{"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16887325,0.00019919602,0.8210118,0.008815082,0.0005676236,0.00017621518,0.000011716596,0.000009032089,0.00033608553],"genre_scores_gemma":[0.9916433,0.00019766377,0.007385757,0.0004220826,0.0003088346,0.000003327839,2.8084494e-7,0.000013187816,0.000025568957],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956374,0.000646525,0.0016956079,0.00041014288,0.0013729983,0.00023733593],"domain_scores_gemma":[0.9879391,0.008891676,0.0015738191,0.00038924653,0.0010159636,0.00019018802],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0077648773,0.0001808078,0.0003458508,0.00048341491,0.00010178722,0.00041579528,0.00046156457,0.000096638905,0.00013375643],"category_scores_gemma":[0.02402307,0.00010230447,0.00009338329,0.00055809424,0.00007244056,0.0018391425,0.00007077818,0.00030892112,0.0003546541],"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":[0.0009119318,0.00011649412,0.0003690003,0.0000015006699,0.000009402905,0.000041358842,0.0010216263,0.000039358867,0.014056932,0.00042802302,0.00033459376,0.9826698],"study_design_scores_gemma":[0.00056082685,0.001376948,0.0077179475,0.0010697829,0.000060434002,0.0010112831,0.014441216,0.0068605617,0.09619217,0.8236904,0.046208166,0.00081027014],"about_ca_topic_score_codex":0.00005515596,"about_ca_topic_score_gemma":0.0002527363,"teacher_disagreement_score":0.9818595,"about_ca_system_score_codex":0.000115084455,"about_ca_system_score_gemma":0.000120235185,"threshold_uncertainty_score":0.984198},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019435087,0.00022554728,0.9784831,0.017090557,0.001749546,0.0002865417,0.0000039890983,0.000043922875,0.00017331353],"genre_scores_gemma":[0.32334083,0.00076917425,0.6715561,0.0013234259,0.0028726298,0.00004785211,0.0000013391243,0.000026212318,0.00006240115],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979928,0.00017939246,0.0008537931,0.00027432284,0.0003389329,0.00036075473],"domain_scores_gemma":[0.9938442,0.003487683,0.001176802,0.00032979768,0.001011865,0.00014963631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012509471,0.00014882967,0.00021039457,0.00009967365,0.00020547761,0.00013513834,0.0007445979,0.00006214223,0.000019748148],"category_scores_gemma":[0.0024353086,0.0001049427,0.00016344525,0.00052978244,0.00006450514,0.0025723227,0.000089964335,0.0002472271,0.0000893526],"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":[0.00021104996,0.0001187777,0.0000074240843,0.0000034627762,0.000022341323,0.000010241961,0.00011240121,0.0019500875,0.001942843,0.023906302,0.0017503928,0.9699647],"study_design_scores_gemma":[0.0002414103,0.0022775729,0.000061098755,0.00030186752,0.00016223743,0.001114301,0.00029939137,0.06673808,0.06384546,0.7145958,0.14965181,0.0007110019],"about_ca_topic_score_codex":0.0000018576574,"about_ca_topic_score_gemma":0.000006315515,"teacher_disagreement_score":0.96925366,"about_ca_system_score_codex":0.0000902721,"about_ca_system_score_gemma":0.00009702393,"threshold_uncertainty_score":0.42794392},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13781449,0.00037816304,0.8579162,0.0026127659,0.0011149494,0.00008726092,0.0000032266669,0.000023641496,0.00004935158],"genre_scores_gemma":[0.6507153,0.00030426806,0.34863558,0.000072076145,0.0002482211,5.95051e-7,2.647135e-7,0.000008422735,0.000015290303],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976972,0.0006532931,0.0007577377,0.00022197323,0.0004561699,0.00021367444],"domain_scores_gemma":[0.99556404,0.001255131,0.0014612334,0.00026209812,0.0013604393,0.00009703226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003313206,0.00014395712,0.00028136946,0.00025008107,0.00010373328,0.00011242774,0.00045677606,0.00011839389,0.000017906515],"category_scores_gemma":[0.004606754,0.00010059838,0.00014536336,0.0004529405,0.00009429234,0.002744359,0.000061091254,0.00030139796,0.00002322459],"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":[0.00023245314,0.00023049807,0.00007307651,0.000011349126,0.00003298932,0.00004695354,0.0005741958,0.0007898788,0.09283673,0.00015089003,0.00007769026,0.9049433],"study_design_scores_gemma":[0.0005585489,0.0018009531,0.004615035,0.0015815287,0.00027749347,0.0022626477,0.0016380538,0.1064695,0.8707214,0.0055765174,0.0036361427,0.0008621835],"about_ca_topic_score_codex":0.000019939973,"about_ca_topic_score_gemma":0.000007536411,"teacher_disagreement_score":0.9040811,"about_ca_system_score_codex":0.00007023468,"about_ca_system_score_gemma":0.00015723443,"threshold_uncertainty_score":0.5515048},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97989476,0.00006519912,0.0036687402,0.008997531,0.0009264682,0.0006176315,0.0000043713753,0.00003718176,0.005788118],"genre_scores_gemma":[0.9943562,0.000016268657,0.0012394013,0.0027814123,0.0013496121,0.0000059891695,8.9439413e-7,0.00003867021,0.00021155736],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967294,0.00039715983,0.001099389,0.0002493864,0.0011938945,0.0003308117],"domain_scores_gemma":[0.9965081,0.00096276007,0.0008671506,0.0005037461,0.0009008748,0.00025734963],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027396183,0.00025885162,0.000503633,0.0004854237,0.00018800919,0.000055933437,0.00030850436,0.00014578002,0.0009707946],"category_scores_gemma":[0.002389417,0.00014906164,0.00025440974,0.0004571529,0.00017741049,0.0004836182,0.000027147049,0.00096515205,0.00008158474],"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":[0.018147813,0.017335452,0.012497365,0.00008782157,0.0013383792,0.00867075,0.007562931,0.0015575743,0.7012325,0.0035084577,0.0030846773,0.22497629],"study_design_scores_gemma":[0.0073453737,0.092432186,0.057719897,0.005002794,0.0040492374,0.0042093014,0.040110588,0.0012475036,0.75745577,0.004330148,0.024014026,0.0020831765],"about_ca_topic_score_codex":0.000016466647,"about_ca_topic_score_gemma":0.000004340119,"teacher_disagreement_score":0.2228931,"about_ca_system_score_codex":0.00013263078,"about_ca_system_score_gemma":0.00021184831,"threshold_uncertainty_score":0.9999425},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0677224,0.00005930598,0.9296693,0.0018294808,0.00042366638,0.00006706912,5.479639e-7,0.00003416825,0.00019401526],"genre_scores_gemma":[0.5129047,0.000034666427,0.4868652,0.00008492515,0.00009836347,4.907022e-7,4.5644914e-8,0.000007823161,0.0000037916404],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99697644,0.0008343333,0.0009446259,0.00024423207,0.0007323982,0.00026797407],"domain_scores_gemma":[0.9921809,0.004279998,0.0018462621,0.00044876276,0.0011183156,0.00012580075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007857236,0.00015246157,0.00038860991,0.00023417758,0.00013777142,0.00017293826,0.00087460375,0.000067017216,0.000028590737],"category_scores_gemma":[0.0036533664,0.00009278991,0.00009246094,0.000680131,0.000059651844,0.0038110206,0.000095112286,0.000258053,0.000013098493],"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":[0.00022636933,0.00027262385,0.0016863473,0.00002550484,0.000066697525,0.00010036821,0.0036874164,0.0014321869,0.053593382,0.0062895915,0.000012770487,0.93260676],"study_design_scores_gemma":[0.00042556404,0.0046236985,0.012783026,0.00163955,0.00028022702,0.0051652123,0.0065016365,0.25043452,0.6700004,0.045720246,0.0013894418,0.0010365107],"about_ca_topic_score_codex":0.000054004267,"about_ca_topic_score_gemma":0.00004696168,"teacher_disagreement_score":0.93157023,"about_ca_system_score_codex":0.0000508491,"about_ca_system_score_gemma":0.00024726693,"threshold_uncertainty_score":0.43736845},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008634175,0.00022310035,0.9811569,0.0076566855,0.00086396484,0.0002853347,0.000005745063,0.000017741639,0.0011563447],"genre_scores_gemma":[0.545685,0.00049114047,0.45327887,0.0001394751,0.00033961135,0.000006800643,0.0000019549425,0.000016014972,0.000041114003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968818,0.00030971773,0.0011627404,0.00031284898,0.001057627,0.00027528894],"domain_scores_gemma":[0.9934896,0.0012354717,0.0026086282,0.0005892504,0.0018762997,0.00020072746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002663777,0.00017299177,0.0004090189,0.00035897293,0.00044577624,0.0005311326,0.0011904307,0.00010329579,0.000048285983],"category_scores_gemma":[0.006074046,0.00015418211,0.00009856254,0.0002500683,0.0002697656,0.0031176799,0.0002503195,0.00066319684,0.00007562501],"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":[0.00037432162,0.00092347676,0.00044242042,0.000042446856,0.00011205719,0.0000631898,0.0010275387,0.0017940473,0.0010139171,0.007224898,0.00023209667,0.9867496],"study_design_scores_gemma":[0.00023750261,0.002185554,0.0050434126,0.00037455923,0.000122062585,0.00041666633,0.0010255538,0.90961444,0.02565648,0.053302474,0.0015968385,0.00042444855],"about_ca_topic_score_codex":0.00006393766,"about_ca_topic_score_gemma":0.000009455164,"teacher_disagreement_score":0.98632514,"about_ca_system_score_codex":0.000078205485,"about_ca_system_score_gemma":0.00024655307,"threshold_uncertainty_score":0.72716385},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92970395,0.000020965288,0.052977297,0.0148264365,0.00031569155,0.00022807517,7.155944e-7,0.000014783991,0.0019120865],"genre_scores_gemma":[0.99771076,0.00003200622,0.0015768294,0.00022920466,0.0002909879,0.0000046210835,7.8907203e-7,0.000021314525,0.00013349939],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99775916,0.000040904913,0.0009143573,0.00021255278,0.0007699595,0.0003030797],"domain_scores_gemma":[0.99488956,0.0003252051,0.0022113447,0.000572201,0.0019883448,0.000013327439],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0036523181,0.00014034829,0.00025054626,0.001229478,0.00039080478,0.00033149213,0.00097342633,0.0001331682,0.000044280077],"category_scores_gemma":[0.00919754,0.00013163415,0.000067968555,0.00078773947,0.00048834615,0.004098018,0.0004632037,0.0007452824,0.00006967626],"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":[0.0027382644,0.0028751798,0.017685995,0.00054032466,0.00017867744,0.00018660507,0.000935531,0.10130241,0.09843616,0.36468893,0.00021046521,0.41022146],"study_design_scores_gemma":[0.00023596035,0.00039362395,0.004957535,0.00065135787,0.00017686248,0.000055303295,0.0133554265,0.39562523,0.023563756,0.5565612,0.0038961873,0.0005275659],"about_ca_topic_score_codex":0.00020047213,"about_ca_topic_score_gemma":0.000049459468,"teacher_disagreement_score":0.4096939,"about_ca_system_score_codex":0.00022509991,"about_ca_system_score_gemma":0.000074101015,"threshold_uncertainty_score":0.9991484},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008288835,0.00024072643,0.97364473,0.0017479308,0.012336945,0.00026198762,0.000039173414,0.000056897818,0.003382753],"genre_scores_gemma":[0.94943964,0.0002656358,0.043886278,0.00010778414,0.006217836,0.0000068582676,0.000006742486,0.000047377645,0.000021846094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996911,0.00064386934,0.000928076,0.0001820963,0.00092381047,0.00041118526],"domain_scores_gemma":[0.99478227,0.002908518,0.00072008825,0.0005342355,0.0008895318,0.00016532869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008504457,0.00017832701,0.00036737882,0.00019715438,0.00045009007,0.00021684445,0.0005318212,0.00011326703,0.000030090507],"category_scores_gemma":[0.0020526634,0.00016019407,0.00016173204,0.0002595862,0.00006986827,0.0006044382,0.000041259136,0.001151769,0.00007358746],"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":[0.0010163278,0.000522359,0.000075081705,0.0001066783,0.00019199737,0.0002914503,0.00037659195,0.8548037,0.000992378,0.0018922988,0.022647416,0.11708375],"study_design_scores_gemma":[0.000094582814,0.0015106257,0.000119320466,0.00050389767,0.00007817872,0.00014258006,0.00071838836,0.80958307,0.06642005,0.00050766463,0.12009101,0.00023063541],"about_ca_topic_score_codex":0.00008210961,"about_ca_topic_score_gemma":0.0000059471818,"teacher_disagreement_score":0.9411508,"about_ca_system_score_codex":0.00019131762,"about_ca_system_score_gemma":0.00012468062,"threshold_uncertainty_score":0.6532525},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6865583,0.00032113018,0.3124924,0.0000648583,0.00012027853,0.0001655156,0.000004834743,0.000017141974,0.000255503],"genre_scores_gemma":[0.86397153,0.00027681384,0.13565245,0.000015416312,0.00005288811,8.8284986e-7,0.0000014802062,0.000027112543,0.0000014500131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985927,0.0001479671,0.0006623468,0.00014395983,0.0002627477,0.00019031689],"domain_scores_gemma":[0.9982904,0.0005717229,0.00043713077,0.000069401955,0.00053987367,0.000091469126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001504647,0.00015873516,0.00031085688,0.00020726431,0.000058139976,0.000119336124,0.000069590555,0.00009084239,0.000029001005],"category_scores_gemma":[0.0002755896,0.00014182512,0.000038535272,0.00031960872,0.00008713287,0.0015829393,0.000008467845,0.00037650566,0.000005044424],"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":[0.00057510496,0.00010702987,0.000011157846,0.00033045135,0.00007611754,0.000018849481,0.0031195683,0.013497446,0.1500121,0.00001345859,0.0000015740222,0.8322371],"study_design_scores_gemma":[0.00026656347,0.0007705342,0.000048910057,0.00088515406,0.00044185534,0.001699925,0.010973098,0.6273029,0.35630998,0.00066553743,0.00025935395,0.00037618497],"about_ca_topic_score_codex":0.000009191724,"about_ca_topic_score_gemma":0.000004736947,"teacher_disagreement_score":0.83186096,"about_ca_system_score_codex":0.000054013406,"about_ca_system_score_gemma":0.000053403335,"threshold_uncertainty_score":0.578346},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03550265,0.00092971075,0.9606592,0.0017797797,0.00039245325,0.00011351291,0.00001538617,0.000050280432,0.00055703043],"genre_scores_gemma":[0.9893388,0.0006584924,0.009642434,0.00012351596,0.0001792364,9.835551e-7,0.000001908988,0.00003112378,0.000023512168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970813,0.00018073755,0.0016772281,0.00015587336,0.00068214914,0.00022273032],"domain_scores_gemma":[0.9976564,0.0006271085,0.00060332444,0.00018073263,0.0005816085,0.00035084682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010581326,0.00018966477,0.0006419258,0.00039920764,0.000041103885,0.000058940037,0.00036157286,0.00009170314,0.00036099108],"category_scores_gemma":[0.00059134135,0.00016173188,0.0003356736,0.0017180667,0.000043208154,0.0007758094,0.000013598936,0.00054261205,0.00005014747],"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":[0.00060873723,0.00017600045,0.00003872425,0.0001377503,0.0017215468,0.00014187451,0.014160711,0.65080774,0.24407257,0.0001526887,0.00027625376,0.087705374],"study_design_scores_gemma":[0.000097279975,0.00056790584,0.000040984072,0.00013933924,0.0020702418,0.00004023984,0.0047382102,0.5531237,0.3754465,0.0001445929,0.063272364,0.00031862163],"about_ca_topic_score_codex":0.00002064214,"about_ca_topic_score_gemma":0.0000043942637,"teacher_disagreement_score":0.95383614,"about_ca_system_score_codex":0.00004140299,"about_ca_system_score_gemma":0.000073442796,"threshold_uncertainty_score":0.6595234},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027732871,0.000054703265,0.970021,0.00085290946,0.00015697214,0.00030942902,0.000006729062,0.000072166236,0.00079322676],"genre_scores_gemma":[0.98811746,0.00005083091,0.011541249,0.00016831589,0.00010424368,0.0000071963955,6.8313096e-7,0.000009503319,5.0648504e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896085,0.00014522568,0.00047295436,0.000106494255,0.00025400743,0.000060459657],"domain_scores_gemma":[0.99841434,0.000885425,0.00032344073,0.00024625324,0.000091617854,0.000038946135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013781843,0.000075591954,0.0001524439,0.000093966264,0.000026329364,0.000018028966,0.00029417835,0.000027256043,0.0000046875393],"category_scores_gemma":[0.00064072805,0.00005798863,0.000027991648,0.00020791018,0.000029576715,0.00018767154,0.000033818706,0.0002143687,0.0000014906976],"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":[0.00046029346,0.000687343,0.00004128657,0.000066783825,0.00015062097,0.000008195277,0.004627235,0.14951782,0.021156723,0.0009001666,0.0016987219,0.8206848],"study_design_scores_gemma":[0.00007972215,0.0020417664,0.00020144008,0.000071793234,0.00022495813,0.0000072030884,0.02346266,0.8415953,0.12704752,0.00008402167,0.005062699,0.00012090556],"about_ca_topic_score_codex":0.0000069003113,"about_ca_topic_score_gemma":0.0000022521886,"teacher_disagreement_score":0.9603846,"about_ca_system_score_codex":0.0000124722665,"about_ca_system_score_gemma":0.000019125273,"threshold_uncertainty_score":0.23647076},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024602504,0.00017114411,0.8475772,0.14299358,0.0005684696,0.00019922279,0.000004036786,0.000028064806,0.0059979996],"genre_scores_gemma":[0.92816156,0.0000761269,0.04252844,0.026760146,0.002409179,0.0000031054403,0.0000045277,0.000027253393,0.000029677121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977273,0.00023544847,0.0008031024,0.00019247891,0.00087104324,0.00017067154],"domain_scores_gemma":[0.9963964,0.0016689812,0.0005794307,0.00013266882,0.00093100633,0.00029147475],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0010099133,0.00016681122,0.0004023436,0.00017545365,0.00009970972,0.000030775725,0.00012086275,0.00007601217,0.00025863864],"category_scores_gemma":[0.014096479,0.0001234273,0.0001635789,0.0005412913,0.0000883593,0.0003386536,0.00001633285,0.00071767246,0.00009536196],"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":[0.04412708,0.008163176,0.0023289802,0.0009109403,0.0019785895,0.008592167,0.020326942,0.3139473,0.042374507,0.05822044,0.074273355,0.42475653],"study_design_scores_gemma":[0.0008803405,0.01064333,0.0016125374,0.0007596821,0.0018007405,0.0021128915,0.011580546,0.68235636,0.112018466,0.005023195,0.17065477,0.00055716257],"about_ca_topic_score_codex":0.00001316809,"about_ca_topic_score_gemma":0.000002518504,"teacher_disagreement_score":0.9257013,"about_ca_system_score_codex":0.00009459781,"about_ca_system_score_gemma":0.00027384647,"threshold_uncertainty_score":0.9942082},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008495443,0.00009082591,0.98327583,0.0074947053,0.000021983644,0.00012523099,0.0000021972032,0.000012955788,0.00048082139],"genre_scores_gemma":[0.97288555,0.00011033613,0.02677799,0.00017397375,0.00003227592,0.0000060333346,2.76747e-7,0.0000033058914,0.000010250988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985