big data; Field (mathematics); Artificial intelligence; Computer science; Quality (philosophy); Data science; Data mining; Mathematics","score_opus":0.5806620427637665,"score_gpt":0.5614138072899363,"score_spread":0.019248235473830255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389684056","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":[0.26177332,0.06758937,0.5108232,0.060169917,0.0011848783,0.00081458484,0.00073272095,0.0008154651,0.09609646],"genre_scores_gemma":[0.8250692,0.026738014,0.14120278,0.002518747,0.0003601977,0.0003117956,0.0004148057,0.00007975624,0.0033048035],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99243414,0.0038506999,0.00046291188,0.00074738765,0.002105808,0.00039914576],"domain_scores_gemma":[0.9659082,0.022498798,0.003503546,0.00266345,0.004680672,0.00074534776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009810813,0.0006978126,0.00086083426,0.004827064,0.000969086,0.0062255007,0.001317077,0.0011131215,0.0018502842],"category_scores_gemma":[0.03179141,0.00030756416,0.00090569846,0.007489564,0.0020431126,0.009374811,0.0015241236,0.0015082059,0.00038427082],"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":[0.00014366405,0.0007675872,0.06876877,0.0036990119,0.000590878,0.000096621145,0.002498155,0.017521486,0.0013988157,0.20585525,0.007326255,0.6913335],"study_design_scores_gemma":[0.00021659762,0.0013678123,0.1368591,0.008497376,0.0010403872,0.00046891154,0.017156538,0.17448908,0.017169945,0.46614566,0.17632122,0.0002674764],"about_ca_topic_score_codex":0.0038751995,"about_ca_topic_score_gemma":0.003430528,"teacher_disagreement_score":0.009810813,"about_ca_system_score_codex":0.0027964343,"about_ca_system_score_gemma":0.0056687146,"threshold_uncertainty_score":0.051885188},"labels":[],"label_agreement":null},{"id":"W4390486145","doi":"10.23977/jaip.2023.060807","title":"The application of artificial intelligence in computer network technology in the data age","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and Big Data Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Big data; China; Data science; Context (archaeology); Computer science; Artificial intelligence; Political science; Geography; Data mining","score_opus":0.12208585014779036,"score_gpt":0.39033612280819585,"score_spread":0.26825027266040546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390486145","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028383052,0.20519716,0.39554137,0.086014904,0.0048390133,0.00024840725,0.00024141918,0.0002601648,0.27927458],"genre_scores_gemma":[0.5015728,0.27263847,0.19111422,0.009462471,0.008529468,0.00034320264,0.00015717084,0.00008693385,0.0160953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998028,0.0009803231,0.0001236044,0.00018116504,0.0005864946,0.00010031711],"domain_scores_gemma":[0.9963947,0.0027561688,0.00016927207,0.00022490593,0.00037520216,0.00007972653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00257998,0.0005004978,0.0004040974,0.0026202493,0.0010614799,0.0048359963,0.0007626961,0.0018299966,0.0012445909],"category_scores_gemma":[0.0051046233,0.00031459777,0.0004382661,0.0035652793,0.004299831,0.0077680084,0.0015877503,0.002674641,0.0004189524],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001732179,0.000045509103,0.0024040213,0.0005039866,0.000031924796,0.00035032383,0.000679737,0.0059624496,0.00069922826,0.8874863,0.0066154734,0.09520382],"study_design_scores_gemma":[0.000007733135,0.00005572415,0.0020601496,0.00085030304,0.000032795422,0.0005690214,0.0008905093,0.03345993,0.0013632876,0.7495145,0.21113865,0.00005729313],"about_ca_topic_score_codex":0.0021365108,"about_ca_topic_score_gemma":0.0019736362,"teacher_disagreement_score":0.0048359963,"about_ca_system_score_codex":0.0020613805,"about_ca_system_score_gemma":0.0016877258,"threshold_uncertainty_score":0.014956415},"labels":[],"label_agreement":null},{"id":"W4390486156","doi":"10.23977/jaip.2023.060809","title":"The Transformation of Photography by Artificial Intelligence Generative AI Technology","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generative grammar; Photography; Transformation (genetics); Computer science; Artificial intelligence; Generative Design; Computer technology; Expression (computer science); Visual arts; Multimedia; Art; Engineering","score_opus":0.05027242621003028,"score_gpt":0.3606889180681049,"score_spread":0.31041649185807463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390486156","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036615428,0.009029349,0.17361148,0.014969253,0.0013640479,0.0001525894,0.00010372552,0.0005208904,0.7636333],"genre_scores_gemma":[0.74658215,0.010039924,0.14633648,0.002625763,0.0010623408,0.0001612056,0.00008608527,0.0003303128,0.092775814],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991553,0.00038038526,0.000028387361,0.00009925441,0.00028093788,0.000055706314],"domain_scores_gemma":[0.9989743,0.0005535038,0.000061486746,0.0002480193,0.000116679235,0.000046039724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011788107,0.000390783,0.0002142292,0.0014600375,0.0013824471,0.0056328126,0.0005730838,0.0009461814,0.0068457457],"category_scores_gemma":[0.0026038561,0.0002814925,0.00047719883,0.0005711053,0.010037594,0.0047313883,0.0026165608,0.0017400715,0.00092193636],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002161216,0.000020402747,0.00035304716,0.00012452713,0.0000074628333,0.00027073608,0.005705611,0.0009106734,0.002583612,0.93750584,0.0047956496,0.047700807],"study_design_scores_gemma":[0.00003052796,0.00007792243,0.0018449904,0.00044538177,0.00002236931,0.0013919628,0.0049432833,0.0068874895,0.0041207033,0.508659,0.47152647,0.000049871778],"about_ca_topic_score_codex":0.0018543546,"about_ca_topic_score_gemma":0.0021648637,"teacher_disagreement_score":0.0068457457,"about_ca_system_score_codex":0.0018635793,"about_ca_system_score_gemma":0.0008626254,"threshold_uncertainty_score":0.022901297},"labels":[],"label_agreement":null},{"id":"W4390486182","doi":"10.23977/jaip.2023.060808","title":"Machine learning: Training model with the case study","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Computational Physics and Python Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Python (programming language); Artificial intelligence; Deep learning; Machine learning; Convolutional neural network; Adversarial system; Generative adversarial network; Task (project management); Process (computing); Generative grammar; Test data; Software engineering; Programming language","score_opus":0.13802522876525897,"score_gpt":0.38147018229984325,"score_spread":0.24344495353458429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390486182","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":[0.13057214,0.00577093,0.79302496,0.01293071,0.0005724341,0.000297911,0.0029503487,0.0016872626,0.0521932],"genre_scores_gemma":[0.7193863,0.002675964,0.25715598,0.0006770894,0.00040543947,0.0004778322,0.002744894,0.00032104252,0.016155416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990715,0.0004129236,0.00004548512,0.00020280444,0.00018889377,0.00007842219],"domain_scores_gemma":[0.9978574,0.0014646954,0.00007362215,0.00025586176,0.00027373392,0.00007466353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017689982,0.00059046835,0.00045109645,0.00052649993,0.0004837077,0.0014808528,0.001125586,0.0017444985,0.006764783],"category_scores_gemma":[0.0069772326,0.00026553494,0.00074517523,0.000929892,0.00079184916,0.0015989168,0.0011343689,0.002357741,0.0014125851],"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":[0.00025021826,0.0004382731,0.008157934,0.0004135943,0.00009646525,0.0012692877,0.00034696984,0.6533083,0.0010671474,0.16701956,0.035638772,0.1319936],"study_design_scores_gemma":[0.00002009979,0.000037864567,0.000566819,0.000052763673,0.000011949857,0.00013750799,0.000051915737,0.9463712,0.000728791,0.04110569,0.010905657,0.000009712164],"about_ca_topic_score_codex":0.007937018,"about_ca_topic_score_gemma":0.0073244288,"teacher_disagreement_score":0.007937018,"about_ca_system_score_codex":0.0012015707,"about_ca_system_score_gemma":0.00073259894,"threshold_uncertainty_score":0.022630394},"labels":[],"label_agreement":null},{"id":"W4390520494","doi":"10.23977/jaip.2023.060810","title":"The current research status of knowledge graph in bridge and its application prospects","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"BIM and Construction Integration","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bridge (graph theory); Current (fluid); Computer science; Graph; Engineering; Theoretical computer science; Electrical engineering; Medicine","score_opus":0.10441379885104311,"score_gpt":0.40917963390742224,"score_spread":0.3047658350563791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390520494","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038897507,0.5737964,0.23519427,0.041150987,0.0027397638,0.00026716638,0.0013723213,0.0011864399,0.10539521],"genre_scores_gemma":[0.28043917,0.57449126,0.12751776,0.0037558812,0.0025969478,0.0002022205,0.0024341918,0.00020459856,0.00835796],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978405,0.00068817637,0.0001621967,0.0004768443,0.0006946276,0.00013752648],"domain_scores_gemma":[0.99010956,0.006585651,0.00050123525,0.0005592158,0.0018651107,0.00037926636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00430251,0.0005555663,0.00073587464,0.006277819,0.0011059842,0.0050686817,0.0018360855,0.0017412497,0.0054970663],"category_scores_gemma":[0.009190819,0.00042197475,0.0008615604,0.009048933,0.0024392481,0.014258472,0.0019171486,0.0017872731,0.0012896777],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007081001,0.00011842828,0.005764973,0.00414944,0.000068231566,0.00028504748,0.0012904812,0.0046625077,0.0010363802,0.22894587,0.02227988,0.7313279],"study_design_scores_gemma":[0.00001639515,0.000120449215,0.004533655,0.0036122482,0.00019242297,0.001015847,0.005656173,0.036607657,0.0023170202,0.29547572,0.6503105,0.0001418972],"about_ca_topic_score_codex":0.007418788,"about_ca_topic_score_gemma":0.0047625285,"teacher_disagreement_score":0.007418788,"about_ca_system_score_codex":0.0023562573,"about_ca_system_score_gemma":0.004077528,"threshold_uncertainty_score":0.022754133},"labels":[],"label_agreement":null},{"id":"W4390650889","doi":"10.23977/jaip.2023.060902","title":"The Application of Artificial Intelligence in Enterprise Auditing","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Impact of AI and Big Data on Business and Society","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Audit; Computer science; Knowledge management; Business; Artificial intelligence; Accounting","score_opus":0.18127263371062693,"score_gpt":0.45661400416965137,"score_spread":0.2753413704590244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390650889","genre_codex":"other","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":[0.035544246,0.20131658,0.27478886,0.107505485,0.003403934,0.00045193304,0.00028661196,0.0005379074,0.3761644],"genre_scores_gemma":[0.61757314,0.1377133,0.21862543,0.008408361,0.003314431,0.0003131593,0.00020574227,0.0001035542,0.013742974],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99263257,0.0041761184,0.0004407061,0.00049339334,0.0020570615,0.00020016971],"domain_scores_gemma":[0.98804224,0.008974378,0.0006239873,0.0008929839,0.0012242493,0.00024219789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006277037,0.0005123841,0.00058114924,0.0037472218,0.0012781565,0.0064233355,0.0011845884,0.0019073287,0.0021911904],"category_scores_gemma":[0.014174045,0.00039095257,0.00054238393,0.0042321077,0.005609816,0.005210085,0.0024960092,0.0025447384,0.000642465],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006735911,0.00016289383,0.0060617826,0.0017564473,0.00013908886,0.0005107267,0.0016613611,0.011591889,0.0010244944,0.5793113,0.014364409,0.38334832],"study_design_scores_gemma":[0.00002213282,0.00009391914,0.0044877646,0.0022491452,0.00004495618,0.0005848729,0.0016996644,0.025074523,0.0012983894,0.77603143,0.1883141,0.00009911818],"about_ca_topic_score_codex":0.0024357338,"about_ca_topic_score_gemma":0.0018543837,"teacher_disagreement_score":0.0064233355,"about_ca_system_score_codex":0.0026289958,"about_ca_system_score_gemma":0.0026389721,"threshold_uncertainty_score":0.03319657},"labels":[],"label_agreement":null},{"id":"W4390650925","doi":"10.23977/jaip.2023.060903","title":"The Role of Artificial Intelligence in Construction Management: A Case Study of Smart Worksite Systems","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"BIM and Construction Integration","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Engineering; Knowledge management","score_opus":0.03066380018887798,"score_gpt":0.29638175419797114,"score_spread":0.26571795400909315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390650925","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":[0.93626505,0.00028722253,0.007615091,0.0029332119,0.000019555715,0.00024347218,0.00009327944,0.000054418237,0.052488733],"genre_scores_gemma":[0.99000823,0.00029582265,0.0050837076,0.00011879269,0.000010460163,0.00006183866,0.000055850967,0.000019443949,0.004345768],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9956821,0.002925795,0.00012631017,0.00017394603,0.00060117146,0.0004906104],"domain_scores_gemma":[0.9950883,0.0033157251,0.00031859797,0.000428766,0.00039506727,0.00045364545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036121805,0.00038294666,0.00039719266,0.0016446335,0.007356418,0.0050107646,0.0017354814,0.0033455817,0.002739562],"category_scores_gemma":[0.004967331,0.00027195955,0.00045665857,0.003036566,0.0054950095,0.0035364004,0.0033573173,0.0017957182,0.00044169778],"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":[0.00054252613,0.0053479043,0.098683774,0.0008540207,0.00017592665,0.033232052,0.33339804,0.081634924,0.0050773723,0.31525734,0.01865681,0.107139274],"study_design_scores_gemma":[0.00018785923,0.0010565701,0.04501628,0.00040872238,0.00009103914,0.0036629813,0.5738111,0.11723704,0.0048010414,0.038332246,0.21526572,0.00012947344],"about_ca_topic_score_codex":0.022713909,"about_ca_topic_score_gemma":0.048896115,"teacher_disagreement_score":0.022713909,"about_ca_system_score_codex":0.004639331,"about_ca_system_score_gemma":0.0027949295,"threshold_uncertainty_score":0.045163393},"labels":[],"label_agreement":null},{"id":"W4390651032","doi":"10.23977/jaip.2023.060901","title":"Tomato picking robot based on deep learning","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Robot; Computer vision; Human–computer interaction","score_opus":0.05290586088582658,"score_gpt":0.3035048828069606,"score_spread":0.25059902192113404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390651032","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":[0.06411386,0.0011719414,0.9159052,0.0005337899,0.00027450893,0.000121955345,0.00020616374,0.0067013134,0.010971245],"genre_scores_gemma":[0.81665957,0.0006429668,0.1651948,0.00046553626,0.00005591746,0.00016456385,0.00054554566,0.0000915831,0.016179517],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986076,0.000009102952,0.000005874433,0.00004828987,0.000043626176,0.00003240545],"domain_scores_gemma":[0.9999056,0.000016962947,0.000011569447,0.00000980551,0.00003984289,0.000016275675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017307895,0.00054406904,0.0005061788,0.00031857914,0.0004322279,0.00039789965,0.0010140375,0.00067724905,0.00281899],"category_scores_gemma":[0.00027379894,0.00035237495,0.0004278682,0.0002617385,0.00027287874,0.0006776681,0.0005982093,0.0006755689,0.00079780986],"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":[0.0003642269,0.00030649413,0.0039206306,0.00032294865,0.00012786877,0.00044934667,0.00012426794,0.35038695,0.064585544,0.004755976,0.011589384,0.56306636],"study_design_scores_gemma":[0.00001630949,0.00009871769,0.0008232008,0.000011502266,0.000021551039,0.00006020259,0.00001463163,0.989493,0.0060701803,0.0012412701,0.00213105,0.000018428907],"about_ca_topic_score_codex":0.00861036,"about_ca_topic_score_gemma":0.00947293,"teacher_disagreement_score":0.00861036,"about_ca_system_score_codex":0.00047536538,"about_ca_system_score_gemma":0.0009034115,"threshold_uncertainty_score":0.01712048},"labels":[],"label_agreement":null},{"id":"W4390761247","doi":"10.23977/jaip.2023.060904","title":"Research and Implementation of Innovative Design of Paper Cuttings Pattern Based on Artificial Intelligence Technology","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Chemistry and Chemical Engineering","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Creativity; Field (mathematics); Artificial intelligence; Cutting; Computer science; Engineering; Engineering management; Mathematics; Psychology","score_opus":0.08249057712618672,"score_gpt":0.39433316014109193,"score_spread":0.3118425830149052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390761247","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":[0.04487777,0.0013097438,0.9285125,0.00034557062,0.00014167117,0.00014923199,0.000029261773,0.00041636615,0.024217946],"genre_scores_gemma":[0.5895268,0.0017801502,0.39728692,0.00012526795,0.000052836564,0.00019981143,0.000107896725,0.00007948867,0.010840907],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991698,0.00014941918,0.00005331324,0.00014392214,0.0004088785,0.000074618554],"domain_scores_gemma":[0.99958664,0.000097112934,0.00005065802,0.00008391697,0.00015092037,0.00003067356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007305503,0.00046781474,0.00038753467,0.0007516254,0.00043725353,0.0016091977,0.0013783191,0.0007856312,0.0025941078],"category_scores_gemma":[0.0012486647,0.00029922582,0.00071520486,0.00068292645,0.0006496945,0.0015276333,0.00067936326,0.00046582185,0.00039577472],"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":[0.00017709407,0.00020566737,0.004485936,0.0011085224,0.00014133762,0.0007112397,0.0010697623,0.120482676,0.10953417,0.16073395,0.002938612,0.598411],"study_design_scores_gemma":[0.00007266658,0.0005433097,0.003901577,0.00017714224,0.00015823841,0.000798619,0.0005221935,0.79008496,0.08516724,0.04811239,0.070381075,0.00008057262],"about_ca_topic_score_codex":0.0009732934,"about_ca_topic_score_gemma":0.000731586,"teacher_disagreement_score":0.0025941078,"about_ca_system_score_codex":0.0005673922,"about_ca_system_score_gemma":0.0010791899,"threshold_uncertainty_score":0.008678198},"labels":[],"label_agreement":null},{"id":"W4390948725","doi":"10.23977/jaip.2023.060814","title":"Research and Implementation of Innovative Design of Paper Cuttings Pattern Based on Artificial Intelligence Technology","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Creativity; Field (mathematics); Artificial intelligence; Cutting; Engineering; Computer science; Engineering management; Mathematics; Psychology","score_opus":0.18375312574241467,"score_gpt":0.4663903465380153,"score_spread":0.2826372207956006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390948725","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":[0.048084006,0.0013624228,0.9206049,0.00039637106,0.00015726434,0.00016468482,0.00002942693,0.00043889278,0.02876209],"genre_scores_gemma":[0.5727559,0.001761118,0.41256586,0.00013297959,0.000054149186,0.00020835327,0.00010123126,0.00008876752,0.012331714],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991026,0.00017796662,0.00005701874,0.00015087046,0.000433491,0.00007799694],"domain_scores_gemma":[0.99950755,0.00012382436,0.000057120353,0.00010266858,0.00017316428,0.000035698333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008030338,0.00046359905,0.00037285712,0.00081359525,0.00046922508,0.0017700199,0.0014227789,0.0007977035,0.0029028293],"category_scores_gemma":[0.001466582,0.00030581272,0.00069962745,0.0007320993,0.0007377428,0.001746033,0.0007240832,0.0004896832,0.00043450686],"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":[0.00018157226,0.00020630259,0.004138158,0.0011089509,0.00013059664,0.00075167726,0.0014248434,0.08637288,0.09947259,0.1941602,0.0031050576,0.6089473],"study_design_scores_gemma":[0.00008925034,0.00061376084,0.004448639,0.00023446616,0.0001831829,0.001054189,0.0007870438,0.735776,0.095234774,0.06675562,0.0947276,0.000095487914],"about_ca_topic_score_codex":0.00090628595,"about_ca_topic_score_gemma":0.0007003383,"teacher_disagreement_score":0.0029028293,"about_ca_system_score_codex":0.00060568826,"about_ca_system_score_gemma":0.0010799962,"threshold_uncertainty_score":0.009710968},"labels":[],"label_agreement":null},{"id":"W4390952029","doi":"10.23977/jaip.2023.060812","title":"The Application of Artificial Intelligence in Enterprise Auditing","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Research studies in Vietnam","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Audit; Computer science; Mindset; Audit plan; Information technology audit; Performance audit; Joint audit; Scope (computer science); Business intelligence; Internal audit; Work (physics); Information security audit; Process management; Knowledge management; Business; Accounting; Artificial intelligence; Engineering; Computer security","score_opus":0.05115084884072752,"score_gpt":0.3894133173915131,"score_spread":0.33826246855078557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390952029","genre_codex":"other","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":[0.052073054,0.18495567,0.21031618,0.07886639,0.0028803064,0.00044645657,0.0002202705,0.00047495854,0.46976665],"genre_scores_gemma":[0.70261467,0.11475902,0.15753338,0.0050177504,0.0017231115,0.00020719618,0.00014531851,0.00007741618,0.017922165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952178,0.0027959403,0.0002782328,0.00031808444,0.0012302812,0.00015975433],"domain_scores_gemma":[0.99458015,0.0037883925,0.00030234852,0.00043777944,0.0007402768,0.00015106179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038051284,0.00034037788,0.0003698175,0.0023652753,0.0010902766,0.0044600917,0.00081967196,0.0011508415,0.0020710437],"category_scores_gemma":[0.007916738,0.00025043782,0.00037005622,0.0030263169,0.0037721274,0.0032980337,0.0016957144,0.0015382788,0.00043518769],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004682011,0.00012608286,0.006378847,0.0016013762,0.00008749527,0.0005700546,0.0025688899,0.007852205,0.0011761369,0.49823368,0.0131039275,0.46825448],"study_design_scores_gemma":[0.00001884148,0.00012000113,0.008047715,0.00270141,0.000044802036,0.000783156,0.0033743016,0.022445574,0.0018730654,0.6134837,0.34701005,0.00009737563],"about_ca_topic_score_codex":0.003724427,"about_ca_topic_score_gemma":0.0026504262,"teacher_disagreement_score":0.0044600917,"about_ca_system_score_codex":0.0023217802,"about_ca_system_score_gemma":0.002799839,"threshold_uncertainty_score":0.02012372},"labels":[],"label_agreement":null},{"id":"W4390952031","doi":"10.23977/jaip.2023.060813","title":"The Role of Artificial Intelligence in Construction Management: A Case Study of Smart Worksite Systems","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"BIM and Construction Integration","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Digitization; Context (archaeology); Big data; Cloud computing; Knowledge management; Industry 4.0; Computer science; Quality (philosophy); Data science; Engineering; Telecommunications","score_opus":0.03066380018887798,"score_gpt":0.29638175419797114,"score_spread":0.26571795400909315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390952031","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":[0.93626505,0.00028722253,0.007615091,0.0029332119,0.000019555715,0.00024347218,0.00009327944,0.000054418237,0.052488733],"genre_scores_gemma":[0.99000823,0.00029582265,0.0050837076,0.00011879269,0.000010460163,0.00006183866,0.000055850967,0.000019443949,0.004345768],"study_design_codex":"qualitative","study_design_gemma":"case_report","domain_scores_codex":[0.9956821,0.002925795,0.00012631017,0.00017394603,0.00060117146,0.0004906104],"domain_scores_gemma":[0.9950883,0.0033157251,0.00031859797,0.000428766,0.00039506727,0.00045364545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036121805,0.00038294666,0.00039719266,0.0016446335,0.007356418,0.0050107646,0.0017354814,0.0033455817,0.002739562],"category_scores_gemma":[0.004967331,0.00027195955,0.00045665857,0.003036566,0.0054950095,0.0035364004,0.0033573173,0.0017957182,0.00044169778],"study_design_candidate":"case_report","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054252613,0.0053479043,0.098683774,0.0008540207,0.00017592665,0.033232052,0.33339804,0.081634924,0.0050773723,0.31525734,0.01865681,0.107139274],"study_design_scores_gemma":[0.00018785923,0.0010565701,0.04501628,0.00040872238,0.00009103914,0.0036629813,0.5738111,0.11723704,0.0048010414,0.038332246,0.21526572,0.00012947344],"about_ca_topic_score_codex":0.022713909,"about_ca_topic_score_gemma":0.048896115,"teacher_disagreement_score":0.022713909,"about_ca_system_score_codex":0.004639331,"about_ca_system_score_gemma":0.0027949295,"threshold_uncertainty_score":0.045163393},"labels":[],"label_agreement":null},{"id":"W4390952039","doi":"10.23977/jaip.2023.060811","title":"Tomato picking robot based on deep learning","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Upload; Robot; Computer science; Artificial intelligence; Maturity (psychological); Field (mathematics); Cloud computing; Simulation; Computer vision; Mathematics; Operating system","score_opus":0.05290586088582658,"score_gpt":0.3035048828069606,"score_spread":0.25059902192113404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390952039","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":[0.06411386,0.0011719414,0.9159052,0.0005337899,0.00027450893,0.000121955345,0.00020616374,0.0067013134,0.010971245],"genre_scores_gemma":[0.81665957,0.0006429668,0.1651948,0.00046553626,0.00005591746,0.00016456385,0.00054554566,0.0000915831,0.016179517],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99986076,0.000009102952,0.000005874433,0.00004828987,0.000043626176,0.00003240545],"domain_scores_gemma":[0.9999056,0.000016962947,0.000011569447,0.00000980551,0.00003984289,0.000016275675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017307895,0.00054406904,0.0005061788,0.00031857914,0.0004322279,0.00039789965,0.0010140375,0.00067724905,0.00281899],"category_scores_gemma":[0.00027379894,0.00035237495,0.0004278682,0.0002617385,0.00027287874,0.0006776681,0.0005982093,0.0006755689,0.00079780986],"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":[0.0003642269,0.00030649413,0.0039206306,0.00032294865,0.00012786877,0.00044934667,0.00012426794,0.35038695,0.064585544,0.004755976,0.011589384,0.56306636],"study_design_scores_gemma":[0.00001630949,0.00009871769,0.0008232008,0.000011502266,0.000021551039,0.00006020259,0.00001463163,0.989493,0.0060701803,0.0012412701,0.00213105,0.000018428907],"about_ca_topic_score_codex":0.00861036,"about_ca_topic_score_gemma":0.00947293,"teacher_disagreement_score":0.00861036,"about_ca_system_score_codex":0.00047536538,"about_ca_system_score_gemma":0.0009034115,"threshold_uncertainty_score":0.01712048},"labels":[],"label_agreement":null},{"id":"W4391139655","doi":"10.23977/jaip.2024.070102","title":"Conditional Diffusion Model for X-Ray Segmentation Data Generation","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Diffusion; Computer science; Artificial intelligence; Physics","score_opus":0.1984553765418792,"score_gpt":0.440134561485128,"score_spread":0.24167918494324878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391139655","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":[0.012374535,0.0002870448,0.983093,0.0004074886,0.00006629165,0.00005767901,0.00025144932,0.00201166,0.0014507821],"genre_scores_gemma":[0.65517443,0.00046527162,0.3337246,0.0005034443,0.00008441617,0.00029671675,0.0016415983,0.0005761563,0.0075333337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996395,0.00007025698,0.000021881728,0.0001289877,0.00010191552,0.00003736106],"domain_scores_gemma":[0.99925226,0.00041247957,0.000058685993,0.00007457221,0.00015993073,0.000041912757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009882142,0.0007152335,0.0005261289,0.0006943253,0.00029422017,0.0008035463,0.0014406041,0.0010930658,0.0036425446],"category_scores_gemma":[0.0029532856,0.00043852837,0.0008091608,0.0005311339,0.0007258263,0.0011385542,0.00088602054,0.0015454656,0.0006308835],"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":[0.00018631034,0.00005731261,0.00061329943,0.0000863188,0.00003280119,0.00012063529,0.00007288603,0.88162446,0.008389572,0.017209098,0.0037446741,0.087862626],"study_design_scores_gemma":[0.0000039894035,0.000008122751,0.00003498658,0.000002045159,0.0000019820282,0.000013062039,0.0000017429527,0.9960366,0.0014297017,0.0020403932,0.00042450422,0.0000028520963],"about_ca_topic_score_codex":0.0109392,"about_ca_topic_score_gemma":0.00964072,"teacher_disagreement_score":0.0109392,"about_ca_system_score_codex":0.0013407865,"about_ca_system_score_gemma":0.0009680078,"threshold_uncertainty_score":0.021751046},"labels":[],"label_agreement":null},{"id":"W4391140106","doi":"10.23977/jaip.2024.070101","title":"Application of Deep Learning in Cross-Lingual Sentiment Analysis for Natural Language Processing","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Natural language processing; Sentiment analysis; Computer science; Artificial intelligence; Natural (archaeology); Deep learning; History; Archaeology","score_opus":0.03160256699623334,"score_gpt":0.39883897351690345,"score_spread":0.36723640652067013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391140106","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":[0.084151015,0.0021025992,0.8936394,0.0025508874,0.00055122474,0.00024627856,0.0012780122,0.0025241396,0.01295652],"genre_scores_gemma":[0.65381145,0.0016959942,0.33433107,0.0007924849,0.00029236556,0.00026752477,0.002705594,0.00024103156,0.0058625108],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987835,0.00048681154,0.00013192027,0.00022609926,0.00026418385,0.000107389],"domain_scores_gemma":[0.9981395,0.00073657616,0.00017236221,0.0001936605,0.0006934549,0.00006438709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002469136,0.0008439377,0.0004993182,0.0015615239,0.00057537126,0.0017590123,0.0005703339,0.0006803922,0.002573469],"category_scores_gemma":[0.005934635,0.00025390185,0.0007913366,0.0013593134,0.00041058916,0.002169156,0.001544727,0.0015265965,0.0012277991],"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":[0.00031063167,0.00036057073,0.0121292835,0.0005583484,0.00036352873,0.00036926506,0.0008174569,0.051282395,0.0342887,0.016314741,0.017749395,0.86545575],"study_design_scores_gemma":[0.000017577198,0.00010094481,0.0044631124,0.000104199615,0.00008872738,0.00010660769,0.0004475268,0.93651915,0.013647703,0.031041833,0.013427059,0.00003558451],"about_ca_topic_score_codex":0.0033187664,"about_ca_topic_score_gemma":0.0044226632,"teacher_disagreement_score":0.0033187664,"about_ca_system_score_codex":0.0010101208,"about_ca_system_score_gemma":0.0009833842,"threshold_uncertainty_score":0.013058186},"labels":[],"label_agreement":null},{"id":"W4391296218","doi":"10.23977/jaip.2024.070103","title":"Application of Artificial Intelligence in Computer Network Technology in the Age of Big Data","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and Big Data Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Big data; Computer science; Artificial intelligence; Data science; Data mining","score_opus":0.130066825457171,"score_gpt":0.3775966045954637,"score_spread":0.2475297791382927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391296218","genre_codex":"review","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":[0.010555748,0.31799406,0.2364944,0.26367223,0.00771349,0.0001838737,0.0003198705,0.00074889726,0.16231738],"genre_scores_gemma":[0.34367788,0.36562783,0.22008666,0.036568724,0.016772773,0.0003871425,0.00035526158,0.00033892217,0.016184777],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99452484,0.0025323895,0.0002545286,0.0004391978,0.0020555223,0.00019352348],"domain_scores_gemma":[0.98409724,0.011376829,0.0004942326,0.0017555101,0.0016980766,0.0005780545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007055997,0.00071497215,0.00090746325,0.0029162324,0.0014239447,0.007946948,0.0016904667,0.0036945606,0.0036196175],"category_scores_gemma":[0.016986473,0.00045193874,0.00058054825,0.0036256178,0.008067618,0.0128290085,0.004086575,0.0067726597,0.0013816106],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053352807,0.00007217767,0.001565533,0.001047355,0.00008468495,0.00024054335,0.00093450997,0.004025822,0.00063493033,0.802955,0.03302509,0.15536112],"study_design_scores_gemma":[0.000013503021,0.00003289539,0.0005679411,0.000835871,0.000019752928,0.0001837973,0.0005300851,0.010188879,0.00046784797,0.79792666,0.18919608,0.00003673104],"about_ca_topic_score_codex":0.0012215083,"about_ca_topic_score_gemma":0.0010119684,"teacher_disagreement_score":0.007946948,"about_ca_system_score_codex":0.0022066957,"about_ca_system_score_gemma":0.0026542975,"threshold_uncertainty_score":0.037316144},"labels":[],"label_agreement":null},{"id":"W4391547320","doi":"10.23977/jaip.2024.070104","title":"Development of Digital English Education in the Context of Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Innovations and Challenges","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Jiangsu Provincial Department of Education","keywords":"Context (archaeology); Computer science; Artificial intelligence; History","score_opus":0.09538267875696871,"score_gpt":0.38222635217307105,"score_spread":0.28684367341610234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391547320","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66777813,0.003473554,0.00635376,0.015489456,0.00019677708,0.000104883235,0.00004370679,0.00006226293,0.30649748],"genre_scores_gemma":[0.98813975,0.00096666237,0.0028842909,0.0002811964,0.000025307,0.000017877399,0.000016560529,0.0000039505267,0.0076643205],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933773,0.00028557857,0.00004353571,0.000058321424,0.00012765301,0.00014711039],"domain_scores_gemma":[0.99895203,0.0002373444,0.00015934184,0.00004127386,0.0001977376,0.00041231277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097044837,0.00009467417,0.000084375846,0.0008936104,0.0011668613,0.003366099,0.00029135312,0.00034251035,0.0023114735],"category_scores_gemma":[0.0014977918,0.00006366167,0.00011727364,0.00070239796,0.0014256183,0.0024451842,0.0016441669,0.00057867577,0.0002662978],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000663483,0.00060097995,0.06334609,0.0005172085,0.000015620502,0.0025530714,0.06079625,0.0007393462,0.0038211632,0.48789766,0.0062678843,0.3733784],"study_design_scores_gemma":[0.000036549874,0.00042521962,0.16956447,0.0012049146,0.000034532095,0.0017595723,0.15131256,0.0052268123,0.006067683,0.06381595,0.60050195,0.000049811417],"about_ca_topic_score_codex":0.002794374,"about_ca_topic_score_gemma":0.004137757,"teacher_disagreement_score":0.003366099,"about_ca_system_score_codex":0.0022062936,"about_ca_system_score_gemma":0.0047850744,"threshold_uncertainty_score":0.0160079},"labels":[],"label_agreement":null},{"id":"W4391547395","doi":"10.23977/jaip.2024.070105","title":"Vehicle Target Detection Algorithm Based on Improved Faster R-CNN for Remote Sensing Images","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Remote sensing; Computer vision; Algorithm; Pattern recognition (psychology); Geology","score_opus":0.04545809046148359,"score_gpt":0.34413275237751073,"score_spread":0.29867466191602715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391547395","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":[0.06453107,0.0008335899,0.92507064,0.00023208365,0.00012866058,0.00012490744,0.00019478801,0.0035116437,0.005372717],"genre_scores_gemma":[0.57727885,0.00080438686,0.40915665,0.00048680798,0.00010195159,0.00014901046,0.0008136313,0.00021538699,0.010993303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996469,0.000023453129,0.000017547749,0.0001279392,0.00011828313,0.00006582029],"domain_scores_gemma":[0.99974686,0.00003916214,0.00003370506,0.00004241413,0.00012242926,0.000015440444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044535022,0.000934922,0.0006730927,0.00077253487,0.00026593855,0.0005627724,0.0012223498,0.00068522297,0.0023824773],"category_scores_gemma":[0.0007502961,0.00036098645,0.00069501746,0.00054752105,0.00026336988,0.0013095351,0.00066630245,0.00060962647,0.00083794544],"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":[0.0002684497,0.00012353946,0.0033019378,0.00018414171,0.00014519709,0.00032820643,0.00008465379,0.12726995,0.12539819,0.0043615783,0.0051378794,0.73339635],"study_design_scores_gemma":[0.000015442421,0.000098502765,0.001606726,0.00001071505,0.000046108446,0.00021230323,0.000015779377,0.9669767,0.027554402,0.0010213878,0.0024215323,0.000020388256],"about_ca_topic_score_codex":0.00846849,"about_ca_topic_score_gemma":0.00901909,"teacher_disagreement_score":0.00846849,"about_ca_system_score_codex":0.00070077553,"about_ca_system_score_gemma":0.0007132357,"threshold_uncertainty_score":0.016838372},"labels":[],"label_agreement":null},{"id":"W4392400363","doi":"10.23977/jaip.2024.070110","title":"Exploration of Deep Learning Evaluation from the Perspective of Multimodal Data Analysis","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Computer science; Deep learning; Artificial intelligence; Machine learning; Natural language processing; Data science","score_opus":0.28501128768420153,"score_gpt":0.5199762326703001,"score_spread":0.2349649449860986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392400363","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":[0.029243743,0.0055533946,0.95105076,0.0055733626,0.00010477472,0.00011809024,0.00011536809,0.00014027816,0.008100173],"genre_scores_gemma":[0.79216474,0.0031103506,0.20134215,0.0006159816,0.00027700883,0.00020768483,0.00014627344,0.000071857576,0.0020639854],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929864,0.004411349,0.00038131158,0.00065955427,0.0013035677,0.00025787094],"domain_scores_gemma":[0.9883952,0.008381407,0.0006516263,0.0005218673,0.0016703776,0.00037955452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011107684,0.0010290816,0.0010783713,0.00243312,0.00045646992,0.0036062184,0.0010210692,0.0012062611,0.002226249],"category_scores_gemma":[0.024891552,0.0003299117,0.0006984746,0.0014950417,0.0020636723,0.004529995,0.00265188,0.0022573671,0.00014355633],"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":[0.0003655858,0.00037250933,0.009607821,0.0010503248,0.00042425157,0.00033347367,0.0007263933,0.14903611,0.004398076,0.3922631,0.004134511,0.43728784],"study_design_scores_gemma":[0.000020305057,0.00017554515,0.0014917452,0.00015291445,0.00006528862,0.00009277313,0.00021633026,0.7537773,0.0017954338,0.24000083,0.002182376,0.000029167039],"about_ca_topic_score_codex":0.0020132363,"about_ca_topic_score_gemma":0.0017578405,"teacher_disagreement_score":0.011107684,"about_ca_system_score_codex":0.0022060783,"about_ca_system_score_gemma":0.0023107084,"threshold_uncertainty_score":0.058743775},"labels":[],"label_agreement":null},{"id":"W4392565694","doi":"10.23977/jaip.2024.070111","title":"The impact and challenges of AI on the legal industry","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Business","score_opus":0.15713039480680663,"score_gpt":0.45816913078100896,"score_spread":0.30103873597420233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392565694","genre_codex":"commentary","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":[0.059058458,0.04532609,0.00826608,0.5267788,0.0015995336,0.00007099516,0.0001198877,0.000072596595,0.35870758],"genre_scores_gemma":[0.88187665,0.062034823,0.007615493,0.030190641,0.0031842585,0.00010560932,0.000094133095,0.0000715539,0.014826837],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.983452,0.008413361,0.0004342285,0.00096854975,0.005252695,0.0014791286],"domain_scores_gemma":[0.9542621,0.031373564,0.002867986,0.0012988492,0.0067723403,0.0034251395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0109820785,0.00039738844,0.00050407567,0.0028783262,0.007970093,0.01579684,0.0015267027,0.005004732,0.008631659],"category_scores_gemma":[0.024747767,0.0004192818,0.00055624236,0.0030739286,0.016364349,0.019809041,0.0061256024,0.00718231,0.0017173176],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006527518,0.00023855362,0.013017422,0.00082852936,0.00004009367,0.0011729117,0.01324281,0.0028616677,0.0005376745,0.75269693,0.042051937,0.17324609],"study_design_scores_gemma":[0.000017121074,0.000108232256,0.010984531,0.002137974,0.00002952833,0.0009101857,0.05073092,0.0058541507,0.0005995038,0.5914277,0.3370864,0.00011372494],"about_ca_topic_score_codex":0.008410718,"about_ca_topic_score_gemma":0.00876193,"teacher_disagreement_score":0.01579684,"about_ca_system_score_codex":0.009081097,"about_ca_system_score_gemma":0.011350418,"threshold_uncertainty_score":0.065888286},"labels":[],"label_agreement":null},{"id":"W4392713417","doi":"10.23977/jaip.2024.070112","title":"Optimization of Charging Strategies for New Energy Vehicles Based on Reinforcement Learning Algorithms","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Computer science; Optimization algorithm; Energy (signal processing); Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.02341516628577652,"score_gpt":0.2862477507464974,"score_spread":0.2628325844607209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392713417","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":[0.060561564,0.0004857474,0.9319533,0.00031901165,0.000102031074,0.00010944311,0.00003085325,0.00031548983,0.0061224545],"genre_scores_gemma":[0.9683894,0.00016004624,0.028892007,0.00012389277,0.000031912656,0.000112032234,0.00004248555,0.000025323354,0.0022230956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956757,0.00014007153,0.000023806377,0.00007920965,0.000088428314,0.00010087405],"domain_scores_gemma":[0.9988815,0.0006447556,0.00016042335,0.000030750798,0.00020612482,0.00007651564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009124924,0.0008471007,0.0013899859,0.00053484325,0.00036889527,0.0007915889,0.000987378,0.0008931583,0.0018838403],"category_scores_gemma":[0.0024915293,0.00039706094,0.0005840776,0.00031222266,0.0006997942,0.0005887061,0.00082512666,0.00089510804,0.0001994201],"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":[0.000029011146,0.000032761887,0.00039212473,0.000022769567,0.000019875675,0.000033258715,0.000017837028,0.98801583,0.00038773494,0.0016473659,0.00027377202,0.009127709],"study_design_scores_gemma":[0.000006444143,0.000010724277,0.000034024393,0.0000015523467,0.0000026106557,0.0000029595728,0.0000029935368,0.999413,0.000043719196,0.00042147853,0.000058991736,0.0000014560464],"about_ca_topic_score_codex":0.0076109273,"about_ca_topic_score_gemma":0.004507445,"teacher_disagreement_score":0.0076109273,"about_ca_system_score_codex":0.0008157775,"about_ca_system_score_gemma":0.0011177443,"threshold_uncertainty_score":0.015133262},"labels":[],"label_agreement":null},{"id":"W4392775576","doi":"10.23977/jaip.2024.070113","title":"The role of digital technology in schools in France","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Education and Technology Integration","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Political science","score_opus":0.027315578420611674,"score_gpt":0.39095934896271184,"score_spread":0.3636437705421002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392775576","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83886284,0.019533964,0.0011204041,0.035571337,0.00033445624,0.000046852278,0.00023082885,0.00016140012,0.10413797],"genre_scores_gemma":[0.9860802,0.0020021007,0.00031265942,0.0006500722,0.00004718543,0.000014386202,0.000035033314,0.000010806256,0.010847605],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.993604,0.0033688536,0.00018506541,0.00056360895,0.0008211151,0.0014574302],"domain_scores_gemma":[0.9954184,0.0015947947,0.00091213785,0.00013497863,0.0005188833,0.0014207149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029430112,0.0003156237,0.00036465714,0.003196759,0.0062573147,0.009451116,0.00069767097,0.002307426,0.008177546],"category_scores_gemma":[0.0036960365,0.00027005732,0.0004106882,0.002311257,0.0041461275,0.0028054377,0.0043460303,0.0015557397,0.0008998296],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000258549,0.00076257123,0.16532218,0.00094728643,0.000082570026,0.00479008,0.13212684,0.0019279697,0.0019003751,0.42246437,0.016597679,0.25281954],"study_design_scores_gemma":[0.0000473883,0.0003668128,0.27233648,0.0013205885,0.000044793105,0.0010696308,0.09418808,0.0009852012,0.00077511225,0.010704957,0.61805594,0.00010508812],"about_ca_topic_score_codex":0.09304762,"about_ca_topic_score_gemma":0.07589991,"teacher_disagreement_score":0.09304762,"about_ca_system_score_codex":0.020207252,"about_ca_system_score_gemma":0.011824999,"threshold_uncertainty_score":0.18501204},"labels":[],"label_agreement":null},{"id":"W4392838374","doi":"10.23977/jaip.2024.070108","title":"The research on banknote authenticity discrimination analysis algorithm based on wavelet transform features","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Banknote; Wavelet; Computer science; Artificial intelligence; Wavelet transform; Pattern recognition (psychology); Algorithm; Computer vision","score_opus":0.12594016363380794,"score_gpt":0.432988656651899,"score_spread":0.30704849301809106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392838374","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":[0.08292,0.002771162,0.90823406,0.00039052317,0.0002698906,0.00008402482,0.0001231096,0.0005414449,0.004665709],"genre_scores_gemma":[0.7483332,0.0052265315,0.23653154,0.0001850262,0.00033026712,0.000115253766,0.0006718851,0.000085357075,0.008520882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992279,0.00009692552,0.0000657058,0.00019273737,0.0003537008,0.00006308906],"domain_scores_gemma":[0.9992505,0.00018876696,0.00007155636,0.000073469455,0.00037890155,0.00003669735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010014309,0.0006259603,0.0008163354,0.0020019456,0.0004196389,0.001113838,0.0004928358,0.0005417753,0.0013661783],"category_scores_gemma":[0.0022393132,0.00022030977,0.0007464283,0.0016994074,0.00045654477,0.002061365,0.00043218568,0.00083831965,0.0007013876],"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":[0.00022826367,0.00012143263,0.0061595584,0.00021289258,0.00007351676,0.00015866253,0.00013056041,0.018743884,0.037412275,0.01136569,0.003159719,0.9222336],"study_design_scores_gemma":[0.000057795853,0.0004479535,0.01746814,0.000097496144,0.0002008719,0.0013410638,0.00032953374,0.8826072,0.0648616,0.0130568445,0.019411575,0.00011995204],"about_ca_topic_score_codex":0.0010517115,"about_ca_topic_score_gemma":0.0006013431,"teacher_disagreement_score":0.0020019456,"about_ca_system_score_codex":0.00039491127,"about_ca_system_score_gemma":0.00054101256,"threshold_uncertainty_score":0.0052961707},"labels":[],"label_agreement":null},{"id":"W4392838595","doi":"10.23977/jaip.2024.070109","title":"Futuristic Predictive Artificial Intelligent Model: \"Prometheus\"","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Homelessness and Social Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.14036955078257027,"score_gpt":0.4900857295258602,"score_spread":0.34971617874329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392838595","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":[0.03979058,0.0026332894,0.8776038,0.009759418,0.00067047967,0.00013961866,0.00061766006,0.0008711865,0.06791385],"genre_scores_gemma":[0.8868279,0.0017821782,0.07877467,0.0013562188,0.0003840378,0.00033323074,0.00042900527,0.00009253414,0.03002017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997795,0.00008277434,0.000008306088,0.000051710016,0.000050207476,0.00002749228],"domain_scores_gemma":[0.9996216,0.00023001368,0.000027447755,0.000027193666,0.00006858573,0.000025110345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069359154,0.0006384657,0.0006416689,0.00038958713,0.00048448044,0.0018176844,0.0016642397,0.0015972765,0.00635983],"category_scores_gemma":[0.0016912298,0.0003271458,0.00055256346,0.00044598492,0.0010475961,0.0011880203,0.0012965928,0.001541733,0.00095232285],"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":[0.00012398954,0.00007168385,0.0014969828,0.00015292305,0.00007901589,0.0002736103,0.00019545582,0.78392774,0.0006092534,0.16238874,0.007606334,0.043074224],"study_design_scores_gemma":[0.00000997802,0.000021463768,0.00007750518,0.000017644908,0.000010891623,0.000021294869,0.000013244572,0.97074425,0.00009990594,0.026337635,0.0026394948,0.0000066338757],"about_ca_topic_score_codex":0.006192162,"about_ca_topic_score_gemma":0.004173267,"teacher_disagreement_score":0.00635983,"about_ca_system_score_codex":0.00080930383,"about_ca_system_score_gemma":0.0011257667,"threshold_uncertainty_score":0.0212757},"labels":[],"label_agreement":null},{"id":"W4392838684","doi":"10.23977/jaip.2024.070107","title":"Research on the Application of ChatGPT in the Interdisciplinars of Higher Education","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Mathematics education; Psychology; Pedagogy","score_opus":0.34362634982504486,"score_gpt":0.5845134608992846,"score_spread":0.2408871110742397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392838684","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":[0.55978835,0.020332895,0.15769824,0.020329736,0.0018148663,0.0012297356,0.0004646886,0.0011930248,0.23714854],"genre_scores_gemma":[0.94811034,0.005789707,0.03254251,0.0020060958,0.000392477,0.0010077509,0.00022301944,0.00015741319,0.0097707035],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98037153,0.015348925,0.00058471545,0.0011615714,0.0020252997,0.0005079093],"domain_scores_gemma":[0.78734446,0.17477101,0.008772532,0.0149022145,0.0078005353,0.0064092954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018892521,0.00045311835,0.00047241832,0.0033171582,0.0032839288,0.0064010364,0.002140742,0.0019188549,0.0108755445],"category_scores_gemma":[0.079493366,0.00038153608,0.00046284046,0.004162078,0.0049100374,0.009769344,0.008309827,0.0027006483,0.001711652],"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":[0.00030740153,0.000533102,0.028260915,0.006799703,0.00011393262,0.0011544082,0.22285472,0.0013630112,0.0059235906,0.128237,0.010334769,0.5941175],"study_design_scores_gemma":[0.00017907844,0.0017391931,0.07577005,0.01403186,0.00027122427,0.004223099,0.26689464,0.00544681,0.009160976,0.082150996,0.5398656,0.00026642412],"about_ca_topic_score_codex":0.0013140768,"about_ca_topic_score_gemma":0.00289401,"teacher_disagreement_score":0.018892521,"about_ca_system_score_codex":0.0031982802,"about_ca_system_score_gemma":0.0055943374,"threshold_uncertainty_score":0.09991443},"labels":[],"label_agreement":null},{"id":"W4392985035","doi":"10.23977/jaip.2024.070115","title":"Robot Trajectory Planning and Simulation Based on Matlab Robotics Toolbox","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Toolbox; Robotics; Artificial intelligence; MATLAB; Computer science; Trajectory; Robot; Computer vision; Control engineering; Simulation; Human–computer interaction; Engineering; Programming language; Physics","score_opus":0.07610961322348701,"score_gpt":0.3674769715951302,"score_spread":0.2913673583716432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392985035","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":[0.010314155,0.00017230453,0.9463119,0.00013243136,0.00008965448,0.00016420525,0.0011533708,0.025182007,0.016480016],"genre_scores_gemma":[0.23945345,0.0006737692,0.73321444,0.00013922856,0.000029780387,0.0017589668,0.0027566636,0.0033001744,0.018673567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996991,0.00006613229,0.000031095035,0.00004842437,0.00012033397,0.00003482927],"domain_scores_gemma":[0.99950886,0.00019717986,0.000040555893,0.00006667071,0.00016382852,0.000022928683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005582452,0.00090359914,0.0007270433,0.00075094163,0.00045528184,0.0005709719,0.0012446169,0.00061769586,0.0218208],"category_scores_gemma":[0.0010850277,0.00035289532,0.0005692244,0.0004983739,0.0003320888,0.0005979446,0.0007125988,0.00087944657,0.0035663466],"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":[0.00031910176,0.00019676844,0.0015416271,0.0011456619,0.00009596728,0.0005427421,0.00045255083,0.7572982,0.018564729,0.03851677,0.025021173,0.15630467],"study_design_scores_gemma":[0.00005157495,0.00006072085,0.00030052563,0.00005079986,0.000018472298,0.00013513946,0.000037638267,0.962122,0.009441347,0.0040469524,0.023706587,0.000028346629],"about_ca_topic_score_codex":0.005429564,"about_ca_topic_score_gemma":0.003191712,"teacher_disagreement_score":0.0218208,"about_ca_system_score_codex":0.00033993315,"about_ca_system_score_gemma":0.0008569146,"threshold_uncertainty_score":0.07299787},"labels":[],"label_agreement":null},{"id":"W4392985066","doi":"10.23977/jaip.2024.070114","title":"Graph Convolutional Networks for Aspect-Based Sentiment Analysis","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Computer science; Graph; Artificial intelligence; Natural language processing; Data science; Theoretical computer science","score_opus":0.05102887343383029,"score_gpt":0.35596288285447353,"score_spread":0.30493400942064325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392985066","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":[0.04997789,0.0011643369,0.9380387,0.0006467968,0.00015807668,0.00007077,0.0007061013,0.0032820497,0.005955279],"genre_scores_gemma":[0.7595486,0.0013976158,0.22600771,0.00032882366,0.00012109743,0.00012601823,0.0023833176,0.00027229675,0.00981453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998723,0.000022419492,0.000007734799,0.000036315683,0.00003335885,0.000027778477],"domain_scores_gemma":[0.99979645,0.00006153841,0.000033520995,0.00002836297,0.000067293375,0.00001283041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003076313,0.0007381929,0.0003090715,0.00076098996,0.0002740682,0.00058542355,0.0006997352,0.00062301883,0.0022351067],"category_scores_gemma":[0.00095702754,0.00028821322,0.0006490561,0.0008746602,0.0003024692,0.0010074367,0.0004677151,0.00095295184,0.0007303715],"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":[0.0003004094,0.0001635815,0.0039678006,0.00021987992,0.00029160903,0.00026499634,0.0001504462,0.3700214,0.05268213,0.04721038,0.017632196,0.5070952],"study_design_scores_gemma":[0.000003335309,0.0000143477555,0.0005342577,0.0000061450296,0.000017687875,0.0000164543,0.000007096466,0.9837114,0.0025095714,0.0116360625,0.0015381831,0.0000054970233],"about_ca_topic_score_codex":0.011278371,"about_ca_topic_score_gemma":0.016620768,"teacher_disagreement_score":0.011278371,"about_ca_system_score_codex":0.0009908259,"about_ca_system_score_gemma":0.0005617895,"threshold_uncertainty_score":0.022425413},"labels":[],"label_agreement":null},{"id":"W4393072406","doi":"10.23977/jaip.2024.070116","title":"The Construction of ACM Practice Bases for the Cultivation of University Students' Technological","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Higher Education and Teaching Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Computer science; Mathematics education; Sociology; Engineering ethics; Engineering; Psychology","score_opus":0.08956385754955418,"score_gpt":0.43535112763217765,"score_spread":0.34578727008262344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393072406","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":[0.47939515,0.00060228904,0.33577526,0.013016324,0.0005584483,0.00096220954,0.000100323356,0.0010987646,0.16849126],"genre_scores_gemma":[0.7827531,0.0003901452,0.19358012,0.0005617416,0.00013173136,0.0006647587,0.00015449015,0.0001768374,0.021587068],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98934096,0.005528292,0.00050498545,0.00091481395,0.0026912133,0.0010196503],"domain_scores_gemma":[0.96463716,0.008783614,0.0026613292,0.0077897394,0.006063188,0.010065058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01360577,0.00050596247,0.00034153002,0.0025023762,0.004733515,0.013037493,0.0020963158,0.0017025198,0.0047243587],"category_scores_gemma":[0.024479358,0.00061984133,0.0004201316,0.0018786141,0.0064518214,0.005380754,0.012956134,0.0034967943,0.0015618264],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006921024,0.0013298762,0.02363417,0.0002479389,0.000026392841,0.00054182723,0.08262509,0.002885236,0.0083635375,0.37220412,0.014137403,0.4939352],"study_design_scores_gemma":[0.00007019091,0.0010803101,0.028250545,0.0011882086,0.000038176768,0.0008381231,0.09052003,0.017320406,0.00851906,0.22159673,0.63037056,0.00020766952],"about_ca_topic_score_codex":0.0013808951,"about_ca_topic_score_gemma":0.0030557443,"teacher_disagreement_score":0.01360577,"about_ca_system_score_codex":0.0036151116,"about_ca_system_score_gemma":0.011249944,"threshold_uncertainty_score":0.071955085},"labels":[],"label_agreement":null},{"id":"W4393284893","doi":"10.23977/jaip.2024.070117","title":"Optimization and Application of Natural Language Processing Models Based on Deep Learning","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Natural (archaeology); Deep learning; Artificial intelligence; Natural language processing; History; Archaeology","score_opus":0.03176149367351076,"score_gpt":0.3681730571005958,"score_spread":0.336411563427085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393284893","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":[0.021444025,0.0008250793,0.9719721,0.0006379192,0.000070461036,0.000056530866,0.00008528746,0.0005837251,0.004324954],"genre_scores_gemma":[0.7017672,0.0011890534,0.29019022,0.0003248031,0.000078233665,0.00031712657,0.00038316147,0.00029764025,0.005452553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995678,0.00014911122,0.000026768765,0.00010998516,0.00009415518,0.000052126717],"domain_scores_gemma":[0.999113,0.0005682243,0.00007492842,0.000060133287,0.00015182118,0.00003183914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011344856,0.001000178,0.0008555134,0.00049085694,0.0003456092,0.0011651054,0.0010333089,0.0010997537,0.0017091801],"category_scores_gemma":[0.004080424,0.00066681195,0.00074881746,0.00051508524,0.00066861697,0.0016953519,0.0012341524,0.0018013375,0.00040310613],"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":[0.000017597788,0.000022964192,0.0002334128,0.000053179407,0.000022609029,0.000023472794,0.000022459713,0.9698504,0.00074127235,0.00639488,0.00050023576,0.022117557],"study_design_scores_gemma":[0.0000013430098,0.0000039712904,0.000015878575,0.00000314303,0.0000018490986,0.0000020545294,0.0000023727437,0.9976502,0.00015569266,0.002026965,0.00013540196,0.0000011378589],"about_ca_topic_score_codex":0.0075995405,"about_ca_topic_score_gemma":0.008661992,"teacher_disagreement_score":0.0075995405,"about_ca_system_score_codex":0.0013435001,"about_ca_system_score_gemma":0.0019213769,"threshold_uncertainty_score":0.015110612},"labels":[],"label_agreement":null},{"id":"W4393284909","doi":"10.23977/jaip.2024.070118","title":"Research on Cloud Detection in Non-agricultural Image Based on Long Time Series Data","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cloud computing; Series (stratigraphy); Time series; Agriculture; Computer science; Image (mathematics); Data science; Remote sensing; Data mining; Computer vision; Geography; Machine learning; Geology; Operating system; Archaeology","score_opus":0.09050421242554399,"score_gpt":0.37417365167042094,"score_spread":0.283669439244877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393284909","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":[0.49804875,0.0054502203,0.49039444,0.00078984257,0.00032192597,0.00011307845,0.0007756977,0.00065997685,0.0034461205],"genre_scores_gemma":[0.9096978,0.0038536917,0.08257229,0.00012054812,0.00027102177,0.000048517388,0.0016310586,0.00006312718,0.0017420629],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99939203,0.00006791228,0.000045070825,0.0001806754,0.00024047462,0.00007389363],"domain_scores_gemma":[0.9984993,0.00061517477,0.00028879746,0.00015704101,0.00035399257,0.000085799154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008081529,0.0006587527,0.0005925433,0.0022230633,0.00037006597,0.00093480543,0.0007779703,0.0005224922,0.0005443876],"category_scores_gemma":[0.002423996,0.0002352125,0.00069010234,0.0029023353,0.00035023468,0.0017257909,0.00028029035,0.00060849567,0.00027878056],"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":[0.00031150138,0.00041460997,0.07725569,0.0005872031,0.00032328928,0.00070970546,0.00024743332,0.14994118,0.056270793,0.0067956615,0.0028276574,0.7043153],"study_design_scores_gemma":[0.000005958747,0.000073538016,0.03645939,0.000026109374,0.000041730458,0.0001521269,0.00013676706,0.95086455,0.008490117,0.001910375,0.0018147088,0.000024705954],"about_ca_topic_score_codex":0.008123487,"about_ca_topic_score_gemma":0.007038726,"teacher_disagreement_score":0.008123487,"about_ca_system_score_codex":0.0005484625,"about_ca_system_score_gemma":0.00061958114,"threshold_uncertainty_score":0.016152382},"labels":[],"label_agreement":null},{"id":"W4393375471","doi":"10.23977/jaip.2024.070120","title":"Research on the application of decision tree algorithm in private universities","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Education Department of Jiangxi Province","keywords":"Decision tree; Computer science; Decision tree learning; ID3 algorithm; Tree (set theory); Operations research; Algorithm; Incremental decision tree; Engineering; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.10967275925352202,"score_gpt":0.39873106224180244,"score_spread":0.2890583029882804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393375471","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":[0.09070247,0.003670711,0.89175206,0.0019256377,0.00026365678,0.00016421109,0.0001710746,0.00049267535,0.010857619],"genre_scores_gemma":[0.6643615,0.003667521,0.32841104,0.00026239213,0.00028385187,0.00014918552,0.00032674172,0.00006425525,0.0024734742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993295,0.0035454377,0.00042353082,0.0008073316,0.0016291883,0.0002993909],"domain_scores_gemma":[0.9642972,0.028967742,0.0010622938,0.0012675088,0.0039572557,0.00044799648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00816725,0.00057421427,0.0011677159,0.0029548148,0.0010249912,0.002664395,0.0016738982,0.0016301412,0.0027965913],"category_scores_gemma":[0.028341403,0.00050640176,0.00090549275,0.0050817025,0.00088244956,0.0048155743,0.00068141555,0.0017881154,0.00068719033],"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":[0.00032227833,0.00067527144,0.021695802,0.0004222687,0.00029216133,0.00017503828,0.00067162287,0.26386783,0.0016457523,0.072798096,0.004274988,0.6331589],"study_design_scores_gemma":[0.000026487269,0.00014745852,0.0026229601,0.000092241375,0.000043406464,0.000115903116,0.00027091062,0.9497968,0.0012803889,0.03848329,0.0070908465,0.000029267778],"about_ca_topic_score_codex":0.008319558,"about_ca_topic_score_gemma":0.0042069554,"teacher_disagreement_score":0.008319558,"about_ca_system_score_codex":0.0014205303,"about_ca_system_score_gemma":0.0021413034,"threshold_uncertainty_score":0.043193102},"labels":[],"label_agreement":null},{"id":"W4393375513","doi":"10.23977/jaip.2024.070119","title":"Rule-based Matching and Hidden Markov Model-based Warning for Brushing Behavior","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Matching (statistics); Computer science; Artificial intelligence; Hidden Markov model; Markov chain; Pattern recognition (psychology); Machine learning; Statistics; Mathematics","score_opus":0.03661174568892891,"score_gpt":0.3350181258495341,"score_spread":0.2984063801606052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393375513","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":[0.07151509,0.0001804937,0.92051804,0.00021788105,0.00009496766,0.00017762222,0.00019410277,0.0048471075,0.0022547152],"genre_scores_gemma":[0.82884777,0.00011968596,0.16808979,0.00018490817,0.000030626823,0.0001097089,0.00031880042,0.00009183471,0.0022068757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998931,0.00018673023,0.00011164067,0.00030592564,0.00036866678,0.00009598665],"domain_scores_gemma":[0.99742764,0.0012927172,0.0003523762,0.00025440633,0.00054479425,0.00012802386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013625108,0.00061901804,0.0010822775,0.00091893756,0.00037536037,0.000699049,0.0011936659,0.0008639902,0.0012645826],"category_scores_gemma":[0.0066934726,0.00037170565,0.000714572,0.00046291546,0.0003368993,0.0014920358,0.0007734564,0.00087383675,0.0006400592],"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":[0.0010196615,0.0009926908,0.022607915,0.00031037242,0.00025699925,0.00050638017,0.00053017674,0.29244256,0.023076808,0.0073370445,0.003802982,0.6471164],"study_design_scores_gemma":[0.000018526303,0.00008969759,0.0018948026,0.000013201255,0.00003002952,0.00007206579,0.00002432333,0.9901237,0.0045278813,0.002516123,0.0006674464,0.000022190152],"about_ca_topic_score_codex":0.0058332654,"about_ca_topic_score_gemma":0.0044214753,"teacher_disagreement_score":0.0058332654,"about_ca_system_score_codex":0.0004658281,"about_ca_system_score_gemma":0.0010062265,"threshold_uncertainty_score":0.011598647},"labels":[],"label_agreement":null},{"id":"W4393982528","doi":"10.23977/jaip.2024.070121","title":"The path and exploration of building the first-class course of machine vision","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Mechatronics Education and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Division of Graduate Education; Xijing University","keywords":"Course (navigation); Class (philosophy); Path (computing); Artificial intelligence; Computer science; Computer vision; Engineering; Aerospace engineering; Programming language","score_opus":0.03539001989032565,"score_gpt":0.35530919325894744,"score_spread":0.3199191733686218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393982528","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2550586,0.006735511,0.15507013,0.0849718,0.0030892123,0.0015935916,0.00037708442,0.001038854,0.49206516],"genre_scores_gemma":[0.62847996,0.0033363227,0.23296918,0.0039247363,0.00041025598,0.00046826815,0.00046693458,0.00022859816,0.12971576],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990495,0.0002556481,0.000029383706,0.00014770878,0.00022808916,0.00028964356],"domain_scores_gemma":[0.99814427,0.00014368613,0.00008321692,0.0001048566,0.00045882497,0.001065205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023575816,0.0004188186,0.00015884888,0.0010362788,0.003963823,0.00367501,0.0009739812,0.0012365663,0.010229021],"category_scores_gemma":[0.0023231935,0.00024951497,0.00035371722,0.0005908092,0.001984042,0.0040778765,0.003177118,0.0030573995,0.0021929513],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067941626,0.00055953674,0.011405105,0.00035068535,0.0000081541875,0.00038478262,0.0072661377,0.00085158786,0.0043097944,0.5451907,0.027119672,0.40248597],"study_design_scores_gemma":[0.00005126728,0.00056276005,0.024770826,0.0005945421,0.000016048338,0.00048837333,0.009009757,0.004248647,0.008057373,0.09946496,0.8526612,0.0000742338],"about_ca_topic_score_codex":0.009577493,"about_ca_topic_score_gemma":0.0098259915,"teacher_disagreement_score":0.010229021,"about_ca_system_score_codex":0.006429708,"about_ca_system_score_gemma":0.020006597,"threshold_uncertainty_score":0.046650946},"labels":[],"label_agreement":null},{"id":"W4394794775","doi":"10.23977/jaip.2024.070122","title":"Research on Integrating Forgetting Behavior into Student Models for Online Learning Systems","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Forgetting; Computer science; Online learning; Psychology; Human–computer interaction; Mathematics education; Cognitive psychology; Multimedia","score_opus":0.18647257979184936,"score_gpt":0.5091405438312837,"score_spread":0.3226679640394343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394794775","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":[0.16394663,0.0019397641,0.82849,0.00108882,0.00012475024,0.0001300357,0.0003687213,0.0017596965,0.0021515854],"genre_scores_gemma":[0.90453124,0.001052736,0.09067724,0.00023015156,0.0001131895,0.00011658133,0.00054124685,0.00013932001,0.002598256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985921,0.00046135907,0.000115582116,0.00047038094,0.00021521564,0.0001453368],"domain_scores_gemma":[0.9907838,0.006330846,0.0008335016,0.0007019188,0.0010109367,0.0003390382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003424567,0.0017294703,0.0012200588,0.001219841,0.00045418236,0.0018460795,0.0020867626,0.0013292751,0.0018430181],"category_scores_gemma":[0.018814608,0.00068476493,0.0011430465,0.000994911,0.00070047815,0.0053946157,0.0009939361,0.002802579,0.00053989474],"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":[0.0003607836,0.00046436378,0.03386126,0.00038383264,0.00039669737,0.0001797455,0.0012102856,0.73925036,0.004198065,0.009771858,0.0016204265,0.20830232],"study_design_scores_gemma":[0.0000043068535,0.000052529802,0.0009157124,0.000015360623,0.000031271844,0.000022519238,0.000030065696,0.9945076,0.00055779173,0.0035047433,0.00034798792,0.000010083778],"about_ca_topic_score_codex":0.015279542,"about_ca_topic_score_gemma":0.014146893,"teacher_disagreement_score":0.015279542,"about_ca_system_score_codex":0.0017765928,"about_ca_system_score_gemma":0.0014596407,"threshold_uncertainty_score":0.030381203},"labels":[],"label_agreement":null},{"id":"W4394794784","doi":"10.23977/jaip.2024.070123","title":"Research on the Hybrid Teaching Mode of Mechanical Fundamentals in the Context of Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Ideological and Political Education","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Xinjiang Province","keywords":"Context (archaeology); Mode (computer interface); Computer science; Artificial intelligence; Human–computer interaction; Biology","score_opus":0.30115735116366826,"score_gpt":0.5330648643803769,"score_spread":0.23190751321670866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394794784","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":[0.8412418,0.0042940984,0.12132095,0.0014156699,0.00014076111,0.00015722009,0.000057196863,0.0001890809,0.031183293],"genre_scores_gemma":[0.9727649,0.0017481184,0.021975817,0.00011379845,0.00003522875,0.000060753657,0.00003294631,0.000011030044,0.0032574045],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995177,0.00015507796,0.0000235841,0.00011491371,0.00012587481,0.00006296866],"domain_scores_gemma":[0.9992694,0.0003279172,0.000104598344,0.000060538267,0.0001503333,0.00008719896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088608393,0.00030443503,0.00022465335,0.0004405945,0.00028796165,0.001332897,0.00053294736,0.00050267705,0.0020395468],"category_scores_gemma":[0.0023871502,0.00014108286,0.0003515279,0.0005412576,0.0004510502,0.0022233361,0.00064009364,0.000495944,0.00028939114],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026202403,0.0008382722,0.060210478,0.0016411376,0.00016823133,0.00059949246,0.006332772,0.017228536,0.063392244,0.045240097,0.0021763935,0.8019103],"study_design_scores_gemma":[0.00018916423,0.004236878,0.24628861,0.0012448814,0.00096005446,0.0024252678,0.017744083,0.44770354,0.08079194,0.08607225,0.11210456,0.00023871062],"about_ca_topic_score_codex":0.0011295946,"about_ca_topic_score_gemma":0.0014662873,"teacher_disagreement_score":0.0020395468,"about_ca_system_score_codex":0.00052174693,"about_ca_system_score_gemma":0.0009121556,"threshold_uncertainty_score":0.0068230033},"labels":[],"label_agreement":null},{"id":"W4394847734","doi":"10.23977/jaip.2024.070124","title":"A Multimodal Diffusion-based Interior Design AI with ControlNet","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Architecture, Design, and Social History","field":"Arts and Humanities","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Diffusion; Computer science; Physics; Thermodynamics","score_opus":0.05200603958579433,"score_gpt":0.30418155609975056,"score_spread":0.25217551651395625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394847734","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":[0.010177389,0.00008738864,0.98072666,0.00012250595,0.000042799515,0.000065495406,0.000059794038,0.0016695284,0.0070485445],"genre_scores_gemma":[0.30492717,0.0002086647,0.68431556,0.000101737634,0.000023952462,0.00025804018,0.00018038288,0.00046822487,0.009516222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967134,0.000070328875,0.000011092668,0.000079654455,0.00014710618,0.000020501582],"domain_scores_gemma":[0.99956924,0.00020777293,0.000029664772,0.000068563626,0.00009517982,0.000029571533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005821849,0.00080754247,0.0005102511,0.0008381048,0.000435252,0.0014384353,0.0012189465,0.00077166193,0.0089516295],"category_scores_gemma":[0.0017026812,0.0004298597,0.00073035446,0.00056402734,0.0008741757,0.0014032939,0.0014942382,0.0006856972,0.0007431113],"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":[0.00028769474,0.0001330823,0.000824556,0.0003518424,0.00004815754,0.00032585397,0.0010436754,0.6196758,0.055015024,0.056489415,0.0039906343,0.26181427],"study_design_scores_gemma":[0.000014126692,0.00003840791,0.00008678268,0.000021290043,0.0000062154036,0.00004980365,0.000026468913,0.9854431,0.0041483636,0.005415976,0.0047348854,0.000014519959],"about_ca_topic_score_codex":0.003510639,"about_ca_topic_score_gemma":0.0037260163,"teacher_disagreement_score":0.0089516295,"about_ca_system_score_codex":0.0006989907,"about_ca_system_score_gemma":0.0006595536,"threshold_uncertainty_score":0.029946208},"labels":[],"label_agreement":null},{"id":"W4395683571","doi":"10.23977/jaip.2024.070125","title":"Study