857,0.00015422408,0.0007749503,0.00012202257,0.0002804751,0.00008261201],"domain_scores_gemma":[0.99776894,0.00034798382,0.0013165819,0.00022787706,0.00028782443,0.000050806888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007686944,0.00006480722,0.0002266172,0.00023558279,0.00007269248,0.000032710715,0.00074676354,0.00003709316,0.000026974329],"category_scores_gemma":[0.00064312463,0.00004946545,0.0001836035,0.0032373287,0.00006491578,0.0003898439,0.00006897802,0.00039566157,0.0000033763686],"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":[0.00020623344,0.0010183642,0.005605438,0.000041810057,0.0005827786,0.000007436946,0.009245368,0.09470446,0.048215583,0.15720846,0.000090459,0.68307364],"study_design_scores_gemma":[0.000051953746,0.00044907074,0.0046705264,0.000035442907,0.00053805363,0.00003908019,0.001720875,0.64451236,0.31723475,0.009079872,0.021466536,0.00020145155],"about_ca_topic_score_codex":0.00010855034,"about_ca_topic_score_gemma":0.000022675977,"teacher_disagreement_score":0.9643901,"about_ca_system_score_codex":0.000022415232,"about_ca_system_score_gemma":0.00008290164,"threshold_uncertainty_score":0.20171425},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06382991,0.00072152313,0.26419616,0.65804917,0.0023343044,0.000623996,0.000016107764,0.00009242535,0.010136436],"genre_scores_gemma":[0.9708871,0.000036053563,0.010271168,0.014938712,0.003829952,0.0000027581084,0.000011183476,0.000015228261,0.000007833129],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978154,0.0001708205,0.0007928424,0.00017592778,0.000857217,0.00018783522],"domain_scores_gemma":[0.9965876,0.0014325368,0.00066161656,0.00011654107,0.0009478455,0.00025390377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007155228,0.00015144973,0.00034202277,0.00010041346,0.00014099278,0.000027824153,0.0001224032,0.00005255691,0.00020820527],"category_scores_gemma":[0.005627559,0.00011815696,0.00015540078,0.00043498148,0.00015823735,0.0003055797,0.000014944663,0.0006784279,0.000105732455],"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":[0.0738241,0.005416817,0.011238351,0.00044177374,0.0011837746,0.0021466701,0.004076638,0.37869707,0.029300291,0.26996982,0.09989649,0.1238082],"study_design_scores_gemma":[0.00071967207,0.009129484,0.03488175,0.00041593917,0.0007884021,0.0011210941,0.0034309698,0.89674234,0.0022166749,0.008119879,0.042054694,0.0003790676],"about_ca_topic_score_codex":0.0000045836996,"about_ca_topic_score_gemma":0.0000012626901,"teacher_disagreement_score":0.9070572,"about_ca_system_score_codex":0.00009697124,"about_ca_system_score_gemma":0.00023861059,"threshold_uncertainty_score":0.673712},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63017887,0.005770277,0.19231729,0.15828213,0.007733707,0.0015201729,0.00013546905,0.000121592544,0.003940482],"genre_scores_gemma":[0.99593174,0.0002539977,0.0014345929,0.0015541401,0.00070219603,0.000015657211,0.0000027883893,0.000035915495,0.00006897368],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99563044,0.00079106126,0.0017731478,0.0006685349,0.00059732865,0.0005394772],"domain_scores_gemma":[0.99618214,0.0010746993,0.0013031438,0.0003271009,0.00051094434,0.00060198596],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020663408,0.00034098863,0.00056477945,0.00025085846,0.00019315953,0.00037070096,0.00041822082,0.00027415532,0.0002556985],"category_scores_gemma":[0.005398484,0.00031552068,0.00015004813,0.0005983049,0.0003253376,0.0022146036,0.0002591078,0.0012630889,0.00007283559],"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":[0.0039698137,0.0012106941,0.0012936258,0.00007940707,0.00009183803,0.00035871376,0.00835821,0.00015770095,0.0020742493,0.012371223,0.00089487835,0.96913964],"study_design_scores_gemma":[0.001008113,0.012088495,0.027731663,0.004722218,0.0008859533,0.009632762,0.2504785,0.017708676,0.48478034,0.15436687,0.032753147,0.003843258],"about_ca_topic_score_codex":0.00013784593,"about_ca_topic_score_gemma":0.000083664476,"teacher_disagreement_score":0.9652964,"about_ca_system_score_codex":0.000109288565,"about_ca_system_score_gemma":0.00013396624,"threshold_uncertainty_score":0.99992967},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006932118,0.0023555914,0.8916485,0.09779302,0.00079020485,0.00013182372,5.471652e-7,0.00004919988,0.00029899765],"genre_scores_gemma":[0.9497548,0.000038141374,0.046365783,0.003020943,0.0007495771,0.0000025314064,1.9106862e-7,0.000012464691,0.000055623856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976243,0.00021433923,0.00072298036,0.0002670187,0.0008352716,0.00033611263],"domain_scores_gemma":[0.9978301,0.00034532972,0.0007503548,0.00029372587,0.00061193533,0.00016854134],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015425299,0.00016637218,0.00021649372,0.00006419253,0.000572608,0.0015953581,0.0016389276,0.00006632224,0.000009818441],"category_scores_gemma":[0.0020315468,0.000113873255,0.00012195602,0.00038981086,0.000096176555,0.0029016382,0.00025663347,0.001009974,0.00003215569],"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":[0.00010370326,0.0000833307,0.000016065891,0.000027603608,0.00004301307,0.0002230476,0.017105134,0.0011564504,0.0068909633,0.013169953,0.0002790437,0.9609017],"study_design_scores_gemma":[0.00010451443,0.0003935243,0.00008479883,0.00015396094,0.000086245425,0.0010619798,0.01788234,0.8120346,0.14514247,0.0069316714,0.015658729,0.0004651684],"about_ca_topic_score_codex":0.00004570195,"about_ca_topic_score_gemma":0.0000174148,"teacher_disagreement_score":0.9604365,"about_ca_system_score_codex":0.00006769085,"about_ca_system_score_gemma":0.00040493425,"threshold_uncertainty_score":0.9994411},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12416118,0.000014418139,0.8473489,0.020894226,0.00036167505,0.00029278776,0.000013006208,0.00008833747,0.006825447],"genre_scores_gemma":[0.9866694,0.000014613237,0.011952777,0.0011567674,0.0001739828,0.000006633195,0.0000022384443,0.000017880393,0.000005667965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980639,0.00011414212,0.0007371178,0.00015617708,0.00072317774,0.00020544665],"domain_scores_gemma":[0.9979099,0.0008127206,0.00016638865,0.0001880617,0.0007603713,0.00016256329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020405403,0.00011394619,0.00016330475,0.0001808337,0.000119548,0.0000749122,0.0002207647,0.00006982823,0.00007699564],"category_scores_gemma":[0.0021024668,0.00010513771,0.00008679392,0.000650967,0.00006210516,0.00049468345,0.0000065522718,0.001162556,0.000077045464],"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":[0.00036326563,0.00026744173,0.000026368805,0.000030768908,0.000018399513,0.000020675428,0.0011662627,0.96573853,0.0015888988,0.018204425,0.00031528753,0.012259698],"study_design_scores_gemma":[0.00004341753,0.00047344936,0.00013165403,0.000054609816,0.000024013892,0.000004064406,0.0018255631,0.97251683,0.016123679,0.0057607233,0.0029136096,0.00012836033],"about_ca_topic_score_codex":0.000008946648,"about_ca_topic_score_gemma":0.000015697375,"teacher_disagreement_score":0.86250824,"about_ca_system_score_codex":0.000108520915,"about_ca_system_score_gemma":0.00017899608,"threshold_uncertainty_score":0.5050792},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004992942,0.00014654346,0.97827864,0.009837466,0.00057167985,0.000113597016,0.0000014509124,0.000058853035,0.005998852],"genre_scores_gemma":[0.90097046,0.00045541726,0.097210355,0.0010331046,0.00019886931,0.0000056047456,0.0000028096722,0.000017654065,0.000105737985],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99705005,0.0008663886,0.0009555152,0.00021165282,0.0006870737,0.00022931324],"domain_scores_gemma":[0.99254644,0.0031598278,0.0012429871,0.00025002775,0.0026977279,0.00010298276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028836003,0.00013831479,0.00023813927,0.00015771735,0.00037336836,0.0005268007,0.0004903529,0.000091552785,0.00007374194],"category_scores_gemma":[0.0077297552,0.000107980544,0.00017035859,0.00076796993,0.000095779345,0.0011418132,0.00006452305,0.0007362124,0.00007446558],"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":[0.00026511974,0.0003723188,0.00001879297,0.000011963198,0.00006206975,0.00018158091,0.00041537156,0.0005572816,0.010619025,0.002997717,0.00038130634,0.98411745],"study_design_scores_gemma":[0.0000829138,0.0008853311,0.000020996276,0.00025875607,0.000059520946,0.0003881822,0.0023613537,0.050161492,0.91610545,0.018818604,0.010651943,0.00020544614],"about_ca_topic_score_codex":0.000018540193,"about_ca_topic_score_gemma":0.000020560514,"teacher_disagreement_score":0.983912,"about_ca_system_score_codex":0.00007216715,"about_ca_system_score_gemma":0.00039530668,"threshold_uncertainty_score":0.92537975},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08840524,0.00070446223,0.9064928,0.0025243396,0.0009947445,0.00009064167,0.000007004316,0.000081331935,0.0006993859],"genre_scores_gemma":[0.83450687,0.0005840069,0.16434965,0.00013346461,0.0003957609,0.0000022379197,0.0000015809283,0.00001650894,0.000009918418],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99892527,0.00007439496,0.00052974507,0.000111586334,0.00025315842,0.00010584938],"domain_scores_gemma":[0.9989483,0.00039220543,0.00020203408,0.00011633685,0.00027374545,0.00006738004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000483403,0.0000964233,0.00018122516,0.00012772437,0.000027803946,0.000043544685,0.00007870537,0.00006908871,0.000028662504],"category_scores_gemma":[0.0011306475,0.00009104377,0.00006827504,0.0001636556,0.00005357782,0.0003774008,0.00002294584,0.00036018333,0.0000023247628],"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":[0.00024510673,0.0002107576,0.000014642335,0.000063176885,0.000035943638,0.00006138311,0.00020711315,0.0031208533,0.113948286,0.00014023622,0.00008521616,0.8818673],"study_design_scores_gemma":[0.000037025453,0.00026076057,0.000023732311,0.00016655712,0.000038883616,0.00024102768,0.00027492864,0.092372306,0.9033594,0.0015715669,0.001552724,0.00010109101],"about_ca_topic_score_codex":0.000004674912,"about_ca_topic_score_gemma":0.0000012140589,"teacher_disagreement_score":0.8817662,"about_ca_system_score_codex":0.000039623323,"about_ca_system_score_gemma":0.000029093058,"threshold_uncertainty_score":0.3712657},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.099148236,0.00021396736,0.8757628,0.020435994,0.0016941101,0.00011853893,0.000007505828,0.00006402696,0.002554816],"genre_scores_gemma":[0.6188786,0.000016684178,0.3798105,0.0008472082,0.00039036717,0.0000044445896,8.737447e-7,0.00001839976,0.000032930493],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975751,0.00042660208,0.0009018032,0.00023868028,0.00058697717,0.00027084124],"domain_scores_gemma":[0.9957074,0.0008729406,0.0010859428,0.00023796843,0.0020197204,0.00007601316],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004122616,0.0001385966,0.00028820767,0.00022201455,0.00027255397,0.00049689366,0.0002897135,0.00010513176,0.0014204249],"category_scores_gemma":[0.009974546,0.00012123085,0.000082533224,0.0005789488,0.00010043752,0.0010314105,0.000056423683,0.0003264923,0.00031376712],"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":[0.00018160278,0.0003085319,0.0000047832486,0.00002664909,0.0000074170857,0.00018119792,0.00029698125,0.009003408,0.9157494,0.0026241753,0.00013138847,0.071484506],"study_design_scores_gemma":[0.000044358436,0.00018494218,0.000009922994,0.00013837118,0.00008439756,0.00036428933,0.0033792614,0.035135366,0.9473669,0.0073885918,0.005764147,0.00013944384],"about_ca_topic_score_codex":0.000010945534,"about_ca_topic_score_gemma":0.000006922701,"teacher_disagreement_score":0.5197303,"about_ca_system_score_codex":0.00008521019,"about_ca_system_score_gemma":0.00064548803,"threshold_uncertainty_score":0.9994924},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013534159,0.0005102066,0.9803519,0.0052273707,0.00015128618,0.00007526399,0.000001026924,0.000043263528,0.00010552275],"genre_scores_gemma":[0.6382697,0.00006930023,0.36144948,0.00008797356,0.000074298056,0.0000018876482,0.000003122575,0.000007871241,0.000036312234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981367,0.0004272038,0.0006038235,0.00023818579,0.0004352918,0.00015881789],"domain_scores_gemma":[0.9965323,0.0005382881,0.001417959,0.00013576902,0.0012877978,0.00008789055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014364324,0.00012665233,0.00023688689,0.00020940027,0.00020011474,0.00024318852,0.00018949856,0.000089600966,0.000023300812],"category_scores_gemma":[0.0047142464,0.00011437219,0.00007466988,0.00068074424,0.000042976048,0.0011256686,0.000059576072,0.0007956004,0.000004066223],"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":[0.00022809544,0.0007664388,0.0011578461,0.00017872942,0.00012610825,0.00005066239,0.0017637635,0.2631367,0.09523108,0.018891525,0.000104781655,0.6183643],"study_design_scores_gemma":[0.00004583364,0.00030546368,0.0001699155,0.00009082301,0.000055165616,0.00060957833,0.00023646367,0.9334576,0.06236923,0.0017977672,0.0007611269,0.00010103561],"about_ca_topic_score_codex":0.000017209943,"about_ca_topic_score_gemma":0.000012462635,"teacher_disagreement_score":0.67032087,"about_ca_system_score_codex":0.00006370462,"about_ca_system_score_gemma":0.00040605108,"threshold_uncertainty_score":0.5643734},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.899962,0.0018534228,0.06485054,0.018031754,0.0033287937,0.00016495823,0.000016971544,0.000019716896,0.01177185],"genre_scores_gemma":[0.98383874,0.0021438769,0.012070931,0.00080282177,0.0010421046,0.00000368149,0.000014262013,0.000012555146,0.00007105882],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978691,0.0007806879,0.00040516426,0.00023188518,0.00052879244,0.00018438495],"domain_scores_gemma":[0.99752337,0.0009246897,0.00021134171,0.00015886317,0.0010124008,0.0001693165],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0032385702,0.00009163806,0.00014491712,0.0001605903,0.0001443875,0.00006556358,0.00013389118,0.00025547267,0.000165561],"category_scores_gemma":[0.015325857,0.00007845197,0.00007605595,0.00029898866,0.00037358957,0.000014590141,0.00005365318,0.0006844027,0.000041710486],"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":[0.0012684842,0.00066314574,0.00008478182,0.00002023018,0.00005049638,0.00013396601,0.00009415659,0.000021032949,0.0091249375,0.00043901062,0.0020519982,0.98604774],"study_design_scores_gemma":[0.0005075693,0.009503991,0.00040333258,0.00081128825,0.000140159,0.0020492424,0.014655127,0.0030321272,0.75673807,0.018774029,0.19286785,0.0005171938],"about_ca_topic_score_codex":0.000014113523,"about_ca_topic_score_gemma":0.000049905466,"teacher_disagreement_score":0.98553056,"about_ca_system_score_codex":0.000024805851,"about_ca_system_score_gemma":0.00049597444,"threshold_uncertainty_score":0.9929685},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3737881,0.001009759,0.5133934,0.077057555,0.00053705287,0.00039135778,0.000005045167,0.000030353256,0.033787414],"genre_scores_gemma":[0.99725026,0.000113364316,0.0014488803,0.00032555358,0.00068934617,0.0000035343194,9.472504e-7,0.000008620202,0.00015946555],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99809015,0.0006671334,0.00046648792,0.000146208,0.00045132844,0.00017867323],"domain_scores_gemma":[0.9955334,0.0024272872,0.00029092302,0.00021800799,0.001461062,0.00006931775],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027964646,0.0000864955,0.00015769764,0.0001038041,0.0002650295,0.00032399717,0.0001945229,0.000029117042,0.0009816372],"category_scores_gemma":[0.000813046,0.0000607927,0.00008435129,0.00059445837,0.00006284173,0.0004926011,0.0000774586,0.00073716306,0.000058021113],"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":[0.00026252924,0.00062132714,0.00013310331,0.0000037807324,0.00023427479,0.000053941,0.0014832949,0.006346162,0.013000667,0.78159523,0.005246281,0.1910194],"study_design_scores_gemma":[0.000080062506,0.0009206823,0.00015720079,0.00022460497,0.0002987831,0.00016271017,0.04806989,0.041080903,0.39802402,0.284154,0.22639981,0.0004273246],"about_ca_topic_score_codex":0.00005346078,"about_ca_topic_score_gemma":0.000017590191,"teacher_disagreement_score":0.6234622,"about_ca_system_score_codex":0.00003143557,"about_ca_system_score_gemma":0.00014236776,"threshold_uncertainty_score":0.9999316},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017873776,0.00025293446,0.9959038,0.00089185033,0.00048368517,0.000117250594,0.000003907833,0.00010717392,0.0020606616],"genre_scores_gemma":[0.8852473,0.0003679887,0.11375873,0.0003403689,0.00019713028,0.000007217261,0.00001045416,0.000033699154,0.000037124802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833095,0.00011959962,0.00076127064,0.00017657649,0.00039873976,0.00021287081],"domain_scores_gemma":[0.9974892,0.0006074886,0.00032839552,0.00019838662,0.0012995729,0.00007696175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005726863,0.00017886516,0.0002178064,0.00023616511,0.00009675842,0.00020695271,0.00023679626,0.00015435809,0.00032047756],"category_scores_gemma":[0.0011620095,0.00017011663,0.000081235485,0.00071962463,0.000058736274,0.00086463103,0.0000150876995,0.0005005248,0.00004289444],"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":[0.000085483734,0.00014974446,0.0000043752875,0.000027384749,0.000035159737,0.000034587516,0.00021467695,0.9014701,0.0026083656,0.0013185032,0.00019765072,0.093853936],"study_design_scores_gemma":[0.000049707378,0.00016892648,0.0000019601043,0.00015252853,0.000047750473,0.000066733824,0.0013433581,0.605888,0.38999307,0.001664278,0.00047831715,0.00014536215],"about_ca_topic_score_codex":0.0000048309266,"about_ca_topic_score_gemma":0.0000056682193,"teacher_disagreement_score":0.88506854,"about_ca_system_score_codex":0.0001695374,"about_ca_system_score_gemma":0.00018100922,"threshold_uncertainty_score":0.6937155},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027420629,0.00041386345,0.96901274,0.0018916855,0.00061770674,0.00040588845,0.00006993778,0.00004963034,0.0001179249],"genre_scores_gemma":[0.9772245,0.00007051966,0.022274917,0.00015841806,0.00020110108,0.000033085027,0.0000025062118,0.000026924197,0.000007982502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986149,0.00007021802,0.0006973022,0.00012777939,0.00030509708,0.00018467596],"domain_scores_gemma":[0.9981202,0.0007004906,0.0005484067,0.00017331955,0.00040561246,0.00005196339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068306335,0.0001307272,0.00028839966,0.00030533172,0.0001529766,0.000019397088,0.00