on Eco-Management Program of Status of Illegal Trade in Wildlife","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Regional Development and Environment","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wildlife trade; Wildlife; Business; Environmental planning; Geography; Environmental protection; Ecology; Biology","score_opus":0.08843209001986224,"score_gpt":0.41039273991412545,"score_spread":0.3219606498942632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395683571","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":[0.9979317,0.000029536126,0.00022437361,0.000052486088,0.0000024431176,0.000015762114,0.00008352929,0.0000032177975,0.0016568796],"genre_scores_gemma":[0.99818856,0.000054978398,0.00025784783,0.00000862684,0.0000018093843,0.000015644087,0.00010806183,0.0000011695529,0.0013632906],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99968994,0.00011899561,0.00001747819,0.000042629068,0.00006332527,0.000067606015],"domain_scores_gemma":[0.99844354,0.0003418788,0.0005672227,0.000066940964,0.00032296358,0.00025747012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007502464,0.000092996124,0.00006818694,0.0012591352,0.00036878945,0.00041831666,0.0002975284,0.00020612334,0.0021917485],"category_scores_gemma":[0.0018953552,0.00006418958,0.00012115668,0.0007961365,0.00022227432,0.0005478178,0.0004070604,0.00022509354,0.00022780318],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061061604,0.00018067336,0.9607634,0.000033555312,0.000015131647,0.00032517672,0.003659744,0.00041204272,0.0005887094,0.00053363264,0.00042351423,0.033003364],"study_design_scores_gemma":[0.0000012558863,0.0001812698,0.9884802,0.000019123125,0.0000067916403,0.00018166439,0.0081176115,0.0012037846,0.00023134626,0.0001640522,0.0014075653,0.000005270151],"about_ca_topic_score_codex":0.006104944,"about_ca_topic_score_gemma":0.014841754,"teacher_disagreement_score":0.006104944,"about_ca_system_score_codex":0.000566038,"about_ca_system_score_gemma":0.0005609061,"threshold_uncertainty_score":0.012138844},"labels":[],"label_agreement":null},{"id":"W4396232533","doi":"10.23977/jaip.2024.070201","title":"Integration of GIS and Artificial Intelligence Algorithms in Rural Landscape Protection and Planning","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Environmental Sustainability and Technology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Geography","score_opus":0.03295397204240975,"score_gpt":0.3143551854141359,"score_spread":0.28140121337172613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396232533","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":[0.05263341,0.004431502,0.91845423,0.0017118421,0.00013766883,0.00012725231,0.00008654729,0.00064225553,0.021775352],"genre_scores_gemma":[0.52370226,0.0037873546,0.4694938,0.00024095605,0.0000886799,0.000068813555,0.00011689482,0.00006705744,0.0024341363],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987923,0.0004994596,0.000074579264,0.00017837311,0.00038827298,0.00006708656],"domain_scores_gemma":[0.99860066,0.00085081905,0.00010508177,0.00015255022,0.00025316438,0.00003768048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017753763,0.0007169812,0.0004257993,0.0024074009,0.00038836623,0.0017696004,0.0006535663,0.000698663,0.0013283088],"category_scores_gemma":[0.0029927506,0.0004123529,0.0005747018,0.0026675253,0.0011667985,0.0031955633,0.0015702067,0.0007936758,0.00027626165],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087046414,0.00012697687,0.012878369,0.00049866166,0.00021033466,0.0003690221,0.0007557652,0.1232234,0.008691075,0.06200129,0.0023269707,0.7888312],"study_design_scores_gemma":[0.000028668035,0.00038573932,0.016065182,0.00034863118,0.00022809586,0.00078704214,0.0017967786,0.769896,0.022589423,0.118135616,0.06960559,0.00013324521],"about_ca_topic_score_codex":0.0029371658,"about_ca_topic_score_gemma":0.0038569919,"teacher_disagreement_score":0.0029371658,"about_ca_system_score_codex":0.00082731055,"about_ca_system_score_gemma":0.0008343808,"threshold_uncertainty_score":0.009389162},"labels":[],"label_agreement":null},{"id":"W4396648043","doi":"10.23977/jaip.2024.070202","title":"Exploration on Classification of Vocal Music Theme Based on Intelligent Multi Image Feature Fusion","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Theme (computing); Feature (linguistics); Computer science; Artificial intelligence; Fusion; Speech recognition; Pattern recognition (psychology); Image (mathematics); Computer vision; Linguistics; World Wide Web","score_opus":0.1289324711000114,"score_gpt":0.3534052212668629,"score_spread":0.2244727501668515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396648043","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":[0.19357458,0.0009979873,0.80046564,0.00035838195,0.00014237256,0.00012164944,0.000101006764,0.000861788,0.0033765424],"genre_scores_gemma":[0.85016185,0.0006542976,0.14626774,0.00015541508,0.0000833327,0.00010719873,0.00029858015,0.000043261058,0.0022283415],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992415,0.00008016206,0.00005206241,0.00015618566,0.0003424261,0.00012766774],"domain_scores_gemma":[0.9996619,0.00007671524,0.00003265479,0.00002913346,0.00017411364,0.00002547447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087105937,0.0006842405,0.0008942466,0.0014856474,0.0004132505,0.00085373933,0.0005378933,0.0007684119,0.000910439],"category_scores_gemma":[0.0013911433,0.000205963,0.0014310892,0.00091445463,0.00036007853,0.0013386339,0.0006567073,0.00059333845,0.00029651378],"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":[0.0005147678,0.00029930638,0.011936014,0.00027313794,0.00018502741,0.00046595518,0.00053733116,0.05669006,0.11631501,0.0030681754,0.0025501428,0.80716497],"study_design_scores_gemma":[0.00002150985,0.0002818534,0.011026301,0.000023272716,0.000114023926,0.000255199,0.00031210497,0.9625901,0.022000773,0.0017488453,0.0015852613,0.00004075256],"about_ca_topic_score_codex":0.0030889048,"about_ca_topic_score_gemma":0.0014744062,"teacher_disagreement_score":0.0030889048,"about_ca_system_score_codex":0.00029856272,"about_ca_system_score_gemma":0.00047784133,"threshold_uncertainty_score":0.0061418414},"labels":[],"label_agreement":null},{"id":"W4396648150","doi":"10.23977/jaip.2024.070203","title":"Hot Spots, Trends and Implications in Foreign Research on Artificial Intelligence Literacy—Visualization Analysis Based on CiteSpace","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Visualization; Computer science; Artificial intelligence","score_opus":0.21475235975970133,"score_gpt":0.5422547278052419,"score_spread":0.32750236804554056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396648150","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85042334,0.08914133,0.0037949583,0.0077647436,0.000399316,0.0002468076,0.015630858,0.00032539017,0.0322733],"genre_scores_gemma":[0.9779285,0.0141489245,0.002117962,0.0002770534,0.000316407,0.0001535463,0.0038386981,0.00004999821,0.0011689004],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99427444,0.0013234637,0.0014434564,0.0006568026,0.0018965853,0.00040527622],"domain_scores_gemma":[0.9380222,0.03848116,0.0143535985,0.0013706294,0.006381643,0.0013908396],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0060667046,0.00034487472,0.00082958565,0.07500142,0.0012079127,0.005603055,0.00047868816,0.00056013226,0.0037272712],"category_scores_gemma":[0.036219966,0.00017208666,0.00080590387,0.09905932,0.001214062,0.0051798453,0.0022739312,0.000501488,0.0004784911],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028392687,0.00007409691,0.755624,0.010557612,0.0008879255,0.0011424066,0.026954304,0.00041254936,0.0017823135,0.014454859,0.011601258,0.17622471],"study_design_scores_gemma":[0.000023989613,0.00009549969,0.87466305,0.005142805,0.001122803,0.0016378025,0.053850796,0.0012004368,0.00097563094,0.008334293,0.0528676,0.00008543223],"about_ca_topic_score_codex":0.003488254,"about_ca_topic_score_gemma":0.0072217467,"teacher_disagreement_score":0.9939333,"about_ca_system_score_codex":0.0014279153,"about_ca_system_score_gemma":0.0020603568,"threshold_uncertainty_score":0.032084227},"labels":[],"label_agreement":null},{"id":"W4396648162","doi":"10.23977/jaip.2024.070205","title":"Application Analysis of Computer Database System in Information Management","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Database; Data administration; Database design; Database schema","score_opus":0.02845429262256656,"score_gpt":0.3561355482053922,"score_spread":0.3276812555828257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396648162","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":[0.31879002,0.043340705,0.42574838,0.005650516,0.0007970598,0.0010532966,0.001420257,0.0011857959,0.20201384],"genre_scores_gemma":[0.8932734,0.012056757,0.08629545,0.0003160677,0.00020491639,0.00018264842,0.0008258099,0.000035406392,0.0068095545],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974716,0.0009106907,0.00019096326,0.00024257877,0.0010380874,0.000146145],"domain_scores_gemma":[0.99641174,0.001680821,0.00019237389,0.00019817635,0.0014264125,0.00009037104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019851045,0.0002224853,0.00026151724,0.0034750653,0.0005436777,0.0025893694,0.0006394184,0.00048960926,0.003011815],"category_scores_gemma":[0.0050311508,0.000121577126,0.0002909132,0.0057531586,0.00040060916,0.0024027205,0.0004691909,0.00043821993,0.00057023176],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048577136,0.0002586384,0.064521566,0.002123414,0.0001759245,0.0011900555,0.0022890558,0.01999241,0.013979986,0.19981325,0.017042039,0.67812794],"study_design_scores_gemma":[0.000117261814,0.00082583533,0.10769392,0.0008853552,0.00046253653,0.004639288,0.0053233393,0.37858775,0.04291804,0.0926396,0.36571687,0.00019027446],"about_ca_topic_score_codex":0.0038051885,"about_ca_topic_score_gemma":0.0016027725,"teacher_disagreement_score":0.0038051885,"about_ca_system_score_codex":0.0011517,"about_ca_system_score_gemma":0.001114709,"threshold_uncertainty_score":0.010498345},"labels":[],"label_agreement":null},{"id":"W4396648166","doi":"10.23977/jaip.2024.070204","title":"The Application Research of Artificial Intelligence in Human Resource Management","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and HR Technologies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Human resource management; Data science; Knowledge management; Artificial intelligence; Cognitive science; Psychology","score_opus":0.1306300144834098,"score_gpt":0.4183293687577783,"score_spread":0.28769935427436855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396648166","genre_codex":"other","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":[0.044532076,0.20967197,0.13235721,0.04357465,0.0020164547,0.00035085686,0.00015834619,0.00024613718,0.5670923],"genre_scores_gemma":[0.75814587,0.15803152,0.06187972,0.0046526245,0.0023325789,0.00024690182,0.000114737704,0.00005074798,0.014545346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968917,0.0015843519,0.00018365847,0.00031867292,0.0008790571,0.00014261602],"domain_scores_gemma":[0.9955603,0.003377475,0.00023085385,0.0002541103,0.00047349097,0.000103787774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030464458,0.00040119217,0.00040010593,0.0025873529,0.0010736121,0.004032815,0.0007527568,0.0013069747,0.0027774519],"category_scores_gemma":[0.0053722113,0.00018441335,0.00047527,0.0042346166,0.003640775,0.003962099,0.001209982,0.001387022,0.00049312226],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023728615,0.000116496885,0.0045879763,0.0016741955,0.000079046746,0.00027289678,0.0018987735,0.004262801,0.00080716773,0.7024821,0.006869047,0.27692574],"study_design_scores_gemma":[0.000024621726,0.00015752966,0.011728881,0.0027778817,0.00010823552,0.0006104909,0.0034438309,0.019477203,0.0021064156,0.61810833,0.34135726,0.00009929523],"about_ca_topic_score_codex":0.0029400354,"about_ca_topic_score_gemma":0.0017614001,"teacher_disagreement_score":0.004032815,"about_ca_system_score_codex":0.002488695,"about_ca_system_score_gemma":0.0032593189,"threshold_uncertainty_score":0.01805675},"labels":[],"label_agreement":null},{"id":"W4396895558","doi":"10.23977/jaip.2024.070206","title":"Changes in Government Attention to AI Topics in the Perspective of Framing Theory—Taking the Report of AI-related Articles in People's Daily Online as an Example","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Framing (construction); Perspective (graphical); E-Government; Psychology; Cognitive psychology; Epistemology; Computer science; Artificial intelligence; Political science; History; World Wide Web; Philosophy","score_opus":0.1000575900390677,"score_gpt":0.4462465644470833,"score_spread":0.34618897440801566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396895558","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":[0.8829144,0.0033833736,0.0049800975,0.030598374,0.00063006766,0.00008292599,0.00019251232,0.00005113274,0.077167146],"genre_scores_gemma":[0.99564266,0.0007663047,0.0005599512,0.00093821675,0.00018570333,0.000045905526,0.00005967981,0.000016504113,0.0017849262],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.98311687,0.010725011,0.0006339339,0.00089004793,0.0034959228,0.0011381161],"domain_scores_gemma":[0.93894196,0.03743421,0.011011444,0.0024572841,0.008384158,0.0017709985],"candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.015445675,0.00041110461,0.00037006437,0.008050971,0.00577608,0.008957807,0.0006595404,0.0018700637,0.0020882683],"category_scores_gemma":[0.040013965,0.00032225426,0.00035349303,0.008526756,0.009599628,0.007097752,0.0040826877,0.0024661275,0.00024408476],"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":[0.00009740821,0.00008299672,0.057793237,0.00036540048,0.00004726855,0.0012326183,0.8172035,0.00024434697,0.0025473076,0.06396998,0.005814106,0.0506019],"study_design_scores_gemma":[0.000012532867,0.00010035434,0.07722127,0.0006079654,0.000049114456,0.00034035862,0.7874831,0.0006748574,0.001361158,0.014143628,0.11790076,0.00010483708],"about_ca_topic_score_codex":0.009772942,"about_ca_topic_score_gemma":0.0077514024,"teacher_disagreement_score":0.991949,"about_ca_system_score_codex":0.00833432,"about_ca_system_score_gemma":0.0040693567,"threshold_uncertainty_score":0.08168554},"labels":[],"label_agreement":null},{"id":"W4396974164","doi":"10.23977/jaip.2024.070207","title":"Vision Recognition and Positioning Optimization of Industrial Robots Based on Deep Learning","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Robot; Deep learning; Human–computer interaction","score_opus":0.049158147363785924,"score_gpt":0.32083091646376694,"score_spread":0.271672769099981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396974164","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":[0.02304577,0.00029073062,0.97443897,0.000108389315,0.000037927697,0.000018303681,0.000035142544,0.00087913946,0.0011455069],"genre_scores_gemma":[0.8158319,0.00027522113,0.17966154,0.00024652813,0.00004561395,0.00007296168,0.00025925893,0.00011057852,0.0034964473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996075,0.000044797904,0.000019913823,0.00014792722,0.00011600027,0.00006399463],"domain_scores_gemma":[0.99970776,0.00007434568,0.000052313106,0.000039207323,0.00010493604,0.000021411839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040289044,0.00087705493,0.0007096187,0.0005495929,0.00025578952,0.0005600594,0.0011667296,0.0007828865,0.001295285],"category_scores_gemma":[0.0012767056,0.0004062608,0.00060197274,0.00056392653,0.000404267,0.0008369867,0.0008747965,0.0007755262,0.00033654828],"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":[0.00010352964,0.000082745944,0.0017066076,0.00010288377,0.000065344415,0.00007626651,0.00006347959,0.5887859,0.02049939,0.002401435,0.0017840505,0.3843284],"study_design_scores_gemma":[0.0000038731137,0.000022872924,0.0003060962,0.0000034023255,0.0000064794317,0.000015638241,0.000005425232,0.99599385,0.0026179485,0.0007786234,0.0002415693,0.0000041722474],"about_ca_topic_score_codex":0.009075469,"about_ca_topic_score_gemma":0.007863686,"teacher_disagreement_score":0.009075469,"about_ca_system_score_codex":0.00081078976,"about_ca_system_score_gemma":0.00096675754,"threshold_uncertainty_score":0.018045247},"labels":[],"label_agreement":null},{"id":"W4398150292","doi":"10.23977/jaip.2024.070208","title":"Research on Criminal Risks in the Age of Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Criminal behaviour; Criminology; Psychology; Artificial intelligence; Computer science","score_opus":0.5121956153807471,"score_gpt":0.5818795449995181,"score_spread":0.06968392961877101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398150292","genre_codex":"other","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":[0.24096793,0.068305604,0.023647511,0.15856983,0.0010031546,0.00017764508,0.00014032712,0.000051906336,0.5071361],"genre_scores_gemma":[0.9480353,0.03284567,0.003432253,0.0052900156,0.00075563736,0.00010406312,0.00003559678,0.000015446994,0.009486013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939441,0.0032411139,0.00023218553,0.000511892,0.0016776469,0.0003929599],"domain_scores_gemma":[0.97410643,0.016484236,0.0038800554,0.001081254,0.003441603,0.0010064016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007210593,0.00042627612,0.0004149711,0.0031472177,0.0036505316,0.007354169,0.0009864945,0.0028216424,0.00408364],"category_scores_gemma":[0.021276725,0.00024764173,0.00036859402,0.0025206555,0.011718402,0.013651519,0.0024350293,0.003862248,0.00038044553],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021533178,0.0000779891,0.012506163,0.00015889696,0.00002053218,0.00023585348,0.0077097635,0.0004997534,0.00008779511,0.9434577,0.0028537333,0.032370377],"study_design_scores_gemma":[0.000010560791,0.00016274414,0.021848995,0.0016889808,0.000054524473,0.0008629633,0.030172363,0.003385095,0.00045058902,0.81563646,0.12566493,0.00006183154],"about_ca_topic_score_codex":0.0028582814,"about_ca_topic_score_gemma":0.0030775825,"teacher_disagreement_score":0.007354169,"about_ca_system_score_codex":0.0044578924,"about_ca_system_score_gemma":0.003990572,"threshold_uncertainty_score":0.03813374},"labels":[],"label_agreement":null},{"id":"W4398150337","doi":"10.23977/jaip.2024.070209","title":"Research on Computer Network Application Based on Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.14161156908077924,"score_gpt":0.40944426077745805,"score_spread":0.2678326916966788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398150337","genre_codex":"other","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":[0.036626235,0.20758605,0.33034578,0.02021471,0.00268111,0.00023523229,0.000168396,0.0004945515,0.40164796],"genre_scores_gemma":[0.5788233,0.28342068,0.09363511,0.0028110873,0.0036741926,0.00030055782,0.0002512189,0.000101560196,0.036982305],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987494,0.00027463626,0.000071706716,0.00024026934,0.0005740782,0.00008989775],"domain_scores_gemma":[0.9985655,0.0007644166,0.00009236073,0.00013527503,0.0003865204,0.000055960398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009453736,0.000390599,0.00040818748,0.0016353171,0.0006683239,0.002885472,0.00093968865,0.0011242931,0.0024122393],"category_scores_gemma":[0.0025024416,0.00021597701,0.00045757735,0.0030038701,0.0017662218,0.005240586,0.0007573824,0.0014425912,0.0005921165],"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":[0.000021918697,0.00006791653,0.0024866278,0.0011438826,0.000052165415,0.00025100174,0.00048813218,0.0074913027,0.0022474946,0.7817236,0.007943273,0.19608276],"study_design_scores_gemma":[0.000023625693,0.000154854,0.004840956,0.0011273129,0.00010030693,0.0012309368,0.00062168384,0.07564567,0.006014718,0.4901324,0.42001662,0.00009099058],"about_ca_topic_score_codex":0.0018272785,"about_ca_topic_score_gemma":0.000932317,"teacher_disagreement_score":0.002885472,"about_ca_system_score_codex":0.0016458231,"about_ca_system_score_gemma":0.0016970839,"threshold_uncertainty_score":0.011941314},"labels":[],"label_agreement":null},{"id":"W4398787632","doi":"10.23977/jaip.2024.070210","title":"Research on periodic intelligent inspection and maintenance of offshore platform equipment","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Submarine pipeline; Marine engineering; Engineering; Construction engineering; Computer science; Forensic engineering; Geotechnical engineering","score_opus":0.1385219177261825,"score_gpt":0.3941637408425306,"score_spread":0.2556418231163481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398787632","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":[0.34335256,0.024501283,0.60225195,0.0017605447,0.0003084957,0.0001190594,0.00009400579,0.00029169206,0.027320387],"genre_scores_gemma":[0.93537,0.008773622,0.05169918,0.00008174437,0.00017971564,0.000032325086,0.000080986414,0.000017183398,0.0037652296],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947447,0.00008765588,0.000035238023,0.0001735575,0.00017792374,0.000051192845],"domain_scores_gemma":[0.9981641,0.0007741726,0.0004136367,0.00020621106,0.00037699385,0.00006495957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007545602,0.0003693702,0.00031940755,0.0010129629,0.00033666124,0.00079744007,0.0011051833,0.00074561033,0.0007839761],"category_scores_gemma":[0.0030687777,0.00021145628,0.00035625484,0.00073024305,0.00065744936,0.0016311287,0.00035757912,0.0003894052,0.0001536343],"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":[0.00019430341,0.0003249895,0.026620358,0.0016302157,0.00012956353,0.00054906425,0.0012711955,0.116886206,0.036065016,0.09099437,0.0025097046,0.722825],"study_design_scores_gemma":[0.000036438327,0.0015046522,0.06524201,0.00064773654,0.00028960366,0.0017253102,0.0017562311,0.7862353,0.026917547,0.076130256,0.03939685,0.00011807212],"about_ca_topic_score_codex":0.0027906508,"about_ca_topic_score_gemma":0.0015039208,"teacher_disagreement_score":0.0027906508,"about_ca_system_score_codex":0.0006743155,"about_ca_system_score_gemma":0.0006799216,"threshold_uncertainty_score":0.005548775},"labels":[],"label_agreement":null},{"id":"W4399473667","doi":"10.23977/jaip.2024.070211","title":"Practical Analysis of Building Robot Operating Systems Based on Scientific Research Projects","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robot; Systems engineering; Computer science; Engineering; Construction engineering; Architectural engineering; Engineering management; Artificial intelligence","score_opus":0.1814808648081909,"score_gpt":0.4481547136411717,"score_spread":0.26667384883298084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399473667","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":[0.09431948,0.00058058964,0.85219777,0.000652388,0.000092671,0.0010855162,0.00026838575,0.0036603417,0.04714285],"genre_scores_gemma":[0.5157232,0.0010310961,0.47022197,0.000081713835,0.00006043622,0.0012175746,0.0007227312,0.00036651824,0.010574732],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99652845,0.0010055369,0.00024092435,0.00036879582,0.0016394717,0.00021677844],"domain_scores_gemma":[0.9955388,0.0015893899,0.00049407466,0.0006884388,0.0015141539,0.0001750572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032601957,0.00073705654,0.00036829355,0.002292941,0.001007295,0.0021795046,0.0011264655,0.0005698859,0.0076056747],"category_scores_gemma":[0.010772051,0.00042483557,0.00064277434,0.0014879502,0.0007761277,0.0023894203,0.001337678,0.0005708315,0.0023370152],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002796575,0.00035991534,0.017555064,0.0012056337,0.00006994238,0.00066391827,0.0012369653,0.20760414,0.026425162,0.16638683,0.0076914546,0.57052124],"study_design_scores_gemma":[0.00008538057,0.0009854413,0.01829155,0.000324934,0.00011415142,0.00085515325,0.001542258,0.800261,0.021242136,0.069950566,0.08624173,0.00010568965],"about_ca_topic_score_codex":0.0020813013,"about_ca_topic_score_gemma":0.0014719715,"teacher_disagreement_score":0.0076056747,"about_ca_system_score_codex":0.0012502071,"about_ca_system_score_gemma":0.0027521912,"threshold_uncertainty_score":0.025443494},"labels":[],"label_agreement":null},{"id":"W4399473690","doi":"10.23977/jaip.2024.070212","title":"Design and Deconstruction of the Intelligent System of College Physical Education in the Era of 5G + Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deconstruction (building); Engineering; Engineering management; Artificial intelligence; Mathematics education; Systems engineering; Computer science; Engineering ethics; Psychology","score_opus":0.061413473432014844,"score_gpt":0.37641900646691134,"score_spread":0.3150055330348965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399473690","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":[0.09513665,0.0005234035,0.8558234,0.0010282545,0.00016247224,0.00045635313,0.000082553066,0.0011372897,0.04564961],"genre_scores_gemma":[0.8770206,0.00040146182,0.11115619,0.00017647937,0.000025696741,0.00031824977,0.00006858793,0.000041161056,0.010791564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99965334,0.00010989538,0.00002206152,0.00008145209,0.00008645091,0.000046875906],"domain_scores_gemma":[0.9998864,0.000024823212,0.000013118505,0.00001950856,0.00003346779,0.000022620854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034884212,0.0003120816,0.00026297846,0.0002446535,0.0006742403,0.0013706009,0.00068578345,0.00062923634,0.002685107],"category_scores_gemma":[0.00043177043,0.00018786995,0.0005033478,0.00014399133,0.0008404528,0.0008942488,0.0008338526,0.00051782967,0.00039498604],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005448284,0.00032690188,0.009649301,0.0005857675,0.00016740631,0.0016325457,0.0026412774,0.38169426,0.091692574,0.32106185,0.004185156,0.18581817],"study_design_scores_gemma":[0.00007315665,0.0004420553,0.0025285494,0.00005818356,0.00010060334,0.0002536274,0.00038179918,0.9221835,0.013565343,0.019129079,0.04123924,0.0000448858],"about_ca_topic_score_codex":0.003623985,"about_ca_topic_score_gemma":0.0023360325,"teacher_disagreement_score":0.003623985,"about_ca_system_score_codex":0.0006261354,"about_ca_system_score_gemma":0.001016906,"threshold_uncertainty_score":0.008982539},"labels":[],"label_agreement":null},{"id":"W4399544028","doi":"10.23977/jaip.2024.070213","title":"Research on Detection of Floating Objects in River and Lake Based on AI Image Recognition","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Technologies in Various Fields","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Image (mathematics); Remote sensing; Pattern recognition (psychology); Geology","score_opus":0.09374987358419443,"score_gpt":0.41784268712656947,"score_spread":0.32409281354237507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399544028","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":[0.22828016,0.0055476045,0.7538744,0.00075621053,0.00021545298,0.00009729247,0.00018013484,0.0013841224,0.009664592],"genre_scores_gemma":[0.82679373,0.0037135875,0.16352162,0.00030680373,0.00011851586,0.00005555638,0.0003427361,0.000060307782,0.005087123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995956,0.00004268427,0.000023272216,0.000112172944,0.00017115036,0.000055189423],"domain_scores_gemma":[0.9995869,0.00012622072,0.0000613951,0.000039842344,0.00015607465,0.000029618228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049950264,0.000523609,0.00044530266,0.0012940796,0.00032939063,0.00088652177,0.0008194779,0.0005525998,0.00065850164],"category_scores_gemma":[0.0010510784,0.00025304584,0.00050061353,0.0010563019,0.0005973689,0.0016719407,0.0006144345,0.0005477583,0.00028112507],"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":[0.00019905332,0.00015413853,0.019083142,0.00060268014,0.00016769198,0.00049262744,0.0004830683,0.07189045,0.11095417,0.007271344,0.0027149864,0.78598666],"study_design_scores_gemma":[0.000011696677,0.00023592442,0.019159159,0.00007887879,0.00011456739,0.0004855426,0.0003151161,0.90546834,0.06029993,0.0049450416,0.008819637,0.000066152286],"about_ca_topic_score_codex":0.0047129374,"about_ca_topic_score_gemma":0.0043987907,"teacher_disagreement_score":0.0047129374,"about_ca_system_score_codex":0.00048416853,"about_ca_system_score_gemma":0.00061891926,"threshold_uncertainty_score":0.009371042},"labels":[],"label_agreement":null},{"id":"W4399579097","doi":"10.23977/jaip.2024.070215","title":"Research on the Influence Mechanism of AI Platform Technology Innovation on Marketing Conversion Rate of Partners—Take Hualin International AI Technology Innovation and Application as an Example","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mechanism (biology); Business; Technology innovation; Knowledge management; Marketing; Industrial organization; Computer science; Physics","score_opus":0.12316625242977118,"score_gpt":0.41493761680930946,"score_spread":0.29177136437953827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399579097","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":[0.9553839,0.0015285264,0.005174673,0.0007890923,0.000058676214,0.00012516124,0.00010589252,0.00003794778,0.036796182],"genre_scores_gemma":[0.9970988,0.00063572766,0.0005079241,0.000034407774,0.000022229508,0.000027925984,0.00003586133,0.0000046591713,0.0016324493],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983866,0.00038335004,0.00009657038,0.00030143742,0.00057914935,0.00025290114],"domain_scores_gemma":[0.9914164,0.0054446547,0.001145713,0.00026509564,0.0012072659,0.00052084593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023452132,0.00029261666,0.00036535188,0.0028955168,0.0007572128,0.0028744517,0.00038440182,0.00062352023,0.0064993873],"category_scores_gemma":[0.009996067,0.00018605929,0.00084563333,0.002041859,0.00082468335,0.0033035697,0.00096102466,0.00075149257,0.00043308805],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063802773,0.0006109222,0.6894651,0.00080649025,0.000383427,0.0018179548,0.008091998,0.0057302797,0.0091228625,0.09147134,0.002818866,0.18904273],"study_design_scores_gemma":[0.00009414768,0.0005476763,0.90641606,0.0002585354,0.0005883161,0.0011683645,0.013037765,0.026819872,0.010573731,0.021368504,0.01896379,0.00016314241],"about_ca_topic_score_codex":0.0037763838,"about_ca_topic_score_gemma":0.001933986,"teacher_disagreement_score":0.0064993873,"about_ca_system_score_codex":0.0015272326,"about_ca_system_score_gemma":0.0014628049,"threshold_uncertainty_score":0.021742582},"labels":[],"label_agreement":null},{"id":"W4399579111","doi":"10.23977/jaip.2024.070214","title":"Application Research of Machine Learning Algorithms in Medical Diagnosis","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Algorithm","score_opus":0.3394661028316501,"score_gpt":0.6083834135074792,"score_spread":0.2689173106758291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399579111","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":[0.016094893,0.15383397,0.7708315,0.010725465,0.0015637437,0.00020104148,0.00021790988,0.0006076737,0.04592383],"genre_scores_gemma":[0.44414127,0.112257704,0.43172145,0.0022379109,0.0021345837,0.00020442656,0.00039053705,0.00015817226,0.0067539657],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961539,0.0016140138,0.00028455394,0.0004954948,0.0013139357,0.00013813259],"domain_scores_gemma":[0.9899294,0.007733749,0.00033209505,0.00047117853,0.0014217745,0.00011181906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004918418,0.0008340537,0.00092924945,0.0040903497,0.0005751056,0.0031333535,0.0011652851,0.0019350667,0.0026102022],"category_scores_gemma":[0.017342089,0.00034556718,0.0012064897,0.0037531184,0.0015687495,0.002619113,0.0011946225,0.00202872,0.0010077842],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012441681,0.00017414872,0.009053032,0.0023986008,0.0003433696,0.00045155475,0.00045988636,0.059059594,0.0034284722,0.18339692,0.00776363,0.7333464],"study_design_scores_gemma":[0.00006662425,0.00034928828,0.0061065485,0.002237135,0.00031929908,0.0023709496,0.0006250231,0.45823652,0.014391937,0.33740294,0.1777174,0.00017637086],"about_ca_topic_score_codex":0.0017315503,"about_ca_topic_score_gemma":0.0007924311,"teacher_disagreement_score":0.004918418,"about_ca_system_score_codex":0.0015858262,"about_ca_system_score_gemma":0.0017772779,"threshold_uncertainty_score":0.026011348},"labels":[],"label_agreement":null},{"id":"W4399579146","doi":"10.23977/jaip.2024.070216","title":"Research on Industry University Research Cooperation in Artificial Intelligence Technology","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Reforms and Innovations","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Engineering management; Engineering; Engineering ethics; Artificial intelligence; Management science; Computer science; Manufacturing engineering","score_opus":0.22745544289788036,"score_gpt":0.4787829700228468,"score_spread":0.2513275271249664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399579146","genre_codex":"other","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":[0.44065994,0.007864121,0.035349723,0.029697768,0.00036025606,0.00029852125,0.000117463496,0.00011522945,0.4855371],"genre_scores_gemma":[0.9837901,0.0027649193,0.0030881295,0.00086737185,0.000105450876,0.00010074386,0.00006382008,0.000021394408,0.009198146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.988755,0.006919738,0.00032141415,0.0009920932,0.0015070306,0.0015047485],"domain_scores_gemma":[0.9715649,0.0153244855,0.0032025364,0.001621363,0.005240851,0.003045935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010394498,0.00027572695,0.00039614708,0.0024896266,0.0030774393,0.007182521,0.0009824253,0.0015746205,0.010454233],"category_scores_gemma":[0.023095053,0.00021885011,0.0005095029,0.0064840564,0.004035518,0.011872432,0.0024752414,0.0019843169,0.0010713357],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012453517,0.0003086012,0.032080505,0.00028838636,0.000044981265,0.00016397417,0.012048167,0.0018682805,0.00059761433,0.8800341,0.005090791,0.06735003],"study_design_scores_gemma":[0.00023131694,0.000638594,0.1384548,0.0013038999,0.0002268766,0.00095733016,0.090295635,0.022312328,0.0053425697,0.43831307,0.30175483,0.00016871619],"about_ca_topic_score_codex":0.004029924,"about_ca_topic_score_gemma":0.002147502,"teacher_disagreement_score":0.010454233,"about_ca_system_score_codex":0.0064247814,"about_ca_system_score_gemma":0.009191683,"threshold_uncertainty_score":0.054971993},"labels":[],"label_agreement":null},{"id":"W4399692280","doi":"10.23977/jaip.2024.070217","title":"Exploration of the Theory and Application of Artificial Intelligence in Emotion Recognition","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cognitive science; Artificial intelligence; Psychology; Computer science; Cognitive psychology","score_opus":0.12725423445059586,"score_gpt":0.43081979930549086,"score_spread":0.303565564854895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399692280","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023007179,0.102244295,0.59510064,0.0401096,0.0017332359,0.00016027708,0.00020688491,0.00024164609,0.23719631],"genre_scores_gemma":[0.7420306,0.07731249,0.1601732,0.005320697,0.0026193296,0.00048575798,0.00018535828,0.00009186381,0.011780725],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983956,0.0008163297,0.00011455221,0.0002552633,0.00034092492,0.00007735704],"domain_scores_gemma":[0.9965894,0.0027162319,0.00014791865,0.00023017838,0.00025591708,0.000060328475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028297782,0.0006811169,0.0006222978,0.0021166701,0.00087981956,0.0047759544,0.0010239361,0.0016259592,0.0027434079],"category_scores_gemma":[0.006557449,0.00038375796,0.00091366086,0.0014985864,0.0061714016,0.0051556253,0.0016555074,0.0030759634,0.00055891206],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015485288,0.000031755575,0.0010336156,0.00041884775,0.000046597524,0.00013765713,0.0008674188,0.0042511513,0.000652094,0.9324801,0.0018179746,0.0582473],"study_design_scores_gemma":[0.0000069188586,0.000032756965,0.0010525736,0.0003697944,0.000020485792,0.0002466993,0.00046320126,0.021571614,0.00039307823,0.94898874,0.026828496,0.000025491503],"about_ca_topic_score_codex":0.0011637632,"about_ca_topic_score_gemma":0.0006496118,"teacher_disagreement_score":0.0047759544,"about_ca_system_score_codex":0.0015053019,"about_ca_system_score_gemma":0.0012145252,"threshold_uncertainty_score":0.014965475},"labels":[],"label_agreement":null},{"id":"W4399932745","doi":"10.23977/jaip.2024.070218","title":"Application and discussion of computer communication technology in artificial intelligence field","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Field (mathematics); Computer science; Cognitive science; Artificial intelligence; Management