021938705,0.00006308195,0.00010440948],"category_scores_gemma":[0.00038839414,0.00013240354,0.0001272718,0.00054637366,0.000048736318,0.00027537928,0.000014563008,0.00050125254,0.000006600783],"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":[0.00033224118,0.00030150014,0.000010901444,0.00004536274,0.000058377784,0.000013550968,0.00007455129,0.84812266,0.04594906,0.012341535,0.00006112241,0.092689164],"study_design_scores_gemma":[0.00057524204,0.0024015578,0.00003716651,0.00012577359,0.00028414014,0.0002356627,0.0021760173,0.7735639,0.1564979,0.05482263,0.0088623455,0.00041763505],"about_ca_topic_score_codex":0.000016373942,"about_ca_topic_score_gemma":0.000003084351,"teacher_disagreement_score":0.9498039,"about_ca_system_score_codex":0.00014890784,"about_ca_system_score_gemma":0.00006454113,"threshold_uncertainty_score":0.539926},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027547246,0.00026971556,0.9551729,0.016537867,0.00017870918,0.00024118513,0.0000020641671,0.00000705651,0.000043251544],"genre_scores_gemma":[0.9076795,0.000113797636,0.09196426,0.00015594771,0.000055626893,0.000024753677,9.3413996e-7,0.0000039359165,0.0000012594332],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976182,0.00026776292,0.0011776856,0.00019845062,0.0005719402,0.00016598195],"domain_scores_gemma":[0.9965114,0.0014308197,0.0012013358,0.0004459964,0.00037826304,0.000032155687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003735571,0.00009825467,0.00017694311,0.0001963263,0.00039932207,0.000090877475,0.0016365009,0.000045508463,0.0000039793426],"category_scores_gemma":[0.00035005648,0.00006527544,0.00004307502,0.0012893865,0.00022242153,0.00052208384,0.000473251,0.0006327322,0.000005321176],"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":[0.00006828199,0.00029230703,0.000023961504,0.0000045322786,0.000011604673,0.000004138586,0.002523305,0.0012908627,0.0019254159,0.24404101,0.000029892235,0.7497847],"study_design_scores_gemma":[0.00007145994,0.0022772858,0.0008037016,0.00009017647,0.0000802681,0.0010738985,0.039115902,0.35800678,0.168694,0.3612256,0.06809299,0.00046795164],"about_ca_topic_score_codex":0.000029578136,"about_ca_topic_score_gemma":0.000043865748,"teacher_disagreement_score":0.88013226,"about_ca_system_score_codex":0.000051556886,"about_ca_system_score_gemma":0.00019874416,"threshold_uncertainty_score":0.3071303},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047994757,0.00009559902,0.99363923,0.0049948716,0.00045653566,0.00015719008,0.0000010732684,0.00009000627,0.00008556427],"genre_scores_gemma":[0.24758533,0.000035984176,0.75163835,0.0005222498,0.00007037601,0.000015013378,0.000004903375,0.000011260192,0.00011655993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969113,0.00054559513,0.001021373,0.00032053737,0.00084997324,0.000351238],"domain_scores_gemma":[0.99363536,0.0033341802,0.0017946131,0.00032374478,0.00082563586,0.000086496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036275652,0.00016506489,0.00034640246,0.0007428097,0.00068079843,0.00015959743,0.0012446889,0.000060109312,0.00013447525],"category_scores_gemma":[0.007476763,0.00016725565,0.00032709463,0.0018567779,0.0000452757,0.0010269997,0.00046721273,0.0016462029,0.000004129768],"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":[0.00016788767,0.00015433136,0.000022167069,0.000004630703,0.00021989566,0.000039080423,0.0005878233,0.78211164,0.00025972188,0.009720537,0.000014340852,0.20669793],"study_design_scores_gemma":[0.000065970875,0.0013107475,0.000008968932,0.0000065077434,0.00019473016,0.00009349213,0.0025904689,0.95987105,0.0029264325,0.0149909565,0.017769389,0.00017130486],"about_ca_topic_score_codex":0.000025760432,"about_ca_topic_score_gemma":0.000013481625,"teacher_disagreement_score":0.24710537,"about_ca_system_score_codex":0.00029857495,"about_ca_system_score_gemma":0.00021711658,"threshold_uncertainty_score":0.89509237},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011313541,0.0001324086,0.9871261,0.0010847606,0.00014200991,0.00008741081,0.00009221562,0.000008703696,0.000012845327],"genre_scores_gemma":[0.69818354,0.000038383445,0.30141982,0.00025173335,0.000038100403,0.0000012978768,0.000061237595,0.000004611472,0.0000012690103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974459,0.00061016093,0.00073653355,0.0004649826,0.00061109074,0.00013133358],"domain_scores_gemma":[0.9959939,0.0013472,0.0014444877,0.0009277954,0.00021860897,0.00006800162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061099776,0.00010518082,0.00022399481,0.00035534654,0.00033872016,0.0003161082,0.0011198826,0.000024089773,0.000028593797],"category_scores_gemma":[0.0014422401,0.00009782253,0.000024280844,0.0008019139,0.000048511374,0.0037389298,0.0006370517,0.00033484987,0.0000010614415],"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":[0.00069211086,0.00046177002,0.00038637544,0.000078106794,0.00022613163,0.000026244837,0.0008929716,0.026764497,0.0028212825,0.000947952,0.00028603876,0.96641654],"study_design_scores_gemma":[0.00009249402,0.00037626567,0.00015614797,0.00003713233,0.00025530026,0.0001013652,0.0021916516,0.97461164,0.019911237,0.001927884,0.0002047535,0.00013410015],"about_ca_topic_score_codex":0.00006346087,"about_ca_topic_score_gemma":0.0000099084455,"teacher_disagreement_score":0.9662824,"about_ca_system_score_codex":0.00003310414,"about_ca_system_score_gemma":0.00033951178,"threshold_uncertainty_score":0.3989087},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012398182,0.00009430972,0.9965853,0.00023947215,0.00044238413,0.00010294235,0.0000076257543,0.00005206172,0.0012360858],"genre_scores_gemma":[0.8402459,0.00013057607,0.15934359,0.000047624944,0.00015471494,0.000008090344,0.000005262607,0.0000310737,0.000033152242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879736,0.00008560327,0.00052829465,0.000085493004,0.00037889587,0.00012436186],"domain_scores_gemma":[0.99904233,0.00025684646,0.0003334147,0.00012447513,0.0002005148,0.000042444597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006931947,0.00009969163,0.0001699738,0.00017118521,0.000117758216,0.00002596928,0.00013784342,0.000031806947,0.00015356587],"category_scores_gemma":[0.00040149028,0.000102612044,0.00007018128,0.00021941015,0.000028732715,0.0004613432,0.000023317536,0.00039335326,0.0000068361724],"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":[0.00020325666,0.00011072447,0.0000028928587,0.000028330061,0.00003740972,0.000011600545,0.00023565697,0.98484814,0.002684747,0.00048650708,0.00006155133,0.0112892],"study_design_scores_gemma":[0.000061027262,0.0009321519,0.0000124186445,0.00004801798,0.00009525433,0.0000630841,0.0009523005,0.9117729,0.073243394,0.0029299268,0.009735292,0.00015424233],"about_ca_topic_score_codex":0.0000044545795,"about_ca_topic_score_gemma":5.871566e-7,"teacher_disagreement_score":0.8390061,"about_ca_system_score_codex":0.00010911304,"about_ca_system_score_gemma":0.000042252843,"threshold_uncertainty_score":0.41843978},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76442456,0.00009784236,0.1546343,0.06430617,0.008440439,0.0009216009,0.000013746111,0.000021520695,0.0071397987],"genre_scores_gemma":[0.9968629,0.000047587535,0.0021748065,0.00033347154,0.00047535863,0.0000243562,0.0000015157569,0.000011624287,0.000068362155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953802,0.0010805939,0.0015345841,0.00019320006,0.0015579101,0.00025347024],"domain_scores_gemma":[0.99431455,0.0019084935,0.0023857702,0.00035298636,0.0009720213,0.00006618394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007171432,0.00014950901,0.00023171662,0.00016348185,0.0011022643,0.00008854186,0.0012906321,0.00007475845,0.00044039372],"category_scores_gemma":[0.0018621776,0.00009004927,0.00020592782,0.00070916273,0.00071276905,0.00039437963,0.0001432352,0.00060652435,0.0000056434637],"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":[0.0056246174,0.007215399,0.00090465613,0.000048155383,0.0003508316,0.0000069319885,0.084836766,0.04866263,0.022818603,0.4371155,0.0014346391,0.39098126],"study_design_scores_gemma":[0.00014659525,0.0018935978,0.0027568291,0.00021895353,0.0002452412,0.00006742488,0.32683554,0.023063065,0.2446108,0.3403455,0.059216518,0.0005999287],"about_ca_topic_score_codex":0.00042578886,"about_ca_topic_score_gemma":0.0010800802,"teacher_disagreement_score":0.39038134,"about_ca_system_score_codex":0.00026462498,"about_ca_system_score_gemma":0.0010268863,"threshold_uncertainty_score":0.8477837},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035873264,0.00013712555,0.9441561,0.019427592,0.00005514647,0.00027403573,0.000010777964,0.0000074475242,0.000058506987],"genre_scores_gemma":[0.9916167,0.00044325355,0.0075780735,0.00028505243,0.000050674524,0.000011815846,0.00000420179,0.0000040540513,0.0000061696296],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799323,0.0006169083,0.0006712546,0.00016036432,0.0004203718,0.0001378964],"domain_scores_gemma":[0.9944549,0.0036451065,0.0009257902,0.0002587753,0.0006612225,0.000054210916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008382655,0.00005870703,0.00015924087,0.00020013138,0.0001859883,0.000060341397,0.00029702482,0.00004204302,0.0000033508652],"category_scores_gemma":[0.0004397763,0.00004343384,0.000030194726,0.00073961355,0.00016727613,0.00058196636,0.00005332608,0.0002718833,0.0000137477555],"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":[0.00035183417,0.00069191435,0.00010560933,0.000079970334,0.00004875443,0.0000015156734,0.00065094617,0.0027275174,0.008433783,0.12509851,0.0005667172,0.86124295],"study_design_scores_gemma":[0.00026270188,0.0028909196,0.004208592,0.00029393952,0.00006753531,0.0000767908,0.012718957,0.64674324,0.020310786,0.30933344,0.002913399,0.00017971962],"about_ca_topic_score_codex":0.00009182996,"about_ca_topic_score_gemma":0.000031145853,"teacher_disagreement_score":0.95574343,"about_ca_system_score_codex":0.000033867014,"about_ca_system_score_gemma":0.00017224488,"threshold_uncertainty_score":0.29052776},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1464956,0.000049762395,0.84849006,0.002725399,0.0015028777,0.00010434594,0.000010841543,0.00009251353,0.0005286257],"genre_scores_gemma":[0.9980373,0.0002432814,0.0011536852,0.00016409578,0.0003194468,0.0000027279,0.0000348937,0.000021018197,0.000023573799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885255,0.00007283251,0.0005099607,0.000086252694,0.00031077644,0.00016761699],"domain_scores_gemma":[0.9987599,0.00047541613,0.00023304136,0.00008966335,0.0003542708,0.00008770854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022690411,0.00010666327,0.00013920879,0.00023081107,0.00014839457,0.00012924557,0.00009097838,0.00006889918,0.00007767247],"category_scores_gemma":[0.00037076662,0.00009956431,0.000069345806,0.00042638014,0.000030119987,0.0005849186,0.000007184391,0.00027455302,0.00013001137],"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":[0.00003978307,0.000057923207,0.000023121524,0.000013891961,0.00007961561,0.000068381705,0.0007221933,0.9490795,0.014052693,0.03062281,0.0012408728,0.003999204],"study_design_scores_gemma":[0.00012843218,0.00041235803,0.001159333,0.00020132044,0.00032548004,0.00029122733,0.020117681,0.8259661,0.09740866,0.0516185,0.0019273295,0.00044356985],"about_ca_topic_score_codex":0.000005818158,"about_ca_topic_score_gemma":0.000010353047,"teacher_disagreement_score":0.8515417,"about_ca_system_score_codex":0.000103916,"about_ca_system_score_gemma":0.00002970689,"threshold_uncertainty_score":0.4060115},"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 &amp; 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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63125074,0.000008673366,0.3670996,0.001172339,0.00018062955,0.00014450103,0.0000013379318,0.000017032338,0.00012515883],"genre_scores_gemma":[0.9677939,0.00029448842,0.03179708,0.00004130856,0.000038941795,7.96651e-7,0.0000010682944,0.000008653109,0.00002380975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979363,0.0005054006,0.0005589736,0.00024797104,0.0005240084,0.00022731541],"domain_scores_gemma":[0.99566615,0.0023928373,0.0011292702,0.0004902512,0.0002804315,0.00004105202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002795536,0.00010607269,0.00022638017,0.00030341162,0.00019187087,0.000037746016,0.0010115164,0.000102142265,0.000026301599],"category_scores_gemma":[0.0013407136,0.000096709526,0.000082026534,0.0005713944,0.00035005363,0.0007404622,0.00021929933,0.0006497134,0.00005258885],"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":[0.002546379,0.0010097751,0.000097545635,0.00003323148,0.000061310544,0.000056647044,0.0067696595,0.7524298,0.039139125,0.031840473,0.000048382542,0.16596769],"study_design_scores_gemma":[0.00026589277,0.006875461,0.00044627232,0.00009477267,0.000043195076,0.000017321841,0.02466282,0.4851124,0.45754093,0.021999542,0.002723679,0.00021773973],"about_ca_topic_score_codex":0.00023485841,"about_ca_topic_score_gemma":0.00005883635,"teacher_disagreement_score":0.41840178,"about_ca_system_score_codex":0.00012357629,"about_ca_system_score_gemma":0.00014030702,"threshold_uncertainty_score":0.39437002},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27910173,0.00007855151,0.71287274,0.0027101552,0.0030748053,0.00017603405,0.000011360421,0.00010101625,0.0018735698],"genre_scores_gemma":[0.99309087,0.00047107652,0.005934919,0.00018959859,0.000245078,0.0000022794277,0.000007640827,0.000009145393,0.000049391903],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99785185,0.00045340665,0.00085044437,0.00016212721,0.0005029078,0.00017928118],"domain_scores_gemma":[0.9954189,0.0013887914,0.0012101865,0.00018600452,0.001708881,0.00008722297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034679833,0.00009989226,0.00020096914,0.0004689283,0.00011085146,0.00014848392,0.00036758775,0.00007667393,0.000104739884],"category_scores_gemma":[0.0036434063,0.00009469289,0.000104483595,0.0013629053,0.00004268789,0.0027508277,0.000055069988,0.00032953452,0.0005866137],"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":[0.00025073288,0.00032572073,0.000060845283,0.000022081742,0.00007075313,0.000057532772,0.0010256058,0.00031048132,0.010901849,0.0013258752,0.0013059257,0.9843426],"study_design_scores_gemma":[0.00027115995,0.0025339287,0.013474815,0.00041016325,0.00022037269,0.0010489199,0.006514808,0.07958518,0.78105533,0.102738164,0.011313053,0.00083412346],"about_ca_topic_score_codex":0.00013644279,"about_ca_topic_score_gemma":0.00007499721,"teacher_disagreement_score":0.98350847,"about_ca_system_score_codex":0.00004470703,"about_ca_system_score_gemma":0.00015407108,"threshold_uncertainty_score":0.7539928},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005135128,0.00024124335,0.9922277,0.00023507359,0.0015945525,0.00023818685,0.000008125684,0.00013127434,0.00018871645],"genre_scores_gemma":[0.9965403,0.000060129936,0.0029599378,0.000053805477,0.00033035222,0.0000044246344,0.0000011299552,0.000030236948,0.000019694067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826133,0.00021215911,0.00077647006,0.000111452755,0.00042945213,0.00020913444],"domain_scores_gemma":[0.99787176,0.00097560324,0.00040940443,0.00018139806,0.00047576876,0.00008606568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001179621,0.00015390012,0.00031207706,0.00028198594,0.000067283036,0.00004876878,0.00017279248,0.00011326806,0.0000098765295],"category_scores_gemma":[0.0005253699,0.00013456013,0.00010971224,0.00048512302,0.000023608358,0.0004475444,0.0000074447016,0.0003617721,0.00009337091],"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":[0.00038351322,0.00006382504,8.5985516e-7,0.00007636374,0.00004777566,0.00010416357,0.00037788294,0.9124391,0.08363031,0.00022292098,0.00011868367,0.0025345958],"study_design_scores_gemma":[0.00009175147,0.00029256183,0.000007590442,0.00026040137,0.000052524603,0.000065753346,0.004843125,0.8947569,0.099248886,0.00010436203,0.00015080681,0.00012531888],"about_ca_topic_score_codex":0.000005408487,"about_ca_topic_score_gemma":9.218386e-7,"teacher_disagreement_score":0.9914052,"about_ca_system_score_codex":0.000107356485,"about_ca_system_score_gemma":0.00004010525,"threshold_uncertainty_score":0.5487203},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06985837,0.00020090194,0.4357813,0.46675175,0.00324382,0.0024179532,0.00018721967,0.0002380206,0.021320678],"genre_scores_gemma":[0.9902012,0.000097483135,0.0052517266,0.0024817493,0.0017740169,0.000026319096,0.00013123428,0.000026428514,0.000009889002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99667835,0.00017834036,0.0012593693,0.00042412287,0.0010792352,0.0003805545],"domain_scores_gemma":[0.9915758,0.004509567,0.0009751561,0.0018728023,0.001050188,0.000016523783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0131959645,0.00016281851,0.00025309555,0.0015569855,0.0003954838,0.00033021535,0.0036237268,0.00013501276,0.00004960771],"category_scores_gemma":[0.008106563,0.00012830875,0.00004830694,0.0067307493,0.000384301,0.0017737717,0.0006407908,0.0017143954,0.00048313735],"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":[0.00028558317,0.0011349324,0.00013915036,0.00003718582,0.000037745758,0.000078979145,0.00022038538,0.0041012857,0.00022367085,0.1508939,0.005227399,0.8376198],"study_design_scores_gemma":[0.00011435494,0.00053260225,0.00058560143,0.00072075747,0.00025782714,0.0000420153,0.122869216,0.60411185,0.004668424,0.13877188,0.12671801,0.0006074798],"about_ca_topic_score_codex":0.0004186512,"about_ca_topic_score_gemma":0.00037199547,"teacher_disagreement_score":0.9203428,"about_ca_system_score_codex":0.000037030444,"about_ca_system_score_gemma":0.00018802584,"threshold_uncertainty_score":0.9704899},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86570853,0.000048800786,0.12237651,0.0070749177,0.00051216804,0.00019213515,6.460961e-7,0.000098380944,0.0039878995],"genre_scores_gemma":[0.99482876,0.000019141597,0.0047394405,0.0001985396,0.00019101643,0.00000408476,0.0000043672494,0.000006915338,0.000007719718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989581,0.000007721153,0.0006215971,0.00008379723,0.00023199746,0.00009677856],"domain_scores_gemma":[0.9980989,0.00011130402,0.0010653622,0.00009343081,0.0006238889,0.0000071310265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010937522,0.00006670966,0.00013634768,0.00043853853,0.000121312085,0.00005216895,0.00011674035,0.000056558303,0.00000813882],"category_scores_gemma":[0.00037025794,0.00006198623,0.000024649922,0.0008515981,0.00007073778,0.0009174977,0.00005182466,0.00015849886,0.00008240628],"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":[0.00006025684,0.00010481248,0.0014837933,0.00012463953,0.000037254104,0.000030306592,0.00023041654,0.0002205427,0.0028885803,0.7866271,0.0001674624,0.2080248],"study_design_scores_gemma":[0.0003985031,0.00019111886,0.017648736,0.0013960506,0.0009305396,0.000828055,0.22577502,0.06422015,0.103675425,0.07861518,0.5051104,0.0012108246],"about_ca_topic_score_codex":0.00006199206,"about_ca_topic_score_gemma":0.000025805988,"teacher_disagreement_score":0.7080119,"about_ca_system_score_codex":0.000023789544,"about_ca_system_score_gemma":0.000029819133,"threshold_uncertainty_score":0.2527725},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08890315,0.000027493348,0.90800244,0.0025765686,0.00013412777,0.00016391308,6.12583e-7,0.00015276362,0.000038957656],"genre_scores_gemma":[0.55313575,0.000046893532,0.44567445,0.00083865324,0.00025555273,0.0000072912226,0.0000014029773,0.00001790661,0.000022063927],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978641,0.00019406373,0.0006389887,0.00033927424,0.0006647329,0.0002988232],"domain_scores_gemma":[0.99707437,0.00021205624,0.00073891465,0.0005401793,0.0012288246,0.00020567664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001084331,0.00017096155,0.00025535646,0.00051277236,0.00022795323,0.00036598544,0.00083951687,0.00007322101,0.000010667787],"category_scores_gemma":[0.00046824623,0.00013674714,0.00006327155,0.0027055424,0.000034974288,0.003566454,0.00015934311,0.00038497747,0.000041166528],"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":[0.0003623505,0.00041917944,0.00009319669,0.00000871648,0.0000747587,0.00017714304,0.0048554563,0.2786302,0.4168127,0.015532489,0.00024115462,0.28279266],"study_design_scores_gemma":[0.000033705408,0.0006037976,0.00004764417,0.000047203797,0.000070753784,0.00019126009,0.0019974315,0.7279571,0.24838099,0.014945974,0.00542383,0.00030028468],"about_ca_topic_score_codex":0.000050369745,"about_ca_topic_score_gemma":0.00003104513,"teacher_disagreement_score":0.46423262,"about_ca_system_score_codex":0.0001342427,"about_ca_system_score_gemma":0.00015158071,"threshold_uncertainty_score":0.55763865},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023103939,0.000027392973,0.9889298,0.0069529163,0.0033019874,0.0002637261,0.000004659527,0.000067307534,0.00022116998],"genre_scores_gemma":[0.0595489,0.00007889771,0.9370447,0.0014808449,0.0017821292,0.000017763561,0.0000055415157,0.000019994062,0.00002127085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971882,0.00096964673,0.00079369184,0.00031585433,0.00038754725,0.00034504852],"domain_scores_gemma":[0.9896737,0.007900182,0.0009855946,0.00029439302,0.0010149608,0.00013121689],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008789445,0.00017297716,0.0003040735,0.0003350387,0.00023801126,0.00033380973,0.00055992045,0.00012667048,0.000012574859],"category_scores_gemma":[0.008805704,0.0001525611,0.00023970504,0.0009861416,0.00003244577,0.0012233515,0.000047059875,0.00046846154,0.000032919528],"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":[0.0013921276,0.00016599255,0.0000041779317,0.000009722123,0.000062543055,0.000053243893,0.0004177285,0.11357359,0.0017591133,0.0022810462,0.00048464272,0.8797961],"study_design_scores_gemma":[0.00015736188,0.0011306818,0.00001885027,0.000037438265,0.000047375193,0.000037152357,0.00030286753,0.95306754,0.023149,0.018468183,0.0034044106,0.00017916322],"about_ca_topic_score_codex":0.000027252581,"about_ca_topic_score_gemma":0.000016408416,"teacher_disagreement_score":0.8796169,"about_ca_system_score_codex":0.000071356735,"about_ca_system_score_gemma":0.0002810642,"threshold_uncertainty_score":0.99954355},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016380161,0.00003104521,0.95295537,0.02968342,0.00029488388,0.00033553864,0.0000049929226,0.000040887655,0.00027371562],"genre_scores_gemma":[0.94889194,0.0002738175,0.050145924,0.00028063843,0.00033911434,0.000047109566,0.0000042783195,0.000011275062,0.000005901965],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969434,0.0004566816,0.0011296831,0.00032816813,0.00080135843,0.00034067084],"domain_scores_gemma":[0.9936011,0.0036003762,0.00074166217,0.00066619343,0.0013241221,0.000066533205],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0068567023,0.00012375915,0.00021498452,0.0012435666,0.00021881486,0.00014735141,0.0016829991,0.00014276245,0.00001977053],"category_scores_gemma":[0.0018806666,0.0000942488,0.00007345322,0.005789768,0.00028317643,0.00087879173,0.0003077615,0.0010095387,0.0008107836],"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":[0.00006638152,0.00028559152,0.0000071373215,0.0000054833067,0.000010882477,0.000014819706,0.00046788456,0.0017440863,0.0024062209,0.2932752,0.00023470602,0.7014816],"study_design_scores_gemma":[0.000017047472,0.00069142465,0.00008118953,0.00010897932,0.000014875217,0.00009189058,0.0049450803,0.32100588,0.10066715,0.56800926,0.004194567,0.00017266249],"about_ca_topic_score_codex":0.000057000143,"about_ca_topic_score_gemma":0.00004607753,"teacher_disagreement_score":0.9325118,"about_ca_system_score_codex":0.0000886718,"about_ca_system_score_gemma":0.00019054078,"threshold_uncertainty_score":0.9999672},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07741496,0.000034703764,0.9144266,0.0074276635,0.00037347627,0.00010760054,9.239028e-7,0.000016539103,0.00019749325],"genre_scores_gemma":[0.99235517,0.00008614895,0.0072820717,0.000079197576,0.00016715423,0.0000015559011,2.5606272e-7,0.000004265719,0.000024161804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848545,0.00017288019,0.00058945187,0.00009349763,0.0005167998,0.00014193132],"domain_scores_gemma":[0.9970119,0.0009914903,0.0010662561,0.00016688554,0.00072652724,0.00003696657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024847623,0.000051714236,0.00017253715,0.00028674983,0.0003459186,0.000029426572,0.00041212444,0.000051842748,0.0000025611935],"category_scores_gemma":[0.0021016577,0.000034499037,0.00010874668,0.0038567132,0.00019325496,0.00017532583,0.00005471094,0.00015890584,0.0000023991188],"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":[0.000026738277,0.00003054952,0.0001308888,0.0000011577757,0.0000830263,0.0000010451129,0.013683931,0.20076276,0.00075130485,0.029222518,0.000012859758,0.7552932],"study_design_scores_gemma":[0.000031823925,0.00018928612,0.001485248,0.000081471764,0.00069085154,0.000009531404,0.103482984,0.7704328,0.00952282,0.09228975,0.021604124,0.00017932252],"about_ca_topic_score_codex":0.000065649125,"about_ca_topic_score_gemma":0.00011656494,"teacher_disagreement_score":0.91494024,"about_ca_system_score_codex":0.0000337797,"about_ca_system_score_gemma":0.00012521844,"threshold_uncertainty_score":0.26605612},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0192856,0.000041632615,0.9681039,0.011836497,0.0004688034,0.00015960394,7.679448e-7,0.0000657248,0.000037511898],"genre_scores_gemma":[0.96264446,0.000035842935,0.037225228,0.000022746473,0.000058755446,0.000004312645,5.536898e-7,0.0000053077288,0.0000027869953],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977969,0.0002501634,0.0012310087,0.00017904644,0.00038687757,0.00015601495],"domain_scores_gemma":[0.99685335,0.00058648724,0.0015964507,0.00034494276,0.00058268185,0.00003608006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002209873,0.00009040007,0.00031652578,0.0009399984,0.00004150547,0.00002406993,0.00058823585,0.0002129463,0.0000010945229],"category_scores_gemma":[0.0012980538,0.000067287525,0.00006275391,0.00231822,0.000114029564,0.0010128556,0.0001616855,0.00035996106,0.000027469345],"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":[0.00012177363,0.00037697755,0.00065108447,0.0001039284,0.000027640177,0.000076104574,0.0022833424,0.003876832,0.013801081,0.8265448,0.000076537195,0.15205988],"study_design_scores_gemma":[0.00009878707,0.0008141973,0.00025161036,0.00035893742,0.00003561213,0.00069441687,0.008478564,0.8472602,0.103986315,0.036576577,0.0012768889,0.00016792775],"about_ca_topic_score_codex":0.000023174678,"about_ca_topic_score_gemma":0.000010196391,"teacher_disagreement_score":0.94335884,"about_ca_system_score_codex":0.00005893611,"about_ca_system_score_gemma":0.000115407995,"threshold_uncertainty_score":0.27439058},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00595457,0.000145147,0.9900028,0.0028870231,0.0006311196,0.00027847456,0.00002542607,0.000048096972,0.000027335582],"genre_scores_gemma":[0.96418804,0.000057008907,0.03509588,0.00017927257,0.00044038653,0.000017132328,0.000006071063,0.0000070555534,0.0000091480815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998942,0.00008822725,0.00034443516,0.00024058121,0.00022903095,0.00015570999],"domain_scores_gemma":[0.99788755,0.0010268539,0.00036947918,0.00042164195,0.00020785118,0.000086622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014639782,0.00008217055,0.00012104834,0.000089472815,0.000242115,0.00029148432,0.0005611046,0.00004870296,9.884116e-7],"category_scores_gemma":[0.00054339395,0.000069318,0.000023350345,0.00034767002,0.00003561681,0.0013346907,0.00018457005,0.00018246725,0.000007961986],"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":[0.00035689826,0.00025979616,0.00018834692,0.000107394284,0.000060034217,0.00005783232,0.00029695532,0.017095558,0.0028045322,0.015772793,0.0017456143,0.96125424],"study_design_scores_gemma":[0.000042824042,0.0003345114,0.00061442837,0.000040145293,0.00016561466,0.00015973137,0.000268488,0.9831245,0.002068871,0.00042055422,0.012663246,0.000097075506],"about_ca_topic_score_codex":0.000014018409,"about_ca_topic_score_gemma":0.00001624612,"teacher_disagreement_score":0.9660289,"about_ca_system_score_codex":0.000013155115,"about_ca_system_score_gemma":0.000044044387,"threshold_uncertainty_score":0.2826706},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07057234,0.000036942005,0.92797583,0.00056747894,0.0002892446,0.0001988185,0.0000015315994,0.00009760851,0.0002602286],"genre_scores_gemma":[0.97753817,0.000029393055,0.022157019,0.00010496882,0.00012377316,0.000012757313,0.000003276801,0.000017905651,0.000012736048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991446,0.000012631955,0.00045689277,0.000085467116,0.00017070617,0.00012971125],"domain_scores_gemma":[0.9981392,0.0013266528,0.00015511508,0.000082955616,0.00022710222,0.000068943256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000707057,0.00008482714,0.00010879351,0.00021309494,0.00012423006,0.00007327812,0.00006839149,0.000051540097,0.000011303505],"category_scores_gemma":[0.0006503966,0.00008395544,0.000039834096,0.00020179074,0.000014669299,0.00029541,0.0000076135298,0.00014378851,0.000025088882],"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":[0.00004248386,0.000021566495,0.0000055517917,0.000009293001,0.000013993524,0.0000012193864,0.0006452924,0.7976684,0.00052663387,0.0004218899,0.000038185175,0.2006055],"study_design_scores_gemma":[0.00004685694,0.000060974005,0.00006869373,0.000028431454,0.000021969669,0.0000038185162,0.0004987251,0.96964085,0.006813717,0.0006569942,0.022071356,0.00008763202],"about_ca_topic_score_codex":0.0000014183144,"about_ca_topic_score_gemma":0.0000033069161,"teacher_disagreement_score":0.90696585,"about_ca_system_score_codex":0.00008101189,"about_ca_system_score_gemma":0.00007128459,"threshold_uncertainty_score":0.34236038},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05642817,0.0001581717,0.9228464,0.01727329,0.0011473771,0.0020474622,0.000025953681,0.00004770353,0.0000254834],"genre_scores_gemma":[0.94444966,0.00050372386,0.053573303,0.000841493,0.00045633537,0.000088110435,0.000027742017,0.000029734325,0.000029907866],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99435467,0.00089401857,0.0033719295,0.00021052087,0.00063674606,0.0005320988],"domain_scores_gemma":[0.9891913,0.003076473,0.004359209,0.0003164721,0.0027696495,0.0002868812],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0092306575,0.00020591781,0.00057815685,0.0014071804,0.0004845183,0.000032028864,0.0003306801,0.00031551303,0.000091523965],"category_scores_gemma":[0.012565681,0.000186628,0.000113600305,0.0011969131,0.00013869433,0.0020124144,0.000041954634,0.00064646406,0.000037661463],"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":[0.004322857,0.00070627284,0.0006999598,0.00081844634,0.00016020452,0.000004262911,0.020962935,0.062351502,0.009011475,0.047601435,0.0012698553,0.8520908],"study_design_scores_gemma":[0.00035216735,0.010967119,0.0000908775,0.00061371876,0.00012735136,0.00001795385,0.07880417,0.811137,0.050365902,0.0423674,0.0048529683,0.00030334617],"about_ca_topic_score_codex":0.0003769281,"about_ca_topic_score_gemma":0.0003043939,"teacher_disagreement_score":0.88802147,"about_ca_system_score_codex":0.0002513867,"about_ca_system_score_gemma":0.0027166319,"threshold_uncertainty_score":0.9957519},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00050321204,0.000037758207,0.9945736,0.0021042277,0.0015254773,0.000116989984,0.0000024891608,0.000079885736,0.0010563844],"genre_scores_gemma":[0.14722718,0.0002253325,0.8515722,0.0005124514,0.0003686418,0.0000026920245,0.000007417059,0.000030167676,0.000053869466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969389,0.00038516574,0.0010703916,0.00028730067,0.0010002864,0.00031795018],"domain_scores_gemma":[0.9949328,0.0018483715,0.0014918179,0.00044952697,0.0011510346,0.00012648973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019469132,0.00018444122,0.00030039818,0.0005808995,0.00010268211,0.00021698742,0.0008073777,0.00012766218,0.000052562613],"category_scores_gemma":[0.0019350601,0.00017601161,0.00013031291,0.002046946,0.00007338479,0.0016391531,0.000113509755,0.00038738063,0.00003647137],"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":[0.00011700565,0.0002750664,0.0000028202323,0.00000792519,0.00002168746,0.000076495504,0.00021477739,0.92496634,0.00025842324,0.0020849626,0.00013518943,0.07183933],"study_design_scores_gemma":[0.00007179445,0.0005069599,0.000002902328,0.000090512825,0.00002944615,0.00003689635,0.00012458932,0.986922,0.011513241,0.0002103805,0.00033373327,0.00015751563],"about_ca_topic_score_codex":0.000011952706,"about_ca_topic_score_gemma":6.8726456e-7,"teacher_disagreement_score":0.14672397,"about_ca_system_score_codex":0.00009286441,"about_ca_system_score_gemma":0.00021071054,"threshold_uncertainty_score":0.71775454},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4188562,0.000290333,0.5642839,0.0120024765,0.001450587,0.00077444635,0.00014241676,0.00018190812,0.0020177092],"genre_scores_gemma":[0.9710297,0.00024143573,0.027800895,0.00031330733,0.00040906935,0.000038064445,0.000015743413,0.00004937107,0.00010243927],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973915,0.00034268814,0.0010472434,0.00029596474,0.00049784634,0.0004247141],"domain_scores_gemma":[0.9954858,0.002379107,0.00068143674,0.00013522741,0.0011877249,0.0001307345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030601334,0.00023322855,0.0003941208,0.00022423858,0.0004119804,0.00012467978,0.0002649765,0.00012785745,0.00007820709],"category_scores_gemma":[0.0053630113,0.00021667256,0.00025639473,0.00054656446,0.00006954722,0.0011584476,0.00009228653,0.0005043807,0.00016491284],"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":[0.0051515494,0.000904319,0.00031984362,0.00013931115,0.0004513377,0.0007007597,0.011483663,0.00018188081,0.264252,0.0010897216,0.0137834605,0.70154214],"study_design_scores_gemma":[0.000767793,0.024523074,0.006913078,0.000785529,0.004400921,0.0033204192,0.38487473,0.034377865,0.44313613,0.043923855,0.049605627,0.0033709828],"about_ca_topic_score_codex":0.00012441674,"about_ca_topic_score_gemma":0.000013439337,"teacher_disagreement_score":0.69817114,"about_ca_system_score_codex":0.00010879534,"about_ca_system_score_gemma":0.00006342423,"threshold_uncertainty_score":0.88356507},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038465152,0.003801738,0.9234602,0.059079543,0.0034240964,0.00064570765,0.0000072548182,0.00015340523,0.005581529],"genre_scores_gemma":[0.84306914,0.032736924,0.107428245,0.015471409,0.0010939066,0.00005840535,0.0000065557642,0.000082817925,0.000052602187],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950685,0.0004306728,0.0026836223,0.00040096923,0.0009406999,0.00047553182],"domain_scores_gemma":[0.99133784,0.0035928274,0.0021358996,0.00078219676,0.0019886978,0.00016257001],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005386178,0.00027715045,0.00063768704,0.00074301293,0.0001274775,0.00018902341,0.002206704,0.00013000502,0.00015203652],"category_scores_gemma":[0.0046800296,0.00025690612,0.0002042747,0.0052216435,0.00012709119,0.0027359633,0.000428753,0.00094878394,0.00073583215],"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":[0.00029968415,0.0006379049,0.000012436119,0.0011213556,0.000096429336,0.00016708246,0.0062367353,0.005360646,0.0050011408,0.6592685,0.00064191077,0.32115617],"study_design_scores_gemma":[0.0000538486,0.00064585736,0.00007338177,0.008270005,0.00020530292,0.0007611997,0.02521648,0.049387272,0.1385378,0.7554177,0.020541336,0.00088981166],"about_ca_topic_score_codex":0.0001361989,"about_ca_topic_score_gemma":0.00014596833,"teacher_disagreement_score":0.8392226,"about_ca_system_score_codex":0.00013442998,"about_ca_system_score_gemma":0.00044650974,"threshold_uncertainty_score":0.9999883},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023487268,0.000056523662,0.98756284,0.009349352,0.0002755871,0.00032270764,9.865275e-7,0.000013289523,0.00007001662],"genre_scores_gemma":[0.307321,0.0002578435,0.69175684,0.00032634375,0.00020536927,0.000054362816,0.000001983482,0.000008082254,0.00006812582],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99885297,0.00009894574,0.000613228,0.00012711078,0.00019494098,0.00011282866],"domain_scores_gemma":[0.9974299,0.0008929656,0.0007027917,0.00017666946,0.0007349072,0.000062785606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018405708,0.000060834533,0.000110716195,0.00018126318,0.000088140245,0.000088206325,0.00027823055,0.00002848423,0.000013075724],"category_scores_gemma":[0.0006419723,0.00005734419,0.000043474756,0.00044922126,0.000020777792,0.000987601,0.000042346353,0.00007575202,0.000009624898],"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":[0.00006227808,0.00016167184,0.000023183436,0.000020296846,0.000029496883,0.000002395477,0.0017525995,0.004183077,0.05260904,0.0737041,0.0071833795,0.8602685],"study_design_scores