science; Engineering; Psychology; Mathematics","score_opus":0.038100929986728954,"score_gpt":0.33412430962010053,"score_spread":0.29602337963337155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399932745","genre_codex":"review","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":[0.004364285,0.44526517,0.021284219,0.08616043,0.017295975,0.00013491465,0.00010205291,0.00007914027,0.4253138],"genre_scores_gemma":[0.1686583,0.5746532,0.01684123,0.07298348,0.04557316,0.0008463823,0.00019141735,0.00012810712,0.120124765],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972487,0.0012790089,0.00015112743,0.0002467644,0.00086174393,0.00021264695],"domain_scores_gemma":[0.9971861,0.0020463162,0.00018164843,0.00012449783,0.00037799147,0.00008344961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018048416,0.0005940203,0.00043099833,0.0025634496,0.0028240466,0.004975336,0.0012728609,0.005219031,0.0075051202],"category_scores_gemma":[0.0036496383,0.0002069526,0.00070924073,0.0045691817,0.005505623,0.003933787,0.0016692892,0.0056244507,0.0026465973],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000139008225,0.00003625723,0.0004190344,0.00054905575,0.00000967942,0.00047233014,0.0012022184,0.0004496067,0.0004941949,0.89878494,0.048632212,0.048936654],"study_design_scores_gemma":[0.0000030292467,0.000033726734,0.0006460415,0.0007337394,0.000006137575,0.0007403064,0.00042195694,0.0006098501,0.00028196131,0.1157783,0.8807302,0.000014794948],"about_ca_topic_score_codex":0.0016721835,"about_ca_topic_score_gemma":0.0010310434,"teacher_disagreement_score":0.0075051202,"about_ca_system_score_codex":0.002415281,"about_ca_system_score_gemma":0.002014743,"threshold_uncertainty_score":0.025107145},"labels":[],"label_agreement":null},{"id":"W4400046063","doi":"10.23977/jaip.2024.070219","title":"Analysis of artificial intelligence technology in electrical automation control","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Engineering and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Automation; Field (mathematics); Artificial intelligence; Artificial neural network; Computer science; Deep learning; Control (management); Realization (probability); Engineering","score_opus":0.03023993130562038,"score_gpt":0.3101774300947645,"score_spread":0.27993749878914415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400046063","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":[0.066115715,0.05289835,0.63441247,0.015588627,0.0013763967,0.00011303368,0.00030740045,0.0003383965,0.22884962],"genre_scores_gemma":[0.92960143,0.019384595,0.031600624,0.0008925912,0.000979486,0.00009528648,0.00012812982,0.00005166131,0.017266236],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993876,0.00011854536,0.000035956888,0.00009620368,0.00030465514,0.000057024947],"domain_scores_gemma":[0.9992316,0.00042096723,0.000082750434,0.000054108805,0.00018679052,0.000023838013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006594926,0.00051499245,0.0004096319,0.001253369,0.00039027724,0.0020774938,0.0004579205,0.0008243918,0.0026747787],"category_scores_gemma":[0.0022479915,0.00019101633,0.0004165767,0.0011015675,0.0014241394,0.0020242801,0.00061595277,0.0012301579,0.00041980215],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040641207,0.000056012388,0.0020280802,0.00027906187,0.00006866879,0.0002251898,0.00017721058,0.08328294,0.0024289312,0.8278806,0.0041067763,0.07942581],"study_design_scores_gemma":[0.000010492416,0.00007029682,0.0038676548,0.00024457902,0.00003557343,0.00017691166,0.00013342092,0.31086013,0.0018730039,0.6512937,0.031393144,0.000041113293],"about_ca_topic_score_codex":0.0017735916,"about_ca_topic_score_gemma":0.0006487095,"teacher_disagreement_score":0.0026747787,"about_ca_system_score_codex":0.0014580297,"about_ca_system_score_gemma":0.0007802991,"threshold_uncertainty_score":0.010578811},"labels":[],"label_agreement":null},{"id":"W4400202027","doi":"10.23977/jaip.2024.070220","title":"The Analysis of Technological Ethical Issues in Generative Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Transformation in Law","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generative grammar; Engineering ethics; Management science; Artificial intelligence; Psychology; Computer science; Engineering","score_opus":0.11111652397026055,"score_gpt":0.3703909155430275,"score_spread":0.25927439157276694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400202027","genre_codex":"other","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":[0.07061744,0.0044313828,0.28598455,0.08513521,0.0006398311,0.00020614084,0.00012359247,0.00008767889,0.55277413],"genre_scores_gemma":[0.96247953,0.0012472281,0.020248469,0.002335428,0.00047080158,0.00025644363,0.00004550664,0.0000745044,0.012842008],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98705715,0.008727827,0.00043543146,0.0006236578,0.00207432,0.0010815612],"domain_scores_gemma":[0.9593957,0.03273127,0.002088652,0.003010121,0.002018745,0.000755526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014211186,0.0003873282,0.0005954834,0.0024444843,0.0048516304,0.008404299,0.0013751712,0.0051445654,0.0052584247],"category_scores_gemma":[0.037312772,0.0005195834,0.0013169057,0.0021548818,0.03344087,0.009234578,0.0046631284,0.005666374,0.00046403697],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010425758,0.0000031230825,0.00006491838,0.00000696429,0.0000013549198,0.000026701515,0.00037672755,0.00036308778,0.00001144688,0.9983802,0.00018076654,0.00058370683],"study_design_scores_gemma":[0.000002131144,0.0000017918634,0.00006649867,0.000020150621,0.0000016229301,0.000037603866,0.00023860193,0.0016413085,0.000031883174,0.99443996,0.0035155881,0.0000028945408],"about_ca_topic_score_codex":0.0018361554,"about_ca_topic_score_gemma":0.0014251717,"teacher_disagreement_score":0.014211186,"about_ca_system_score_codex":0.0062333085,"about_ca_system_score_gemma":0.0038702602,"threshold_uncertainty_score":0.07515687},"labels":[],"label_agreement":null},{"id":"W4400624556","doi":"10.23977/jaip.2024.070221","title":"Overview of the development history and current design status of quadrupedal animal robots","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quadrupedalism; Current (fluid); Robot; Development (topology); Computer science; Engineering; Architectural engineering; Artificial intelligence; Biology; Electrical engineering; Mathematics","score_opus":0.1439715762219871,"score_gpt":0.3461202399844585,"score_spread":0.2021486637624714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400624556","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020018155,0.8246202,0.0643737,0.002469106,0.0006081043,0.00012413319,0.00032700252,0.00033858203,0.087120935],"genre_scores_gemma":[0.09486108,0.8011838,0.07457682,0.000981269,0.0006256694,0.00019032767,0.00066277996,0.00012284095,0.026795363],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99961567,0.00005858027,0.000052903186,0.00007714535,0.00016356485,0.000032076816],"domain_scores_gemma":[0.99953234,0.00014681133,0.00006699268,0.00002791089,0.00018418324,0.000041826665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006405544,0.00040007127,0.000271324,0.00166939,0.0005045314,0.0012337986,0.00040284204,0.00055523694,0.0035328348],"category_scores_gemma":[0.0008339884,0.00039652246,0.00025377527,0.0015172239,0.00042902093,0.0016031563,0.0005273555,0.0004983278,0.0017662545],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007337135,0.000053033345,0.0015300561,0.0040703746,0.00002320212,0.00011452024,0.00043374742,0.0032596958,0.006574846,0.029259764,0.009771382,0.94483596],"study_design_scores_gemma":[0.000007633017,0.00028560497,0.0040230844,0.0019735717,0.00004188869,0.00071067654,0.00030735412,0.0030620943,0.0029862744,0.009438736,0.9771213,0.000041834555],"about_ca_topic_score_codex":0.0013074497,"about_ca_topic_score_gemma":0.0016501113,"teacher_disagreement_score":0.0035328348,"about_ca_system_score_codex":0.00061589375,"about_ca_system_score_gemma":0.0007272928,"threshold_uncertainty_score":0.011818469},"labels":[],"label_agreement":null},{"id":"W4400742028","doi":"10.23977/jaip.2024.070222","title":"Research on the application risks and countermeasures of ChatGPT generative artificial intelligence in social work","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generative grammar; Work (physics); Computer science; Artificial intelligence; Management science; Risk analysis (engineering); Engineering; Business; Mechanical engineering","score_opus":0.3664768682941309,"score_gpt":0.5388112842218302,"score_spread":0.17233441592769932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400742028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38826263,0.013095767,0.21890226,0.1101712,0.0015340736,0.0013500246,0.00043182555,0.00262231,0.26362997],"genre_scores_gemma":[0.9642793,0.0018302511,0.021717511,0.0043301987,0.0003006238,0.0004864728,0.00016866716,0.00021298967,0.0066739316],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9623584,0.025299404,0.00076838396,0.001900619,0.00844174,0.0012314485],"domain_scores_gemma":[0.7455687,0.20098568,0.010459128,0.024664816,0.014841788,0.00347994],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04295998,0.00061005424,0.0004290753,0.0024284928,0.004153166,0.008261238,0.0032626323,0.0033422937,0.010371514],"category_scores_gemma":[0.17734599,0.00050035137,0.00071415526,0.0019243694,0.009790295,0.009001732,0.0065094107,0.005015307,0.0021067294],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005275791,0.0007558123,0.05650154,0.0011154308,0.00015536505,0.00060740806,0.042594086,0.0053355345,0.00223717,0.45255294,0.020527152,0.41708997],"study_design_scores_gemma":[0.00028404198,0.0014372189,0.047879312,0.0054250374,0.00037474866,0.0031281293,0.04271169,0.058762487,0.011242865,0.5475179,0.28096062,0.00027586814],"about_ca_topic_score_codex":0.004904961,"about_ca_topic_score_gemma":0.00444184,"teacher_disagreement_score":0.95704,"about_ca_system_score_codex":0.0054118973,"about_ca_system_score_gemma":0.004998486,"threshold_uncertainty_score":0.22719675},"labels":[],"label_agreement":null},{"id":"W4400884786","doi":"10.23977/jaip.2024.070225","title":"Customer-centric AI in Banking: Using AIGC to Improve Personalized Services","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pace; Service (business); Process (computing); Field (mathematics); Financial services; Process management; Computer science; Knowledge management; Business; Marketing; Finance","score_opus":0.08197201308368479,"score_gpt":0.3723089516657292,"score_spread":0.2903369385820444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400884786","genre_codex":"other","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":[0.22190201,0.02336708,0.3294644,0.026132358,0.00078136346,0.00073970493,0.00051453046,0.002342437,0.39475605],"genre_scores_gemma":[0.8624342,0.009364339,0.11497098,0.0029119716,0.00038894103,0.00015902365,0.00032483175,0.000101643505,0.009344004],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987489,0.00058642036,0.00005906013,0.00010834493,0.00039006872,0.000107197004],"domain_scores_gemma":[0.9979328,0.0008950244,0.00020178373,0.00025089743,0.0005999336,0.000119596916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015940989,0.0003847875,0.00022349798,0.0026563292,0.0007199051,0.0039027394,0.00081920583,0.0010563537,0.0029526395],"category_scores_gemma":[0.0040689358,0.00015515344,0.000301199,0.0037302792,0.0011619395,0.005939374,0.0015181878,0.0008700022,0.00083537557],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011947331,0.00023406334,0.01463097,0.0009918656,0.00008212268,0.00057006825,0.0041042194,0.008036542,0.0057723075,0.2128859,0.01528637,0.73728615],"study_design_scores_gemma":[0.0000959975,0.0006192236,0.025869729,0.0017239067,0.0003554712,0.0023336972,0.012605331,0.11590328,0.02214165,0.33544806,0.48272425,0.00017942017],"about_ca_topic_score_codex":0.0020497388,"about_ca_topic_score_gemma":0.0027620962,"teacher_disagreement_score":0.0039027394,"about_ca_system_score_codex":0.0016552394,"about_ca_system_score_gemma":0.001776861,"threshold_uncertainty_score":0.012009621},"labels":[],"label_agreement":null},{"id":"W4400884904","doi":"10.23977/jaip.2024.070223","title":"Research on Path Planning Algorithm Based on Fast Target Detection","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Path (computing); Computer science; Motion planning; Algorithm; Artificial intelligence; Computer network; Robot","score_opus":0.12485595220064316,"score_gpt":0.4259096461640093,"score_spread":0.3010536939633661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400884904","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":[0.008490308,0.000595781,0.98862016,0.00010795389,0.000048198723,0.000041880685,0.00004946153,0.00089854875,0.0011477175],"genre_scores_gemma":[0.23200625,0.0015956442,0.7621514,0.0001204251,0.000050622657,0.00022237233,0.00048975105,0.00020507244,0.0031584988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993843,0.000080916994,0.00003739201,0.00024283378,0.00019967457,0.000054815544],"domain_scores_gemma":[0.99935657,0.00024437145,0.00006473458,0.00006548928,0.00023896502,0.00002994243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004889952,0.0011300091,0.00093619316,0.0016949002,0.0007426218,0.0009094875,0.0014086418,0.0007440353,0.0024051655],"category_scores_gemma":[0.0017094201,0.0005836536,0.0007451078,0.0021323296,0.0006394294,0.0020886322,0.00082956196,0.0009462224,0.0005351576],"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":[0.00013655984,0.000069964524,0.0018927471,0.00032931124,0.000080890204,0.000114824834,0.00020976382,0.3419592,0.013861722,0.022399316,0.004073906,0.61487174],"study_design_scores_gemma":[0.000030884665,0.000089592584,0.00058304856,0.00001855827,0.000027709899,0.0001441928,0.000049937316,0.98034155,0.0057322257,0.0086258035,0.004327138,0.000029306004],"about_ca_topic_score_codex":0.012254843,"about_ca_topic_score_gemma":0.005309286,"teacher_disagreement_score":0.012254843,"about_ca_system_score_codex":0.0008663673,"about_ca_system_score_gemma":0.0023120476,"threshold_uncertainty_score":0.024367034},"labels":[],"label_agreement":null},{"id":"W4400885212","doi":"10.23977/jaip.2024.070224","title":"Research on the Transformation and Upgrading of Manufacturing Industry in the Era of AI Empowerment","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Empowerment; Transformation (genetics); Manufacturing engineering; Manufacturing; Business; Engineering; Engineering management; Economic growth; Marketing; Economics; Chemistry","score_opus":0.18222509542770599,"score_gpt":0.49791836002548395,"score_spread":0.31569326459777797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400885212","genre_codex":"other","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":[0.21042758,0.016847607,0.017786384,0.060492735,0.0005571885,0.00010785781,0.0000540305,0.00004977599,0.6936769],"genre_scores_gemma":[0.96958244,0.009476423,0.0028050803,0.001475869,0.00019493597,0.000045804976,0.00002083998,0.000008911632,0.016389647],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979704,0.0009813893,0.00007315197,0.00025024926,0.00041742212,0.0003073132],"domain_scores_gemma":[0.9962132,0.0022604289,0.00052188395,0.00024240515,0.00042683064,0.00033520025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031823867,0.00026269603,0.00020557317,0.0017818894,0.0025578155,0.0039236695,0.0005855946,0.0009428059,0.007445264],"category_scores_gemma":[0.0044730618,0.00013286762,0.00035085593,0.002603028,0.008074089,0.008778059,0.0018809662,0.0017257491,0.00044484364],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015883445,0.000043975833,0.0045751473,0.00020725215,0.000008218718,0.000212742,0.020501323,0.00031718274,0.00030602026,0.92513347,0.0018829157,0.046795867],"study_design_scores_gemma":[0.000029959116,0.00019846468,0.036096018,0.0012816061,0.0000418054,0.0006085304,0.06609996,0.003109359,0.0020291235,0.48282382,0.40763935,0.000041947493],"about_ca_topic_score_codex":0.0043271114,"about_ca_topic_score_gemma":0.0046161995,"teacher_disagreement_score":0.007445264,"about_ca_system_score_codex":0.004551438,"about_ca_system_score_gemma":0.0072527323,"threshold_uncertainty_score":0.03302318},"labels":[],"label_agreement":null},{"id":"W4400991984","doi":"10.23977/jaip.2024.070301","title":"Application and Performance Evaluation of DES Data Encryption Algorithm in Computer Information Security Technology","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Technology and Security Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Encryption; Computer security; Information security; Confidentiality; Firewall (physics); Data security; Computer security model; Cloud computing security; Algorithm; Cloud computing; Business","score_opus":0.05407743757655093,"score_gpt":0.35082446559979236,"score_spread":0.29674702802324143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400991984","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":[0.6603055,0.006918952,0.28414366,0.0007564222,0.0004377065,0.00044113657,0.00035865378,0.0019700062,0.044668008],"genre_scores_gemma":[0.9481374,0.001494976,0.04716819,0.000037015743,0.00003113633,0.00005694737,0.00029984282,0.000038390495,0.0027361212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969945,0.00095532666,0.00025802568,0.00023173561,0.0013690955,0.0001913487],"domain_scores_gemma":[0.997101,0.00089416414,0.00018436906,0.00024612976,0.0015068795,0.000067435816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019269867,0.00062685047,0.0004743661,0.0017486529,0.0004808561,0.0010174284,0.00042382418,0.00054729794,0.0014461515],"category_scores_gemma":[0.004804255,0.000114874754,0.00035115075,0.0014254609,0.00032130754,0.0014890805,0.00038039061,0.00036775044,0.00040057994],"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":[0.0023827362,0.0006110619,0.027455963,0.0014136591,0.0002487411,0.00063957076,0.0006692935,0.19418733,0.11078804,0.03434689,0.0057945424,0.6214622],"study_design_scores_gemma":[0.000068538546,0.0020448118,0.010456918,0.00008062794,0.00011278704,0.00092565396,0.00035493114,0.7348257,0.23490305,0.003928981,0.012203919,0.00009409628],"about_ca_topic_score_codex":0.0013818258,"about_ca_topic_score_gemma":0.0006660282,"teacher_disagreement_score":0.0019269867,"about_ca_system_score_codex":0.0009325467,"about_ca_system_score_gemma":0.0006557954,"threshold_uncertainty_score":0.010191023},"labels":[],"label_agreement":null},{"id":"W4400992218","doi":"10.23977/jaip.2024.070302","title":"Design of Key Technologies for Robot End Effectors","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Technology and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Key (lock); Robot end effector; Computer science; Robot; Human–computer interaction; Artificial intelligence; Computer security","score_opus":0.05588464101422634,"score_gpt":0.31310736498961395,"score_spread":0.2572227239753876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400992218","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":[0.013420157,0.00070756295,0.9812449,0.00007740832,0.00010257303,0.00024678305,0.000039280585,0.00033684834,0.003824489],"genre_scores_gemma":[0.28280157,0.0010692446,0.7094291,0.00012499942,0.00003949817,0.0007381137,0.00014563561,0.000079500176,0.005572348],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993749,0.00008085661,0.00006449741,0.00011956889,0.00029462864,0.000065599124],"domain_scores_gemma":[0.9995437,0.000082104474,0.00010856888,0.00006074721,0.00017924956,0.000025578905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008564485,0.00092477625,0.0004658046,0.0006916539,0.00038094493,0.0008608289,0.001158903,0.001088904,0.0017259578],"category_scores_gemma":[0.000988751,0.00046867708,0.00050676777,0.0002718512,0.0005278101,0.0013264574,0.0008372835,0.00079097995,0.0012499219],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002241508,0.000112106674,0.0011133588,0.0015398661,0.000045140037,0.00045400258,0.00033447557,0.046557635,0.6690023,0.06753243,0.001629859,0.21145466],"study_design_scores_gemma":[0.00012584878,0.0031961554,0.0032174652,0.0003213333,0.00012726123,0.0017206335,0.00021989753,0.25788826,0.6123842,0.018268744,0.102392256,0.00013802934],"about_ca_topic_score_codex":0.00009952671,"about_ca_topic_score_gemma":0.00012618786,"teacher_disagreement_score":0.0017259578,"about_ca_system_score_codex":0.00039377337,"about_ca_system_score_gemma":0.0006176122,"threshold_uncertainty_score":0.0057739615},"labels":[],"label_agreement":null},{"id":"W4401423641","doi":"10.23977/jaip.2024.070305","title":"Research on the Path of Artificial Intelligence Empowering High-quality Economic Development","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Regional Development and Environment","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Path (computing); Quality (philosophy); Artificial intelligence; Computer science","score_opus":0.2555765903278317,"score_gpt":0.47243140455280397,"score_spread":0.21685481422497227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401423641","genre_codex":"other","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":[0.16241749,0.022360412,0.057985857,0.14303292,0.0009166991,0.00025841847,0.00024133743,0.00020460712,0.6125823],"genre_scores_gemma":[0.9433987,0.02138747,0.018652078,0.0025103164,0.0001437403,0.00012161526,0.00006920678,0.000034081975,0.013682761],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99879825,0.0005287785,0.000048243106,0.00018936566,0.00024612845,0.00018926653],"domain_scores_gemma":[0.9976398,0.0010710218,0.00034085204,0.0001323272,0.000530755,0.0002853017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023041198,0.00033336648,0.00022560277,0.0017682826,0.0022595418,0.0057159173,0.000565754,0.0013071228,0.0073238597],"category_scores_gemma":[0.0043646423,0.00017078727,0.0003608877,0.002903779,0.0059918775,0.008314244,0.0020622362,0.0021096105,0.0008190501],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013682244,0.00004362151,0.0036062356,0.0001605691,0.0000115234625,0.00009285701,0.0015787858,0.00057960907,0.00018551567,0.9614254,0.002872971,0.029429154],"study_design_scores_gemma":[0.000025966934,0.00008678995,0.00869532,0.00065833883,0.000040906456,0.00018098053,0.008973898,0.0045498414,0.0011149382,0.8296814,0.1459545,0.000037095167],"about_ca_topic_score_codex":0.0029018272,"about_ca_topic_score_gemma":0.0031976968,"teacher_disagreement_score":0.0073238597,"about_ca_system_score_codex":0.003580932,"about_ca_system_score_gemma":0.0072953985,"threshold_uncertainty_score":0.025981545},"labels":[],"label_agreement":null},{"id":"W4401863963","doi":"10.23977/jaip.2024.070307","title":"Research on path planning of patrol robot based on multi-algorithm fusion","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Motion planning; Computer science; Path (computing); Fusion; Robot; Artificial intelligence; Algorithm; Computer vision; Computer network","score_opus":0.18942506317209146,"score_gpt":0.4519253544763342,"score_spread":0.26250029130424274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401863963","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":[0.009173041,0.0004896711,0.9880189,0.00008690808,0.000050545757,0.000029879215,0.000013664316,0.00025355828,0.0018838602],"genre_scores_gemma":[0.5788645,0.0012787916,0.4158049,0.00010188319,0.00006536407,0.00019861921,0.00011251466,0.00007547791,0.0034979675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993699,0.00008352818,0.000041386138,0.0001876268,0.0002511699,0.00006647277],"domain_scores_gemma":[0.9995838,0.00012486956,0.00005280855,0.00004562086,0.00016697384,0.00002601422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059657125,0.00087237044,0.000947017,0.0010606828,0.00092103466,0.0009538784,0.0012151893,0.0009489815,0.0011957746],"category_scores_gemma":[0.0011074498,0.0004956637,0.0009672886,0.0012315706,0.0007390657,0.0022125544,0.0011208929,0.000984076,0.000230819],"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":[0.00012236508,0.000060168626,0.0015470262,0.00024368342,0.000109682325,0.00017456389,0.00028115974,0.65067244,0.017510196,0.020878343,0.001528644,0.3068718],"study_design_scores_gemma":[0.000017451312,0.00009914298,0.00035750357,0.000013798976,0.000024096387,0.00010328665,0.00003827548,0.9879209,0.003949524,0.0049635307,0.0024895298,0.000022908796],"about_ca_topic_score_codex":0.0053763175,"about_ca_topic_score_gemma":0.0019318003,"teacher_disagreement_score":0.0053763175,"about_ca_system_score_codex":0.0007610552,"about_ca_system_score_gemma":0.0014524009,"threshold_uncertainty_score":0.010690033},"labels":[],"label_agreement":null},{"id":"W4401864111","doi":"10.23977/jaip.2024.070306","title":"The Training Process and Methods for LLMs Using an Own Knowledge Base","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Rights Management and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Training (meteorology); Process (computing); Base (topology); Knowledge base; Computer science; Artificial intelligence; Geography; Mathematics","score_opus":0.1852679543090059,"score_gpt":0.48146182818981326,"score_spread":0.29619387388080737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401864111","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":[0.004011109,0.00009197474,0.9918568,0.00031957912,0.000026268453,0.00015411069,0.00010392318,0.0021917128,0.0012446212],"genre_scores_gemma":[0.08788157,0.00015939573,0.908934,0.00022376567,0.000031208478,0.0005204393,0.0005215137,0.0005401212,0.0011880061],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99379265,0.0032091197,0.00041182636,0.0012055185,0.0011991976,0.0001817168],"domain_scores_gemma":[0.9771374,0.013751028,0.0006440777,0.005824373,0.0023283,0.00031495333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0108944625,0.00087378675,0.00062240014,0.0015977861,0.0007935344,0.0023146328,0.0033226032,0.0011604014,0.004248995],"category_scores_gemma":[0.055474088,0.00088278594,0.0011183231,0.0010094922,0.0016513342,0.0068356213,0.0039545656,0.0043550627,0.0021096626],"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":[0.00022174072,0.0004284293,0.0052320007,0.0006328605,0.00017082175,0.00016015615,0.0020130072,0.113669194,0.008282325,0.08355038,0.007936991,0.7777021],"study_design_scores_gemma":[0.00004841046,0.00016275066,0.0011965284,0.00030684512,0.000055867753,0.00018335687,0.00036509964,0.8657426,0.017191889,0.083661124,0.031026939,0.0000585412],"about_ca_topic_score_codex":0.0062150043,"about_ca_topic_score_gemma":0.0075768647,"teacher_disagreement_score":0.0108944625,"about_ca_system_score_codex":0.0016547636,"about_ca_system_score_gemma":0.0033883164,"threshold_uncertainty_score":0.057616115},"labels":[],"label_agreement":null},{"id":"W4402313454","doi":"10.23977/jaip.2024.070309","title":"Research on the Application of Artificial Intelligence in Commercial Auto Insurance","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Insurance and Financial Risk Management","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":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Business","score_opus":0.16810540103124122,"score_gpt":0.39327899739748756,"score_spread":0.22517359636624634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402313454","genre_codex":"other","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":[0.09774313,0.25400236,0.103398874,0.041019995,0.0008122212,0.00018588455,0.00013088212,0.00018467408,0.502522],"genre_scores_gemma":[0.7316059,0.19421534,0.054704137,0.0031487467,0.00092427246,0.00012341345,0.00014205802,0.00004823519,0.015087758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980259,0.0008820285,0.00012624447,0.0002367452,0.0006175864,0.00011152208],"domain_scores_gemma":[0.98713326,0.01089824,0.00043938842,0.00043352373,0.00093085883,0.00016482771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036350333,0.00048741486,0.0003814015,0.0024208263,0.0009580052,0.0046347724,0.0009855302,0.0015853114,0.0036357294],"category_scores_gemma":[0.008405645,0.00026217834,0.0005000331,0.003690287,0.0036648333,0.005302378,0.0011022777,0.0020388074,0.00063675595],"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":[0.000044887453,0.00016630033,0.0052737184,0.0012667672,0.00008682636,0.00025791008,0.0020617377,0.009043543,0.0012793206,0.6896546,0.004106955,0.2867574],"study_design_scores_gemma":[0.000027984679,0.0002953135,0.015397124,0.004584749,0.000083823834,0.0009095048,0.0045041726,0.04277452,0.0048714406,0.5490041,0.37743902,0.00010827548],"about_ca_topic_score_codex":0.002989682,"about_ca_topic_score_gemma":0.0017699475,"teacher_disagreement_score":0.0046347724,"about_ca_system_score_codex":0.0026130464,"about_ca_system_score_gemma":0.0021560295,"threshold_uncertainty_score":0.019224107},"labels":[],"label_agreement":null},{"id":"W4402314381","doi":"10.23977/jaip.2024.070308","title":"A Sentiment Analysis Framework Integrating Systemic Functional Grammar and Appraisal Theory","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Systemic functional grammar; Systemic functional linguistics; Computer science; Grammar; Appraisal theory; Natural language processing; Linguistics; Psychology; Philosophy; Neuroscience","score_opus":0.03968067122095253,"score_gpt":0.35484816853118684,"score_spread":0.3151674973102343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402314381","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":[0.011274168,0.0003858373,0.95575434,0.0021506848,0.00012724777,0.0002563237,0.00019349344,0.00020851832,0.029649379],"genre_scores_gemma":[0.440056,0.00064883125,0.5515243,0.0005146311,0.00032099494,0.00073755195,0.00036982357,0.00012507432,0.005702769],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982023,0.0009696651,0.000107837746,0.00025666077,0.00036401945,0.00009952715],"domain_scores_gemma":[0.9978725,0.0011331794,0.00019608732,0.00009877132,0.000603908,0.00009564903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048943153,0.0010031674,0.00061908,0.0032770808,0.0011889696,0.0031630564,0.0008804364,0.00083821977,0.003077341],"category_scores_gemma":[0.005421647,0.00030639788,0.0013933695,0.0017532753,0.0038777606,0.003941627,0.0013547144,0.0013097154,0.000742297],"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":[0.0000218835,0.000033306005,0.0016772015,0.00018401719,0.00003844903,0.0002613853,0.0032477544,0.00699478,0.001983697,0.92328435,0.003848834,0.05842423],"study_design_scores_gemma":[0.00001654263,0.00004482098,0.0012284655,0.00011544702,0.000034783956,0.00014994094,0.0011203167,0.061627924,0.0004972961,0.91169673,0.023433857,0.000033954602],"about_ca_topic_score_codex":0.002893411,"about_ca_topic_score_gemma":0.0025997828,"teacher_disagreement_score":0.0048943153,"about_ca_system_score_codex":0.0020080225,"about_ca_system_score_gemma":0.0024105946,"threshold_uncertainty_score":0.025883913},"labels":[],"label_agreement":null},{"id":"W4402397235","doi":"10.23977/jaip.2024.070310","title":"Problems in the Optimization Work of Speech-Text Auto-Recognition and Relevant Possible Solutions","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Computational Techniques in Science and Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Speech recognition; Computer science; Work (physics); Natural language processing; Artificial intelligence; Engineering; Mechanical engineering","score_opus":0.08779264845985786,"score_gpt":0.33993210203511753,"score_spread":0.2521394535752597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402397235","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":[0.010485883,0.0069493833,0.95962524,0.014401145,0.000480834,0.00013594597,0.00010183058,0.00034542067,0.00747437],"genre_scores_gemma":[0.20158926,0.0057784696,0.7798366,0.0013385385,0.0014647203,0.0005201392,0.0002497636,0.0007505721,0.008471914],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97421527,0.015540412,0.0016952432,0.00431696,0.0036702976,0.0005617508],"domain_scores_gemma":[0.92556727,0.06430188,0.00211333,0.003353026,0.0043142443,0.0003501719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026734091,0.0016674149,0.0023560298,0.0017329719,0.0019896652,0.0067290645,0.002999344,0.0037572463,0.004320928],"category_scores_gemma":[0.095022045,0.0014922795,0.0013601254,0.0026339565,0.005589676,0.0085781505,0.0030717785,0.0036571992,0.001503545],"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":[0.0002950743,0.00023601647,0.0023399056,0.0020378048,0.00023118698,0.0003977955,0.002830225,0.18015361,0.002578273,0.31374678,0.012401869,0.48275158],"study_design_scores_gemma":[0.00007267589,0.00016930928,0.0016260551,0.00057608844,0.00006220224,0.00053961,0.0020708402,0.37022898,0.0059618526,0.5887394,0.029761536,0.0001914154],"about_ca_topic_score_codex":0.0045999466,"about_ca_topic_score_gemma":0.0027002876,"teacher_disagreement_score":0.026734091,"about_ca_system_score_codex":0.0028870269,"about_ca_system_score_gemma":0.0032355417,"threshold_uncertainty_score":0.14138514},"labels":[],"label_agreement":null},{"id":"W4402546005","doi":"10.23977/jaip.2024.070311","title":"\"AI+RPA\" and the Intelligent Development of Enterprise Financial Shared Service Centers","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Impact of AI and Big Data on Business and Society","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Business; Service (business); Development (topology); Knowledge management; Finance; Engineering management; Process management; Computer science; Engineering; Marketing","score_opus":0.14326676839517974,"score_gpt":0.41876347726985574,"score_spread":0.27549670887467603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402546005","genre_codex":"other","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":[0.0530019,0.00986641,0.3123281,0.07692609,0.001317324,0.00029438187,0.00016366578,0.0011112409,0.54499084],"genre_scores_gemma":[0.7871077,0.005875928,0.17031802,0.004191051,0.00040054705,0.00022584769,0.00011694373,0.00013182037,0.03163216],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99717486,0.0012587323,0.00011828232,0.00028514455,0.0008523209,0.00031062326],"domain_scores_gemma":[0.99782985,0.00079849095,0.0002878117,0.00029919666,0.00039422986,0.0003903261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026385128,0.00034197854,0.0001811488,0.0011407015,0.0020632767,0.006178846,0.0012310441,0.0018029208,0.0031686467],"category_scores_gemma":[0.0035687876,0.0002262162,0.0004099325,0.001542065,0.005201827,0.007069226,0.0048451796,0.0021062214,0.000942952],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029303108,0.000049482045,0.0011975615,0.00015687615,0.00001823491,0.00023918747,0.0016400961,0.002750412,0.00065652194,0.8830978,0.012137464,0.09802706],"study_design_scores_gemma":[0.000025909952,0.000083882864,0.0026380725,0.00038003077,0.000026057716,0.0005906855,0.003016062,0.022673532,0.0027935528,0.4969176,0.47079146,0.00006316184],"about_ca_topic_score_codex":0.0026300973,"about_ca_topic_score_gemma":0.0025206471,"teacher_disagreement_score":0.006178846,"about_ca_system_score_codex":0.0034208763,"about_ca_system_score_gemma":0.004247904,"threshold_uncertainty_score":0.024820268},"labels":[],"label_agreement":null},{"id":"W4402613813","doi":"10.23977/jaip.2024.070314","title":"YOLOv1 to YOLOv10: A Comprehensive Review of YOLO Variants and Their Application in Medical Image Detection","year":2024,"lang":"en","type":"review","venue":"Journal of Artificial Intelligence Practice","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Shanghai Municipal Health Commission","keywords":"Computer science; Computational biology; Artificial intelligence; Computer vision; Biology","score_opus":0.11248203914133413,"score_gpt":0.4740037598143264,"score_spread":0.36152172067299226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402613813","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001297237,0.9959506,0.0011898959,0.00055318075,0.00028030024,0.000017096763,0.000050588398,0.000034155375,0.0017944811],"genre_scores_gemma":[0.0014448123,0.9939805,0.0020448875,0.00073509355,0.00037251465,0.000028048244,0.00011523844,0.000020545656,0.0012583862],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995943,0.00007802707,0.00007026359,0.00008060202,0.00014988307,0.000026910586],"domain_scores_gemma":[0.9988457,0.0006515882,0.00011706544,0.00003406766,0.00030261895,0.00004895984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010908418,0.00096005877,0.0011329898,0.004687986,0.00032619966,0.0011298782,0.0010443461,0.0011146878,0.0054378333],"category_scores_gemma":[0.003164178,0.00043169098,0.0008265463,0.0035247447,0.0006497784,0.0016746322,0.0007705328,0.0017356442,0.004264313],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006910672,0.000052738982,0.00019275358,0.011299909,0.000093904135,0.00009518781,0.000050101262,0.00028080377,0.0008622582,0.0036648647,0.03358192,0.94975644],"study_design_scores_gemma":[0.000018030323,0.00015932454,0.0010400395,0.0062646745,0.00016009418,0.0012144231,0.000043243428,0.0003021454,0.0010608406,0.0030490137,0.98664016,0.000047865466],"about_ca_topic_score_codex":0.0017694413,"about_ca_topic_score_gemma":0.0026557597,"teacher_disagreement_score":0.0054378333,"about_ca_system_score_codex":0.0006994472,"about_ca_system_score_gemma":0.0011813343,"threshold_uncertainty_score":0.018191338},"labels":[],"label_agreement":null},{"id":"W4402613910","doi":"10.23977/jaip.2024.070312","title":"Analysis