_gemma":[0.0001386846,0.001120185,0.00042591893,0.00007010688,0.00015137169,0.00017730633,0.014361346,0.15106076,0.65127224,0.14717555,0.033778798,0.0002677348],"about_ca_topic_score_codex":0.000041813822,"about_ca_topic_score_gemma":0.000006792141,"teacher_disagreement_score":0.8600007,"about_ca_system_score_codex":0.000027501681,"about_ca_system_score_gemma":0.00040105564,"threshold_uncertainty_score":0.23384282},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000069773528,0.9844905,0.013056929,0.00011725474,0.00079767796,0.0011502713,0.000015922997,0.000052172734,0.00031229883],"genre_scores_gemma":[0.00026922606,0.9977272,0.0011470295,0.000016461492,0.00069246354,0.00007228415,0.0000028234333,0.00006876034,0.0000037331472],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99496776,0.00048711197,0.0034433182,0.0003135667,0.00049201556,0.00029625543],"domain_scores_gemma":[0.99293715,0.003895907,0.0020291412,0.00034831295,0.00065070216,0.00013880896],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025151516,0.00041301455,0.0020666954,0.0007418632,0.00006996606,0.00008184454,0.0003702334,0.00023823329,0.000012661292],"category_scores_gemma":[0.0035007976,0.0003650933,0.0003430714,0.0014067222,0.000102706654,0.0003701953,0.000076604585,0.0013694139,0.00009557355],"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":[0.000017720684,0.00007002299,3.2779035e-8,0.025432695,0.0001192678,0.00013297415,0.00010161105,0.00042491214,0.0000133099575,0.0012152753,0.000035317094,0.97243685],"study_design_scores_gemma":[0.000016567654,0.0003421377,3.5288517e-7,0.06988478,0.0009076899,0.001575835,0.0013096674,0.0010522346,0.00024936302,0.0034899742,0.92066675,0.00050462864],"about_ca_topic_score_codex":0.000039759554,"about_ca_topic_score_gemma":0.000017201071,"teacher_disagreement_score":0.97193223,"about_ca_system_score_codex":0.00013765878,"about_ca_system_score_gemma":0.00019383115,"threshold_uncertainty_score":0.9998801},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.965193,0.0013631836,0.030488774,0.00043733226,0.00019493088,0.00031977321,0.000008656705,0.00008585737,0.0019085198],"genre_scores_gemma":[0.9953409,0.0015181104,0.0030091756,0.0000047188582,0.00005307785,0.0000052087457,0.0000036565143,0.000022139891,0.000043035314],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772286,0.00018900105,0.0013311305,0.00014109223,0.00041853308,0.00019738106],"domain_scores_gemma":[0.99660116,0.0013956396,0.001063232,0.00038676694,0.00047960415,0.00007358441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003079737,0.00014773566,0.00057076936,0.00094518875,0.000050659037,0.00004574924,0.00034193957,0.000100976715,0.000021903808],"category_scores_gemma":[0.00074230833,0.00015406846,0.00010895217,0.0024709988,0.00007008873,0.0006765992,0.00006765914,0.00032272516,0.0001082194],"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":[0.00043573504,0.00015861043,0.008449461,0.0010547225,0.0013879498,0.000019135445,0.007907242,0.21225998,0.7422257,0.005525202,0.000090008885,0.020486249],"study_design_scores_gemma":[0.00004200436,0.00006227034,0.0011882725,0.00035767967,0.00028612083,0.00003489236,0.0035645883,0.9187129,0.07503455,0.000018852446,0.00055885385,0.00013899371],"about_ca_topic_score_codex":0.000056335353,"about_ca_topic_score_gemma":0.000008462721,"teacher_disagreement_score":0.70645297,"about_ca_system_score_codex":0.00014810868,"about_ca_system_score_gemma":0.000040792274,"threshold_uncertainty_score":0.62827295},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37402818,0.000047883776,0.36358595,0.25407615,0.0013982705,0.00077566854,0.0000048390816,0.00023305212,0.0058500087],"genre_scores_gemma":[0.9950097,0.00006449995,0.0027886573,0.0010773999,0.0009989887,0.000024959147,0.000006394343,0.000018875931,0.000010522167],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99775696,0.00008402636,0.0010395097,0.00021379195,0.0006001193,0.00030559787],"domain_scores_gemma":[0.9940444,0.00167957,0.0011007483,0.00036862108,0.002795249,0.000011360206],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005213052,0.00013054346,0.00021425002,0.0020509502,0.00039098962,0.00015578493,0.0007232973,0.00019326023,0.00009639084],"category_scores_gemma":[0.0035345873,0.00010084142,0.0000862463,0.0062557585,0.0005614241,0.000993621,0.0001978385,0.0011565648,0.002122689],"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":[0.00011679217,0.00023046664,0.00004287445,0.000014385676,0.000032288244,0.000012164512,0.0000666491,0.0005496213,0.0018506524,0.5632405,0.0011874075,0.43265617],"study_design_scores_gemma":[0.000018721554,0.00024136655,0.00007222033,0.0001255807,0.00007229937,0.000034676093,0.02028413,0.024776649,0.050147567,0.88641346,0.017639639,0.00017371937],"about_ca_topic_score_codex":0.00012573079,"about_ca_topic_score_gemma":0.00005382955,"teacher_disagreement_score":0.6209815,"about_ca_system_score_codex":0.000042152722,"about_ca_system_score_gemma":0.000059574355,"threshold_uncertainty_score":0.99865425},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004721302,0.00008611411,0.9602338,0.03316751,0.00074872375,0.00032898295,0.0000018879321,0.0003103939,0.0004012905],"genre_scores_gemma":[0.83529675,0.00032064586,0.16360125,0.0004098698,0.00030979555,0.00003081785,0.0000014442804,0.000019271838,0.0000101788555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670416,0.00032161304,0.000986444,0.00054023816,0.0009183658,0.00052917795],"domain_scores_gemma":[0.99383,0.002999214,0.0006856417,0.00063512044,0.0017311965,0.00011881895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004254361,0.00020186543,0.00028069402,0.0024188582,0.0003880161,0.00024546133,0.0014925497,0.0003548791,0.000014621437],"category_scores_gemma":[0.005626955,0.00019318063,0.000072620955,0.005147181,0.00034154215,0.0013270841,0.00048900914,0.0017468089,0.0006051203],"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":[0.000104385144,0.0001641117,0.0000025304034,0.00001047655,0.000012062445,0.00009458041,0.00018052995,0.0074805785,0.0018765784,0.2697269,0.00007929382,0.72026795],"study_design_scores_gemma":[0.000014816565,0.0009394741,0.0000048919287,0.00006272055,0.000009858178,0.00012462634,0.0009525811,0.47010845,0.09223284,0.43352032,0.0018728903,0.0001565372],"about_ca_topic_score_codex":0.000005899797,"about_ca_topic_score_gemma":0.0000056657473,"teacher_disagreement_score":0.8305754,"about_ca_system_score_codex":0.00014538057,"about_ca_system_score_gemma":0.00017874209,"threshold_uncertainty_score":0.78776777},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12952742,0.00029880408,0.86419797,0.0028149458,0.00036113092,0.00033079478,0.0000028255947,0.00020731823,0.002258807],"genre_scores_gemma":[0.95858926,0.0006369518,0.040645327,0.000024717512,0.000074701566,0.0000048894367,0.0000013626097,0.000013945689,0.0000088205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976784,0.0003489567,0.00069448596,0.00030372787,0.0006164292,0.00035799362],"domain_scores_gemma":[0.99715763,0.0008409828,0.0005263723,0.0010226876,0.00035893358,0.000093375376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029869718,0.00018491643,0.00034427433,0.00029355107,0.00019450883,0.00039163022,0.0036076522,0.00010047504,0.000002576555],"category_scores_gemma":[0.004584841,0.0001356391,0.00007562833,0.0014053261,0.00010522105,0.001360175,0.0025419623,0.0007231437,0.000013211527],"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":[0.0022345025,0.00095870765,0.0010521075,0.00046592313,0.0015836223,0.0011150526,0.002319595,0.8098292,0.00075034343,0.07794958,0.012207337,0.089534074],"study_design_scores_gemma":[0.00015299683,0.0024006227,0.00042919593,0.00050613435,0.00012577612,0.0007606989,0.0047159465,0.93680114,0.0025168613,0.050731406,0.0004877367,0.00037149797],"about_ca_topic_score_codex":0.00005956213,"about_ca_topic_score_gemma":0.000002405733,"teacher_disagreement_score":0.82906187,"about_ca_system_score_codex":0.00008153291,"about_ca_system_score_gemma":0.00008895541,"threshold_uncertainty_score":0.6703975},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003390187,0.00004283651,0.9863479,0.008540043,0.0014333398,0.00013970055,4.4551365e-7,0.000047565107,0.000057957113],"genre_scores_gemma":[0.02841518,0.000042333082,0.9707158,0.00032832922,0.00041369096,0.000008484133,6.7991846e-7,0.0000075830917,0.000067916844],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984222,0.00006706497,0.0004901017,0.00022286146,0.00053644186,0.00026130048],"domain_scores_gemma":[0.9972571,0.0013412432,0.0003957285,0.0002324291,0.00066774205,0.000105742874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022214353,0.00011418946,0.00016315815,0.00025741712,0.00014346372,0.00016617475,0.0005606354,0.000032594893,0.0000055994933],"category_scores_gemma":[0.00083979155,0.000087563334,0.00008031507,0.00085570983,0.000020099882,0.0021538446,0.00011812373,0.00017902082,0.00003865355],"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":[0.0002409675,0.00025217407,0.000007699773,0.000027985692,0.00009090627,0.00010059602,0.006437383,0.12904985,0.021725371,0.15931125,0.0013534278,0.6814024],"study_design_scores_gemma":[0.00005346747,0.0006474355,0.000031748204,0.000047302743,0.000019900739,0.00021191685,0.0003746325,0.9092265,0.05928538,0.0076165586,0.022339664,0.00014549764],"about_ca_topic_score_codex":0.000006279742,"about_ca_topic_score_gemma":0.000001214046,"teacher_disagreement_score":0.78017664,"about_ca_system_score_codex":0.000057441877,"about_ca_system_score_gemma":0.00016521208,"threshold_uncertainty_score":0.3570729},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97762996,0.00075505825,0.0032119378,0.013225635,0.00044518762,0.00042925298,0.0000043529626,0.000005939805,0.0042926497],"genre_scores_gemma":[0.99864686,0.0008754577,0.0003262632,0.00003711092,0.00007649317,0.0000029977373,6.1944476e-8,0.0000033094755,0.00003147973],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99790776,0.00045033378,0.0008077016,0.00009498404,0.0005886537,0.00015055791],"domain_scores_gemma":[0.99569374,0.0022240048,0.0012311497,0.00017956765,0.00062824955,0.00004327774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059823575,0.000058323778,0.00017805905,0.000047098903,0.00022595536,0.000035681358,0.0004465507,0.000047361413,0.0000065537583],"category_scores_gemma":[0.0046289074,0.000038215352,0.00007015226,0.0006273026,0.0009511552,0.00052394083,0.00006613671,0.00022780517,0.0000027516794],"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":[0.00059138174,0.00018939812,0.0010436451,0.00009675346,0.00002024187,0.0000037552506,0.011968923,0.00078571774,0.024286332,0.9174473,0.000042606604,0.04352393],"study_design_scores_gemma":[0.00011914143,0.0007021463,0.015731351,0.00067847804,0.00036435953,0.000033614488,0.14177394,0.0018526048,0.5914474,0.10324661,0.14373481,0.00031553162],"about_ca_topic_score_codex":0.0053508156,"about_ca_topic_score_gemma":0.005093602,"teacher_disagreement_score":0.8142007,"about_ca_system_score_codex":0.000039133065,"about_ca_system_score_gemma":0.00026922158,"threshold_uncertainty_score":0.8088868},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.982424,0.00015939704,0.009525141,0.004237459,0.0018064049,0.0004155973,0.000028604303,0.000018309158,0.001385086],"genre_scores_gemma":[0.9968068,0.000051763873,0.0022367006,0.00039328047,0.0003944732,0.0000040353957,0.000001965623,0.000012236787,0.00009875991],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9980415,0.00032838935,0.00089498487,0.00013388405,0.00039330262,0.00020791824],"domain_scores_gemma":[0.9973474,0.0007267721,0.0010384967,0.00023417834,0.0006120739,0.000041061678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033779112,0.00011439848,0.00028461165,0.00023342654,0.00039683355,0.0002743041,0.00052272447,0.00003959832,0.00021859964],"category_scores_gemma":[0.00042242155,0.00006163834,0.0002727582,0.000609685,0.00008709495,0.0012071918,0.000054136362,0.0001652802,0.000003681237],"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":[0.0013328523,0.001050941,0.0017953137,0.00024860314,0.0031823795,0.00004081227,0.44288048,0.096865304,0.084462605,0.2948252,0.00036532772,0.07295019],"study_design_scores_gemma":[0.0000972626,0.00038343546,0.00081679644,0.00026079072,0.002854154,0.000017754977,0.53761816,0.3683007,0.01806232,0.009083109,0.06219413,0.0003113904],"about_ca_topic_score_codex":0.0014849106,"about_ca_topic_score_gemma":0.005788239,"teacher_disagreement_score":0.28574207,"about_ca_system_score_codex":0.000047975333,"about_ca_system_score_gemma":0.000049026603,"threshold_uncertainty_score":0.32299733},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046792496,0.000024729408,0.97813815,0.015654158,0.0006360053,0.00019656973,0.000003962841,0.0003223027,0.00034490216],"genre_scores_gemma":[0.80775964,0.00048218595,0.19120495,0.00019903349,0.00028689564,0.000017202614,0.000002035704,0.000016809046,0.000031269086],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975026,0.00015413856,0.001200316,0.00031016118,0.0005711649,0.0002615784],"domain_scores_gemma":[0.99662995,0.0008013975,0.0012484504,0.00046659613,0.0007788826,0.000074701165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016014476,0.00017472873,0.00028088366,0.0006676502,0.00020256489,0.00017089509,0.0009836073,0.00015242395,0.000023819348],"category_scores_gemma":[0.0011235229,0.00012607875,0.0001823415,0.001923859,0.00016009131,0.0012036593,0.00023175443,0.00055026804,0.00033778936],"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":[0.000091992515,0.0002740502,0.000002430208,0.00000954189,0.000020385673,0.000023425566,0.0004985706,0.0012290695,0.0044297706,0.08425509,0.0003462578,0.90881944],"study_design_scores_gemma":[0.000013320752,0.0013169757,0.00002125391,0.0001502601,0.00003205249,0.0001350791,0.0031954595,0.07101809,0.5637335,0.35492828,0.005217674,0.00023800782],"about_ca_topic_score_codex":0.0000123755135,"about_ca_topic_score_gemma":0.0000035575388,"teacher_disagreement_score":0.9085814,"about_ca_system_score_codex":0.000063599124,"about_ca_system_score_gemma":0.00009256512,"threshold_uncertainty_score":0.5141342},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55541676,0.000005137212,0.4420887,0.0019059742,0.00012881547,0.0002237927,0.000003927625,0.000018648447,0.00020824689],"genre_scores_gemma":[0.9678893,0.000027753948,0.031873927,0.00016101715,0.000030815696,0.0000042689367,0.0000010087814,0.0000051727293,0.000006733789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983904,0.00019774628,0.0006240058,0.00017172827,0.0005016898,0.000114475486],"domain_scores_gemma":[0.99715596,0.0013544783,0.0007948487,0.00024747502,0.00040219404,0.000045047946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000958941,0.00008986733,0.0001986525,0.0004273629,0.000049619754,0.000028535185,0.00051371776,0.00003498774,0.000005431258],"category_scores_gemma":[0.0005416172,0.00007946209,0.000053159358,0.0011257153,0.000060520964,0.00066767464,0.00010065956,0.00026070437,0.000017185372],"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":[0.0015255417,0.0070477375,0.0027754272,0.00004599575,0.000071333,0.00025766046,0.009406125,0.68457454,0.006049488,0.16065936,0.0005998204,0.12698698],"study_design_scores_gemma":[0.00054216466,0.007813245,0.036895685,0.00059831445,0.00005611076,0.000015103978,0.0072403685,0.57658505,0.17518924,0.19289051,0.0017302976,0.00044387765],"about_ca_topic_score_codex":0.00004325832,"about_ca_topic_score_gemma":0.0000034574348,"teacher_disagreement_score":0.41247255,"about_ca_system_score_codex":0.00004631787,"about_ca_system_score_gemma":0.000086485714,"threshold_uncertainty_score":0.324037},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7123527,0.00013479732,0.28488615,0.0001256801,0.0019207423,0.0002295422,0.0000011208921,0.00011741919,0.00023185468],"genre_scores_gemma":[0.9976874,0.0002455297,0.0017567037,0.000007124094,0.00023461535,0.000003405227,1.3393944e-7,0.000025242854,0.000039834988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792296,0.00036963334,0.0009801507,0.000100095895,0.00043891498,0.00018825775],"domain_scores_gemma":[0.9976541,0.0011430818,0.0005957747,0.000166811,0.00035735228,0.000082873805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024226145,0.0001235118,0.00027610952,0.00023825868,0.000103511986,0.000060486287,0.00020990011,0.00007018861,0.000036081194],"category_scores_gemma":[0.0017393129,0.000114231254,0.00008904546,0.0007923593,0.000021136173,0.0006026762,0.000021325337,0.0005418999,0.00019356028],"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":[0.0002568121,0.00018500072,0.0001928765,0.000021476546,0.00020314197,0.00010649205,0.01448128,0.87345326,0.006651741,0.000018517443,0.00023936405,0.10419007],"study_design_scores_gemma":[0.00019787898,0.0034011856,0.0031357112,0.00016874932,0.00032809176,0.0002819921,0.07897019,0.8633679,0.045784287,0.000571905,0.0033625811,0.00042952536],"about_ca_topic_score_codex":0.00017209118,"about_ca_topic_score_gemma":0.000017862627,"teacher_disagreement_score":0.2853347,"about_ca_system_score_codex":0.000049322975,"about_ca_system_score_gemma":0.000030849995,"threshold_uncertainty_score":0.46582153},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6860281,0.00010783832,0.30576736,0.0015311409,0.0029454082,0.00017849471,0.0000027986448,0.0005423484,0.0028965112],"genre_scores_gemma":[0.9968164,0.00023918255,0.002113289,0.00014994445,0.0006117616,0.000008122602,0.0000043563264,0.000030703144,0.000026247762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838674,0.00010764302,0.0006999464,0.00016063756,0.00031764692,0.00032735956],"domain_scores_gemma":[0.9982691,0.0009725051,0.00021951865,0.00016708924,0.0002804579,0.000091324095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014039028,0.00016190375,0.0002240358,0.00028890942,0.0001756247,0.000052999963,0.00021061086,0.00017076201,0.00015041223],"category_scores_gemma":[0.0008610579,0.00016313107,0.000113268135,0.00066671614,0.0000695301,0.0005400228,0.000028454066,0.0008512003,0.00038376],"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":[0.00025916452,0.00011325178,0.00019020849,0.000016169235,0.00009007895,0.00014786456,0.00021344064,0.4885991,0.016927006,0.00012840907,0.00039888226,0.49291644],"study_design_scores_gemma":[0.00008903637,0.00063781603,0.0032336253,0.00038821442,0.00013906912,0.00015815222,0.0013118524,0.60600597,0.3823371,0.0044231857,0.00081255496,0.00046340364],"about_ca_topic_score_codex":0.0000028152972,"about_ca_topic_score_gemma":0.000019731806,"teacher_disagreement_score":0.49245304,"about_ca_system_score_codex":0.00016521852,"about_ca_system_score_gemma":0.00006279458,"threshold_uncertainty_score":0.6652292},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105788596,0.00029858874,0.8814704,0.009922867,0.0016265317,0.00026127353,0.0000034328623,0.00006078032,0.0005675737],"genre_scores_gemma":[0.9577795,0.00035865442,0.041386224,0.00021517946,0.00023584458,0.000005810398,0.0000021532976,0.000008268374,0.000008340426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977038,0.00036952316,0.00076013996,0.0002561126,0.0007303823,0.00018006306],"domain_scores_gemma":[0.9947915,0.0020915759,0.0008180257,0.00026041866,0.0019487924,0.00008966217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050164983,0.00012950202,0.00020859553,0.0005464792,0.00014311525,0.00011966092,0.0003883849,0.00013426137,0.00003821854],"category_scores_gemma":[0.0078060688,0.00012389927,0.00004627538,0.00091923214,0.00018065571,0.0010173172,0.00007895374,0.0003635608,0.00001454566],"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":[0.0002226102,0.00032680124,0.00015653895,0.000020336942,0.00003644882,0.000016619108,0.003158408,0.3968073,0.0006323127,0.14985766,0.0000737914,0.44869116],"study_design_scores_gemma":[0.000031975913,0.0005422323,0.00028303635,0.00006353596,0.00007977426,0.000058965823,0.0027892895,0.90949285,0.023489997,0.06270218,0.0003294136,0.0001367274],"about_ca_topic_score_codex":0.000021631275,"about_ca_topic_score_gemma":0.000009947719,"teacher_disagreement_score":0.85199094,"about_ca_system_score_codex":0.00005331104,"about_ca_system_score_gemma":0.00041681915,"threshold_uncertainty_score":0.9345158},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06378004,0.00034628078,0.8936154,0.038190924,0.0028823784,0.00026459235,0.0000015286672,0.000039505532,0.0008793345],"genre_scores_gemma":[0.9821541,0.00042993808,0.016755605,0.0003739331,0.00019284006,0.0000057924403,3.9933425e-7,0.000017574133,0.00006985994],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950674,0.00053893105,0.0021922581,0.0002367938,0.0015298917,0.00043473075],"domain_scores_gemma":[0.98921597,0.006564976,0.0018965461,0.0005518844,0.001610057,0.00016053714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009204003,0.00020745894,0.00034037684,0.0002442688,0.0004966904,0.0003530865,0.0022307832,0.00012443324,0.000012041717],"category_scores_gemma":[0.005475637,0.00012262557,0.00021342498,0.0016502436,0.00052577927,0.0012325615,0.00038494135,0.0010458074,0.00017754623],"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":[0.00019104821,0.00037142832,0.000009466466,0.000024838777,0.00008560835,0.000026189993,0.019052766,0.0028621089,0.0022008065,0.31311932,0.00017529198,0.66188115],"study_design_scores_gemma":[0.000046326128,0.0013381693,0.00016942242,0.00043347056,0.00012464597,0.00050273875,0.07145004,0.03984557,0.41431734,0.43905452,0.032189522,0.0005282356],"about_ca_topic_score_codex":0.000014698973,"about_ca_topic_score_gemma":0.000060697097,"teacher_disagreement_score":0.918374,"about_ca_system_score_codex":0.00006134249,"about_ca_system_score_gemma":0.001079653,"threshold_uncertainty_score":0.6555245},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9299452,0.000046179575,0.036392286,0.018045457,0.0043356717,0.00069409,0.0000162117,0.0002179597,0.010306971],"genre_scores_gemma":[0.9967759,0.00022950102,0.0018867024,0.00031952394,0.00060336647,0.00003580377,0.0000019169943,0.000024301533,0.00012295786],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.994949,0.000871194,0.0015974035,0.00037001033,0.0016909997,0.00052139914],"domain_scores_gemma":[0.9921977,0.0041249404,0.0017078614,0.00037390876,0.0013797269,0.0002158468],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005338604,0.00023376246,0.00046431308,0.00071968074,0.0004767731,0.00023572869,0.0008242333,0.0003005174,0.00015636145],"category_scores_gemma":[0.04130865,0.00022850648,0.00022999693,0.002650978,0.00087075547,0.001622454,0.000058466696,0.00073639443,0.00022135126],"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":[0.0023003023,0.0020044402,0.0006385565,0.000027624345,0.000038168313,0.00015372538,0.035112686,0.0044594184,0.0039906353,0.1632314,0.0002628766,0.78778017],"study_design_scores_gemma":[0.000111823254,0.0026091922,0.0013123534,0.0007395979,0.00018707308,0.00001038022,0.6326697,0.002022284,0.22326848,0.1163866,0.019537259,0.0011452695],"about_ca_topic_score_codex":0.00020015826,"about_ca_topic_score_gemma":0.00028066768,"teacher_disagreement_score":0.7866349,"about_ca_system_score_codex":0.0002091072,"about_ca_system_score_gemma":0.0004986969,"threshold_uncertainty_score":0.96676683},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010598629,0.000053653857,0.9920045,0.0064216577,0.00017027979,0.00009135856,4.930378e-7,0.00009649759,0.0001016656],"genre_scores_gemma":[0.5859073,0.0013513026,0.41186893,0.00050003215,0.0002784022,0.000018520685,0.000025540752,0.000016004627,0.00003399705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851215,0.00026310928,0.0005108093,0.00019116576,0.0003833103,0.0001394591],"domain_scores_gemma":[0.99777454,0.00062699715,0.0006813029,0.00011189823,0.0007138525,0.00009143137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017179443,0.00009384631,0.0001351037,0.00038570276,0.00019956264,0.00030915922,0.0001815137,0.00006198475,0.000015275513],"category_scores_gemma":[0.0013527505,0.000092431925,0.00006736235,0.0010480753,0.00003127495,0.003099115,0.00003452422,0.00027429845,0.0002536099],"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":[0.000017725666,0.00005273712,0.0000030227372,0.0000052047885,0.00001638161,0.000008535683,0.00087405776,0.058939897,0.0015414036,0.0002957823,0.000023238208,0.938222],"study_design_scores_gemma":[0.00003581274,0.0001341148,0.000013631305,0.00002141753,0.000041595446,0.000034573128,0.0038599214,0.97344774,0.0147033315,0.0064666728,0.0011363286,0.00010483763],"about_ca_topic_score_codex":0.000011939665,"about_ca_topic_score_gemma":0.0000042301585,"teacher_disagreement_score":0.93811715,"about_ca_system_score_codex":0.000043107466,"about_ca_system_score_gemma":0.00005039184,"threshold_uncertainty_score":0.37692645},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014647279,0.00008971877,0.9945562,0.0013042311,0.0016181703,0.0006766969,0.0000022746663,0.00011432991,0.00017363798],"genre_scores_gemma":[0.31996155,0.000012463997,0.6795003,0.000104247025,0.00032195114,0.000006462366,7.8576136e-7,0.0000249378,0.00006735192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99571055,0.00068126456,0.0019163935,0.00034768909,0.00067231455,0.00067176775],"domain_scores_gemma":[0.988654,0.0074262503,0.0018202685,0.00043587378,0.001492646,0.00017092169],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.012391567,0.00024921028,0.0008486484,0.0004397332,0.0002766974,0.0003274846,0.0011445956,0.00014220762,0.0000051573115],"category_scores_gemma":[0.008709045,0.00018803708,0.00028411995,0.00059831096,0.000050575665,0.00129424,0.000121041114,0.000480956,0.00006236115],"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":[0.0016193422,0.00012319992,0.0000085003685,0.00010054007,0.00029138787,0.00035476938,0.0035541821,0.89910996,0.04836117,0.0034300683,0.0001409007,0.042905986],"study_design_scores_gemma":[0.0005433483,0.000639033,0.0000037416003,0.00029621206,0.00016303099,0.00031688457,0.0029113167,0.93761736,0.05570277,0.0014866148,0.00012325752,0.00019643918],"about_ca_topic_score_codex":0.00005449792,"about_ca_topic_score_gemma":0.0000010981645,"teacher_disagreement_score":0.31849682,"about_ca_system_score_codex":0.00013096911,"about_ca_system_score_gemma":0.00022655618,"threshold_uncertainty_score":0.999641},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09883031,0.00010034874,0.8969661,0.0023243832,0.0014000146,0.00010627901,4.7187933e-7,0.00014880866,0.00012325762],"genre_scores_gemma":[0.8671978,0.00011130505,0.13222508,0.00032155612,0.00012393217,0.0000023162997,2.121268e-7,0.000013347324,0.00000440642],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980231,0.0001603796,0.00065591995,0.00029216433,0.0005403327,0.00032812785],"domain_scores_gemma":[0.99679035,0.0017507107,0.00061867986,0.0004612328,0.0002849148,0.00009414215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014472061,0.00016938699,0.00022693096,0.0005272683,0.00034527355,0.00028814247,0.0006378591,0.00014389229,0.000004537656],"category_scores_gemma":[0.0059538363,0.00016388601,0.00013125334,0.0011668357,0.000073203184,0.0015059053,0.00020276621,0.0007910523,0.000025822561],"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":[0.00021181627,0.00020158736,0.00007201337,0.00002214741,0.000113367765,0.0011400055,0.0020099666,0.30966523,0.07087477,0.019603727,0.000030164882,0.5960552],"study_design_scores_gemma":[0.00004958167,0.0008438877,0.000025902511,0.0001570093,0.00006290845,0.0005884093,0.002750071,0.7090174,0.26396558,0.02183629,0.00045316957,0.0002497751],"about_ca_topic_score_codex":0.000034615372,"about_ca_topic_score_gemma":0.000008134158,"teacher_disagreement_score":0.7683675,"about_ca_system_score_codex":0.00020785576,"about_ca_system_score_gemma":0.00012779793,"threshold_uncertainty_score":0.71277285},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014440562,0.000019405259,0.9831739,0.0019700935,0.00012638354,0.00008902019,6.55414e-7,0.000055246208,0.00012470683],"genre_scores_gemma":[0.9925899,0.00011570696,0.006949344,0.000265718,0.00005599539,0.000007830943,5.1347564e-7,0.000004800827,0.000010172644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986071,0.00018187371,0.0005647386,0.0001923476,0.00031051986,0.00014341596],"domain_scores_gemma":[0.99836177,0.0005841423,0.00048338145,0.00020937831,0.00029583756,0.00006550486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019030178,0.00008588684,0.0001588007,0.00079755276,0.00012524324,0.00015170043,0.00023897187,0.0000588933,0.00000957581],"category_scores_gemma":[0.0006699049,0.000081184524,0.00008428104,0.003341056,0.000031573312,0.0007450108,0.000040022525,0.00022766668,0.000021306543],"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":[0.00019626186,0.0002694399,0.0020635037,0.000010592105,0.000063194166,0.000050477454,0.00045566392,0.5118427,0.0020037463,0.008261757,0.000057976293,0.47472465],"study_design_scores_gemma":[0.000024461095,0.00023869591,0.004717564,0.000011468975,0.000026581565,0.000021441705,0.00018118483,0.97208846,0.018212486,0.0028333887,0.0015524967,0.00009174608],"about_ca_topic_score_codex":0.00007094599,"about_ca_topic_score_gemma":0.00012250438,"teacher_disagreement_score":0.97814935,"about_ca_system_score_codex":0.00007134771,"about_ca_system_score_gemma":0.00004944909,"threshold_uncertainty_score":0.3310609},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1802077,0.000049227787,0.7348344,0.074106924,0.0029287194,0.0003524958,0.0000053592876,0.00031759372,0.007197629],"genre_scores_gemma":[0.8962601,0.000026518308,0.09089303,0.010716753,0.0020235367,0.0000024574883,0.000058977028,0.0000144195255,0.0000042161373],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989877,0.000008483248,0.000532547,0.00009333272,0.00022640014,0.00015154779],"domain_scores_gemma":[0.99817914,0.00028033694,0.00089808705,0.00016205397,0.00047174768,0.000008616521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013410223,0.00008557616,0.0001177827,0.0004754874,0.00028070682,0.00043002353,0.00023687116,0.00007079693,0.000015546535],"category_scores_gemma":[0.000813091,0.00007728004,0.000017021905,0.0011171821,0.000098144104,0.0075551113,0.00012297522,0.00025897357,0.000082547645],"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":[0.00015596859,0.000043156,0.001036904,0.000042565993,0.000020806481,0.000014383612,0.00010616901,0.010097768,0.000014687523,0.022855693,0.007001909,0.95861],"study_design_scores_gemma":[0.00007959385,0.00006923281,0.0004341732,0.00020082928,0.00010935783,0.00006154991,0.0057242243,0.9377424,0.00006714148,0.0099942,0.045360338,0.00015692227],"about_ca_topic_score_codex":0.000021366812,"about_ca_topic_score_gemma":0.000011400851,"teacher_disagreement_score":0.95845306,"about_ca_system_score_codex":0.000014283766,"about_ca_system_score_gemma":0.00004318831,"threshold_uncertainty_score":0.54772735},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93234617,0.0013173665,0.029405802,0.035317063,0.000679945,0.0002582447,0.000013879482,0.0000130704075,0.0006484339],"genre_scores_gemma":[0.9972971,0.0012989541,0.0012429493,0.00006337389,0.00008512886,0.0000017891135,3.5392128e-7,0.000003497289,0.000006870321],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9985089,0.00021806329,0.00039029677,0.0001617248,0.00057713105,0.0001439114],"domain_scores_gemma":[0.99576217,0.0033893164,0.0002804759,0.00033007227,0.00021064676,0.000027333852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032735597,0.00007145495,0.0001436396,0.0000728235,0.000121660516,0.000057053647,0.00078019494,0.000028006518,0.0000030730248],"category_scores_gemma":[0.002449747,0.000039429495,0.000025728512,0.000665768,0.00016130056,0.00038995492,0.00046001098,0.00041435382,0.0000045788765],"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":[0.0006562587,0.000752981,0.0062494115,0.00004948559,0.00049386505,0.000476084,0.06546968,0.0165264,0.015240472,0.5992921,0.0023509574,0.29244232],"study_design_scores_gemma":[0.00025598612,0.0015271908,0.148398,0.0024172198,0.00014288203,0.00017740647,0.4885912,0.058726586,0.17006275,0.10733888,0.021571307,0.0007905911],"about_ca_topic_score_codex":0.00057865644,"about_ca_topic_score_gemma":0.0001007196,"teacher_disagreement_score":0.4919532,"about_ca_system_score_codex":0.000053617692,"about_ca_system_score_gemma":0.00005417943,"threshold_uncertainty_score":0.2932753},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072067883,0.000100037105,0.99002993,0.0015553881,0.00073354115,0.00031723097,5.605253e-7,0.00004496727,0.000011551944],"genre_scores_gemma":[0.39417443,0.00004788873,0.6052933,0.00015098836,0.00030991843,0.000013642314,5.199117e-7,0.0000074069744,0.0000019283193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982096,0.0002792786,0.0007398316,0.00021103896,0.00034193022,0.00021830438],"domain_scores_gemma":[0.9933695,0.004549128,0.0008989173,0.00036304555,0.0007119253,0.000107466076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005500913,0.00010554516,0.00023517419,0.00013034427,0.00015461283,0.00010890498,0.00051629444,0.000114594775,0.000001984194],"category_scores_gemma":[0.0030178262,0.00008793127,0.000077904646,0.0007980833,0.000073903764,0.001272308,0.000048280148,0.00022486586,0.000011588824],"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":[0.00053750264,0.00024406235,0.00029269495,0.00009894483,0.000040113726,0.000005672366,0.0011572826,0.24334805,0.0006023659,0.055747114,0.00018046147,0.69774574],"study_design_scores_gemma":[0.000041371546,0.0008026282,0.00020009383,0.00007458637,0.000034872726,0.000028423225,0.00033908006,0.8336061,0.010961935,0.15185845,0.0019402396,0.00011221228],"about_ca_topic_score_codex":0.000013340886,"about_ca_topic_score_gemma":0.000002117311,"teacher_disagreement_score":0.6976335,"about_ca_system_score_codex":0.000027566522,"about_ca_system_score_gemma":0.00016278077,"threshold_uncertainty_score":0.36128378},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005167215,0.000070013135,0.96151793,0.027751002,0.00070313935,0.0005378298,0.000004415209,0.000041357303,0.0042071133],"genre_scores_gemma":[0.9694678,0.0005345878,0.028849246,0.00046086835,0.00047582423,0.00007954413,0.0000057877965,0.000014580195,0.0001117202],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99654317,0.00046105738,0.0010836808,0.000341564,0.001246136,0.00032437852],"domain_scores_gemma":[0.9940202,0.0025042177,0.00088451453,0.00097059226,0.0015080413,0.00011238718],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0067236726,0.00013800722,0.00018340712,0.00065867876,0.00038285722,0.00026712634,0.0021413215,0.00007988872,0.000029869081],"category_scores_gemma":[0.001237827,0.000102937614,0.000105330764,0.0036177018,0.00020113967,0.0010093232,0.00033158934,0.0006328231,0.00096363516],"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":[0.00005784858,0.0003791906,0.0000017056979,0.000009345306,0.000022677732,0.0000044168396,0.00057587033,0.0007239455,0.0011560855,0.56459635,0.0016191372,0.4308534],"study_design_scores_gemma":[0.000019484525,0.00073004863,0.00018724574,0.00016479411,0.00007270366,0.000081104736,0.033161614,0.08230792,0.10575314,0.6960589,0.08113907,0.00032392086],"about_ca_topic_score_codex":0.000060757266,"about_ca_topic_score_gemma":0.000013647742,"teacher_disagreement_score":0.96430063,"about_ca_system_score_codex":0.00010511459,"about_ca_system_score_gemma":0.0003287899,"threshold_uncertainty_score":0.9998142},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10393689,0.00028911824,0.7977482,0.095029354,0.0011888322,0.000638163,0.0000012742386,0.000040960163,0.0011272226],"genre_scores_gemma":[0.99682707,0.00045794024,0.0018574503,0.00044007946,0.0003924634,0.000015937545,2.2375718e-7,0.0000060263883,0.0000028355175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960511,0.0010976573,0.0012203669,0.00024268046,0.0010306307,0.00035759623],"domain_scores_gemma":[0.98697484,0.010370963,0.0009657233,0.0006655414,0.0009827691,0.000040160277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013755347,0.00011820908,0.00024762205,0.0006398368,0.00026309877,0.00010197105,0.0019976618,0.0001778772,0.000022268967],"category_scores_gemma":[0.0053962455,0.00007763341,0.000111104535,0.00652475,0.00029016763,0.00056785747,0.00025909927,0.0015901064,0.000068970796],"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":[0.00026222895,0.00026742092,0.000014857497,0.000011155821,0.000016034639,0.000016916543,0.002501346,0.011705942,0.0009577071,0.7250665,0.0008240612,0.25835583],"study_design_scores_gemma":[0.000010820062,0.0010998794,0.000029603283,0.000102125545,0.000011819185,0.000049881357,0.009056255,0.1493549,0.08847368,0.7471177,0.0046043564,0.00008895881],"about_ca_topic_score_codex":0.0001236935,"about_ca_topic_score_gemma":0.00013000975,"teacher_disagreement_score":0.89289016,"about_ca_system_score_codex":0.000044759443,"about_ca_system_score_gemma":0.00014097497,"threshold_uncertainty_score":0.69083095},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9817443,0.00002344147,0.0076095895,0.008808443,0.00043187948,0.00015577574,0.0000038742774,0.000026374428,0.0011963265],"genre_scores_gemma":[0.99728143,0.000046429792,0.0018762761,0.00026683934,0.00049958174,0.00000249566,0.000002967485,0.000013496783,0.00001045264],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981675,0.000041947667,0.0011095344,0.00012413676,0.00037785355,0.00017904124],"domain_scores_gemma":[0.99521744,0.0006479188,0.001974207,0.00016425061,0.0019861953,0.000009961399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009918334,0.000119097815,0.00028219348,0.0005238293,0.0001446419,0.000039787432,0.0003914751,0.000090762245,0.000060313807],"category_scores_gemma":[0.0017403418,0.00009523159,0.000100116325,0.001960751,0.00018792643,0.0009051773,0.00012906255,0.0003369925,0.000037971397],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020845875,0.0015584566,0.009114514,0.0001354125,0.0012224079,0.000009967597,0.023975981,0.011155503,0.2426086,0.64313054,0.0021319413,0.06287208],"study_design_scores_gemma":[0.0001411584,0.00016046443,0.0034797671,0.0002580031,0.00033527924,0.000011388262,0.12414496,0.008673586,0.810812,0.048894435,0.0028790755,0.00020985643],"about_ca_topic_score_codex":0.016851537,"about_ca_topic_score_gemma":0.0013917808,"teacher_disagreement_score":0.5942361,"about_ca_system_score_codex":0.00006842943,"about_ca_system_score_gemma":0.00014955572,"threshold_uncertainty_score":0.9896953},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75864476,0.00088703784,0.23539037,0.002903099,0.0015372027,0.00024415564,0.0000073091,0.000047559857,0.00033853357],"genre_scores_gemma":[0.9961873,0.00090567284,0.0023392886,0.00018419545,0.0003492404,0.0000050811313,0.0000028321488,0.000020561745,0.000005805143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99637204,0.00026672703,0.001681081,0.00028036078,0.0010497163,0.0003500842],"domain_scores_gemma":[0.99677867,0.0013027135,0.0010322933,0.00021285054,0.0004662151,0.00020725913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039157113,0.00018713585,0.00039793114,0.0010156629,0.000121300436,0.00014191095,0.00045907262,0.00017095165,0.000072438976],"category_scores_gemma":[0.0042403173,0.00018355386,0.00016368381,0.0024234315,0.00043374763,0.0012710375,0.000116125055,0.0008547837,0.00006893461],"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":[0.00083857885,0.00055257365,0.011608515,0.00010160007,0.00010366974,0.0008304367,0.0021377155,0.0010881447,0.0082971845,0.052901104,0.00011496226,0.9214255],"study_design_scores_gemma":[0.00025680807,0.0012254734,0.028502434,0.0016045093,0.0005218413,0.006261456,0.07189828,0.4282686,0.20088285,0.25520417,0.0040098475,0.0013637061],"about_ca_topic_score_codex":0.00032204838,"about_ca_topic_score_gemma":0.00023148679,"teacher_disagreement_score":0.9200618,"about_ca_system_score_codex":0.000042720203,"about_ca_system_score_gemma":0.00013853572,"threshold_uncertainty_score":0.7485109},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9269548,0.0001243166,0.0010505434,0.06322513,0.00086978555,0.00035491548,0.000008010314,0.000072303344,0.0073402124],"genre_scores_gemma":[0.9986647,0.00025714014,0.00024364793,0.00017621138,0.0005131927,0.000003934169,4.4061287e-7,0.000015366073,0.00012541682],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9954274,0.0014846461,0.0006370383,0.00020090093,0.0017616171,0.0004883776],"domain_scores_gemma":[0.98587394,0.010806717,0.0005819756,0.00029760288,0.0023118097,0.00012797056],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008264127,0.00012640313,0.00022936324,0.0006738794,0.0007985475,0.0001478851,0.00083725806,0.00018983158,0.00028256324],"category_scores_gemma":[0.026624326,0.00008870844,0.00021821317,0.0019843709,0.0006157815,0.00066332816,0.00008860953,0.0017380795,0.00039073822],"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":[0.0019024257,0.0019280709,0.0026059486,0.0000477746,0.00062145636,0.00048078422,0.1802825,0.048003685,0.0050342404,0.34262174,0.025000028,0.39147133],"study_design_scores_gemma":[0.00015509817,0.011142613,0.019257843,0.0021290048,0.00015414058,0.00012722169,0.8431831,0.0015072892,0.030781362,0.07974593,0.011176479,0.00063988526],"about_ca_topic_score_codex":0.0011714834,"about_ca_topic_score_gemma":0.0006464286,"teacher_disagreement_score":0.6629006,"about_ca_system_score_codex":0.00036085036,"about_ca_system_score_gemma":0.0009497676,"threshold_uncertainty_score":0.98157483},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032837197,0.00008568152,0.93504,0.0031947466,0.0016332386,0.00069721765,0.0000013968821,0.00047852573,0.026032008],"genre_scores_gemma":[0.9926914,0.00002340587,0.005871901,0.0004668598,0.000861992,0.0000031072648,0.000002717079,0.00003135717,0.000047276015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742657,0.000049564856,0.0012145643,0.00022463528,0.0007420194,0.00034266643],"domain_scores_gemma":[0.99651504,0.0007753676,0.001885949,0.00032112564,0.00048081242,0.00002170503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035560543,0.00021329673,0.00036975238,0.0010677557,0.00029760072,0.00023844333,0.0006907675,0.00010823115,0.00003923095],"category_scores_gemma":[0.00076534325,0.00018389233,0.00015350986,0.0012726337,0.00010684985,0.000841428,0.00021779607,0.0004450254,0.00032714882],"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":[0.0014068015,0.0006289162,0.00011819644,0.00080388715,0.00024047394,0.0006501348,0.00047712913,0.37581083,0.0012175946,0.40037382,0.006451227,0.21182099],"study_design_scores_gemma":[0.00032584567,0.000975631,0.00017194597,0.0026094613,0.0007876725,0.00006834975,0.13771869,0.7262038,0.027463602,0.022484051,0.08026084,0.00093014474],"about_ca_topic_score_codex":0.000039489427,"about_ca_topic_score_gemma":0.0000022449108,"teacher_disagreement_score":0.9598542,"about_ca_system_score_codex":0.00010912867,"about_ca_system_score_gemma":0.000026413283,"threshold_uncertainty_score":0.74989116},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061487593,0.00010722556,0.9274084,0.00894463,0.00015643687,0.00027772776,5.99779e-7,0.00010579897,0.0015115927],"genre_scores_gemma":[0.8025967,0.0001533288,0.19685443,0.00022296078,0.00007020052,0.000029652612,4.2106151e-7,0.000010152321,0.00006218741],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802077,0.00014465954,0.00072638993,0.0003087466,0.00044435074,0.00035510372],"domain_scores_gemma":[0.9983198,0.00037940562,0.00052279717,0.00045655822,0.00023679639,0.00008461703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00307791,0.000147818,0.0002415087,0.00074922014,0.00013566589,0.00012587884,0.0010532669,0.00010100745,0.000007374445],"category_scores_gemma":[0.00028404774,0.00012488812,0.000060110342,0.0026788712,0.00009156245,0.00043808887,0.0002040033,0.00060827663,0.00005735341],"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":[0.00017623305,0.0008588083,0.00017107054,0.000045120367,0.00009976548,0.00074288895,0.0054434068,0.011168482,0.0010169791,0.7501274,0.00016213905,0.22998771],"study_design_scores_gemma":[0.00037867334,0.0011293391,0.0007757501,0.00029007744,0.00011410997,0.001995382,0.04152502,0.72102773,0.055240933,0.16497314,0.011664758,0.00088506954],"about_ca_topic_score_codex":0.000030164472,"about_ca_topic_score_gemma":0.000041132724,"teacher_disagreement_score":0.7411091,"about_ca_system_score_codex":0.00007892284,"about_ca_system_score_gemma":0.0000836256,"threshold_uncertainty_score":0.50927895},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24250059,0.000309908,0.74928564,0.0067429407,0.00078956544,0.00019520284,0.0000031111706,0.000043064338,0.00012999206],"genre_scores_gemma":[0.9802123,0.00046620757,0.018932385,0.00006907249,0.0002990897,0.000008130906,0.0000010883125,0.000006709492,0.0000050188496],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99812627,0.00023073991,0.0010160571,0.00020956829,0.0002494744,0.00016790527],"domain_scores_gemma":[0.99469894,0.0022290729,0.0011003905,0.0002668617,0.001639557,0.000065148335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003346187,0.00011277421,0.00022404695,0.0003073645,0.00019432747,0.00010262892,0.00040354356,0.000116029645,0.0000060212055],"category_scores_gemma":[0.005461408,0.00009596032,0.00005236411,0.00095073105,0.00031616667,0.0017154469,0.00014426264,0.0004083854,0.0000054111124],"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":[0.000072074814,0.00012860331,0.00009073844,0.000025690371,0.0000332841,0.000042812895,0.01671989,0.00091026287,0.0015135448,0.36964118,0.000042844276,0.61077905],"study_design_scores_gemma":[0.000053472064,0.0008673079,0.00018600648,0.00010358438,0.00015887195,0.0016562273,0.17524864,0.16384053,0.12665595,0.5278657,0.0030276438,0.00033608236],"about_ca_topic_score_codex":0.00010191006,"about_ca_topic_score_gemma":0.00007482014,"teacher_disagreement_score":0.7377117,"about_ca_system_score_codex":0.000019575855,"about_ca_system_score_gemma":0.0001393531,"threshold_uncertainty_score":0.653821},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2422813,0.0004133877,0.72002745,0.01862511,0.0044224933,0.00075909426,0.000011415569,0.0000768055,0.013382934],"genre_scores_gemma":[0.99555725,0.00012679366,0.003926543,0.00011757775,0.00016002441,0.000006486711,0.000001980361,0.000010281439,0.00009305736],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99607265,0.0002320272,0.0019419786,0.00022289784,0.0011409408,0.0003895144],"domain_scores_gemma":[0.99334335,0.004633886,0.0011111726,0.00042557207,0.00039868854,0.00008731584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004082571,0.00018246373,0.00032797272,0.00033764372,0.0001499586,0.0005064031,0.0019393415,0.00006513782,0.000007667175],"category_scores_gemma":[0.004470797,0.000111140165,0.00016873369,0.0019419136,0.00030301942,0.00207519,0.00025827423,0.00044904609,0.00017164879],"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":[0.00024049675,0.00070688775,0.00008890534,0.00002149844,0.000061102815,0.0002301491,0.016096747,0.00073954795,0.0012355082,0.097190596,0.0004021281,0.8829864],"study_design_scores_gemma":[0.0001376456,0.0038594697,0.0016457022,0.00069312175,0.00013395712,0.00083529705,0.094788566,0.032192256,0.2952366,0.38867572,0.18065156,0.0011501317],"about_ca_topic_score_codex":0.0000065649797,"about_ca_topic_score_gemma":0.00008861034,"teacher_disagreement_score":0.8818363,"about_ca_system_score_codex":0.00004413197,"about_ca_system_score_gemma":0.00030038427,"threshold_uncertainty_score":0.53522843},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24111885,0.00025565655,0.73946124,0.016447103,0.00086986896,0.00066786323,0.0000045448583,0.00050215976,0.0006726896],"genre_scores_gemma":[0.99860597,0.00019514916,0.00095596106,0.000014799368,0.00017656904,0.000028699324,5.451828e-7,0.000018752182,0.0000035514497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978472,0.00016427538,0.00096211873,0.00013845321,0.0005331222,0.00035485654],"domain_scores_gemma":[0.9959736,0.0029700224,0.00025423412,0.00027521697,0.00049164274,0.000035323937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004273099,0.00012713886,0.00025404873,0.0015403887,0.0000858309,0.00004731724,0.00051739014,0.0002804153,0.000010246153],"category_scores_gemma":[0.007115953,0.00010087497,0.00006465106,0.004156122,0.00016576711,0.00024413223,0.000033459473,0.0016217404,0.00014116436],"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":[0.00014556007,0.000093658105,0.000008171463,0.000011019848,0.00003259989,0.000020917425,0.00018416154,0.38631663,0.017689709,0.24866536,0.00011748192,0.34671474],"study_design_scores_gemma":[0.000022110728,0.0003834655,0.000028067177,0.00006572833,0.000017375729,0.0000298728,0.004292731,0.68668956,0.2310525,0.07639051,0.000918735,0.00010937168],"about_ca_topic_score_codex":0.000025119687,"about_ca_topic_score_gemma":0.00000904252,"teacher_disagreement_score":0.7574871,"about_ca_system_score_codex":0.00017220221,"about_ca_system_score_gemma":0.00008051573,"threshold_uncertainty_score":0.8518975},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90538776,0.00020324766,0.05933132,0.031478148,0.0024366307,0.00043790237,0.0000027292715,0.000045637607,0.00067661924],"genre_scores_gemma":[0.9985737,0.00015787876,0.0006183419,0.00045407857,0.00015399355,0.000005745548,7.5175834e-8,0.000011731998,0.000024457753],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99589145,0.0021341431,0.00087786024,0.00023411987,0.00053614046,0.0003262982],"domain_scores_gemma":[0.9709241,0.027671944,0.0007456146,0.00034313122,0.00027799665,0.00003722148],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008857463,0.00015445994,0.0002859642,0.00040939735,0.00018287447,0.00016964151,0.0014096941,0.00008714394,0.000009106242],"category_scores_gemma":[0.011759851,0.00008556535,0.00014314556,0.0018743017,0.0004090108,0.00061703305,0.00019899047,0.0008033464,0.000106226194],"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":[0.003147759,0.00083626615,0.00012986358,0.000097807424,0.00012938387,0.0007858552,0.009694215,0.029431297,0.6346291,0.11452204,0.001964276,0.20463215],"study_design_scores_gemma":[0.00007497062,0.0007989574,0.00012294624,0.0001486147,0.000031153144,0.0006546485,0.00766349,0.007164111,0.93580323,0.03460236,0.01281184,0.00012364802],"about_ca_topic_score_codex":0.000015574451,"about_ca_topic_score_gemma":0.0000101832275,"teacher_disagreement_score":0.30117416,"about_ca_system_score_codex":0.000042085594,"about_ca_system_score_gemma":0.00008805189,"threshold_uncertainty_score":0.9965645},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05718864,0.00019420772,0.9397301,0.0022730737,0.00032499837,0.00017365349,1.3001188e-7,0.00006184095,0.000053358162],"genre_scores_gemma":[0.90227264,0.00011627753,0.097455405,0.000017610035,0.00009537647,0.0000033961032,6.9405985e-8,0.000008465542,0.000030777475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977345,0.00077164476,0.0005710234,0.00017356017,0.0004655433,0.00028373828],"domain_scores_gemma":[0.9934974,0.004927185,0.0009126792,0.00025991647,0.0002964299,0.000106429594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00717146,0.00012653407,0.0002049352,0.0001676217,0.00036983308,0.0005570495,0.000635032,0.0000451409,0.0000015450354],"category_scores_gemma":[0.0038212975,0.00007931035,0.0001317625,0.0005742246,0.00010275548,0.00071965635,0.00012900394,0.0005992058,0.00001620674],"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":[0.00012014416,0.00022941212,0.00020517463,0.0000031100565,0.000084980216,0.000049152088,0.006345741,0.07066436,0.00028084446,0.004183863,0.00003998269,0.9177932],"study_design_scores_gemma":[0.0005756532,0.038017895,0.03911371,0.0010108444,0.0004851379,0.0025976659,0.039644834,0.7602991,0.0312433,0.021637442,0.06354009,0.0018343353],"about_ca_topic_score_codex":0.000103275524,"about_ca_topic_score_gemma":0.0000024176168,"teacher_disagreement_score":0.9159589,"about_ca_system_score_codex":0.00007590328,"about_ca_system_score_gemma":0.00018549648,"threshold_uncertainty_score":0.53716415},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5057222,0.00065512053,0.10762493,0.38385373,0.00055733504,0.0006848554,0.000009258392,0.00003918329,0.0008533753],"genre_scores_gemma":[0.9955032,0.00016863276,0.0037488553,0.00016539381,0.0003839862,0.000008054914,0.000007694781,0.000012704173,0.0000014879141],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99723375,0.00020042475,0.0015008299,0.00017403737,0.00073890743,0.00015205113],"domain_scores_gemma":[0.9960497,0.0023735128,0.0006972222,0.0001379111,0.0006989364,0.000042742708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020101606,0.00015177112,0.0005621554,0.0012500474,0.000052485073,0.0000057300817,0.00014931826,0.00011198476,0.000021498161],"category_scores_gemma":[0.0023036033,0.00009553462,0.000110195375,0.002646023,0.000491089,0.0006812123,0.00001444016,0.0008572813,0.0000019997954],"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":[0.011498386,0.0066494867,0.014249518,0.000574878,0.0006357499,0.00079508097,0.06812728,0.06619362,0.3823822,0.2503121,0.0003924319,0.19818927],"study_design_scores_gemma":[0.00192567,0.0069651855,0.23895249,0.0030962634,0.00039712386,0.0015416594,0.06998888,0.040483262,0.0064953184,0.62980765,0.00006551662,0.00028096995],"about_ca_topic_score_codex":0.0000868413,"about_ca_topic_score_gemma":0.0002511842,"teacher_disagreement_score":0.489781,"about_ca_system_score_codex":0.00013441761,"about_ca_system_score_gemma":0.00015989403,"threshold_uncertainty_score":0.38957888},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022990196,0.00006350429,0.98770136,0.006523995,0.001974931,0.00017979475,0.0000028351508,0.00017260308,0.0010819692],"genre_scores_gemma":[0.45814344,0.000049761078,0.53890496,0.0014931037,0.0012350928,0.0000052494006,0.000004427498,0.00005733474,0.00010663838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941773,0.0011511064,0.0010697085,0.0005063234,0.0022975407,0.0007980391],"domain_scores_gemma":[0.98689055,0.009915227,0.00078324246,0.0006630966,0.0014384324,0.00030943556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011198339,0.00025078087,0.00036816043,0.0014496294,0.0005250798,0.00060305384,0.0015324734,0.00017085839,0.000014399608],"category_scores_gemma":[0.0067997756,0.000223129,0.00014953731,0.0034177278,0.000101492005,0.0012408949,0.00017012519,0.0019503352,0.0007337709],"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":[0.0001657312,0.00021492378,0.000010005188,0.000006439892,0.000028423061,0.0022433584,0.0010153044,0.8423795,0.00028653315,0.0013210158,0.000933117,0.15139565],"study_design_scores_gemma":[0.00007946945,0.001619314,0.000083169914,0.0004074472,0.000014893213,0.00011043929,0.0017575264,0.9849939,0.006152567,0.0028207665,0.0017375626,0.00022297903],"about_ca_topic_score_codex":0.000038973165,"about_ca_topic_score_gemma":1.7504952e-7,"teacher_disagreement_score":0.45584443,"about_ca_system_score_codex":0.0002099725,"about_ca_system_score_gemma":0.0004207225,"threshold_uncertainty_score":0.9431385},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040413734,0.02568078,0.5376237,0.38872895,0.0041551925,0.0012015845,0.00044180336,0.00031391496,0.001440364],"genre_scores_gemma":[0.80230236,0.1184936,0.07582576,0.00056264777,0.002496658,0.00008841866,0.000037477443,0.000051823215,0.00014123034],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99692214,0.00017528389,0.0015232606,0.0003870554,0.0006873332,0.0003049541],"domain_scores_gemma":[0.99124515,0.005399262,0.0011795993,0.0009210657,0.0010948284,0.00016007078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004352304,0.00018483549,0.0003516348,0.0006