of Errors at the Lexical Level in Post-editing for Medical Texts","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Natural language processing; Linguistics; Computer science; Philosophy","score_opus":0.1220433936815191,"score_gpt":0.40878029721595965,"score_spread":0.28673690353444053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402613910","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":[0.9772209,0.0007321806,0.009919331,0.00030627966,0.00029748812,0.0002639578,0.002009335,0.0008828007,0.00836772],"genre_scores_gemma":[0.970147,0.0003642437,0.019914106,0.000093965486,0.0000879947,0.00011166119,0.0029828444,0.0004526848,0.005845405],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9924493,0.0019048299,0.001374621,0.00087992096,0.0030818293,0.00030952532],"domain_scores_gemma":[0.85399276,0.10962173,0.010929494,0.0062318286,0.018262414,0.00096180063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032701867,0.00044671632,0.00036676278,0.004724605,0.0011186923,0.0024032462,0.0008159493,0.0009933074,0.0039325915],"category_scores_gemma":[0.07548419,0.0001936117,0.00023198857,0.0037052755,0.0010638706,0.0015635354,0.0014331916,0.00085430447,0.0024615629],"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":[0.0026346957,0.00072510744,0.20651397,0.0028857805,0.00017329954,0.0125262635,0.08537279,0.0017793153,0.059311334,0.0026715298,0.012658322,0.6127476],"study_design_scores_gemma":[0.00007844582,0.0016379483,0.63471717,0.0012599331,0.00039349057,0.0209806,0.05180219,0.024249617,0.14798732,0.00510107,0.11140128,0.0003909198],"about_ca_topic_score_codex":0.0019272089,"about_ca_topic_score_gemma":0.0024265076,"teacher_disagreement_score":0.004724605,"about_ca_system_score_codex":0.00055343815,"about_ca_system_score_gemma":0.00090954936,"threshold_uncertainty_score":0.017294645},"labels":[],"label_agreement":null},{"id":"W4402613916","doi":"10.23977/jaip.2024.070313","title":"Ethical Implications of AI in Autonomous Systems: Balancing Innovation and Responsibility","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Engineering ethics; Sociology; Environmental ethics; Business; Engineering; Philosophy","score_opus":0.10812906248274655,"score_gpt":0.47966674814169086,"score_spread":0.3715376856589443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402613916","genre_codex":"commentary","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":[0.09000365,0.008939025,0.25364906,0.3440614,0.001290505,0.0001688778,0.000036543115,0.00006873975,0.3017822],"genre_scores_gemma":[0.97003675,0.0016938152,0.017596724,0.0059022866,0.00043621683,0.0001294928,0.0000102298445,0.000028607468,0.0041659484],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96177,0.028316751,0.00071107806,0.0015046075,0.006481869,0.0012156803],"domain_scores_gemma":[0.93764937,0.047726408,0.0038269698,0.0032060107,0.004983865,0.0026072962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029025977,0.00051220297,0.0005477494,0.0017324035,0.0067046853,0.009180442,0.0014885376,0.006219017,0.0017303276],"category_scores_gemma":[0.039957203,0.00033960227,0.00052122923,0.0008971475,0.0572517,0.01084479,0.008119915,0.0072872,0.0003694473],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007829619,0.000017045513,0.0005560189,0.000033827742,0.0000060494876,0.00011939387,0.004796205,0.0010177804,0.000157596,0.9846038,0.0007530045,0.007931547],"study_design_scores_gemma":[0.000006417483,0.000015566231,0.00022791662,0.00009894899,0.0000032516386,0.0001023275,0.0024357787,0.0016035974,0.00016300617,0.9811365,0.014195488,0.000011216865],"about_ca_topic_score_codex":0.0024137523,"about_ca_topic_score_gemma":0.0017766901,"teacher_disagreement_score":0.029025977,"about_ca_system_score_codex":0.005823818,"about_ca_system_score_gemma":0.012800403,"threshold_uncertainty_score":0.15350586},"labels":[],"label_agreement":null},{"id":"W4402731657","doi":"10.23977/jaip.2024.070315","title":"Siamese Network-Based Text Similarity Algorithm Research","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Similarity (geometry); Computer science; Algorithm; Artificial intelligence","score_opus":0.1152017498653385,"score_gpt":0.4580224853169203,"score_spread":0.3428207354515818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402731657","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":[0.021956066,0.0023643954,0.9685686,0.00053155556,0.00022732736,0.0000866178,0.00010535089,0.00069932296,0.0054608798],"genre_scores_gemma":[0.5141857,0.0032892004,0.46000177,0.00045584224,0.00053490914,0.00022209324,0.00083862926,0.00031479265,0.020157019],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990721,0.00019397643,0.00008098604,0.0002946894,0.0003085966,0.00004966424],"domain_scores_gemma":[0.99834204,0.00061583915,0.00015071307,0.00022202646,0.00059645536,0.00007293681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011636474,0.00059953186,0.0009264801,0.0019885027,0.0004900215,0.0014730484,0.0016223043,0.0011645384,0.0035592658],"category_scores_gemma":[0.0055883033,0.0002911157,0.0007460724,0.0023003817,0.0008724536,0.0037124362,0.0009014721,0.0011971747,0.0011726252],"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":[0.00020131485,0.00023726614,0.0019167518,0.00032533915,0.00022189916,0.0001518987,0.0001857807,0.30934343,0.010061865,0.093121186,0.006338702,0.5778945],"study_design_scores_gemma":[0.000009768495,0.00004234917,0.00032477622,0.000007882115,0.000013533646,0.000044002587,0.0000135547,0.97825146,0.0021163877,0.016388113,0.0027793748,0.000008793008],"about_ca_topic_score_codex":0.006421442,"about_ca_topic_score_gemma":0.0045077316,"teacher_disagreement_score":0.006421442,"about_ca_system_score_codex":0.0013674032,"about_ca_system_score_gemma":0.0011658281,"threshold_uncertainty_score":0.012768149},"labels":[],"label_agreement":null},{"id":"W4402926085","doi":"10.23977/jaip.2024.070317","title":"Research on Security and Privacy Protection Policies of Artificial Intelligence in Primary and Secondary Education","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Internet privacy; Computer security; Primary (astronomy); Business; Computer science","score_opus":0.14991443019016387,"score_gpt":0.488235069586499,"score_spread":0.3383206393963351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402926085","genre_codex":"other","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":[0.14488569,0.032467786,0.13857584,0.12413656,0.0009995409,0.00032135323,0.00040723316,0.0001600208,0.55804604],"genre_scores_gemma":[0.94885534,0.0122479545,0.017589932,0.007031195,0.00068833627,0.00021424258,0.0001625488,0.000047629874,0.013162731],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97354126,0.013071225,0.001504798,0.0030838002,0.006473883,0.0023249825],"domain_scores_gemma":[0.83411235,0.12237427,0.012648753,0.013587341,0.013995234,0.0032820196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028482644,0.00049498386,0.0007772186,0.00282283,0.00369037,0.012747486,0.0019047646,0.004793197,0.007101934],"category_scores_gemma":[0.0806809,0.0006076006,0.0012795798,0.0034533334,0.013032494,0.021058794,0.0029898565,0.008687736,0.0011821537],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041674757,0.00008504075,0.0028869286,0.00012746955,0.000017509881,0.00005650157,0.0015122263,0.0007956487,0.00018070538,0.96771514,0.0015153438,0.02506574],"study_design_scores_gemma":[0.000026584887,0.00011384539,0.00556415,0.0012405348,0.00004884312,0.00031420018,0.002652368,0.0054737134,0.0024740384,0.930385,0.051652335,0.0000543706],"about_ca_topic_score_codex":0.005853409,"about_ca_topic_score_gemma":0.0022863692,"teacher_disagreement_score":0.028482644,"about_ca_system_score_codex":0.009739746,"about_ca_system_score_gemma":0.012976482,"threshold_uncertainty_score":0.15063244},"labels":[],"label_agreement":null},{"id":"W4402926156","doi":"10.23977/jaip.2024.070316","title":"Enhanced Credit Score Prediction Using Ensemble Deep Learning Model","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Ensemble learning; Credit score; Machine learning; Computer science; Deep learning; Econometrics; Actuarial science; Economics","score_opus":0.06190819399885802,"score_gpt":0.308857786487653,"score_spread":0.24694959248879497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402926156","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":[0.36290076,0.0022008861,0.62310743,0.00086979393,0.00027379775,0.00006947785,0.0010854697,0.0033980687,0.006094313],"genre_scores_gemma":[0.95217407,0.00033971484,0.0421849,0.00018138076,0.00006513816,0.0000427674,0.0009729138,0.000038257996,0.00400077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999754,0.00006682521,0.000013353283,0.00004729042,0.000067165245,0.00005125651],"domain_scores_gemma":[0.99938524,0.00022265347,0.000057212907,0.000058272824,0.00024302237,0.000033638094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011562267,0.00071220705,0.00088075566,0.0010644312,0.00022296183,0.0007572811,0.00087711297,0.0007929021,0.0012378838],"category_scores_gemma":[0.001735516,0.0002647702,0.000444284,0.0010011933,0.00018857635,0.0010021557,0.000591597,0.001048231,0.0004867938],"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":[0.00011654229,0.0001638369,0.0069805253,0.000025849638,0.0000856156,0.00007033077,0.000026831374,0.860291,0.00127268,0.0012942267,0.003779559,0.12589304],"study_design_scores_gemma":[0.000001596388,0.0000065585996,0.00026054392,0.000001962756,0.0000036114102,0.0000033246401,0.000001171738,0.99916685,0.000147635,0.0003276479,0.00007721565,0.000001867296],"about_ca_topic_score_codex":0.014552132,"about_ca_topic_score_gemma":0.016356438,"teacher_disagreement_score":0.014552132,"about_ca_system_score_codex":0.0006409819,"about_ca_system_score_gemma":0.00079579663,"threshold_uncertainty_score":0.028934896},"labels":[],"label_agreement":null},{"id":"W4402926304","doi":"10.23977/jaip.2024.070319","title":"Analysis of the Impact of Modern VR Technology on Digital Media Art Design","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Digital media; Multimedia; Human–computer interaction; Computer graphics (images); World Wide Web","score_opus":0.07153108678162956,"score_gpt":0.3808735978599268,"score_spread":0.30934251107829724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402926304","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":[0.6019247,0.0044465815,0.027010558,0.0007811187,0.000058631067,0.000118650896,0.00015619362,0.00012913033,0.36537436],"genre_scores_gemma":[0.9871148,0.0017263731,0.0046025245,0.000039015064,0.000018019568,0.000019740708,0.00005308554,0.000025706257,0.0064007044],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982664,0.00054625596,0.00006064788,0.000106597596,0.00087739545,0.00014271901],"domain_scores_gemma":[0.9959971,0.0026775133,0.00032957777,0.0001657218,0.000712746,0.00011732399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094860996,0.00022897558,0.00011983329,0.002016101,0.00050760584,0.0024397098,0.00035580798,0.00038610262,0.007393748],"category_scores_gemma":[0.005864257,0.00020271153,0.00045321876,0.0016684375,0.0007585265,0.0015814594,0.00061773055,0.00038162683,0.0005777392],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047838115,0.0003947452,0.08908206,0.001294648,0.00013297562,0.0020216235,0.012816014,0.03667365,0.01894254,0.21504404,0.006477137,0.6166422],"study_design_scores_gemma":[0.00006593384,0.0006717175,0.5092253,0.0010990065,0.00054157624,0.0030010298,0.02252485,0.18288533,0.023841245,0.034343902,0.22158311,0.00021702536],"about_ca_topic_score_codex":0.004010979,"about_ca_topic_score_gemma":0.0034233173,"teacher_disagreement_score":0.007393748,"about_ca_system_score_codex":0.0015280363,"about_ca_system_score_gemma":0.0007979969,"threshold_uncertainty_score":0.024734557},"labels":[],"label_agreement":null},{"id":"W4402926389","doi":"10.23977/jaip.2024.070318","title":"Construction of Higher Education Management Cloud Space Based on Machine Learning and Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cloud computing; Space (punctuation); Artificial intelligence; Computer science; Engineering management; Engineering; Operating system","score_opus":0.05020596981179963,"score_gpt":0.38044030972631965,"score_spread":0.33023433991452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402926389","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":[0.36392316,0.0015101046,0.57746035,0.0021212602,0.00062925916,0.0011934023,0.0011284499,0.0051991753,0.046834845],"genre_scores_gemma":[0.89768624,0.00045532492,0.09502327,0.00020179761,0.00009858626,0.0002549188,0.00092327286,0.000082031045,0.0052745226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898285,0.00019089543,0.000058731133,0.0001497521,0.00033457723,0.00028326554],"domain_scores_gemma":[0.9991954,0.00006763859,0.00008633231,0.000156764,0.00023946942,0.0002544019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004721417,0.00035219072,0.0003480703,0.0012752126,0.0017001688,0.0016259692,0.00094646006,0.00040730083,0.0041187312],"category_scores_gemma":[0.00088758883,0.00016530344,0.0005259209,0.0021707842,0.00040151775,0.002330331,0.002601981,0.0005109505,0.0009785938],"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":[0.0009514591,0.0013863579,0.04322297,0.00056518294,0.0001204592,0.0012869221,0.0014591774,0.053267173,0.03486605,0.065323494,0.046266712,0.751284],"study_design_scores_gemma":[0.00037617848,0.00064140296,0.042760894,0.00021327914,0.00017833874,0.0010664618,0.0044347136,0.71720415,0.04659408,0.024980096,0.16133276,0.00021760436],"about_ca_topic_score_codex":0.007875574,"about_ca_topic_score_gemma":0.005666906,"teacher_disagreement_score":0.007875574,"about_ca_system_score_codex":0.0012734697,"about_ca_system_score_gemma":0.0029569876,"threshold_uncertainty_score":0.015659451},"labels":[],"label_agreement":null},{"id":"W4403082358","doi":"10.23977/jaip.2024.070320","title":"Research on Holographic Retrieval and Analysis System for Scientific Research Data Based on SSH Framework and Lucene Engine","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Information retrieval; Computer science","score_opus":0.4608478845350966,"score_gpt":0.5409146072341356,"score_spread":0.08006672269903903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403082358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04521677,0.002153523,0.89819866,0.0010185734,0.0001410228,0.0010262234,0.0023268885,0.033755556,0.016162764],"genre_scores_gemma":[0.29464704,0.0024773984,0.67309743,0.00082389225,0.00018268381,0.00063867384,0.008891725,0.0010399937,0.018201116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672,0.00044855537,0.00040194826,0.00052994216,0.0015881042,0.00031139402],"domain_scores_gemma":[0.9976688,0.0005346839,0.0001908204,0.00061408425,0.0008443489,0.00014737698],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0035965624,0.0007761965,0.0009104286,0.0039911726,0.0010333487,0.0045035845,0.0022891925,0.0007688708,0.005159692],"category_scores_gemma":[0.004199924,0.00043768913,0.0011899027,0.003741213,0.000788699,0.007013539,0.0025823382,0.0008273449,0.0019999908],"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":[0.0010127767,0.00046050543,0.012149073,0.0015001138,0.00033886507,0.0009992265,0.0021026018,0.010159982,0.06895044,0.09146531,0.04238514,0.76847595],"study_design_scores_gemma":[0.0005151994,0.0006776399,0.014974923,0.00035737563,0.00062455406,0.0021905175,0.0016028567,0.3710487,0.27719563,0.056840286,0.27328622,0.000686046],"about_ca_topic_score_codex":0.008284677,"about_ca_topic_score_gemma":0.004867893,"teacher_disagreement_score":0.99640346,"about_ca_system_score_codex":0.0014216697,"about_ca_system_score_gemma":0.0037699924,"threshold_uncertainty_score":0.019020677},"labels":[],"label_agreement":null},{"id":"W4403683151","doi":"10.23977/jaip.2024.070321","title":"Text classification system based on LLM","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Natural language processing; Information retrieval","score_opus":0.07024406697806411,"score_gpt":0.3849638597407743,"score_spread":0.31471979276271017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403683151","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":[0.09725724,0.002130332,0.8191788,0.0014705509,0.0012400924,0.00091323646,0.004041145,0.060187336,0.013581302],"genre_scores_gemma":[0.56543386,0.00078233983,0.3887648,0.0009300755,0.0007957679,0.00088774617,0.0074743074,0.0004192918,0.034511797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991732,0.000082895676,0.000094421164,0.00030462813,0.00026228323,0.00008244258],"domain_scores_gemma":[0.99891114,0.00019459994,0.000119696364,0.00013135422,0.000555917,0.0000873372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085329526,0.0007789778,0.0010027546,0.0026353789,0.0010387142,0.0011838981,0.0012326874,0.0010133779,0.008547458],"category_scores_gemma":[0.002186372,0.00025109676,0.00067344046,0.0016733101,0.0002325338,0.0024029515,0.0009571959,0.0008446022,0.007026755],"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":[0.0008301448,0.00036240564,0.0044335127,0.00040730956,0.00007604071,0.0004950086,0.00020050707,0.006049846,0.049918573,0.00270914,0.034529325,0.8999883],"study_design_scores_gemma":[0.00014219663,0.00047495778,0.0066491193,0.0000771677,0.00020649057,0.00056820747,0.00019047038,0.8978046,0.055505823,0.0062828474,0.031972915,0.00012518077],"about_ca_topic_score_codex":0.0030335174,"about_ca_topic_score_gemma":0.0026246607,"teacher_disagreement_score":0.008547458,"about_ca_system_score_codex":0.0010034938,"about_ca_system_score_gemma":0.0008075163,"threshold_uncertainty_score":0.028594077},"labels":[],"label_agreement":null},{"id":"W4403915104","doi":"10.23977/jaip.2024.070322","title":"Innovation and development strategy of interactive entertainment industry driven by artificial intelligence","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Entertainment; Knowledge management; Computer science; Business; Artificial intelligence; Engineering; Engineering management; Manufacturing engineering; Art; Visual arts","score_opus":0.06889303183124894,"score_gpt":0.3421010197841749,"score_spread":0.27320798795292595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403915104","genre_codex":"other","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":[0.1632413,0.008174354,0.08435059,0.019782439,0.00055683655,0.000527418,0.00012279318,0.0005406111,0.7227037],"genre_scores_gemma":[0.8908448,0.0059512504,0.028634917,0.0012971106,0.00027871423,0.00030415313,0.0001436427,0.000078562276,0.07246679],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99867827,0.0003277623,0.000051468465,0.0002360772,0.00040243694,0.0003040095],"domain_scores_gemma":[0.9993199,0.00011094328,0.000055039298,0.000051832554,0.00022421504,0.00023805758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012092653,0.00046213812,0.00026281326,0.0025316605,0.002257698,0.005811289,0.001196477,0.0017582622,0.0059594666],"category_scores_gemma":[0.0012820209,0.00021348128,0.00063192606,0.001716842,0.0026033411,0.005699648,0.0035895123,0.0010829561,0.0014903467],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006393411,0.0001405189,0.006347513,0.00031294575,0.000029654475,0.001188892,0.004021205,0.0017540057,0.0028874076,0.8765318,0.010796108,0.095925994],"study_design_scores_gemma":[0.0000925499,0.0003328806,0.01230096,0.0005530284,0.00009923247,0.0018340859,0.010925488,0.032765172,0.0072007,0.2592471,0.67455024,0.00009862027],"about_ca_topic_score_codex":0.0025667434,"about_ca_topic_score_gemma":0.0019009516,"teacher_disagreement_score":0.0059594666,"about_ca_system_score_codex":0.0035866601,"about_ca_system_score_gemma":0.0049024387,"threshold_uncertainty_score":0.02602321},"labels":[],"label_agreement":null},{"id":"W4404092351","doi":"10.23977/jaip.2024.070323","title":"Development of a Knowledge Graph for Database Courses through the Integration of Multi-source Educational Data","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Assessment","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Graph database; Graph; Database; Data science; Information retrieval; Theoretical computer science","score_opus":0.2515913928978869,"score_gpt":0.48245180090610873,"score_spread":0.23086040800822183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404092351","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":[0.08774049,0.0006207049,0.7659419,0.0014568904,0.000290888,0.0015828338,0.116061695,0.017088119,0.009216515],"genre_scores_gemma":[0.12953207,0.0004949395,0.6946124,0.00018627652,0.000031214357,0.00072754035,0.17212318,0.0003440448,0.0019483512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865997,0.00019935943,0.00015399486,0.00048023017,0.0004385207,0.000067825524],"domain_scores_gemma":[0.99699473,0.00088317745,0.000263916,0.0008992067,0.00072884903,0.00023014376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011887572,0.00064438384,0.0004565184,0.007338921,0.0007426795,0.0019549904,0.0016690785,0.00087067304,0.0018506144],"category_scores_gemma":[0.0069319126,0.00034882882,0.001071026,0.005555238,0.00036657052,0.0034598112,0.0020837907,0.001727096,0.0012943735],"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":[0.00025019955,0.0012038394,0.047684886,0.0015241791,0.0003779442,0.0006797717,0.0012097976,0.07053377,0.021666082,0.034989387,0.06323345,0.75664663],"study_design_scores_gemma":[0.00009575463,0.0004148256,0.03972571,0.00042604702,0.0002281355,0.00059277064,0.0016856819,0.6226069,0.036930755,0.04846244,0.24866062,0.0001703149],"about_ca_topic_score_codex":0.01708809,"about_ca_topic_score_gemma":0.032931123,"teacher_disagreement_score":0.01708809,"about_ca_system_score_codex":0.00145442,"about_ca_system_score_gemma":0.0025426429,"threshold_uncertainty_score":0.03397727},"labels":[],"label_agreement":null},{"id":"W4404280210","doi":"10.23977/jaip.2024.070324","title":"Systemic Bias in Artificial Intelligence: Focusing on Gender, Racial, and Political Biases","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Politics; Gender bias; Racial bias; Psychology; Political science; Sociology; Social psychology; Artificial intelligence; Computer science; Race (biology); Gender studies; Law","score_opus":0.3386635278398094,"score_gpt":0.500945675962464,"score_spread":0.16228214812265457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404280210","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":[0.59823096,0.009592333,0.082177654,0.05650482,0.0010521105,0.00018742548,0.00023124497,0.000056655455,0.25196686],"genre_scores_gemma":[0.9910562,0.0008107194,0.003466726,0.0019432222,0.000246471,0.000077850025,0.000027273256,0.000026229935,0.0023453843],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9775711,0.015421884,0.00065515004,0.0016612645,0.003897603,0.00079293764],"domain_scores_gemma":[0.95373577,0.03191612,0.0068616034,0.0025444862,0.0038277302,0.001114333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.026309362,0.00037154532,0.000476373,0.002645442,0.0037454793,0.0046920306,0.00070587295,0.0010638006,0.004904861],"category_scores_gemma":[0.066711925,0.00016021534,0.00029167635,0.0024657182,0.015067214,0.007246198,0.005320458,0.0017259497,0.00034301917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013570001,0.00008163262,0.06808274,0.00030252142,0.00007645898,0.00023758452,0.14520499,0.00040481877,0.0009349952,0.67880476,0.0039772233,0.10175663],"study_design_scores_gemma":[0.000039379982,0.00014337471,0.056015454,0.0012541021,0.0001166372,0.0006890941,0.108465,0.0023827686,0.0020754654,0.7516595,0.07708891,0.00007031733],"about_ca_topic_score_codex":0.0024500182,"about_ca_topic_score_gemma":0.0037918824,"teacher_disagreement_score":0.026309362,"about_ca_system_score_codex":0.002458889,"about_ca_system_score_gemma":0.0026076126,"threshold_uncertainty_score":0.13913882},"labels":[],"label_agreement":null},{"id":"W4404670199","doi":"10.23977/jaip.2024.070325","title":"The Current Status, Development Bottlenecks and Future Prospects of the Application of Artificial Intelligence in English Teaching at Basic Period from the Perspective of \"Internet+\"","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Period (music); Perspective (graphical); The Internet; Computer science; Mathematics education; Sociology; Psychology; Artificial intelligence; World Wide Web; Art; Aesthetics","score_opus":0.03612118582590347,"score_gpt":0.38096623279657016,"score_spread":0.3448450469706667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404670199","genre_codex":"empirical","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91628456,0.02749442,0.0033743712,0.033211354,0.00012571749,0.00006470259,0.00022154817,0.00013156673,0.019091727],"genre_scores_gemma":[0.98634094,0.010006722,0.0018728913,0.00028516934,0.00004575359,0.00003468595,0.00007597379,0.000010026509,0.0013278748],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99706143,0.0010094056,0.00028483325,0.00035573338,0.00055843487,0.0007302236],"domain_scores_gemma":[0.9844974,0.00543407,0.0029678666,0.0004093248,0.0028253512,0.0038659947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008823311,0.00018722119,0.0003695,0.002001138,0.0015044134,0.0044058445,0.0011860738,0.00080304034,0.0026896975],"category_scores_gemma":[0.012771542,0.00029869852,0.00022417492,0.0028091257,0.0017783399,0.00748606,0.0017362226,0.00096061185,0.00046903262],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004079931,0.00059203437,0.39456,0.0023950029,0.000040720304,0.00054967485,0.028851362,0.0012067697,0.004263483,0.03165143,0.0034131422,0.5320684],"study_design_scores_gemma":[0.00005814012,0.00097286876,0.78523654,0.0017104659,0.00010109025,0.0010440225,0.11920385,0.0067117573,0.003541514,0.01435646,0.06692176,0.00014153245],"about_ca_topic_score_codex":0.011653301,"about_ca_topic_score_gemma":0.015667642,"teacher_disagreement_score":0.011653301,"about_ca_system_score_codex":0.0036619022,"about_ca_system_score_gemma":0.012129182,"threshold_uncertainty_score":0.04666263},"labels":[],"label_agreement":null},{"id":"W4404697089","doi":"10.23977/jaip.2024.070401","title":"Hierarchical Model of Graphical Human-computer Interface Based on Digital Twin and Visual Perception","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Simulation and Modeling Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Perception; Human–computer interaction; Interface (matter); Graphical user interface; Computer graphics (images); Psychology; Programming language; Neuroscience; Operating system","score_opus":0.04860891507479902,"score_gpt":0.36511831059354505,"score_spread":0.316509395518746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404697089","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":[0.032028448,0.00040640988,0.94594264,0.00035444656,0.00006410866,0.000091682836,0.00012717022,0.00086114794,0.020123854],"genre_scores_gemma":[0.79658365,0.00068193284,0.18596306,0.00013978648,0.000043864162,0.00026050035,0.00027677184,0.00013124527,0.015919143],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992574,0.00014987557,0.000043972705,0.00019278351,0.00027029778,0.000085642496],"domain_scores_gemma":[0.9994134,0.00013424369,0.0000566262,0.000093874005,0.0002217164,0.00008020554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043393503,0.00054826937,0.00049371884,0.000991744,0.0009079639,0.0017607204,0.0013170782,0.00079140073,0.008456499],"category_scores_gemma":[0.001854924,0.00036146288,0.0010422325,0.00060595904,0.0012790943,0.0035419313,0.0013862659,0.00079945865,0.0011871114],"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":[0.00036446287,0.00017098394,0.005667848,0.0003767968,0.00011562932,0.0008355581,0.0023780304,0.20539096,0.03442828,0.6292231,0.0046562706,0.11639209],"study_design_scores_gemma":[0.000026327873,0.00011123906,0.0016606945,0.000030442316,0.00005660918,0.00033311956,0.00021832665,0.90589523,0.0025943627,0.08288309,0.0061423816,0.00004816112],"about_ca_topic_score_codex":0.010485865,"about_ca_topic_score_gemma":0.004351095,"teacher_disagreement_score":0.010485865,"about_ca_system_score_codex":0.0012261278,"about_ca_system_score_gemma":0.0012053399,"threshold_uncertainty_score":0.028289795},"labels":[],"label_agreement":null},{"id":"W4404767726","doi":"10.23977/jaip.2024.070402","title":"Exploration of the Integration Development of Innovation and Entrepreneurship Education in Colleges and Universities Based on AI Technology","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Entrepreneurship; Entrepreneurship education; Engineering management; Engineering; Engineering ethics; Political science; Knowledge management; Mathematics education; Computer science; Psychology","score_opus":0.07450614502856297,"score_gpt":0.37816813784314074,"score_spread":0.3036619928145778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404767726","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":[0.83637667,0.00046502,0.013895218,0.0023724309,0.000026756888,0.0001434181,0.000021102245,0.00005142937,0.14664793],"genre_scores_gemma":[0.99535596,0.00013583717,0.0022746786,0.000037045294,0.000002776106,0.000018909575,0.000006932582,0.0000034076031,0.0021645227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99868387,0.00063561037,0.000033946435,0.00008810881,0.000240428,0.00031798176],"domain_scores_gemma":[0.99895203,0.00034598116,0.00011237989,0.000053496053,0.000112513924,0.00042351664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015061385,0.0001305377,0.00015577368,0.0012372956,0.0020599116,0.004513765,0.0005776883,0.00052140385,0.0035656998],"category_scores_gemma":[0.0022544027,0.00016181667,0.0003018884,0.0013611037,0.0015625554,0.0035849428,0.0033312568,0.0006165239,0.0002938036],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001214241,0.0009824211,0.09800289,0.00025856492,0.00003785951,0.0019170959,0.06291905,0.0027003482,0.003151172,0.5987153,0.001816339,0.22937752],"study_design_scores_gemma":[0.00009037006,0.00072505634,0.23732087,0.00067429774,0.000119452285,0.0016055333,0.33125123,0.06928668,0.006468084,0.20832796,0.1440346,0.00009584549],"about_ca_topic_score_codex":0.0026513997,"about_ca_topic_score_gemma":0.0052966494,"teacher_disagreement_score":0.004513765,"about_ca_system_score_codex":0.0027103918,"about_ca_system_score_gemma":0.0056660543,"threshold_uncertainty_score":0.0196653},"labels":[],"label_agreement":null},{"id":"W4404876113","doi":"10.23977/jaip.2024.070403","title":"Exploration and Application of AI Technology in the Curriculum System of E-commerce Specialty","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Specialty; Curriculum; E-commerce; Engineering management; Computer science; Engineering ethics; Medical education; Engineering; Psychology; Pedagogy; Medicine; World Wide Web","score_opus":0.053372825774296154,"score_gpt":0.4209968240993816,"score_spread":0.36762399832508547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404876113","genre_codex":"other","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":[0.3196008,0.01022633,0.12604621,0.038270254,0.0020395801,0.0005663708,0.00013937936,0.00039071514,0.50272036],"genre_scores_gemma":[0.8559916,0.005228605,0.08564231,0.0023310904,0.00041789707,0.0002849476,0.00013763693,0.00007054771,0.049895447],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99880457,0.00067799416,0.00005967718,0.0000962091,0.00022405433,0.00013748853],"domain_scores_gemma":[0.99826604,0.00059285545,0.00012542834,0.00008902908,0.0004139076,0.0005127419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016269208,0.00017639226,0.00015037128,0.001418497,0.0015556514,0.0047225496,0.0005783199,0.0007221775,0.0049894066],"category_scores_gemma":[0.003686299,0.00012923182,0.00027432386,0.0014541695,0.0016626131,0.0025236954,0.0019775322,0.0012724076,0.0010163031],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077977886,0.0006141186,0.023846773,0.00065171765,0.000015026453,0.0007443027,0.043052793,0.0013107818,0.008844037,0.4016974,0.022655195,0.4964899],"study_design_scores_gemma":[0.000035856505,0.00045129601,0.041257564,0.0014878298,0.000038566162,0.0016742691,0.056451324,0.010327354,0.005259388,0.18892981,0.6940183,0.000068393965],"about_ca_topic_score_codex":0.001126868,"about_ca_topic_score_gemma":0.002045359,"teacher_disagreement_score":0.0049894066,"about_ca_system_score_codex":0.0021087725,"about_ca_system_score_gemma":0.0053659678,"threshold_uncertainty_score":0.016691267},"labels":[],"label_agreement":null},{"id":"W4405307393","doi":"10.23977/jaip.2024.070404","title":"HCM: Icon art design based on diffusion model","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Icon; Diffusion; Computer science; Physics; Programming language; Thermodynamics","score_opus":0.09996065018434111,"score_gpt":0.3703115377194586,"score_spread":0.2703508875351175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405307393","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":[0.03875742,0.0009480045,0.9363083,0.0005324892,0.00017709033,0.00015486243,0.0011375281,0.0038858522,0.018098487],"genre_scores_gemma":[0.6769513,0.0009765427,0.29093742,0.00034948648,0.00010998842,0.00037217676,0.0026759019,0.0008279144,0.026799174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975306,0.00004834535,0.000010282742,0.00009256084,0.00006500696,0.000030859777],"domain_scores_gemma":[0.9997061,0.0001434596,0.000027470081,0.000054907556,0.00004676875,0.000021305721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043532756,0.0007926199,0.0006896257,0.0010380667,0.00046030764,0.0012685057,0.0013581689,0.0014601251,0.0077001527],"category_scores_gemma":[0.0014314388,0.00050759903,0.0015572128,0.0007353801,0.0006012841,0.0009873168,0.0006815539,0.0008860455,0.0014642648],"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":[0.00011931111,0.00007548043,0.0018905,0.00016696552,0.000059552523,0.00017150023,0.00014850023,0.84744525,0.005799032,0.034861274,0.009299077,0.09996351],"study_design_scores_gemma":[0.000007232757,0.000008196147,0.00012640448,0.000004838149,0.000004869189,0.000028168603,0.0000052985615,0.99374926,0.0004188415,0.004050566,0.001590888,0.000005429177],"about_ca_topic_score_codex":0.015628258,"about_ca_topic_score_gemma":0.015803618,"teacher_disagreement_score":0.015628258,"about_ca_system_score_codex":0.0011439453,"about_ca_system_score_gemma":0.0006720197,"threshold_uncertainty_score":0.031074584},"labels":[],"label_agreement":null},{"id":"W4405377440","doi":"10.23977/jaip.2024.070405","title":"Multi-dimensional Evaluation and Practical Reflection on the Intelligent PE Class Model from the Perspective of Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Reflection (computer programming); Class (philosophy); Artificial intelligence; Computer science","score_opus":0.3318447052578401,"score_gpt":0.5222383142377042,"score_spread":0.19039360897986407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405377440","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":[0.8314609,0.00020010083,0.030745536,0.0029376294,0.00017718064,0.00035523137,0.000115473435,0.0001517964,0.13385607],"genre_scores_gemma":[0.983482,0.00013147344,0.006319675,0.000095014104,0.000019015511,0.0001328162,0.000051576455,0.000017769155,0.009750637],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979857,0.0009023278,0.00009786752,0.00022728812,0.0006029142,0.00018383407],"domain_scores_gemma":[0.99780875,0.0005428177,0.00020181137,0.00019629259,0.0008944124,0.00035598478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020835884,0.00026467734,0.00017269321,0.0010798599,0.0011978905,0.0025054037,0.00067630544,0.0005172281,0.004713112],"category_scores_gemma":[0.0059287082,0.00008704934,0.00033158733,0.0006212537,0.0015223381,0.002166971,0.0018501291,0.0007963348,0.0005858729],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030092764,0.0018024417,0.18235086,0.0003101244,0.00005215796,0.0012744042,0.16310091,0.005030635,0.010978209,0.0943802,0.02863594,0.51178324],"study_design_scores_gemma":[0.000059639966,0.0017510308,0.35870817,0.00031581693,0.00008684568,0.001463643,0.41457435,0.052539907,0.007732582,0.037102744,0.12544732,0.00021780157],"about_ca_topic_score_codex":0.0021414817,"about_ca_topic_score_gemma":0.002339479,"teacher_disagreement_score":0.004713112,"about_ca_system_score_codex":0.0014753838,"about_ca_system_score_gemma":0.0009372615,"threshold_uncertainty_score":0.015766978},"labels":[],"label_agreement":null},{"id":"W4405377896","doi":"10.23977/jaip.2024.070406","title":"A Stereo Vision Perception and Control Method for an Intelligent Shift Device","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perception; Stereopsis; Computer vision; Computer science; Artificial intelligence; Control (management); Depth perception; Psychology","score_opus":0.04609163156370081,"score_gpt":0.40922330226780484,"score_spread":0.363131670704104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405377896","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":[0.005876807,0.0001258945,0.9914027,0.00004943126,0.000060667462,0.000031041956,0.000015036412,0.00025781666,0.0021806227],"genre_scores_gemma":[0.5501982,0.00033033066,0.44024605,0.00017937248,0.00008628378,0.00015284271,0.00009809875,0.000055219927,0.008653586],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998216,0.000018017985,0.000007122232,0.000047811092,0.00008851069,0.000016900942],"domain_scores_gemma":[0.9998944,0.000015871565,0.000013671541,0.00001407824,0.00005426302,0.000007668293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026540484,0.00033868567,0.0002638343,0.0003429673,0.00031738624,0.00041697462,0.0005301062,0.0004469819,0.0021749993],"category_scores_gemma":[0.00033990561,0.00015746674,0.00042254676,0.0002480042,0.00025546536,0.00041809762,0.0003433162,0.0004271151,0.0005109936],"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":[0.00018266737,0.00011135254,0.0006969258,0.00019785836,0.000046377674,0.0001311168,0.0002400767,0.06216394,0.17239654,0.01911747,0.0035784084,0.7411373],"study_design_scores_gemma":[0.000038135033,0.00032087188,0.0017235228,0.000021146627,0.00003242883,0.00019863817,0.00006175552,0.9537867,0.02654331,0.0030538535,0.014178861,0.000040831415],"about_ca_topic_score_codex":0.0041281986,"about_ca_topic_score_gemma":0.00285609,"teacher_disagreement_score":0.0041281986,"about_ca_system_score_codex":0.0003994276,"about_ca_system_score_gemma":0.00054941344,"threshold_uncertainty_score":0.008208334},"labels":[],"label_agreement":null},{"id":"W4405722017","doi":"10.23977/jaip.2024.070409","title":"Exploring the Path of Intangible Cultural Heritage and Protection Promoted by Artificial Intelligence: Taking the Eight Wonders of Yanjing as an Example","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Regional Development and Environment","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Path (computing); Cultural heritage; Intangible cultural heritage; Environmental ethics; Artificial intelligence; Computer science; Sociology; Political science; Law; Philosophy; Operating system","score_opus":0.25654221149249945,"score_gpt":0.36730451209152093,"score_spread":0.11076230059902148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405722017","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":[0.7922709,0.0053350776,0.02259385,0.007924038,0.0001559292,0.000069916445,0.00007962815,0.000098747136,0.17147186],"genre_scores_gemma":[0.9816616,0.0014685298,0.005946993,0.00013447594,0.000010393601,0.0000178722,0.000028158725,0.000011270522,0.010720606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99974185,0.00008436624,0.00000968417,0.00003721765,0.000056763565,0.0000701533],"domain_scores_gemma":[0.99962926,0.00012196658,0.00006311891,0.0000427845,0.000069771384,0.000072983516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005944712,0.00023635181,0.00022629407,0.00090186385,0.003455482,0.0037451072,0.00039021848,0.00073239725,0.0026391603],"category_scores_gemma":[0.000598648,0.00015448677,0.00022328242,0.001838786,0.0030350275,0.0030882764,0.0018392159,0.0009503659,0.00015896192],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037196348,0.00014978276,0.06882525,0.0010915551,0.00006470152,0.009438333,0.114899114,0.0044022007,0.016296394,0.5818278,0.0066979933,0.19593485],"study_design_scores_gemma":[0.00004295972,0.00037319717,0.14123562,0.0007084263,0.00016862487,0.0034686716,0.23749214,0.011954305,0.012747042,0.21226604,0.3793223,0.00022075191],"about_ca_topic_score_codex":0.016277468,"about_ca_topic_score_gemma":0.03555696,"teacher_disagreement_score":0.016277468,"about_ca_system_score_codex":0.003153762,"about_ca_system_score_gemma":0.0034217266,"threshold_uncertainty_score":0.03236544},"labels":[],"label_agreement":null},{"id":"W4405722029","doi":"10.23977/jaip.2024.070410","title":"Analysis of the Impact of Machine Learning Research Methods on Labour Market Research—An Example from CNKI","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Data science; Manufacturing engineering; Engineering management; Engineering","score_opus":0.45438877708028885,"score_gpt":0.6298436124041137,"score_spread":0.17545483532382483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405722029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37804267,0.16582584,0.17935327,0.064448565,0.0019474578,0.0007696474,0.0043741185,0.0005950659,0.20464337],"genre_scores_gemma":[0.86770695,0.046412367,0.067131385,0.0020490119,0.0009360443,0.00029285578,0.001439948,0.0002047788,0.013826763],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.98238856,0.008448196,0.0008324062,0.00096973695,0.0068076844,0.0005533985],"domain_scores_gemma":[0.85474503,0.11013061,0.006804003,0.005428505,0.021601148,0.001290729],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.037511934,0.00055536034,0.0008567154,0.010626491,0.0017750931,0.0046923556,0.0010326131,0.00096048834,0.004084734],"category_scores_gemma":[0.08536014,0.00023810474,0.0009399058,0.018271046,0.0017132905,0.0047413525,0.0018792454,0.0021886723,0.0009702256],"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":[0.00050793076,0.00029774147,0.11914535,0.0033374587,0.00045082174,0.00062283565,0.005555405,0.00873832,0.0017267348,0.17965257,0.016258363,0.6637065],"study_design_scores_gemma":[0.00007730322,0.00050231675,0.56522614,0.0058822376,0.000550231,0.0009101743,0.013235376,0.06335468,0.0070607117,0.102213964,0.24073577,0.00025119347],"about_ca_topic_score_codex":0.020977046,"about_ca_topic_score_gemma":0.020605328,"teacher_disagreement_score":0.96248806,"about_ca_system_score_codex":0.008530341,"about_ca_system_score_gemma":0.007398191,"threshold_uncertainty_score":0.1983844},"labels":[],"label_agreement":null},{"id":"W4406045730","doi":"10.23977/jaip.2024.070411","title":"Exploration of Teaching Strategies for Artificial Intelligence-Oriented College Students' Autonomous Learning","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Autonomous learning; Mathematics education; Computer science; Artificial intelligence; Psychology","score_opus":0.11487522375354517,"score_gpt":0.4348073644437311,"score_spread":0.31993214069018594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406045730","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":[0.76212835,0.00040592774,0.15330027,0.0015547437,0.000036991863,0.0005804364,0.00002225874,0.00033591015,0.08163513],"genre_scores_gemma":[0.94580024,0.00015906274,0.04981125,0.00005476711,0.0000039471033,0.00014959999,0.000017650898,0.000009938302,0.0039933966],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9997055,0.00016073385,0.000009375984,0.00004226137,0.000044826997,0.000037266942],"domain_scores_gemma":[0.9995332,0.00023878308,0.000044574084,0.000023735602,0.00006017005,0.00009962345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063599995,0.00026433737,0.00016248785,0.00041569263,0.0005537365,0.0016839004,0.00059791096,0.0004378398,0.0029442247],"category_scores_gemma":[0.0015254398,0.00010119264,0.00028542674,0.00025568763,0.00047271763,0.0013391441,0.00063850154,0.00038446265,0.00027584215],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026467757,0.0034965794,0.030328533,0.0006651009,0.000053378175,0.00053520926,0.039025366,0.0114125665,0.016381878,0.21837954,0.0050236345,0.67443347],"study_design_scores_gemma":[0.00067318836,0.002561133,0.043212168,0.0006995681,0.00031185604,0.0008898358,0.07925787,0.46707562,0.027720075,0.25982141,0.11761127,0.00016603747],"about_ca_topic_score_codex":0.0011094771,"about_ca_topic_score_gemma":0.0017726776,"teacher_disagreement_score":0.0029442247,"about_ca_system_score_codex":0.0008081945,"about_ca_system_score_gemma":0.0013538961,"threshold_uncertainty_score":0.009849429},"labels":[],"label_agreement":null},{"id":"W4406123959","doi":"10.23977/jaip.2024.070412","title":"Analysis of Utilizing Artificial Intelligence to Improve the Efficiency of Digital Media Art Creation","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Media arts; Digital media; Multimedia; Artificial intelligence; Human–computer interaction; Art; World Wide Web; Visual arts","score_opus":0.06448468093853821,"score_gpt":0.37574189386358275,"score_spread":0.31125721292504455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406123959","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":[0.45066196,0.0076302774,0.18072289,0.0037254584,0.00011382575,0.000306026,0.00023196025,0.0005638393,0.3560438],"genre_scores_gemma":[0.9623901,0.002028001,0.030613884,0.0001085756,0.000032269058,0.00006414422,0.00009025104,0.000066287255,0.0046064765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779,0.0007177129,0.00012845213,0.00016578929,0.0010201778,0.00017781268],"domain_scores_gemma":[0.9946197,0.0033277161,0.0004433486,0.0005340547,0.0009586482,0.00011648437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002514252,0.00038837275,0.00028087242,0.0023416346,0.00072170957,0.0037311707,0.00067720644,0.00052407634,0.002749146],"category_scores_gemma":[0.008795248,0.00018434743,0.0004610562,0.0020686188,0.0012317015,0.003260997,0.0008845655,0.00060392055,0.00041361802],"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":[0.00030739998,0.00030352807,0.023852387,0.0011227191,0.00020277327,0.0005516774,0.0019070198,0.072007775,0.012682174,0.39307278,0.0047813347,0.48920846],"study_design_scores_gemma":[0.000060841998,0.00033503972,0.053033933,0.0006523065,0.00036806214,0.0007443425,0.003453551,0.55983615,0.040724088,0.24202293,0.09865201,0.00011677569],"about_ca_topic_score_codex":0.0012866394,"about_ca_topic_score_gemma":0.0010760552,"teacher_disagreement_score":0.0037311707,"about_ca_system_score_codex":0.0013681328,"about_ca_system_score_gemma":0.001021165,"threshold_uncertainty_score":0.013296843},"labels":[],"label_agreement":null},{"id":"W4406296233","doi":"10.23977/jaip.2024.070413","title":"Exploration and Research on the New Ecosystem of Artificial Intelligence + Security Education","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ecosystem; Computer science; Environmental resource management; Artificial intelligence; Ecology; Environmental science; Biology","score_opus":0.27935798082766583,"score_gpt":0.5152784114973524,"score_spread":0.2359204306696866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406296233","genre_codex":"other","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":[0.15421435,0.053911645,0.053587385,0.1806551,0.0020521474,0.00024318197,0.000049446564,0.00025509665,0.5550316],"genre_scores_gemma":[0.89618224,0.036089458,0.040513642,0.0078626685,0.0005043655,0.00019630106,0.000035450415,0.000038980957,0.01857687],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976555,0.0013537473,0.000080208665,0.00017721232,0.00043541935,0.00029793463],"domain_scores_gemma":[0.99662364,0.0017784449,0.00033530654,0.00021266834,0.00037318247,0.0006767784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036635222,0.0002984119,0.000348992,0.0026494232,0.0032050523,0.0116429655,0.0006822441,0.0019112516,0.0034877933],"category_scores_gemma":[0.0023206086,0.0002586056,0.0005352183,0.0032059099,0.010145643,0.014841008,0.0041198637,0.0026165608,0.0004471029],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016252798,0.00018230527,0.0060046366,0.00027519796,0.0000147096835,0.0003206418,0.009070765,0.0004046612,0.00042871112,0.8861006,0.004629899,0.092551656],"study_design_scores_gemma":[0.000023610217,0.00019791623,0.0128305275,0.00148993,0.000038521768,0.0011268258,0.04579635,0.0056761336,0.0008582623,0.6324765,0.29943225,0.000053192034],"about_ca_topic_score_codex":0.001713541,"about_ca_topic_score_gemma":0.00316376,"teacher_disagreement_score":0.0116429655,"about_ca_system_score_codex":0.0037875965,"about_ca_system_score_gemma":0.008652211,"threshold_uncertainty_score":0.02748102},"labels":[],"label_agreement":null},{"id":"W4406296277","doi":"10.23977/jaip.2024.070414","title":"Design Study on Intelligent Storage and Dispensing System for Ship Outfitting Parts","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Engineering; Computer science; Manufacturing engineering; Engineering drawing; Marine engineering","score_opus":0.09941226941357253,"score_gpt":0.3442210291396217,"score_spread":0.24480875972604918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406296277","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":[0.15440078,0.00060634734,0.8292005,0.00027648316,0.00017277422,0.00062296726,0.000085736625,0.0011395429,0.01349484],"genre_scores_gemma":[0.93549126,0.00027309803,0.057966948,0.00006182888,0.000044924323,0.00032052826,0.00006406076,0.000032768185,0.005744525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938583,0.0001332666,0.000045043173,0.00016712538,0.00019892753,0.0000697347],"domain_scores_gemma":[0.9996623,0.00007131492,0.00005655006,0.000029492241,0.0001463477,0.00003401584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006086645,0.0007360697,0.0008224624,0.00051193376,0.0008186299,0.0011518932,0.0012265344,0.00080241845,0.004147551],"category_scores_gemma":[0.00058156654,0.00044099326,0.00064610934,0.00031401555,0.00040090678,0.0007361468,0.0005778959,0.0003199638,0.0004941433],"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":[0.00092729647,0.0004145248,0.005277083,0.0010804512,0.00015144733,0.0011121007,0.0007220422,0.67936194,0.16452344,0.008128535,0.0021729898,0.1361281],"study_design_scores_gemma":[0.000071082584,0.00085811946,0.0019579045,0.00002057954,0.00009164427,0.0002031436,0.00010248668,0.9757606,0.016700037,0.00052074436,0.0036816052,0.000031944663],"about_ca_topic_score_codex":0.0031014492,"about_ca_topic_score_gemma":0.0016391338,"teacher_disagreement_score":0.004147551,"about_ca_system_score_codex":0.00069811265,"about_ca_system_score_gemma":0.0009940914,"threshold_uncertainty_score":0.013874948},"labels":[],"label_agreement":null},{"id":"W4406346988","doi":"10.23977/jaip.2024.070415","title":"A Bionic Pelican-based Rice Field Channeled Applesnail Egg Removal Robot","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pelican; Robot; Field (mathematics); Environmental science; Computer science; Artificial intelligence; Biology; Fishery; Mathematics","score_opus":0.04475586701647346,"score_gpt":0.3044333465610622,"score_spread":0.25967747954458875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406346988","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":[0.5042737,0.0013526055,0.45146275,0.0003677547,0.00025744896,0.00035564243,0.0003032692,0.0060177064,0.035609104],"genre_scores_gemma":[0.8386541,0.00048435997,0.12755755,0.00018356305,0.000028189961,0.00019589574,0.00037012078,0.000063014355,0.032463204],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99992836,0.000003794436,0.0000025465408,0.000020264426,0.000035457448,0.000009684674],"domain_scores_gemma":[0.9999461,0.0000058828628,0.0000086957825,0.000007303279,0.000017914608,0.000014074243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006999287,0.0003108398,0.00024443198,0.00018104346,0.00025538792,0.00018580577,0.00052702683,0.00034656056,0.002666044],"category_scores_gemma":[0.00007869104,0.0001427707,0.00019626383,0.00008878208,0.00022055546,0.0002541678,0.00035226948,0.0002053017,0.00067654304],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028695137,0.00011219573,0.0033135908,0.00043234212,0.00003505361,0.0007552991,0.00021360663,0.011680699,0.8001822,0.0016069006,0.002823647,0.17855754],"study_design_scores_gemma":[0.00023527829,0.00410443,0.037004925,0.00014527382,0.00025280184,0.0059385384,0.00046191708,0.3254608,0.48138195,0.00093551906,0.14387351,0.00020509667],"about_ca_topic_score_codex":0.0016038448,"about_ca_topic_score_gemma":0.0025049367,"teacher_disagreement_score":0.002666044,"about_ca_system_score_codex":0.00013568733,"about_ca_system_score_gemma":0.00037884174,"threshold_uncertainty_score":0.008918822},"labels":[],"label_agreement":null},{"id":"W4406580176","doi":"10.23977/jaip.2024.070416","title":"Cloud Computing Applications and Data Security in Overseas Investment","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cloud computing; Computer security; Computer science; Cloud computing security; Business; Data science; Operating system","score_opus":0.14350549942369958,"score_gpt":0.3934599105306987,"score_spread":0.2499544111069991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406580176","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":[0.533239,0.024876762,0.020130042,0.04938882,0.0005215051,0.000094374154,0.00007446745,0.000054582597,0.37162042],"genre_scores_gemma":[0.9875567,0.004517047,0.0016010274,0.00058506243,0.00009247335,0.00001005297,0.000011561389,0.0000067302194,0.0056194146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"design_other","domain_scores_codex":[0.9989617,0.00046797394,0.000033101034,0.00006475221,0.00023050637,0.00024196909],"domain_scores_gemma":[0.99841595,0.00074508874,0.0003070733,0.00010071585,0.00022258013,0.00020860431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013059519,0.00021553063,0.00012156775,0.00087195245,0.001556139,0.005494077,0.0002926345,0.0011318065,0.0024475884],"category_scores_gemma":[0.002509639,0.00009701489,0.00024021398,0.0016468979,0.0025253883,0.0034587148,0.0014722504,0.0013708639,0.00020775273],"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":[0.000069637084,0.000105340456,0.025102414,0.000106717656,0.00002177093,0.0013492712,0.0030075905,0.00501525,0.0011719295,0.8826458,0.0067627016,0.074641585],"study_design_scores_gemma":[0.00003330547,0.00021915183,0.06667888,0.001235594,0.000052969564,0.00284352,0.03406942,0.05680572,0.0045750453,0.4995364,0.33384097,0.00010899249],"about_ca_topic_score_codex":0.010621273,"about_ca_topic_score_gemma":0.00987335,"teacher_disagreement_score":0.010621273,"about_ca_system_score_codex":0.0033075395,"about_ca_system_score_gemma":0.0029332903,"threshold_uncertainty_score":0.023997962},"labels":[],"label_agreement":null},{"id":"W4406668368","doi":"10.23977/jaip.2024.070417","title":"Literature Review of Path Planning Algorithms for Mobile Robots","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Motion planning; Mobile robot; Path (computing); Artificial intelligence; Robot; Algorithm; Computer network","score_opus":0.07333587596648408,"score_gpt":0.39731690110884765,"score_spread":0.3239810251423636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406668368","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019380421,0.76804876,0.1986515,0.0020227272,0.0019992874,0.000121552664,0.00049071736,0.0007957582,0.025931625],"genre_scores_gemma":[0.024579113,0.8161399,0.14755143,0.0008949383,0.0013348691,0.00019704217,0.0014750101,0.00020300633,0.007624742],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994356,0.00010264905,0.00008136642,0.00011869268,0.00022369313,0.000037919206],"domain_scores_gemma":[0.9985322,0.00082007505,0.00008480899,0.00007981531,0.00045216084,0.000030899817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006100467,0.0011962246,0.00097548566,0.0022640228,0.0007571132,0.0013670393,0.0017323731,0.0011708578,0.008832632],"category_scores_gemma":[0.0029145775,0.0005357185,0.00096662116,0.0058443365,0.00047621844,0.0026242225,0.00082783104,0.0012755073,0.0037991612],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004270771,0.00004128114,0.0002641896,0.008631738,0.000052622236,0.00018343088,0.00010981314,0.01646726,0.0008851374,0.018017583,0.03670749,0.91859674],"study_design_scores_gemma":[0.000027500659,0.00012382647,0.0008824032,0.0052647833,0.00021820358,0.001514441,0.00025334954,0.039199036,0.0017624971,0.03818427,0.91247696,0.00009276597],"about_ca_topic_score_codex":0.0038340222,"about_ca_topic_score_gemma":0.0027376085,"teacher_disagreement_score":0.008832632,"about_ca_system_score_codex":0.00075295125,"about_ca_system_score_gemma":0.00239704,"threshold_uncertainty_score":0.029548109},"labels":[],"label_agreement":null},{"id":"W4407077936","doi":"10.23977/jaip.2025.080101","title":"Design of Multi-functional Agricultural Management Robot Based on Machine Vision","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Agriculture; Artificial intelligence; Robot; Machine vision; Human–computer interaction; Computer vision; Geography","score_opus":0.05705006640804748,"score_gpt":0.3081569919209514,"score_spread":0.2511069255129039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407077936","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":[0.017966522,0.0003788892,0.9710352,0.00015968931,0.0001511327,0.00024164298,0.00003670928,0.0026642974,0.007365901],"genre_scores_gemma":[0.5665418,0.00037021594,0.4234575,0.00025172462,0.00006680658,0.0007894841,0.00015102317,0.000085973224,0.008285433],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952364,0.00003799455,0.000021702346,0.00015492915,0.00019360197,0.00006817068],"domain_scores_gemma":[0.999846,0.000017112492,0.000024179335,0.000014131848,0.000074315125,0.000024345516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003757682,0.0007812927,0.0007818621,0.0006919106,0.000567884,0.0005365799,0.0017074707,0.0011293445,0.0025896293],"category_scores_gemma":[0.0002735383,0.00054165034,0.00058004056,0.0003286234,0.00039487306,0.0006391226,0.00056135375,0.00041778546,0.00089029165],"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":[0.00037678622,0.00034372995,0.0026543073,0.00087970524,0.00014582089,0.0008249436,0.00042031016,0.151433,0.36159772,0.01037063,0.0076590576,0.46329397],"study_design_scores_gemma":[0.00019273674,0.0012897137,0.004539318,0.000056787674,0.00009385644,0.0009774164,0.00009105715,0.93446344,0.037797708,0.0022297967,0.018144345,0.00012382059],"about_ca_topic_score_codex":0.0027663393,"about_ca_topic_score_gemma":0.0017162985,"teacher_disagreement_score":0.0027663393,"about_ca_system_score_codex":0.00040262527,"about_ca_system_score_gemma":0.0010336486,"threshold_uncertainty_score":0.008663237},"labels":[],"label_agreement":null},{"id":"W4407556668","doi":"10.23977/jaip.2025.080102","title":"AI-Driven Situated Cognition Interaction Design for Immersive Learning in Virtual Space Tourism","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Situated; Situated cognition; Virtual space; Space (punctuation); Situated learning; Tourism; Human–computer interaction; Cognition; Computer science; Psychology; Cognitive science; Geography; Artificial intelligence; Mathematics education; Neuroscience","score_opus":0.06554526362776777,"score_gpt":0.38089822328696515,"score_spread":0.3153529596591974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407556668","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":[0.026475973,0.0001495376,0.9561049,0.0003335749,0.00004011312,0.00022399133,0.000023217397,0.00028722573,0.016361399],"genre_scores_gemma":[0.56476426,0.00019083398,0.4254044,0.00016770841,0.000016992877,0.00096667965,0.00006830298,0.00008739267,0.008333467],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987764,0.00071011146,0.00004713693,0.00014786879,0.00023752137,0.00008099081],"domain_scores_gemma":[0.99931145,0.0003860765,0.000043374974,0.00007861222,0.00009987948,0.00008065071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012441516,0.00064498134,0.00025748988,0.00036941457,0.0006958751,0.0022748066,0.0010947644,0.0009404795,0.004738028],"category_scores_gemma":[0.0019020023,0.00034467733,0.0008135712,0.00015785836,0.0017411702,0.0015024565,0.0023743967,0.000963199,0.0006485896],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004580112,0.0006512359,0.0023604934,0.0012408643,0.00018378274,0.00093199825,0.023840941,0.21042794,0.07830563,0.5456046,0.0030619798,0.13293253],"study_design_scores_gemma":[0.00023732953,0.0008229978,0.0017471609,0.00023087158,0.00014772498,0.00074707397,0.004615512,0.723739,0.020582175,0.14769156,0.09930791,0.0001306085],"about_ca_topic_score_codex":0.0012830232,"about_ca_topic_score_gemma":0.0014562724,"teacher_disagreement_score":0.004738028,"about_ca_system_score_codex":0.0009999793,"about_ca_system_score_gemma":0.0008353657,"threshold_uncertainty_score":0.015850306},"labels":[],"label_agreement":null},{"id":"W4407894302","doi":"10.23977/jaip.2025.080104","title":"Exploring the Application of AI in Digital Media Design and Creation","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Engineering drawing; Engineering","score_opus":0.1261649110894142,"score_gpt":0.3745774552872646,"score_spread":0.24841254419785042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407894302","genre_codex":"other","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":[0.1320981,0.009009947,0.33892047,0.01462998,0.000315686,0.00027063512,0.000054664873,0.00041430898,0.50428617],"genre_scores_gemma":[0.8669138,0.0052366587,0.1081041,0.00063090096,0.0001161748,0.00017518834,0.000033901364,0.00007661526,0.018712742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966287,0.0023607693,0.00009943566,0.00020223006,0.00052972935,0.00017911807],"domain_scores_gemma":[0.99315274,0.0058010183,0.0002067623,0.0003866676,0.0002927468,0.00016003338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034485136,0.00051159307,0.00025196298,0.0021062724,0.0021705343,0.008548674,0.0011919428,0.0021364703,0.0046766214],"category_scores_gemma":[0.008127422,0.00036522793,0.00050946797,0.001490818,0.010187029,0.0073892213,0.0036559799,0.0013851185,0.00072424684],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005627812,0.00007914576,0.0017038963,0.0005018081,0.000020310985,0.00066221907,0.018410757,0.007433237,0.0030381784,0.87167525,0.0019235631,0.09449527],"study_design_scores_gemma":[0.000054529326,0.00019535376,0.0018711952,0.0008523682,0.00003749787,0.0015261169,0.020860055,0.05900649,0.006561604,0.6930489,0.21592276,0.00006308951],"about_ca_topic_score_codex":0.0018468394,"about_ca_topic_score_gemma":0.0020155234,"teacher_disagreement_score":0.008548674,"about_ca_system_score_codex":0.0025080733,"about_ca_system_score_gemma":0.0014473536,"threshold_uncertainty_score":0.01823771},"labels":[],"label_agreement":null},{"id":"W4407894334","doi":"10.23977/jaip.2025.080103","title":"Exploration of Rural Micro-Renewal in the Context of AIGC and Cross-Media Integration: A Case Study of an Artistic Practice in Wupu Village","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Regional Development and Environment","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Sociology; Visual arts; Geography; Art; Archaeology","score_opus":0.07765036321261481,"score_gpt":0.40893318128798645,"score_spread":0.33128281807537163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407894334","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":[0.9889509,0.0001514078,0.0017431846,0.00061441347,0.000013842323,0.00007245772,0.000012925359,0.000015315523,0.008425657],"genre_scores_gemma":[0.99623257,0.00012486405,0.0010899715,0.000057018337,0.0000049958808,0.000035615245,0.0000065563177,0.0000072806265,0.0024411434],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9976342,0.00163774,0.000041442585,0.0001612742,0.0001593348,0.00036611012],"domain_scores_gemma":[0.99812835,0.0011789667,0.00013357386,0.00014784813,0.00008544985,0.0003257966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002184725,0.00037487122,0.00039065394,0.0011267221,0.010196727,0.0028309422,0.0015040095,0.0016943766,0.0027624695],"category_scores_gemma":[0.0028732533,0.00030649808,0.00036434145,0.0011367573,0.008143925,0.0017982193,0.0051385993,0.0014573948,0.0002324152],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004803272,0.00018001358,0.0060213045,0.00017891737,0.000009415707,0.012123809,0.9548005,0.00031714723,0.0025488844,0.0074197524,0.0005650333,0.01578704],"study_design_scores_gemma":[0.000008328661,0.00014130388,0.0056456965,0.00010367405,0.000012538359,0.0020426833,0.96701795,0.00053525524,0.0008438634,0.0015925427,0.022036867,0.00001943858],"about_ca_topic_score_codex":0.006327483,"about_ca_topic_score_gemma":0.022300728,"teacher_disagreement_score":0.010196727,"about_ca_system_score_codex":0.0026060469,"about_ca_system_score_gemma":0.0023039225,"threshold_uncertainty_score":0.018908322},"labels":[],"label_agreement":null},{"id":"W4408003657","doi":"10.23977/jaip.2025.080105","title":"Exploration of Formalization Techniques for Geometric Entities in Planar Geometry Proposition Texts","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Proposition; Geometry; Planar; Computer science; Mathematics; Engineering drawing; Algebra over a field; Pure mathematics; Computer graphics (images); Engineering; Linguistics; Philosophy","score_opus":0.03100983818718876,"score_gpt":0.3110163477160621,"score_spread":0.28000650952887335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408003657","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":[0.0041718306,0.00037036385,0.9897091,0.0007301589,0.00007512008,0.000096076204,0.00040928565,0.0014260658,0.0030119917],"genre_scores_gemma":[0.14985265,0.00093990704,0.84060884,0.0003963783,0.00014308839,0.00020813518,0.002546914,0.00068981166,0.0046142675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970708,0.00081937085,0.00037116357,0.00092518784,0.0006696381,0.00014375914],"domain_scores_gemma":[0.99573874,0.0017717481,0.0005026468,0.0010559805,0.0008041738,0.0001265794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026289257,0.0008997315,0.0004960884,0.0026193298,0.0010379417,0.00270865,0.0020682693,0.0008765695,0.007953435],"category_scores_gemma":[0.0087445825,0.00078458444,0.002387158,0.0015552774,0.003970357,0.011401918,0.0026676864,0.0029420545,0.0030080345],"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":[0.000055731052,0.0000448946,0.0009965325,0.0005604095,0.000036645295,0.00040486088,0.0017509583,0.015483595,0.0071183727,0.8505237,0.0059420355,0.11708233],"study_design_scores_gemma":[0.000027143687,0.000088852226,0.0010172236,0.0004590284,0.0000819369,0.0012547267,0.00119862,0.20557947,0.018532164,0.6243516,0.1472807,0.00012845911],"about_ca_topic_score_codex":0.0035942618,"about_ca_topic_score_gemma":0.0047387704,"teacher_disagreement_score":0.007953435,"about_ca_system_score_codex":0.0019084021,"about_ca_system_score_gemma":0.0021988489,"threshold_uncertainty_score":0.026606917},"labels":[],"label_agreement":null},{"id":"W4408004207","doi":"10.23977/jaip.2025.080106","title":"The Impact, Potential Risks, and Countermeasures of Artificial Intelligence on Ideological Education in Higher Education","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ideology; Risk analysis (engineering); Computer science; Political science; Business; Politics; Law","score_opus":0.14449741507964559,"score_gpt":0.49404830516767545,"score_spread":0.34955089008802986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408004207","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":[0.37449703,0.027882468,0.029617729,0.3059686,0.0016668659,0.00034056167,0.0001198109,0.00030041396,0.25960654],"genre_scores_gemma":[0.98600453,0.0033955276,0.004320636,0.004142964,0.00033344314,0.00008573967,0.000012870073,0.00002674828,0.0016775836],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9176258,0.053635605,0.0021370146,0.002968799,0.019661058,0.0039718095],"domain_scores_gemma":[0.81991893,0.12132915,0.02771731,0.010555367,0.014724551,0.0057547498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.047513474,0.0009227694,0.0007253641,0.0042292913,0.006531197,0.014410332,0.002008383,0.00540234,0.004921997],"category_scores_gemma":[0.12183026,0.0006475667,0.0007647371,0.002358276,0.020863613,0.011868282,0.01128003,0.00583376,0.00072796684],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044763176,0.0007304357,0.06587881,0.0009056952,0.00022365949,0.00078482064,0.021586593,0.0026645744,0.0011540899,0.5818668,0.007085083,0.31667182],"study_design_scores_gemma":[0.00021911184,0.0015135048,0.092757866,0.007815438,0.00043909528,0.0019343736,0.06384038,0.008530801,0.0071380497,0.7022826,0.113126576,0.00040217792],"about_ca_topic_score_codex":0.0023914955,"about_ca_topic_score_gemma":0.0030731235,"teacher_disagreement_score":0.047513474,"about_ca_system_score_codex":0.0070513817,"about_ca_system_score_gemma":0.011766111,"threshold_uncertainty_score":0.25127822},"labels":[],"label_agreement":null},{"id":"W4408260145","doi":"10.23977/jaip.2025.080107","title":"+iDigiChat: Intelligent Digital Marketing Service Chatbot for Efficient Customer Service via Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Chatbot; Service (business); Customer service; Computer science; Digital marketing; Business; World Wide Web; Marketing","score_opus":0.048502822209087276,"score_gpt":0.348489677917631,"score_spread":0.2999868557085438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408260145","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":[0.20718473,0.000921494,0.6896958,0.0014019288,0.0005089575,0.0011868482,0.0004412762,0.03488313,0.06377589],"genre_scores_gemma":[0.68144166,0.00037990705,0.28286466,0.00072143413,0.00008930069,0.00052359095,0.00047327654,0.0005581732,0.032947987],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965906,0.00009084619,0.000016349077,0.00006881827,0.00011217793,0.00005275972],"domain_scores_gemma":[0.9995552,0.00016500933,0.00004125477,0.00007505715,0.00008223306,0.000081275175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005255793,0.0006202544,0.00041425155,0.00059851987,0.00070964603,0.0011339238,0.0009309988,0.0008095912,0.0070703905],"category_scores_gemma":[0.001092777,0.00023297963,0.00036346828,0.00031333134,0.0007412395,0.0014498794,0.0014885099,0.0008394867,0.0020312974],"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":[0.0021827458,0.0016449366,0.008043385,0.0018593813,0.00015759593,0.0022790842,0.0038688157,0.025808813,0.21806306,0.06554812,0.044349395,0.6261947],"study_design_scores_gemma":[0.00028254068,0.0016032227,0.008790074,0.00019431613,0.00018132596,0.0021429402,0.0012240735,0.737669,0.078056514,0.024382379,0.1452744,0.00019923404],"about_ca_topic_score_codex":0.0010140148,"about_ca_topic_score_gemma":0.0012145418,"teacher_disagreement_score":0.0070703905,"about_ca_system_score_codex":0.0005045773,"about_ca_system_score_gemma":0.0006424112,"threshold_uncertainty_score":0.023652792},"labels":[],"label_agreement":null},{"id":"W4408363021","doi":"10.23977/jaip.2025.080109","title":"Research on the Optimization of Intrusion Detection System Based on Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Intrusion detection system; Computer science; Artificial intelligence","score_opus":0.08736224670488568,"score_gpt":0.371287588893493,"score_spread":0.2839253421886073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408363021","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":[0.028414788,0.012299936,0.9430668,0.0010402111,0.00023560134,0.00007425385,0.000037002974,0.00039744365,0.014433956],"genre_scores_gemma":[0.79107237,0.024199119,0.17589279,0.00045346317,0.00052102783,0.00021054901,0.0001925161,0.00012862284,0.007329491],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989593,0.00021912945,0.00008081042,0.00025739882,0.00041075607,0.00007259975],"domain_scores_gemma":[0.9993086,0.00031683993,0.0000911709,0.00004764754,0.00021645315,0.000019341389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088010466,0.00082544546,0.0009619454,0.00081859325,0.0003695299,0.0013616262,0.00084567076,0.000626255,0.00094775984],"category_scores_gemma":[0.0021999425,0.00035052732,0.00075830106,0.0009814462,0.0007068575,0.0019109598,0.00044430795,0.000947346,0.00022133006],"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":[0.000109786735,0.00014299374,0.0039529726,0.0008763423,0.00034303888,0.00012370782,0.00015772136,0.5812121,0.015601926,0.08511198,0.0026018138,0.30976558],"study_design_scores_gemma":[0.000012922579,0.00013946366,0.0011384995,0.0000488887,0.00007424618,0.00010358744,0.000033669072,0.96626717,0.005995267,0.0190738,0.0070838635,0.000028622404],"about_ca_topic_score_codex":0.002181531,"about_ca_topic_score_gemma":0.00086237234,"teacher_disagreement_score":0.002181531,"about_ca_system_score_codex":0.0009695722,"about_ca_system_score_gemma":0.00092623295,"threshold_uncertainty_score":0.0070347786},"labels":[],"label_agreement":null},{"id":"W4408363048","doi":"10.23977/jaip.2025.080108","title":"AI for Financial Inclusion: Bailing out the Unbanked in China","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Unbanked; Financial inclusion; China; Inclusion (mineral); Business; Economics; Financial system; Financial services; Political science; Finance; Sociology; Social science","score_opus":0.03358852790797719,"score_gpt":0.33347936794980726,"score_spread":0.2998908400418301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408363048","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":[0.9952678,0.00031305707,0.00010113728,0.0021334686,0.000020373176,0.000028482173,0.00003061285,0.000003307068,0.0021017848],"genre_scores_gemma":[0.9982375,0.00038039192,0.00012722859,0.00047993686,0.00001129176,0.000024940038,0.000031808602,0.0000015764423,0.00070525997],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984309,0.00033169647,0.00013751235,0.00014809957,0.00033851742,0.000613284],"domain_scores_gemma":[0.99753726,0.00039200307,0.00063789514,0.00009645639,0.00026196547,0.0010744948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023392353,0.00035681497,0.00039770606,0.0022722883,0.0064213057,0.0027267481,0.00090869307,0.000746013,0.0030811527],"category_scores_gemma":[0.0032204096,0.00024353334,0.00032560964,0.0028723627,0.002468712,0.003055544,0.0042091534,0.0010378249,0.00014770884],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010639523,0.00044275683,0.716386,0.00050955045,0.00003921699,0.0024177933,0.17693435,0.00021912147,0.0014861439,0.011099465,0.0037134825,0.08664576],"study_design_scores_gemma":[0.00001671602,0.00018556321,0.75711393,0.0003727233,0.000035497393,0.0003823849,0.21867038,0.0015218337,0.0004635271,0.0033653497,0.017814435,0.000057648627],"about_ca_topic_score_codex":0.06435107,"about_ca_topic_score_gemma":0.08740784,"teacher_disagreement_score":0.06435107,"about_ca_system_score_codex":0.0037256854,"about_ca_system_score_gemma":0.010784166,"threshold_uncertainty_score":0.12795305},"labels":[],"label_agreement":null},{"id":"W4408739103","doi":"10.23977/jaip.2025.080111","title":"The Practice and Application of Machine Learning in Data Analysis","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Data science; Machine learning; Artificial intelligence","score_opus":0.22253898466643873,"score_gpt":0.4846734670592484,"score_spread":0.2621344823928097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408739103","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":[0.0032882912,0.045292955,0.8285352,0.06980103,0.0020016925,0.000422324,0.00018755096,0.000444727,0.050026257],"genre_scores_gemma":[0.17325112,0.06467577,0.73573416,0.012701784,0.0065797362,0.0018845563,0.00030380985,0.00032532983,0.0045436844],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.8819167,0.08567638,0.007817597,0.00654972,0.01702947,0.001010224],"domain_scores_gemma":[0.81543916,0.14984696,0.0051696184,0.019954486,0.008281654,0.0013081561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08527953,0.0018467824,0.0023592296,0.008949197,0.0030919516,0.014546157,0.0038704386,0.006733253,0.0021831444],"category_scores_gemma":[0.1170335,0.0013530763,0.0017857723,0.0092730895,0.03439234,0.012053308,0.008110757,0.015490079,0.0024297487],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037941205,0.0001033649,0.0017993082,0.0013933146,0.00014728626,0.00022495515,0.0028117946,0.005252623,0.000452598,0.87597764,0.008653931,0.10314525],"study_design_scores_gemma":[0.000027501259,0.000053030893,0.00057547697,0.0019574792,0.000026750784,0.00026626184,0.00067274977,0.0098629845,0.00067995326,0.9112793,0.07453278,0.000065709435],"about_ca_topic_score_codex":0.0027505269,"about_ca_topic_score_gemma":0.0013185549,"teacher_disagreement_score":0.08527953,"about_ca_system_score_codex":0.005444509,"about_ca_system_score_gemma":0.009824525,"threshold_uncertainty_score":0.45100665},"labels":[],"label_agreement":null},{"id":"W4408739193","doi":"10.23977/jaip.2025.080112","title":"The potential and risks of artificial intelligence in promoting personalized learning","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Engineering Education and Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psychology; Artificial intelligence; Computer science","score_opus":0.050844028477321564,"score_gpt":0.3730890725530418,"score_spread":0.32224504407572024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408739193","genre_codex":"commentary","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":[0.04337499,0.030712405,0.2591321,0.39613864,0.0017751115,0.00033654986,0.00018238803,0.00048822837,0.26785952],"genre_scores_gemma":[0.870138,0.011540918,0.07921929,0.021370362,0.0024396903,0.00066454196,0.0000910145,0.0001889018,0.014347311],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9478551,0.029942015,0.0015239097,0.002833192,0.016317014,0.001528746],"domain_scores_gemma":[0.8407514,0.110063925,0.007762509,0.026559997,0.012069732,0.0027923705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.050757907,0.0008253201,0.0007891313,0.0024668833,0.0033797096,0.011830868,0.0027365517,0.007630993,0.0046562348],"category_scores_gemma":[0.09912647,0.00068749563,0.0008279087,0.002103466,0.017069133,0.023294836,0.010974375,0.009842283,0.0014155599],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012362195,0.000097549164,0.0030806472,0.00024284208,0.00005397894,0.00012788939,0.0010000668,0.004633558,0.0003241026,0.9123019,0.0047157956,0.07329807],"study_design_scores_gemma":[0.00003191154,0.00008281945,0.0009202048,0.00056441594,0.000034135635,0.00022274246,0.00057692593,0.007557089,0.0009746463,0.93340397,0.055586267,0.000044802204],"about_ca_topic_score_codex":0.0013420276,"about_ca_topic_score_gemma":0.0010904969,"teacher_disagreement_score":0.050757907,"about_ca_system_score_codex":0.0038106348,"about_ca_system_score_gemma":0.0051583773,"threshold_uncertainty_score":0.2684366},"labels":[],"label_agreement":null},{"id":"W4408739210","doi":"10.23977/jaip.2025.080113","title":"Intelligent design and dynamic adaptation model of building facade based on artificial intelligence","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"BIM and Construction Integration","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Facade; Adaptation (eye); Architectural engineering; Computer science; Artificial intelligence; Engineering; Civil engineering; Psychology","score_opus":0.06069280218206569,"score_gpt":0.32118288920734994,"score_spread":0.26049008702528426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408739210","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":[0.02075884,0.00021032902,0.97189474,0.000118715034,0.00003087224,0.0000421603,0.00003921382,0.0004901518,0.0064149844],"genre_scores_gemma":[0.88183993,0.00045608237,0.11053109,0.000052548396,0.000023287364,0.00020982667,0.00014067483,0.000063816115,0.006682857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996973,0.000051197716,0.00001404902,0.000085906024,0.0001142958,0.000037273643],"domain_scores_gemma":[0.99987674,0.000028532755,0.000021021426,0.000016123282,0.00004736759,0.0000103064485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027440756,0.0006918527,0.0005682411,0.00073463446,0.0004822961,0.000843266,0.0010871793,0.0007688715,0.0018730583],"category_scores_gemma":[0.00048404952,0.00043779117,0.0008508628,0.00059266755,0.00054470706,0.0009930563,0.0006946579,0.00048301014,0.0003196342],"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":[0.000025514086,0.000024345361,0.0009139882,0.00004623155,0.00002527503,0.0000682638,0.000072109404,0.96032304,0.0037843157,0.007774003,0.0004195718,0.026523426],"study_design_scores_gemma":[0.0000031869185,0.00001377003,0.00019734206,0.00000312997,0.00000957316,0.000020848238,0.0000098540595,0.99730194,0.00045379045,0.0012264091,0.00075402507,0.0000061340306],"about_ca_topic_score_codex":0.011018215,"about_ca_topic_score_gemma":0.0087931575,"teacher_disagreement_score":0.011018215,"about_ca_system_score_codex":0.0007930414,"about_ca_system_score_gemma":0.0010028204,"threshold_uncertainty_score":0.021908164},"labels":[],"label_agreement":null},{"id":"W4408739212","doi":"10.23977/jaip.2025.080110","title":"Innovation and Reconstruction of Early Childhood Education Models Driven by Artificial Intelligence Technology","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Impulse Buying and Technology Impacts","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Early childhood; Artificial intelligence; Cognitive science; Computer science; Psychology; Developmental psychology","score_opus":0.04670483175591433,"score_gpt":0.30463938482806074,"score_spread":0.2579345530721464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408739212","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":[0.43876538,0.0008008117,0.13830888,0.0078285355,0.00009882317,0.00010080304,0.00021325529,0.00019891582,0.41368455],"genre_scores_gemma":[0.9793505,0.0002042777,0.011894581,0.000063455365,0.000005366292,0.00003741543,0.000047250916,0.000028993663,0.008368152],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990054,0.0005789382,0.000022636406,0.00013146535,0.000107858454,0.00015372936],"domain_scores_gemma":[0.9989078,0.00039316792,0.00013280103,0.00022859509,0.00015877203,0.00017888217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016062679,0.000240129,0.0001756985,0.0008414296,0.0015544594,0.004649729,0.0011584901,0.0009278097,0.0051103584],"category_scores_gemma":[0.0032226115,0.00016860965,0.00038953146,0.0005951523,0.0054810653,0.0027683883,0.0026934966,0.00113244,0.00057222386],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013353238,0.000026661777,0.0019346976,0.000010723613,0.0000041184935,0.00010932328,0.0037013956,0.00567264,0.00010664457,0.98322177,0.00039329912,0.0048054634],"study_design_scores_gemma":[0.000021821234,0.00005918686,0.0036477777,0.000096931624,0.000016839096,0.00018674029,0.008507608,0.048731267,0.0006206348,0.8858402,0.0522467,0.000024483692],"about_ca_topic_score_codex":0.007836245,"about_ca_topic_score_gemma":0.008890695,"teacher_disagreement_score":0.007836245,"about_ca_system_score_codex":0.0049934727,"about_ca_system_score_gemma":0.0033277832,"threshold_uncertainty_score":0.036230326},"labels":[],"label_agreement":null},{"id":"W4408896936","doi":"10.23977/jaip.2025.080115","title":"Research on the Innovative Practice of Guangdong TV News Driven by Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Research and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Data science","score_opus":0.22110097667332865,"score_gpt":0.5231394287789279,"score_spread":0.3020384521055993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408896936","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":[0.57939327,0.017398003,0.009811227,0.016947646,0.0004536834,0.00026616294,0.00014565095,0.00009021337,0.37549415],"genre_scores_gemma":[0.97367686,0.008586712,0.0039683366,0.00045936942,0.00014929488,0.000083241524,0.000074580224,0.000009453226,0.012992215],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983211,0.0007273118,0.00010785542,0.00022226671,0.00044776322,0.00017369117],"domain_scores_gemma":[0.9973769,0.0014857773,0.0004226146,0.0001644484,0.0003573992,0.00019295694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002230057,0.00025359218,0.0001684525,0.0024230648,0.0021786138,0.00447399,0.0006565637,0.00072957325,0.003312476],"category_scores_gemma":[0.0032758557,0.00015968474,0.00032105955,0.0035684875,0.0031819453,0.0039945655,0.0012249735,0.0008520526,0.00028655038],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013478963,0.00021005998,0.05076423,0.0025709982,0.000105081235,0.0042365496,0.16052818,0.0013529982,0.0051044137,0.4345098,0.014369588,0.3261133],"study_design_scores_gemma":[0.00007667743,0.00040260365,0.2040746,0.0027606299,0.00026918427,0.003244387,0.16664122,0.0073309615,0.0064912927,0.060540825,0.5480449,0.00012277874],"about_ca_topic_score_codex":0.006049583,"about_ca_topic_score_gemma":0.0069442694,"teacher_disagreement_score":0.006049583,"about_ca_system_score_codex":0.004462561,"about_ca_system_score_gemma":0.004406026,"threshold_uncertainty_score":0.032378256},"labels":[],"label_agreement":null},{"id":"W4408896951","doi":"10.23977/jaip.2025.080114","title":"Research on internal financial fraud identification model of enterprise based on ensemble learning","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Evaluation and Optimization Models","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Ensemble learning; Ensemble forecasting; Business; Computer science; Artificial intelligence","score_opus":0.11475534095560752,"score_gpt":0.4240000696679757,"score_spread":0.3092447287123682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408896951","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":[0.27443886,0.002858218,0.71311927,0.0014887362,0.00015458558,0.000101540136,0.00019953607,0.00042938621,0.0072098863],"genre_scores_gemma":[0.96527725,0.0014564195,0.030338127,0.0001196436,0.00008594412,0.000070779424,0.00021087409,0.000018964338,0.0024219367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898344,0.00030717926,0.000065967455,0.000213154,0.00025519662,0.00017500661],"domain_scores_gemma":[0.9983039,0.0007829527,0.00022666888,0.000095837415,0.0005152931,0.00007532387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003193131,0.00075903337,0.0011914746,0.0018208884,0.0006168096,0.0017038016,0.00141538,0.0008454114,0.001062575],"category_scores_gemma":[0.004565817,0.0003139916,0.0011816099,0.0015113062,0.00046300236,0.002401193,0.00079945254,0.0010887079,0.00023744526],"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":[0.00013585402,0.00016006813,0.035683077,0.000118918506,0.00023871675,0.00027093553,0.00025196522,0.79652864,0.0009629409,0.019281162,0.0027994532,0.1435682],"study_design_scores_gemma":[0.0000018605217,0.000014093779,0.0012163348,0.000011055605,0.000019197998,0.000035986188,0.000019028934,0.9960557,0.00015762389,0.002229034,0.00023469212,0.000005415085],"about_ca_topic_score_codex":0.009505688,"about_ca_topic_score_gemma":0.004662438,"teacher_disagreement_score":0.009505688,"about_ca_system_score_codex":0.0011166693,"about_ca_system_score_gemma":0.0011575707,"threshold_uncertainty_score":0.018900692},"labels":[],"label_agreement":null},{"id":"W4409170815","doi":"10.23977/jaip.2025.080116","title":"Research on the application of artificial intelligence and multi-scale image fusion technology to pedestrian detection in complex street view","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedestrian detection; Pedestrian; Artificial intelligence; Computer vision; Computer science; Scale (ratio); Image fusion; Image (mathematics); Engineering; Transport engineering; Geography; Cartography","score_opus":0.09857359108152107,"score_gpt":0.42034268561936666,"score_spread":0.3217690945378456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409170815","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":[0.033390995,0.005030169,0.95610654,0.0003403153,0.00019434124,0.000049649705,0.00007151283,0.0007890699,0.0040275124],"genre_scores_gemma":[0.6681357,0.008390422,0.31599563,0.00038645955,0.00030754055,0.00007478611,0.00040553874,0.00010487877,0.0061991033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994561,0.00007477787,0.000026785816,0.0002117342,0.00017487581,0.00005566174],"domain_scores_gemma":[0.99955887,0.00011925129,0.000049636103,0.00005910645,0.00018916697,0.000023907847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009923361,0.0010034422,0.0008327203,0.0016386213,0.00038257608,0.0008437418,0.0009715734,0.00095439935,0.0012705837],"category_scores_gemma":[0.0014575704,0.00039219225,0.0013897544,0.001684007,0.00054261537,0.0019171367,0.00071523484,0.0008955491,0.0004757006],"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":[0.00014417662,0.00012300437,0.005069014,0.00029818554,0.00020700676,0.00022380696,0.00015890146,0.12309335,0.024325106,0.010537765,0.0032732994,0.83254653],"study_design_scores_gemma":[0.000005935446,0.00011931807,0.0036585417,0.000030247673,0.000080839505,0.00025876416,0.000053774413,0.9710193,0.013702958,0.005317322,0.005719629,0.000033472566],"about_ca_topic_score_codex":0.0064984327,"about_ca_topic_score_gemma":0.0042216494,"teacher_disagreement_score":0.0064984327,"about_ca_system_score_codex":0.000847846,"about_ca_system_score_gemma":0.0006046013,"threshold_uncertainty_score":0.012921214},"labels":[],"label_agreement":null},{"id":"W4409204231","doi":"10.23977/jaip.2025.080118","title":"E-MART: An Improved Misclassification Aware Adversarial Training with Entropy-Based Uncertainty Measure","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adversarial system; Measure (data warehouse); Computer science; Artificial intelligence; Entropy (arrow of time); Statistics; Machine learning; Mathematics; Econometrics; Data mining","score_opus":0.04528000276109503,"score_gpt":0.33047002718029067,"score_spread":0.28519002441919566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409204231","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":[0.0138287535,0.00039922653,0.9834086,0.00027668677,0.000069123555,0.000042952302,0.00007260429,0.00065152213,0.001250502],"genre_scores_gemma":[0.687303,0.000570852,0.30390307,0.00085970445,0.00024921785,0.00017332264,0.00058369705,0.0003442547,0.006012824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886036,0.0003505311,0.00006232042,0.00024109945,0.00037486613,0.00011095953],"domain_scores_gemma":[0.99816424,0.00097253,0.00019377096,0.00027837238,0.00029201622,0.00009922019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020673738,0.001256625,0.0014305734,0.00074146525,0.00045584032,0.000676652,0.0021954505,0.0013216283,0.0017190597],"category_scores_gemma":[0.004850657,0.00047732398,0.0009614679,0.00050475437,0.0011284956,0.0018676859,0.0024345312,0.0024846203,0.0003131623],"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":[0.00021208818,0.00008521634,0.0014020386,0.000099225006,0.00011520855,0.00015960139,0.00009040806,0.8197639,0.007122353,0.0170813,0.0039395783,0.1499291],"study_design_scores_gemma":[0.000004231285,0.000028148277,0.00008044671,0.000006342365,0.000007507427,0.00003472628,0.000002700399,0.99493945,0.0012653824,0.003300384,0.00032350197,0.0000071904547],"about_ca_topic_score_codex":0.0017017233,"about_ca_topic_score_gemma":0.0016113021,"teacher_disagreement_score":0.0021954505,"about_ca_system_score_codex":0.00073231803,"about_ca_system_score_gemma":0.0008496111,"threshold_uncertainty_score":0.010933459},"labels":[],"label_agreement":null},{"id":"W4409335278","doi":"10.23977/jaip.2025.080201","title":"From Data Governance to Data Intelligence Governance: Transforming Enterprise-Level Data Asset Management","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Data governance; Enterprise data management; Corporate governance; Information governance; Business; Data management; Asset management; Asset (computer security); Knowledge management; Process management; Computer science; Data quality; Data mining; Finance; Computer security; Information system; Management information systems; Political science; Enterprise information system; Marketing","score_opus":0.23694079440058347,"score_gpt":0.4045342955576493,"score_spread":0.16759350115706584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409335278","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":[0.035639558,0.0043589883,0.8313495,0.061677173,0.0006895846,0.00023612438,0.0002015346,0.000878761,0.064968735],"genre_scores_gemma":[0.78125787,0.0044329613,0.20185554,0.0048813736,0.0007195876,0.00024232987,0.00042326245,0.00027779336,0.0059092967],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99225074,0.0039785714,0.00041496803,0.0009746302,0.0017573441,0.0006236767],"domain_scores_gemma":[0.98842853,0.004090726,0.0010057337,0.0034811147,0.0019188038,0.0010750792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012823743,0.00044114946,0.00046706703,0.0022810684,0.0016285651,0.011585807,0.0014336611,0.0019757298,0.0013254895],"category_scores_gemma":[0.016753899,0.00042911395,0.00048477095,0.0035206173,0.009604801,0.015020166,0.008323686,0.004144966,0.0005874724],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014892656,0.00006780089,0.0033371889,0.00014432179,0.000028085658,0.00008749803,0.0020609482,0.0033667358,0.00090731715,0.8965232,0.005894012,0.087568],"study_design_scores_gemma":[0.000016993687,0.000050337523,0.0015900354,0.00051666156,0.000021275055,0.00012342326,0.0027891903,0.01289184,0.001767589,0.8532347,0.12696575,0.00003225592],"about_ca_topic_score_codex":0.0026246519,"about_ca_topic_score_gemma":0.0017206202,"teacher_disagreement_score":0.012823743,"about_ca_system_score_codex":0.0029714422,"about_ca_system_score_gemma":0.007143293,"threshold_uncertainty_score":0.06781924},"labels":[],"label_agreement":null},{"id":"W4409651419","doi":"10.23977/jaip.2025.080206","title":"A two-stage cervical pathology cell detection model based on YOLOv7x and K-means","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Stage (stratigraphy); Pathology; Medicine; Computer science; Biology; Paleontology","score_opus":0.03386071167995914,"score_gpt":0.3352474627157263,"score_spread":0.3013867510357672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409651419","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":[0.10943523,0.0007141216,0.8837055,0.0004236352,0.00014077708,0.00017411506,0.00029333078,0.0013972265,0.0037161522],"genre_scores_gemma":[0.8559556,0.0006291588,0.13026935,0.00023209881,0.000075996126,0.0003538121,0.00085984456,0.00010684536,0.011517288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958307,0.00003760477,0.00002187294,0.00017195345,0.00011197424,0.0000734726],"domain_scores_gemma":[0.9997384,0.000054630877,0.000026674395,0.000019598121,0.00013922718,0.00002149538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047830687,0.00074423495,0.00083851535,0.0006616656,0.00069422903,0.0010206641,0.0020300122,0.0011491068,0.0016371273],"category_scores_gemma":[0.00074312836,0.00055545743,0.0013544261,0.00042948534,0.0005027647,0.0008101688,0.00086042297,0.0007292517,0.00059354695],"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":[0.00040001862,0.00012271127,0.010267354,0.00016977475,0.00014326084,0.0001608085,0.00032671803,0.8070337,0.020208653,0.0056280764,0.0024690244,0.15306985],"study_design_scores_gemma":[0.0000059707286,0.000031421892,0.00069824536,0.0000033596627,0.000015969954,0.00002443455,0.000012481257,0.99721175,0.0011183635,0.0004402621,0.0004283464,0.00000942397],"about_ca_topic_score_codex":0.045803625,"about_ca_topic_score_gemma":0.027060978,"teacher_disagreement_score":0.045803625,"about_ca_system_score_codex":0.0012268861,"about_ca_system_score_gemma":0.0016114041,"threshold_uncertainty_score":0.09107405},"labels":[],"label_agreement":null},{"id":"W4409896213","doi":"10.23977/jaip.2025.080207","title":"Construction and Practice of Supply Chain Optimization Decision System Driven by Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Supply chain; Computer science; Artificial intelligence; Management science; Business; Engineering","score_opus":0.028339082780756707,"score_gpt":0.31140127738793927,"score_spread":0.28306219460718257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409896213","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":[0.00710009,0.0004532285,0.9830496,0.0004734962,0.00007036782,0.00017118512,0.000057715257,0.0004201043,0.008204228],"genre_scores_gemma":[0.48050374,0.00216212,0.5115275,0.00025559886,0.00013995462,0.0006531572,0.00041359657,0.00008041993,0.0042639016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792224,0.0006268821,0.00023773912,0.0004342892,0.0006683239,0.0001105828],"domain_scores_gemma":[0.9992575,0.0002640303,0.0000799139,0.00007044291,0.00028095726,0.000047123085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022206963,0.00087832025,0.0007036874,0.001575107,0.0012393373,0.002615268,0.0012291113,0.0008944349,0.0026115605],"category_scores_gemma":[0.0024456321,0.0004789791,0.001027402,0.0019445274,0.0010802144,0.0021904588,0.0017606657,0.0014099294,0.0004452857],"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":[0.00009595412,0.00013154605,0.0039297,0.000629179,0.00021533613,0.0005164347,0.0009021839,0.5242802,0.006158714,0.212309,0.0039617675,0.24687006],"study_design_scores_gemma":[0.00002752822,0.00006799973,0.0006559852,0.00009948306,0.00005603452,0.00010104613,0.00014161269,0.92792743,0.0022202097,0.05600326,0.012658931,0.000040499075],"about_ca_topic_score_codex":0.0059045968,"about_ca_topic_score_gemma":0.0031151816,"teacher_disagreement_score":0.0059045968,"about_ca_system_score_codex":0.0018805107,"about_ca_system_score_gemma":0.002953023,"threshold_uncertainty_score":0.013644159},"labels":[],"label_agreement":null},{"id":"W4411069680","doi":"10.23977/jaip.2025.080215","title":"The Nature of DeepSeek Used in Teaching","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics education; Psychology; Computer science","score_opus":0.02175311586682528,"score_gpt":0.3778878245567544,"score_spread":0.35613470868992914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411069680","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":[0.5484906,0.0039621973,0.07912238,0.0060455455,0.00023173055,0.000085772124,0.00013375515,0.00041522857,0.36151275],"genre_scores_gemma":[0.9829227,0.00070271123,0.007569383,0.00029458906,0.000041672934,0.00003613536,0.000028230172,0.00011565851,0.008288819],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99165,0.0038357098,0.00026773498,0.0008284616,0.0028635836,0.0005545484],"domain_scores_gemma":[0.9860406,0.009041645,0.0011624984,0.0019919542,0.0009883748,0.0007749332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003930233,0.00029068752,0.0002996909,0.0022798683,0.0018588098,0.010537526,0.0011307519,0.0008121321,0.0045962455],"category_scores_gemma":[0.012942627,0.00032511703,0.00027137212,0.0035105075,0.011866683,0.012166673,0.0050529167,0.0018930311,0.00091785984],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015229027,0.00016610284,0.026615845,0.00057622534,0.000054818487,0.0002799087,0.084199205,0.0010868273,0.00797253,0.48808315,0.003167907,0.3876451],"study_design_scores_gemma":[0.00006025839,0.0004415214,0.06008534,0.0017630701,0.000089706075,0.0014048768,0.1068027,0.0068675624,0.019738479,0.37690395,0.42570487,0.00013757062],"about_ca_topic_score_codex":0.0017198412,"about_ca_topic_score_gemma":0.002041248,"teacher_disagreement_score":0.010537526,"about_ca_system_score_codex":0.0031065517,"about_ca_system_score_gemma":0.0020374078,"threshold_uncertainty_score":0.022539675},"labels":[],"label_agreement":null},{"id":"W4411498821","doi":"10.23977/jaip.2025.080219","title":"The Application of Artificial Intelligence in Marketing: A Review of Research","year":2025,"lang":"en","type":"review","venue":"Journal of Artificial Intelligence Practice","topic":"Impact of AI and Big Data on Business and Society","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Marketing; Business","score_opus":0.43415001664532726,"score_gpt":0.5866785686520105,"score_spread":0.15252855200668325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411498821","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006237934,0.9985613,0.00012121068,0.0005204576,0.00012270593,0.0000036160366,0.0000053244526,0.0000028524976,0.00060022617],"genre_scores_gemma":[0.0007704827,0.99841964,0.00023852024,0.00024337522,0.00020394112,0.0000058075807,0.000007655859,0.0000015619731,0.000109024375],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99889034,0.00032374755,0.00017115833,0.00016185177,0.00040138082,0.000051537703],"domain_scores_gemma":[0.9922495,0.0063449806,0.00032257687,0.00011816043,0.00082366366,0.00014121277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028251708,0.00092135725,0.00158325,0.0053056027,0.0005164468,0.002341374,0.00094541267,0.0021273335,0.0027047882],"category_scores_gemma":[0.0051499107,0.0005483394,0.0007137433,0.007920506,0.0017130703,0.003356808,0.0008615633,0.0022635788,0.0010218001],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005622322,0.00008663342,0.0003093398,0.035475794,0.0001453629,0.00008547725,0.00022485705,0.0005586803,0.00038168996,0.014042716,0.0203754,0.9282577],"study_design_scores_gemma":[0.00002195377,0.00012787904,0.0022413516,0.036963247,0.00021397705,0.0006353092,0.00039334697,0.00036376598,0.00034615688,0.011076513,0.94756275,0.00005389778],"about_ca_topic_score_codex":0.0026212628,"about_ca_topic_score_gemma":0.0035307123,"teacher_disagreement_score":0.0053056027,"about_ca_system_score_codex":0.0015898689,"about_ca_system_score_gemma":0.0030209478,"threshold_uncertainty_score":0.014941096},"labels":[],"label_agreement":null},{"id":"W4411712554","doi":"10.23977/jaip.2025.080220","title":"An Analysis of the Role of Artificial Intelligence in the Personalized Development of Instrumental Music Learning in Colleges and Universities","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Instrumental music; Psychology; Personalized learning; Mathematics education; Computer science; Teaching method; Visual arts; Art; Musical; Cooperative learning","score_opus":0.04185988501894339,"score_gpt":0.35110176277341837,"score_spread":0.309241877754475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411712554","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":[0.98392755,0.00030417534,0.0021201535,0.00025091032,0.0000032060318,0.000023085364,0.000019185367,0.000021799018,0.013330056],"genre_scores_gemma":[0.9991315,0.00007851791,0.00040449444,0.0000071486147,0.0000012061101,0.000003306531,0.0000059694403,0.0000015951142,0.0003663457],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990625,0.0004252249,0.000042110118,0.00010174698,0.00023625632,0.00013207055],"domain_scores_gemma":[0.9958116,0.0024772773,0.00046557977,0.0002300268,0.00047028728,0.0005452832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011698351,0.00010188289,0.000119236465,0.0010427436,0.0005698256,0.0014413786,0.00031309095,0.00028927118,0.0014137365],"category_scores_gemma":[0.0057457825,0.00010001211,0.00018396515,0.0010909213,0.0005623418,0.0010691936,0.0007826228,0.00031112973,0.00015004657],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046835074,0.00058020675,0.6111415,0.00024103273,0.000076246084,0.0006271964,0.0155681,0.00644444,0.0079725385,0.024247797,0.0006816643,0.3319509],"study_design_scores_gemma":[0.000009023249,0.0002476939,0.9612792,0.00004689459,0.000056371533,0.0002443627,0.009101489,0.017368766,0.002801275,0.0037018363,0.005120406,0.00002271358],"about_ca_topic_score_codex":0.0039042635,"about_ca_topic_score_gemma":0.00484472,"teacher_disagreement_score":0.0039042635,"about_ca_system_score_codex":0.0012025394,"about_ca_system_score_gemma":0.0013314735,"threshold_uncertainty_score":0.008725047},"labels":[],"label_agreement":null},{"id":"W4412478750","doi":"10.23977/jaip.2025.080305","title":"Research on Information Transmission Method Selection and Security Protection Based on Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Reforms and Innovations","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Computer science; Transmission (telecommunications); Artificial intelligence; Computer security; Telecommunications","score_opus":0.08953430673084549,"score_gpt":0.43464583646478033,"score_spread":0.34511152973393483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412478750","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":[0.029362282,0.009659326,0.95004946,0.0009514923,0.00014957367,0.000055929293,0.00002136762,0.00014077543,0.009609672],"genre_scores_gemma":[0.86170036,0.013469222,0.118972644,0.00027488492,0.00034002468,0.000108419204,0.00007431579,0.00006761982,0.0049924944],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986603,0.00031626498,0.000093109324,0.0003118244,0.0004944016,0.00012411528],"domain_scores_gemma":[0.9979419,0.0011920886,0.00025524147,0.00018167014,0.00036256167,0.00006644505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018192866,0.0006041215,0.00087055156,0.0011525902,0.00054878043,0.0017236517,0.0012820422,0.0008989358,0.0010343139],"category_scores_gemma":[0.0043995283,0.00036747148,0.00076080946,0.0013937472,0.0015622817,0.0040959143,0.0008073516,0.0011473934,0.00016870614],"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":[0.00012643753,0.00018243749,0.004504176,0.00093435205,0.00026554117,0.0001901529,0.00038422292,0.3626227,0.01282114,0.23664689,0.0021661632,0.37915573],"study_design_scores_gemma":[0.00001926463,0.00013704441,0.0010378122,0.00006710295,0.000071548115,0.00014633252,0.00007978847,0.9256633,0.004779715,0.062278125,0.0056808232,0.000039167167],"about_ca_topic_score_codex":0.0020420826,"about_ca_topic_score_gemma":0.0007624257,"teacher_disagreement_score":0.0020420826,"about_ca_system_score_codex":0.0012691383,"about_ca_system_score_gemma":0.0013153114,"threshold_uncertainty_score":0.009621382},"labels":[],"label_agreement":null},{"id":"W4413463968","doi":"10.23977/jaip.2025.080309","title":"Investigation and Analyses of AI Application among Freshmen","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Psychology; Mathematics education","score_opus":0.24444194933653357,"score_gpt":0.5576605167410802,"score_spread":0.3132185674045466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413463968","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":[0.9986564,0.000105663865,0.000105198626,0.00017142156,0.000004207961,0.000009636529,0.00003838643,0.0000020291454,0.0009071444],"genre_scores_gemma":[0.99701476,0.0002121189,0.00012424341,0.000098722565,0.000011127376,0.000014278516,0.000055451903,0.0000018060367,0.0024674323],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992938,0.00014272127,0.00005631151,0.00011674282,0.00022461497,0.00016567961],"domain_scores_gemma":[0.99717283,0.0006665351,0.00084204,0.00011203155,0.00069349445,0.0005130066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009809822,0.000113889844,0.00014780056,0.0012552884,0.0010621845,0.0007981848,0.0003063342,0.00029722787,0.0041811876],"category_scores_gemma":[0.0039646723,0.00013585408,0.00019022913,0.0008248837,0.0005135488,0.0006663409,0.0006065091,0.00049632683,0.00048618147],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058519756,0.00024927538,0.9271582,0.00008552269,0.000016763408,0.00030946056,0.027480599,0.000031468582,0.0016165959,0.00044124798,0.00071675214,0.04183554],"study_design_scores_gemma":[0.0000016099702,0.0002087046,0.94447744,0.00003633119,0.000015812848,0.00018843295,0.049127273,0.00013430629,0.0006794505,0.000108289285,0.005012246,0.000010227648],"about_ca_topic_score_codex":0.0056580785,"about_ca_topic_score_gemma":0.010041129,"teacher_disagreement_score":0.0056580785,"about_ca_system_score_codex":0.0007469438,"about_ca_system_score_gemma":0.0010261231,"threshold_uncertainty_score":0.013987422},"labels":[],"label_agreement":null},{"id":"W4413463969","doi":"10.23977/jaip.2025.080308","title":"Design and Implementation of Smart Guide Glasses for the Blind Based on Deep Perception and Bone Conduction Technology","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Beihua University","keywords":"Perception; Thermal conduction; Computer science; Psychology; Materials science; Neuroscience","score_opus":0.06522689313569419,"score_gpt":0.37852061875240317,"score_spread":0.313293725616709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413463969","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":[0.11598119,0.0006022709,0.8693607,0.000251996,0.00025091448,0.00033779824,0.00014033518,0.003866851,0.009207993],"genre_scores_gemma":[0.71021956,0.0003962355,0.27937886,0.00025270204,0.00003407767,0.0002615688,0.0000981066,0.00009987962,0.009258905],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997477,0.00002333154,0.000016258506,0.000052897256,0.00009912203,0.00006071077],"domain_scores_gemma":[0.999814,0.000020040108,0.00002623212,0.000019076375,0.00008460946,0.000036053312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021826434,0.0004937878,0.00033328577,0.0005133479,0.0003031245,0.0005222164,0.0012346491,0.0006499958,0.0027799758],"category_scores_gemma":[0.00033616766,0.00028826215,0.00041608632,0.0001417786,0.00027210117,0.0004972796,0.00084324833,0.00026035492,0.0007752229],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047342203,0.00021562219,0.005359367,0.0005221338,0.000109371365,0.001061196,0.00087072025,0.011510855,0.6548964,0.010212268,0.0066155996,0.30815315],"study_design_scores_gemma":[0.00028511405,0.0042001014,0.017731244,0.00020898604,0.00051575043,0.0032767402,0.0009610108,0.41281658,0.43781888,0.005554401,0.11629307,0.00033818287],"about_ca_topic_score_codex":0.002098207,"about_ca_topic_score_gemma":0.0024430428,"teacher_disagreement_score":0.0027799758,"about_ca_system_score_codex":0.00038593906,"about_ca_system_score_gemma":0.00076316355,"threshold_uncertainty_score":0.0092999935},"labels":[],"label_agreement":null},{"id":"W4414418856","doi":"10.23977/jaip.2025.080313","title":"Prioritized Reward of Deep Reinforcement Learning Applied Mobile Manipulation Reaching Tasks","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reinforcement learning; Task (project management); Function (biology); Mobile robot; Mobile manipulator; Base (topology); Robot","score_opus":0.03294986184125855,"score_gpt":0.3167157270523156,"score_spread":0.28376586521105707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414418856","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":[0.18906158,0.0004132055,0.8068625,0.00025254674,0.000058009573,0.0000733903,0.00002990728,0.0005988608,0.0026499506],"genre_scores_gemma":[0.9743688,0.000056337245,0.024324141,0.000046729943,0.000009491863,0.00003986091,0.0000163538,0.000021161575,0.0011170856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962795,0.00012092025,0.000019563427,0.000060979222,0.00010065363,0.000069893824],"domain_scores_gemma":[0.9990115,0.0004965367,0.00012939612,0.00006323855,0.00020940065,0.000089865025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010623473,0.00073112664,0.0006520831,0.00024044013,0.00017140443,0.00042594102,0.0007633825,0.0006699622,0.0012229017],"category_scores_gemma":[0.0030138243,0.00022722333,0.00019166416,0.00017857394,0.0004648713,0.0006416674,0.0006165795,0.00070714956,0.00016203344],"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":[0.00027713962,0.00016947606,0.001278754,0.00010805428,0.0000356609,0.000090850095,0.000048644557,0.90350187,0.011047281,0.0066803144,0.00047346315,0.076288514],"study_design_scores_gemma":[0.000008194081,0.00006743652,0.00013720953,0.0000026992286,0.0000030477715,0.000007570116,0.0000021679853,0.9976164,0.0008872187,0.0011851861,0.0000802326,0.0000026876796],"about_ca_topic_score_codex":0.0017328284,"about_ca_topic_score_gemma":0.001525121,"teacher_disagreement_score":0.0017328284,"about_ca_system_score_codex":0.00077248336,"about_ca_system_score_gemma":0.00070945907,"threshold_uncertainty_score":0.005618274},"labels":[],"label_agreement":null},{"id":"W4414802080","doi":"10.23977/jaip.2025.080314","title":"Research on the Application of Generative Artificial Intelligence in Physical Education Teaching","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Generative grammar; Curriculum; Generative model; Applications of artificial intelligence; Field (mathematics); Physical education","score_opus":0.11756153108944772,"score_gpt":0.4487988400107878,"score_spread":0.33123730892134007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414802080","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":[0.50504124,0.012438184,0.04119556,0.010253563,0.00029776237,0.0004593613,0.000049886883,0.00008760441,0.43017688],"genre_scores_gemma":[0.973911,0.007013793,0.011386361,0.00053410186,0.000050437386,0.00012320453,0.000028320022,0.00001054805,0.0069424137],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99705553,0.0015516841,0.00015723535,0.00028185837,0.0007441938,0.00020947395],"domain_scores_gemma":[0.99018985,0.0068669035,0.0006013848,0.0003979859,0.0014241668,0.0005197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029300551,0.00021491417,0.00021562271,0.0019538363,0.0010796023,0.0037300915,0.00094045675,0.0007312096,0.0051532877],"category_scores_gemma":[0.010915972,0.00023650443,0.00039877187,0.0028710507,0.0024696246,0.0038701685,0.0011186944,0.0010691651,0.00051928434],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006378568,0.00089836016,0.0629164,0.001930242,0.000054432494,0.00058201194,0.057421338,0.0019270638,0.002572005,0.37587684,0.0029209745,0.49283653],"study_design_scores_gemma":[0.00013786156,0.00093304174,0.25299317,0.007263871,0.0004323285,0.0025407998,0.14906408,0.025959719,0.0108870715,0.27036276,0.27923894,0.00018631243],"about_ca_topic_score_codex":0.0048045535,"about_ca_topic_score_gemma":0.0044745016,"teacher_disagreement_score":0.0051532877,"about_ca_system_score_codex":0.0027211406,"about_ca_system_score_gemma":0.005689318,"threshold_uncertainty_score":0.019743383},"labels":[],"label_agreement":null},{"id":"W4415167065","doi":"10.23977/jaip.2025.080315","title":"Case Analysis in AI Practice Courses: A Comparative Study of Tool Wear Prediction Methods","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Metal Alloys Wear and Properties","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Interpretability; Feature engineering; Feature (linguistics); Support vector machine; Artificial neural network; Deep learning; Mean squared error","score_opus":0.13163887574934363,"score_gpt":0.47352677463349574,"score_spread":0.3418878988841521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415167065","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":[0.8984127,0.0057555623,0.07710859,0.0012714741,0.00021260364,0.00059607485,0.0005355606,0.0006979373,0.015409404],"genre_scores_gemma":[0.9562847,0.0021021976,0.039030105,0.000102547194,0.000046810936,0.00016163925,0.00048613135,0.000047437316,0.0017384097],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9940876,0.0021285296,0.000470197,0.0007783362,0.0022283304,0.00030702565],"domain_scores_gemma":[0.959214,0.03006313,0.0023588592,0.0019558717,0.0055061537,0.0009020102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008895811,0.00086250465,0.00064156373,0.004703421,0.00074042834,0.0017985292,0.0018786518,0.0011720747,0.0019951789],"category_scores_gemma":[0.031024259,0.0003193576,0.00087976834,0.003347861,0.00064588344,0.0024208827,0.0013022679,0.0009848563,0.00063955155],"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":[0.0007938786,0.0024037983,0.1327467,0.0017551531,0.00029787575,0.0007627797,0.0051912195,0.040587455,0.002591453,0.0050394163,0.0058146995,0.8020155],"study_design_scores_gemma":[0.00016038826,0.0028327426,0.21160129,0.0020776775,0.00031969737,0.0014730938,0.017418252,0.70167154,0.013379603,0.011746932,0.037077155,0.00024164056],"about_ca_topic_score_codex":0.0050177756,"about_ca_topic_score_gemma":0.0069709974,"teacher_disagreement_score":0.008895811,"about_ca_system_score_codex":0.0020662793,"about_ca_system_score_gemma":0.0013439935,"threshold_uncertainty_score":0.047046125},"labels":[],"label_agreement":null},{"id":"W4416343507","doi":"10.23977/jaip.2025.080317","title":"Research on Underground Non-uniform Fog Removal Method Based on Enhanced Parallel Attention Mechanism","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mechanism (biology); Atmosphere (unit); Air pollution","score_opus":0.1658303170081555,"score_gpt":0.46372427920066633,"score_spread":0.29789396219251085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416343507","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":[0.08262212,0.0030648075,0.9037908,0.0002776574,0.00041700676,0.00010879549,0.0000744292,0.0013719286,0.008272416],"genre_scores_gemma":[0.80854666,0.0023609707,0.1770526,0.00030748098,0.00028593792,0.00009006664,0.00026370338,0.0001507251,0.010941758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997222,0.000017989736,0.000011156081,0.00009293114,0.00011272295,0.000042965123],"domain_scores_gemma":[0.99979895,0.000038935603,0.000014648521,0.000027395661,0.0000988754,0.000021119837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027181362,0.00065197,0.0007014909,0.00066136045,0.00046649223,0.0006075143,0.0015015897,0.00061586295,0.0021975657],"category_scores_gemma":[0.0005121817,0.0002472291,0.0007367501,0.00054368854,0.00033847,0.0018183698,0.00068455614,0.0005145423,0.00030407825],"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":[0.0005433075,0.00029540833,0.0036359543,0.0005162204,0.0002193131,0.000461897,0.00019312368,0.04213635,0.19788063,0.008464375,0.0061625107,0.7394908],"study_design_scores_gemma":[0.00008211183,0.00032104622,0.0072037303,0.000029191362,0.0003078743,0.0008038128,0.00013136897,0.9025596,0.073855534,0.0052251304,0.009411879,0.000068789464],"about_ca_topic_score_codex":0.004822242,"about_ca_topic_score_gemma":0.003696736,"teacher_disagreement_score":0.004822242,"about_ca_system_score_codex":0.00035672775,"about_ca_system_score_gemma":0.0008180766,"threshold_uncertainty_score":0.009588361},"labels":[],"label_agreement":null},{"id":"W4416862949","doi":"10.23977/jaip.2025.080319","title":"A Review of the Basic Applications of Machine Vision in Medical Image Segmentation","year":2025,"lang":"","type":"review","venue":"Journal of Artificial Intelligence Practice","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Image segmentation; Scale-space segmentation; Medical imaging; Segmentation-based object categorization; Machine vision; Image processing","score_opus":0.0457293447663657,"score_gpt":0.43775138917076223,"score_spread":0.39202204440439653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416862949","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015380987,0.9955415,0.0017590448,0.0003887515,0.0002752017,0.000013706792,0.000033081968,0.00002514373,0.0018097842],"genre_scores_gemma":[0.0013519012,0.99566865,0.0018395063,0.00024306691,0.00034519474,0.000017630475,0.000059919585,0.000006982148,0.00046710033],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99938524,0.000114639624,0.000111784895,0.00010298883,0.00024934078,0.000036032692],"domain_scores_gemma":[0.9983216,0.0009432831,0.00012450844,0.000054451684,0.0005070614,0.000049119866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013263816,0.0010292974,0.0011786075,0.0052116495,0.00038119414,0.0011984207,0.001083931,0.001406728,0.003126881],"category_scores_gemma":[0.0029084424,0.00064687,0.0009789108,0.005369725,0.0006976407,0.0020025498,0.00059511064,0.0016356956,0.0025268437],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048040936,0.000068073634,0.000378917,0.02001817,0.00013891445,0.00016235803,0.00008549436,0.0010841417,0.0016411066,0.006090872,0.02891884,0.9413651],"study_design_scores_gemma":[0.000013224763,0.00013495666,0.0021376433,0.0075545236,0.00021510184,0.0016179278,0.00007437451,0.0010316699,0.0013947994,0.0048245112,0.98093516,0.00006622239],"about_ca_topic_score_codex":0.0023573493,"about_ca_topic_score_gemma":0.0024180848,"teacher_disagreement_score":0.0052116495,"about_ca_system_score_codex":0.00086819025,"about_ca_system_score_gemma":0.0016895722,"threshold_uncertainty_score":0.010460496},"labels":[],"label_agreement":null},{"id":"W4416862965","doi":"10.23977/jaip.2025.080318","title":"A Review of the Applications of Machine Vision in Industrial Surface Defect Detection","year":2025,"lang":"","type":"review","venue":"Journal of Artificial Intelligence Practice","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Machine vision; Field (mathematics); Software deployment; Sorting; Key (lock); Object detection; Reflection (computer programming)","score_opus":0.06706877689005532,"score_gpt":0.3773870532628263,"score_spread":0.310318276372771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416862965","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023462166,0.9962288,0.0012896848,0.00023185728,0.00022726241,0.000011813725,0.000038585138,0.000023727494,0.0017137025],"genre_scores_gemma":[0.0016481264,0.9959103,0.0013948331,0.00016892892,0.00022570182,0.000012820054,0.0000704134,0.0000057383786,0.0005631982],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994591,0.00006807078,0.000093760995,0.00011255709,0.0002297849,0.000036714497],"domain_scores_gemma":[0.99844235,0.00080674654,0.00014462677,0.000056299494,0.0004959742,0.00005401685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011827664,0.0011762292,0.0012369968,0.0047577866,0.00033192037,0.0010778512,0.001133786,0.0012253025,0.0034349372],"category_scores_gemma":[0.0021365108,0.0005961205,0.00090210914,0.0056336806,0.00049428135,0.0020814748,0.0006225879,0.0013078632,0.0021493644],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036627378,0.00006675263,0.00042300278,0.01981704,0.00009961083,0.00011681202,0.00006410368,0.0007836658,0.001544467,0.0032284746,0.021480415,0.95233893],"study_design_scores_gemma":[0.000011394878,0.00018815247,0.0029217522,0.006790687,0.00024695383,0.0014601888,0.00010311142,0.00087503647,0.0015761866,0.002714265,0.9830419,0.000070382775],"about_ca_topic_score_codex":0.0021557093,"about_ca_topic_score_gemma":0.002221899,"teacher_disagreement_score":0.0047577866,"about_ca_system_score_codex":0.0006535994,"about_ca_system_score_gemma":0.00152009,"threshold_uncertainty_score":0.011491001},"labels":[],"label_agreement":null},{"id":"W4416984649","doi":"10.23977/jaip.2025.080320","title":"Empowering Security Surveillance with Machine Vision: A Survey of Anomaly Detection Technologies","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Anomaly detection; Intrusion detection system; Object detection; Constant false alarm rate; Path (computing); Sensor fusion; ALARM; False alarm","score_opus":0.024972744917274685,"score_gpt":0.35731769480697906,"score_spread":0.33234494988970437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416984649","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02255983,0.26222417,0.6903065,0.0038387678,0.00055033946,0.00017217112,0.000149791,0.0016072721,0.018591184],"genre_scores_gemma":[0.28519,0.24508134,0.4618911,0.0016005384,0.001618194,0.00021008676,0.0003890516,0.00020208558,0.003817478],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984169,0.0003916964,0.00009973794,0.00028870258,0.00071863655,0.00008432372],"domain_scores_gemma":[0.9981741,0.0009755013,0.00014932253,0.00016348885,0.0004810675,0.00005649873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021596116,0.0007992462,0.0009863959,0.0047713104,0.00045125172,0.0022994876,0.0013016199,0.0013369833,0.0006929788],"category_scores_gemma":[0.002885206,0.0006291176,0.0008369668,0.0032551545,0.0009811452,0.0032934302,0.000981666,0.0014910713,0.00050095265],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058197307,0.00015298842,0.0035020784,0.0014321575,0.00011594985,0.000103613645,0.0002738886,0.0072908853,0.008596191,0.021919556,0.0051751737,0.9513793],"study_design_scores_gemma":[0.000043306838,0.0011346166,0.01631876,0.0027950923,0.000373016,0.0032346996,0.0011749738,0.33406872,0.057636127,0.12183992,0.4609423,0.00043854682],"about_ca_topic_score_codex":0.0012181492,"about_ca_topic_score_gemma":0.0009947892,"teacher_disagreement_score":0.0047713104,"about_ca_system_score_codex":0.0008903387,"about_ca_system_score_gemma":0.00057148916,"threshold_uncertainty_score":0.011421263},"labels":[],"label_agreement":null},{"id":"W4417305733","doi":"10.23977/jaip.2025.080401","title":"Hybrid Detection Method for Concrete Cracks Based on Maskr-CNN and Swin Transformer","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Transformer; Segmentation; Convolutional neural network; Pixel; Dice; Object detection","score_opus":0.018365070322080724,"score_gpt":0.33064229099123066,"score_spread":0.3122772206691499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417305733","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":[0.06869535,0.0007481445,0.92360497,0.0002022256,0.00010128734,0.00014072818,0.00022702926,0.003220012,0.0030601972],"genre_scores_gemma":[0.62580264,0.00081017736,0.363395,0.00034945764,0.00007548835,0.00014271351,0.0008115662,0.00026925173,0.008343696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996214,0.000028779974,0.000020064432,0.0001299261,0.00013660907,0.00006334949],"domain_scores_gemma":[0.9996816,0.000057610676,0.000052483436,0.00005222152,0.00013034971,0.000025741461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004974154,0.001103023,0.0007566917,0.0013374736,0.00028318053,0.0007482243,0.0013727674,0.0009120904,0.0016420064],"category_scores_gemma":[0.0009812163,0.0004789499,0.0007934698,0.0005284162,0.00036956745,0.001639041,0.0009197044,0.00056404265,0.00070018915],"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":[0.00055446534,0.00019681636,0.00696254,0.00026866328,0.00019054204,0.00037435727,0.00016248526,0.085185334,0.13902222,0.005139354,0.004806641,0.7571366],"study_design_scores_gemma":[0.000011367426,0.00010989947,0.001811054,0.000015892923,0.00005758097,0.00033643012,0.000023500386,0.9617194,0.032988563,0.0009960113,0.0019107109,0.00001949875],"about_ca_topic_score_codex":0.0064988583,"about_ca_topic_score_gemma":0.009604144,"teacher_disagreement_score":0.0064988583,"about_ca_system_score_codex":0.00075905444,"about_ca_system_score_gemma":0.00085029344,"threshold_uncertainty_score":0.012922108},"labels":[],"label_agreement":null},{"id":"W4417305734","doi":"10.23977/jaip.2025.080402","title":"Adaptive Inspection Path Planning Algorithm for Oil Pipeline Robots Driven by Fluid Kinetic Energy","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pipeline (software); Adaptability; Pipeline transport; Robot; Reliability (semiconductor); Trajectory; Energy consumption; Energy (signal processing); Motion planning","score_opus":0.03222524555475877,"score_gpt":0.3182485654103979,"score_spread":0.28602331985563917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417305734","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":[0.04136355,0.00014572061,0.95497596,0.00010099751,0.000028562656,0.0000788162,0.000048742048,0.0016109202,0.0016467695],"genre_scores_gemma":[0.5954879,0.0001436956,0.400108,0.00007864833,0.000013259873,0.0002459126,0.00026509608,0.000112424554,0.0035450577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980515,0.000019300169,0.000010629919,0.00007655834,0.000054435746,0.000033918364],"domain_scores_gemma":[0.9997706,0.00007219456,0.00004249822,0.00002081884,0.00007485114,0.000019055491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025594028,0.00068560534,0.0005141584,0.00064966816,0.0004631973,0.00039376604,0.0009199218,0.0005841901,0.0018501388],"category_scores_gemma":[0.0007025727,0.00033322253,0.00039773204,0.00042570723,0.0004027322,0.00041474748,0.0005241048,0.00052089075,0.00030175803],"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":[0.00021226406,0.000074018266,0.001782228,0.00009319035,0.000038521575,0.00020901568,0.00018679719,0.71174943,0.017969579,0.0029268945,0.0022937038,0.26246423],"study_design_scores_gemma":[0.000019595678,0.000046809415,0.0002602643,0.0000031724785,0.0000064786973,0.00004050596,0.000014694986,0.99677604,0.0017623394,0.0005351661,0.00052798,0.0000068916725],"about_ca_topic_score_codex":0.01007266,"about_ca_topic_score_gemma":0.0075942245,"teacher_disagreement_score":0.01007266,"about_ca_system_score_codex":0.00047780035,"about_ca_system_score_gemma":0.001622413,"threshold_uncertainty_score":0.020028055},"labels":[],"label_agreement":null},{"id":"W4417313957","doi":"10.23977/jaip.2025.080404","title":"NF-Net: Crowd Counting Based on Near-Far Network and Dynamic Dual Attention Mechanism","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Henan Provincial Science and Technology Research Project; Henan University","keywords":"Discriminative model; Key (lock); Dual (grammatical number); Noise (video); Perspective (graphical); Curse of dimensionality; Fusion mechanism; Feature extraction; Feature (linguistics)","score_opus":0.03643556909506318,"score_gpt":0.35666828163932757,"score_spread":0.3202327125442644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417313957","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":[0.05460645,0.0006034093,0.9374598,0.00034108953,0.00020560142,0.000093360184,0.0001952809,0.0014175143,0.005077597],"genre_scores_gemma":[0.8849274,0.0005069422,0.105491474,0.00028626126,0.00018658578,0.00014337372,0.00035049254,0.00013383849,0.007973598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951637,0.0000645543,0.000015379237,0.00019447456,0.000117709795,0.000091558904],"domain_scores_gemma":[0.999471,0.00017515414,0.000066838984,0.00005055145,0.00016343735,0.000072942035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007602858,0.0012274612,0.0011891542,0.0013248287,0.00077671884,0.00093814376,0.002646849,0.0011075035,0.0018456619],"category_scores_gemma":[0.0019547932,0.00047782244,0.00078002695,0.0008147462,0.0007714248,0.0023696271,0.0021284453,0.0009426155,0.00043760505],"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":[0.000471076,0.00023465071,0.005684811,0.00015519149,0.00015010206,0.0004328391,0.00027660123,0.66136724,0.012162011,0.019515377,0.008619863,0.29093024],"study_design_scores_gemma":[0.000006488472,0.000027500173,0.00043628688,0.0000064473543,0.000017530758,0.000053331478,0.000015693337,0.9934382,0.0012485769,0.004101218,0.0006365924,0.000012116675],"about_ca_topic_score_codex":0.011448871,"about_ca_topic_score_gemma":0.00910573,"teacher_disagreement_score":0.011448871,"about_ca_system_score_codex":0.0014211702,"about_ca_system_score_gemma":0.0009197307,"threshold_uncertainty_score":0.022764444},"labels":[],"label_agreement":null},{"id":"W4417313967","doi":"10.23977/jaip.2025.080403","title":"Cloud Computing Environment: Research on Big Data Security and Privacy Protection Strategies","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Cloud Data Security Solutions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cloud computing; Cloud computing security; Big data; Information privacy; Data Protection Act 1998; Data security; Data sharing; Privacy by Design; Scalability; Key (lock)","score_opus":0.30011739149995365,"score_gpt":0.4401783506192044,"score_spread":0.14006095911925076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417313967","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":[0.07423069,0.11400998,0.5776538,0.0629075,0.0011514748,0.0005440745,0.00028769358,0.0003937778,0.16882099],"genre_scores_gemma":[0.8497158,0.055724397,0.084164135,0.0037054338,0.0011621803,0.00021157037,0.00018762749,0.000115918505,0.005012969],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9910261,0.0038034061,0.00037271052,0.00090618856,0.0029359725,0.0009555627],"domain_scores_gemma":[0.9785826,0.012281749,0.0019284579,0.0036225724,0.0026510989,0.00093346677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010497989,0.0007812682,0.0008011898,0.0029664214,0.002539096,0.008234606,0.0019253633,0.0029254172,0.0018566948],"category_scores_gemma":[0.015107311,0.0005267938,0.0010744474,0.006359625,0.005711169,0.016610064,0.0034271637,0.0042643268,0.00041017096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037487775,0.00007715296,0.0019987712,0.00034525414,0.000044703836,0.000090572896,0.00040105887,0.008704507,0.00064206356,0.92994875,0.0022655171,0.055444166],"study_design_scores_gemma":[0.000035269546,0.00016839724,0.0031558066,0.0011711167,0.00006713651,0.0007048852,0.002018423,0.0948948,0.006179423,0.78133863,0.11018116,0.000084998836],"about_ca_topic_score_codex":0.0037418404,"about_ca_topic_score_gemma":0.001720001,"teacher_disagreement_score":0.010497989,"about_ca_system_score_codex":0.006000125,"about_ca_system_score_gemma":0.0070177778,"threshold_uncertainty_score":0.055519342},"labels":[],"label_agreement":null},{"id":"W7076051120","doi":"10.23977/jaip.2025.080310","title":"An Empirical Analysis of Neural Network Machine Translation and Human Translation","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Theoretical and Computational Physics","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Machine translation; Translation (biology); Example-based machine translation; Sentence; Computer-assisted translation; Machine translation software usability; Vocabulary; Transfer-based machine translation","score_opus":0.04537968571719844,"score_gpt":0.38915847142421284,"score_spread":0.34377878570701437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7076051120","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":[0.9612769,0.0023008483,0.011044651,0.0020532499,0.00011548132,0.000053719217,0.000641458,0.00006731548,0.022446332],"genre_scores_gemma":[0.99722266,0.0001912602,0.0011028172,0.00009543445,0.000054037424,0.000020368017,0.0003465396,0.000019587247,0.0009471927],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9892135,0.0076137353,0.00055270374,0.0007957165,0.0015661573,0.00025809713],"domain_scores_gemma":[0.88466185,0.09594774,0.007686411,0.0048967144,0.0059968513,0.00081032835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013348294,0.0003183411,0.00037146494,0.0010466459,0.00058844103,0.0013439577,0.00037966913,0.00059540424,0.0046636546],"category_scores_gemma":[0.101000026,0.00013765818,0.0002608198,0.0024877256,0.0015887484,0.002442584,0.00083797344,0.0009346206,0.001041384],"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":[0.0019914964,0.00064410834,0.66027063,0.0010534427,0.000938358,0.0012229133,0.0075007738,0.026753347,0.0025468373,0.03182884,0.00944731,0.25580192],"study_design_scores_gemma":[0.00012460325,0.0009852459,0.78563666,0.0003327662,0.0002742792,0.0019817683,0.005894047,0.14063768,0.0034616122,0.042259682,0.018324576,0.00008711294],"about_ca_topic_score_codex":0.0020463471,"about_ca_topic_score_gemma":0.0018035569,"teacher_disagreement_score":0.013348294,"about_ca_system_score_codex":0.0008775088,"about_ca_system_score_gemma":0.0005353295,"threshold_uncertainty_score":0.07059336},"labels":[],"label_agreement":null},{"id":"W7081997834","doi":"10.23977/jaip.2025.080311","title":"The Impact of Embodied Intelligence and AI Leasing on the Commercialization Process of Humanoid Robots","year":2025,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Commercialization; Standardization; Process (computing); Cost reduction; Robot; Humanoid robot; Key (lock); Scale (ratio); Protocol (science)","score_opus":0.0596680851924472,"score_gpt":0.38264069080419494,"score_spread":0.32297260561174773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7081997834","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":[0.8635906,0.0007606593,0.028000453,0.0017769639,0.000024105937,0.00009944549,0.000045743807,0.000071952,0.105630085],"genre_scores_gemma":[0.9955265,0.000099243785,0.002681855,0.00005258091,0.0000039363276,0.00001662459,0.000011020453,0.0000101760725,0.0015981136],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.997874,0.0009973092,0.000072167706,0.00023651555,0.00050368713,0.00031632773],"domain_scores_gemma":[0.98323625,0.009914099,0.0027392912,0.0015318895,0.0016968872,0.0008815903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039579836,0.00036757378,0.00016989654,0.0009415761,0.0011035858,0.004143368,0.00060155324,0.00084133254,0.005383304],"category_scores_gemma":[0.021531435,0.00019897358,0.00030967023,0.00073370145,0.0031643587,0.0059124003,0.0026097267,0.0010711632,0.00052826235],"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":[0.00097390474,0.0007982934,0.10758014,0.0003982461,0.00014609357,0.0018993164,0.015630225,0.040840194,0.017601315,0.36461356,0.002836862,0.44668183],"study_design_scores_gemma":[0.00015061251,0.0026790155,0.2691307,0.00083031534,0.00028439032,0.002342098,0.064246394,0.1847895,0.045700785,0.32391125,0.105561554,0.00037339004],"about_ca_topic_score_codex":0.002703276,"about_ca_topic_score_gemma":0.0023773964,"teacher_disagreement_score":0.005383304,"about_ca_system_score_codex":0.0025917953,"about_ca_system_score_gemma":0.0014731887,"threshold_uncertainty_score":0.020932078},"labels":[],"label_agreement":null},{"id":"W7118066892","doi":"10.23977/jaip.2025.080405","title":"AI-Driven Reverse Engineering of Biomimetic Structures via GNN-GAN Synergy","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reverse engineering; Property (philosophy); Generative grammar; Graph; Adversarial system; Artificial neural network","score_opus":0.013748474654219642,"score_gpt":0.2938706667970641,"score_spread":0.28012219214284445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118066892","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":[0.027563551,0.00024428,0.9623636,0.0003282703,0.000051166087,0.00003646858,0.000046748406,0.00050865975,0.008857358],"genre_scores_gemma":[0.839046,0.00025477647,0.15232766,0.00029313966,0.00002658906,0.00011926883,0.0001496393,0.00023449361,0.0075485106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984694,0.000042465894,0.0000045561706,0.00003247092,0.00005593771,0.000017621669],"domain_scores_gemma":[0.99968064,0.00017522028,0.00003619864,0.000047902744,0.000038669754,0.000021424312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005231944,0.0006413929,0.00037503795,0.00028846337,0.00016135215,0.00040958053,0.00080191507,0.0006699555,0.0014993879],"category_scores_gemma":[0.0011360478,0.0002845654,0.0004255417,0.00019090215,0.0007897125,0.00069378264,0.0009419584,0.00091406656,0.00032891805],"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":[0.000017240205,0.000013619916,0.0002221417,0.000026393704,0.000011870826,0.000059350168,0.000023323037,0.9657218,0.0050764484,0.015829265,0.0005674708,0.01243111],"study_design_scores_gemma":[0.0000013831318,0.000009455389,0.000019518096,0.0000023356752,0.0000014250488,0.0000141048495,0.0000022970592,0.9946009,0.0005948561,0.0044142483,0.00033796352,0.0000015638716],"about_ca_topic_score_codex":0.0011875615,"about_ca_topic_score_gemma":0.0022083237,"teacher_disagreement_score":0.0014993879,"about_ca_system_score_codex":0.00053210603,"about_ca_system_score_gemma":0.00045046522,"threshold_uncertainty_score":0.0050159693},"labels":[],"label_agreement":null},{"id":"W7118990336","doi":"10.23977/jaip.2025.080406","title":"A Four-Layer Security Governance Framework for LLM-Based AI Agents","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Corporate governance; Trustworthiness; Computer security model; Security information and event management; Phase (matter); Security domain; Process (computing); Information governance","score_opus":0.09132488724048299,"score_gpt":0.4042829723256977,"score_spread":0.3129580850852147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118990336","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":[0.00997857,0.00024684917,0.9589296,0.0045863353,0.000057297493,0.00048774143,0.00008082907,0.00094805635,0.024684709],"genre_scores_gemma":[0.27173018,0.00028045362,0.72075176,0.0004948028,0.00004887539,0.0007335999,0.00016934119,0.000103667546,0.0056872154],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99620456,0.0017350112,0.0003919854,0.00052512804,0.0007564646,0.00038688898],"domain_scores_gemma":[0.9959759,0.0011080696,0.0005900691,0.0010297394,0.0008384873,0.00045772258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072931885,0.00081066653,0.00041589933,0.0019275848,0.0025227796,0.007341311,0.0021895336,0.0027589696,0.0028766037],"category_scores_gemma":[0.0067301258,0.00074511167,0.001139025,0.0007818621,0.006128634,0.007132844,0.005713934,0.00395996,0.00084693317],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015223178,0.000047155987,0.0008464733,0.00005326323,0.000015203447,0.00017348904,0.0012399347,0.014684335,0.0011125595,0.96755064,0.0011281058,0.013133593],"study_design_scores_gemma":[0.000049146005,0.000101145604,0.0006744085,0.00038440383,0.00006109994,0.0003530911,0.0013792444,0.20246026,0.0035227565,0.72213584,0.068803534,0.00007495451],"about_ca_topic_score_codex":0.008196612,"about_ca_topic_score_gemma":0.008471786,"teacher_disagreement_score":0.008196612,"about_ca_system_score_codex":0.0038091922,"about_ca_system_score_gemma":0.007707727,"threshold_uncertainty_score":0.038570583},"labels":[],"label_agreement":null},{"id":"W7124292464","doi":"10.23977/jaip.2025.080407","title":"Application of Artificial Intelligence Technology in Network Security Protection of Power Enterprise","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"AI and Big Data Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adversarial system; Enterprise private network; Information security; Applications of artificial intelligence; Network security; Core (optical fiber); Information technology; Control (management)","score_opus":0.034469498683482654,"score_gpt":0.34374485504179764,"score_spread":0.309275356358315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124292464","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":[0.05763586,0.014961684,0.7919862,0.017266044,0.0005985173,0.00015836483,0.0000849144,0.000455627,0.11685275],"genre_scores_gemma":[0.86542726,0.012712908,0.11441944,0.0010919579,0.0004047734,0.000090419446,0.00006712024,0.000037132635,0.0057490626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99883765,0.0004975803,0.000070212816,0.00013739466,0.0003833678,0.00007369322],"domain_scores_gemma":[0.9982797,0.0010892124,0.00016853366,0.00020284171,0.00021976302,0.00004001079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014206059,0.00036646466,0.00028069495,0.0010767273,0.0007535327,0.0027364541,0.0006993718,0.0010669602,0.0010170925],"category_scores_gemma":[0.0033514937,0.00016870294,0.0003562275,0.0010201926,0.001641304,0.002915312,0.001362938,0.0012598166,0.00021341087],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055193203,0.00011332925,0.0057474994,0.0005354441,0.0001102358,0.0007325595,0.00093094393,0.08553269,0.006053749,0.5499004,0.005974411,0.34431353],"study_design_scores_gemma":[0.000015172449,0.00013244737,0.002643818,0.00044162435,0.00006294913,0.0005264528,0.0008762084,0.38112286,0.010264478,0.52829945,0.07556329,0.000051228806],"about_ca_topic_score_codex":0.0008222512,"about_ca_topic_score_gemma":0.00071739434,"teacher_disagreement_score":0.0027364541,"about_ca_system_score_codex":0.0011146532,"about_ca_system_score_gemma":0.0009423497,"threshold_uncertainty_score":0.008087397},"labels":[],"label_agreement":null},{"id":"W7128178388","doi":"10.23977/jaip.2025.080302","title":"A Study of Innovative Teaching Pathways in AIGC-Enabled Business Programmes--Take the Tax Law Course as an Example","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Assessment and Pedagogy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Course (navigation); Tax law; Work (physics); Commercial law","score_opus":0.17406710799999917,"score_gpt":0.47723923516671873,"score_spread":0.3031721271667196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128178388","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":[0.9843163,0.000049878476,0.0004063182,0.00055960583,0.000013209475,0.0001695366,0.000036368707,0.000007666574,0.014441119],"genre_scores_gemma":[0.99232787,0.00008844457,0.0011783863,0.0001399772,0.0000043095993,0.00018670115,0.00004513194,0.000005633855,0.0060236068],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9980363,0.0010142543,0.00004048217,0.00016021622,0.00028238678,0.00046640637],"domain_scores_gemma":[0.99221087,0.0034430183,0.0005176984,0.0002354089,0.00070996175,0.0028831242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028590888,0.00020313992,0.00023381122,0.0010630084,0.0044697803,0.0049388474,0.0013489993,0.0013237024,0.007626113],"category_scores_gemma":[0.008487642,0.00023014085,0.00021278324,0.0014068123,0.001917073,0.002683279,0.0031500224,0.0024745762,0.0007657606],"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":[0.0013285743,0.044008434,0.18915996,0.000508463,0.00005059079,0.0022699172,0.44037837,0.0023207942,0.005052531,0.10596681,0.0056743706,0.20328127],"study_design_scores_gemma":[0.0004777637,0.0036484618,0.19306114,0.0004180274,0.00004472546,0.00035547226,0.72450954,0.0032972188,0.0031814587,0.012902549,0.058037333,0.00006633259],"about_ca_topic_score_codex":0.019425986,"about_ca_topic_score_gemma":0.03576593,"teacher_disagreement_score":0.019425986,"about_ca_system_score_codex":0.00645571,"about_ca_system_score_gemma":0.0084883645,"threshold_uncertainty_score":0.046839654},"labels":[],"label_agreement":null},{"id":"W7128432250","doi":"10.23977/jaip.2026.090102","title":"Influence of AIGC on Research Activity in Higher Education","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Educational Technology and Pedagogy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Higher education; Standardization; Intellect; Promotion (chess); Intellectual property; Digital transformation; Plan (archaeology)","score_opus":0.23971456106551664,"score_gpt":0.5172729607835119,"score_spread":0.2775583997179953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128432250","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8031826,0.0048485193,0.0038301966,0.006679621,0.00017899465,0.00013186701,0.0003135175,0.00023378212,0.18060097],"genre_scores_gemma":[0.9948835,0.0006769001,0.00091396633,0.00024747287,0.00006230721,0.00001893206,0.000056271194,0.000021581787,0.003119032],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9821525,0.009315833,0.0008365597,0.0013421768,0.0050240373,0.0013287716],"domain_scores_gemma":[0.9223219,0.04129182,0.010535958,0.004043061,0.014977772,0.0068294695],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007417085,0.00022843218,0.00025143454,0.002901622,0.0018535912,0.005528987,0.00051221054,0.00085302786,0.004714752],"category_scores_gemma":[0.03239465,0.0001520136,0.00030636837,0.00431719,0.0025932537,0.0017681076,0.0026892424,0.00095697393,0.00077799446],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044685492,0.00042284848,0.61906993,0.0010581486,0.00013694067,0.0013933947,0.028749447,0.0022518788,0.0040227743,0.06706268,0.009058989,0.26632613],"study_design_scores_gemma":[0.00004645859,0.00035105308,0.8192016,0.00076827535,0.00028309156,0.00079693383,0.029044917,0.0035697864,0.004553772,0.019240884,0.12204298,0.00010026121],"about_ca_topic_score_codex":0.0064682076,"about_ca_topic_score_gemma":0.007462031,"teacher_disagreement_score":0.9925829,"about_ca_system_score_codex":0.0049484014,"about_ca_system_score_gemma":0.0063409316,"threshold_uncertainty_score":0.039225757},"labels":[],"label_agreement":null}]}