030231,0.0005245466,0.00064300734,0.0013596796,0.00012672514,0.000030493478],"category_scores_gemma":[0.004569473,0.0001442775,0.00013170215,0.0021052628,0.0005099311,0.0012760747,0.00048473148,0.0005740578,0.00011467283],"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":[0.000081171434,0.00011441743,0.000023565453,0.000004928822,0.000018709383,0.0000022012484,0.00060289714,0.000009308791,0.0004420716,0.17405494,0.00061716564,0.8240286],"study_design_scores_gemma":[0.000012259391,0.00021496785,0.00021319735,0.000018953224,0.00006566126,0.000052995034,0.015563808,0.0050677997,0.0016072536,0.42473558,0.55229384,0.00015370644],"about_ca_topic_score_codex":0.0000131602865,"about_ca_topic_score_gemma":0.000045260243,"teacher_disagreement_score":0.82387495,"about_ca_system_score_codex":0.00002956194,"about_ca_system_score_gemma":0.000085021085,"threshold_uncertainty_score":0.6200534},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012590019,0.00014894031,0.98097235,0.0034106185,0.0008853101,0.00030691194,0.0000016738587,0.00004012614,0.0016440481],"genre_scores_gemma":[0.8910361,0.0005286815,0.10801428,0.00012357917,0.00021646166,0.00002016275,0.0000010573615,0.000021683232,0.000038025548],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99309504,0.0035530708,0.0017494612,0.00034137125,0.00075643347,0.000504612],"domain_scores_gemma":[0.976164,0.021100203,0.00124397,0.0006778831,0.00064947107,0.0001644873],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.032906394,0.00021454741,0.00044205313,0.00051265175,0.00033950133,0.00042699307,0.001675802,0.00012522544,0.00001122793],"category_scores_gemma":[0.022382263,0.00016053235,0.00021393209,0.002339878,0.0002213141,0.0018692462,0.00028906795,0.0010982858,0.00014297896],"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":[0.00032746923,0.0002503228,0.000018521043,0.000020694753,0.00003940799,0.000026194319,0.0011544499,0.0055371486,0.014164187,0.31844777,0.00005440978,0.65995944],"study_design_scores_gemma":[0.000045936114,0.0010579597,0.0009043245,0.0003101488,0.00006188684,0.00023914121,0.0077820667,0.17800805,0.19700715,0.60117924,0.013011183,0.00039290133],"about_ca_topic_score_codex":0.00012069446,"about_ca_topic_score_gemma":0.000060627397,"teacher_disagreement_score":0.87844604,"about_ca_system_score_codex":0.00016312223,"about_ca_system_score_gemma":0.00038158635,"threshold_uncertainty_score":0.99582636},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2672606,0.00006912067,0.6690354,0.061936196,0.00089094567,0.00021363638,0.000008243612,0.00006754529,0.00051834504],"genre_scores_gemma":[0.9653857,0.000048802594,0.033709455,0.0002656743,0.0004884755,0.00000335765,0.000005866085,0.000016072307,0.00007663934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99363303,0.0018685964,0.0017826252,0.0004326101,0.0018532212,0.00042993634],"domain_scores_gemma":[0.98730105,0.007164391,0.0015769834,0.00053694024,0.0033267166,0.00009394096],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.016425682,0.00019526099,0.00034703166,0.0006228468,0.0005967917,0.00040599136,0.001523737,0.00013230635,0.000019723364],"category_scores_gemma":[0.018602535,0.00014598072,0.00009658808,0.00364316,0.00021007382,0.0010020444,0.00029703285,0.002312981,0.00019174966],"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":[0.0002562901,0.0006487257,0.000021595026,0.0000079082065,0.000114358976,0.000029099343,0.009664765,0.005171372,0.0067262785,0.5999526,0.00080099073,0.37660608],"study_design_scores_gemma":[0.000056244397,0.0011573064,0.0004412215,0.00032236552,0.000048020225,0.000024843888,0.06957191,0.05553101,0.22028963,0.6457248,0.006426545,0.0004061395],"about_ca_topic_score_codex":0.0006601963,"about_ca_topic_score_gemma":0.000043874003,"teacher_disagreement_score":0.69812506,"about_ca_system_score_codex":0.00029198529,"about_ca_system_score_gemma":0.0006887471,"threshold_uncertainty_score":0.99998873},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005635,0.000071781935,0.970648,0.023273185,0.00008514007,0.00021218113,9.68854e-7,0.000039621264,0.000034157343],"genre_scores_gemma":[0.94328535,0.00043285495,0.056183774,0.000034224428,0.000031305335,0.000022556864,8.931307e-7,0.0000054448774,0.0000035932264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983802,0.00022041649,0.0007750635,0.00018995373,0.00029205854,0.00014233399],"domain_scores_gemma":[0.9975777,0.0007416557,0.00086046924,0.00032458553,0.00044608428,0.00004947723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002624026,0.00007842354,0.00018052188,0.00054168864,0.00006331045,0.000050388277,0.0004718094,0.00009194955,6.8547536e-7],"category_scores_gemma":[0.0010521488,0.00007712903,0.000028714254,0.0018369879,0.00010174463,0.00151418,0.000115211325,0.00025555576,0.000015671234],"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":[0.00008838254,0.0002645625,0.000050534214,0.000016565331,0.000021623047,0.000012620282,0.0019571772,0.06706969,0.031024758,0.2057603,0.000070155365,0.6936636],"study_design_scores_gemma":[0.000027577356,0.000109029,0.00013116204,0.000016654472,0.00001183774,0.00004392338,0.0015315992,0.4806031,0.042569328,0.47471464,0.00017717962,0.00006396258],"about_ca_topic_score_codex":0.000048840688,"about_ca_topic_score_gemma":0.000025170883,"teacher_disagreement_score":0.9376504,"about_ca_system_score_codex":0.000058235004,"about_ca_system_score_gemma":0.0001009917,"threshold_uncertainty_score":0.31452307},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7904331,0.0012991617,0.00033376046,0.14375664,0.0008581206,0.0004725538,0.000046320365,0.000020925769,0.062779404],"genre_scores_gemma":[0.995834,0.0020191835,0.00004938228,0.00035667428,0.00030991735,0.0000024336218,0.000012395684,0.000006428038,0.0014096013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9982346,0.0005753865,0.00050165557,0.00007971301,0.0004918127,0.000116809744],"domain_scores_gemma":[0.995507,0.0025690491,0.00046088424,0.00042622993,0.0010144749,0.000022344719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052124565,0.00006969716,0.00012253204,0.00012451316,0.00043965978,0.00033590323,0.0007457931,0.000025384368,0.00025333406],"category_scores_gemma":[0.00042416505,0.000035979207,0.00003797786,0.00022409552,0.0006298805,0.0013220953,0.00033536542,0.00040883676,0.000015818137],"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":[0.00020727012,0.000088946115,0.0000059848276,0.0000331463,0.00009165007,0.0000033664735,0.024397444,0.00008662489,0.00024023923,0.9384981,0.0148975225,0.021449737],"study_design_scores_gemma":[0.000019823268,0.00018735224,0.000050034065,0.00008759611,0.00004749033,0.0000043387267,0.8158014,0.0013452377,0.0005922827,0.028232023,0.15358251,0.000049911247],"about_ca_topic_score_codex":0.00062858535,"about_ca_topic_score_gemma":0.0010206966,"teacher_disagreement_score":0.91026604,"about_ca_system_score_codex":0.000015184317,"about_ca_system_score_gemma":0.000035584733,"threshold_uncertainty_score":0.3381552},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009747892,0.00030842394,0.9854304,0.0030482358,0.00079153804,0.00028135665,0.00003878761,0.000057197743,0.00029615706],"genre_scores_gemma":[0.9801453,0.0017710421,0.017363211,0.00027037202,0.00037758803,0.000010460477,0.000016739421,0.000015906351,0.000029384339],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995661,0.00043400392,0.0019655083,0.00065234734,0.00088350434,0.00040359216],"domain_scores_gemma":[0.9933942,0.0022524213,0.00129046,0.0014365354,0.0013755396,0.0002508219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048632575,0.00023560283,0.0006090526,0.00083692255,0.00025793948,0.0003540856,0.0023613616,0.00012103908,0.000077594465],"category_scores_gemma":[0.0041325754,0.00018984413,0.00022714274,0.004852561,0.00045347612,0.0021515382,0.00096100324,0.00049208605,0.000097465454],"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":[0.00020821481,0.0007248524,0.00018221122,0.00003634643,0.00069461623,0.000031238647,0.0029503342,0.0031761073,0.0032295808,0.026110128,0.00028440656,0.96237195],"study_design_scores_gemma":[0.000030290284,0.00075640355,0.00039387334,0.000052936826,0.0013069486,0.00010396364,0.0058633117,0.8855626,0.037309665,0.056534056,0.011587989,0.00049799314],"about_ca_topic_score_codex":0.00023166256,"about_ca_topic_score_gemma":0.00012002629,"teacher_disagreement_score":0.9703974,"about_ca_system_score_codex":0.00007039096,"about_ca_system_score_gemma":0.0002827997,"threshold_uncertainty_score":0.7741619},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24923633,0.00034524014,0.7420605,0.005921689,0.0013414562,0.00021121265,0.0000022418933,0.00006168194,0.00081966055],"genre_scores_gemma":[0.9904106,0.0002527458,0.009051196,0.00015679444,0.000102088234,0.0000075779785,0.000001144309,0.0000061444457,0.000011703443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963021,0.00045738573,0.0016260209,0.00031416412,0.0009773786,0.0003228983],"domain_scores_gemma":[0.9950651,0.0019122136,0.0012299251,0.00047428202,0.0012527849,0.00006569498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007875367,0.00016156408,0.0005309013,0.0022745077,0.00013138399,0.00009705501,0.000875532,0.00014712212,0.00009217148],"category_scores_gemma":[0.0074622524,0.00014826715,0.00024811676,0.008271225,0.00019275967,0.001277344,0.00022471632,0.0005288631,0.000076078184],"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":[0.0001581981,0.00044882577,0.0004149214,0.000012658243,0.0006648774,0.00015575861,0.012973795,0.051551387,0.0012273695,0.20643327,0.00014208617,0.72581685],"study_design_scores_gemma":[0.00003039134,0.00040454473,0.002206638,0.00005891622,0.00085478637,0.000054224587,0.013664413,0.7012735,0.012651681,0.2676937,0.0008297123,0.00027751186],"about_ca_topic_score_codex":0.000083747116,"about_ca_topic_score_gemma":0.00041029527,"teacher_disagreement_score":0.7411743,"about_ca_system_score_codex":0.00010855955,"about_ca_system_score_gemma":0.0002470004,"threshold_uncertainty_score":0.8933552},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27851138,0.000027341308,0.7175502,0.0027204598,0.0007755591,0.00023710103,0.0000029744965,0.000031383603,0.00014360936],"genre_scores_gemma":[0.94861203,0.000032474287,0.050930686,0.00023364676,0.00016112723,0.0000032775508,3.2252314e-7,0.000016724947,0.000009720089],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9910735,0.0022704601,0.0017254908,0.00037859022,0.0042302315,0.0003217113],"domain_scores_gemma":[0.98345476,0.01029489,0.0026888815,0.0006237233,0.0028433537,0.000094383795],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.022038748,0.00022750827,0.00043150494,0.00068785984,0.00030092837,0.00017188776,0.001947739,0.000124546,0.000025255793],"category_scores_gemma":[0.04257999,0.00017251415,0.00028752538,0.0029977502,0.00013213273,0.0007963379,0.00027360127,0.0011383469,0.000056661047],"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":[0.00021595103,0.0003954543,0.00013274119,0.000010352362,0.00004324604,0.000016099664,0.0007113163,0.7081008,0.00096812117,0.0127886245,0.000013094468,0.27660424],"study_design_scores_gemma":[0.000043860684,0.0009846628,0.0010529095,0.0007881551,0.00018733054,0.000023008462,0.0024591514,0.9328908,0.010263261,0.05098219,0.00015953521,0.00016519196],"about_ca_topic_score_codex":0.0000204084,"about_ca_topic_score_gemma":0.000013477775,"teacher_disagreement_score":0.6701006,"about_ca_system_score_codex":0.00016856521,"about_ca_system_score_gemma":0.0005653119,"threshold_uncertainty_score":0.96548474},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029264906,0.0003214565,0.9542285,0.0139928525,0.00097386143,0.00025530552,0.0000011683627,0.00013104007,0.0008308932],"genre_scores_gemma":[0.70948637,0.00057652075,0.28978825,0.00005678756,0.00005155706,0.000008202327,6.61039e-7,0.000010868457,0.000020807807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974024,0.00029149564,0.0012670303,0.00035069103,0.00039487737,0.00029349528],"domain_scores_gemma":[0.99606675,0.0014457199,0.0014715124,0.0004982002,0.0004729609,0.000044845976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022912922,0.00018536428,0.0003735555,0.002492206,0.00010054585,0.000051522664,0.0006949978,0.00038137627,0.000019594805],"category_scores_gemma":[0.005711593,0.00017544394,0.000058832695,0.0029729044,0.00052639935,0.0010150159,0.00045303692,0.0011807769,0.0000076226443],"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":[0.000085882864,0.00020816394,0.00010381082,0.00002763309,0.000025270963,0.000030389025,0.00035103617,0.00095911435,0.0011476438,0.53273433,0.0000075545713,0.46431917],"study_design_scores_gemma":[0.000029104975,0.00036538672,0.0000828028,0.00018155771,0.000046070574,0.00054949784,0.010552217,0.0030737747,0.055165406,0.92905235,0.0007498457,0.00015197124],"about_ca_topic_score_codex":0.000005085893,"about_ca_topic_score_gemma":0.000004924635,"teacher_disagreement_score":0.68022144,"about_ca_system_score_codex":0.000063133775,"about_ca_system_score_gemma":0.0001696533,"threshold_uncertainty_score":0.7154396},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1521201,0.0006852373,0.80116904,0.043244317,0.0018707714,0.00019877686,0.000008786587,0.000070772745,0.00063217373],"genre_scores_gemma":[0.9700156,0.0010405461,0.027828103,0.000456853,0.0005894454,0.0000059245176,0.0000064060223,0.000009072803,0.000048078186],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962464,0.0007978913,0.0010638445,0.00064875605,0.00085203414,0.0003910601],"domain_scores_gemma":[0.99234205,0.0046333005,0.00074279326,0.0010019527,0.0011353887,0.00014448768],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.011596952,0.00016215515,0.00030859068,0.0012303174,0.00054449984,0.0004580185,0.0015811729,0.00016411643,0.000014442126],"category_scores_gemma":[0.009910428,0.00014774996,0.000063213374,0.0045082094,0.00041518002,0.0019801778,0.0007010113,0.0010387431,0.00007290741],"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":[0.00007753205,0.00036723868,0.00009717106,0.000015817384,0.000079066805,0.000012615765,0.0019532463,0.0004851223,0.00072976487,0.3712247,0.000119669545,0.62483805],"study_design_scores_gemma":[0.000030484718,0.0011779586,0.0052327653,0.000104546045,0.0003417079,0.00028990192,0.053246144,0.092578955,0.0168959,0.82377714,0.0057543553,0.0005701583],"about_ca_topic_score_codex":0.0003931694,"about_ca_topic_score_gemma":0.000104513034,"teacher_disagreement_score":0.8178955,"about_ca_system_score_codex":0.00006956845,"about_ca_system_score_gemma":0.0008930639,"threshold_uncertainty_score":0.99842954},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021505756,0.00019073012,0.9675521,0.029264431,0.00040963697,0.00032362077,0.000005045489,0.000026430109,0.000077415985],"genre_scores_gemma":[0.9077476,0.0009855843,0.090026036,0.00051011733,0.00066513906,0.000034715493,0.00001358928,0.000012386208,0.0000047912786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969709,0.00036240643,0.0014004104,0.00034608727,0.0005480304,0.0003721938],"domain_scores_gemma":[0.99402946,0.003137789,0.0009894193,0.001497179,0.0003024695,0.000043658507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007081185,0.00013733326,0.00023692273,0.00034461298,0.0002264344,0.0002491875,0.0052410834,0.00011199495,0.0000022896772],"category_scores_gemma":[0.0013511932,0.000090033456,0.00005287324,0.0046110335,0.0002671382,0.0012120398,0.00079519587,0.00075152,0.0001016858],"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":[0.000042402626,0.00017682895,0.000054326887,0.000004011152,0.000011443854,0.000046755526,0.00074251945,0.008877623,0.00027632786,0.31395733,0.00052083173,0.67528963],"study_design_scores_gemma":[0.000016298192,0.0001676884,0.0003002293,0.00004512337,0.000020504196,0.00012957718,0.0028660425,0.633834,0.001292253,0.34066647,0.020517103,0.00014472508],"about_ca_topic_score_codex":0.00009520049,"about_ca_topic_score_gemma":0.0005353199,"teacher_disagreement_score":0.9055971,"about_ca_system_score_codex":0.00004423139,"about_ca_system_score_gemma":0.00016913669,"threshold_uncertainty_score":0.97393245},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018200397,0.00029577862,0.9498975,0.028870137,0.0016089937,0.00024072154,0.0000060172415,0.0000787149,0.000801725],"genre_scores_gemma":[0.99248147,0.0008864377,0.0059486716,0.00045517078,0.00017721084,0.000012956373,0.000001662078,0.000013094877,0.000023353654],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969909,0.0001962503,0.0014313754,0.00022678755,0.0007653711,0.0003892962],"domain_scores_gemma":[0.9959754,0.0014578226,0.0009760035,0.00034361627,0.0011210837,0.00012609697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002182408,0.00018524144,0.0002900965,0.0005593334,0.00029804202,0.00038575454,0.001318041,0.000119290555,0.000009600741],"category_scores_gemma":[0.0020776563,0.00013368986,0.00019036143,0.0030834272,0.00036634164,0.0027847528,0.00011816082,0.0005540384,0.00014233052],"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":[0.00015460883,0.000245324,0.000006750657,0.00000934145,0.00006977471,0.000022564473,0.0030398662,0.00048537692,0.015880857,0.2889889,0.0007105213,0.6903861],"study_design_scores_gemma":[0.000020117184,0.00096638827,0.0000033726608,0.000045762696,0.000032552394,0.000101879574,0.007640049,0.03441582,0.6771864,0.25646564,0.022940585,0.00018136752],"about_ca_topic_score_codex":0.000015993952,"about_ca_topic_score_gemma":0.000017468477,"teacher_disagreement_score":0.9742811,"about_ca_system_score_codex":0.000036569432,"about_ca_system_score_gemma":0.00018443339,"threshold_uncertainty_score":0.54517144},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04638359,0.000023126717,0.9387176,0.014193084,0.000088351466,0.00013476118,5.4062826e-7,0.000040376784,0.00041856707],"genre_scores_gemma":[0.9809253,0.0000125261695,0.018631913,0.00022237207,0.00012271096,0.000006507584,2.9651721e-7,0.000007908637,0.000070464346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879575,0.00017567418,0.00035030447,0.00014898115,0.00038388796,0.00014542416],"domain_scores_gemma":[0.9972341,0.0014909755,0.00047178596,0.00021434034,0.0005173194,0.00007148687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017518424,0.00008871318,0.00011806229,0.00011513237,0.0003870192,0.0002938876,0.0005143561,0.000015576985,0.0000036487706],"category_scores_gemma":[0.00029307939,0.000056798814,0.000052775842,0.0010583557,0.00003869416,0.0007888092,0.000099491685,0.0004569521,0.00004550864],"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":[0.000033941964,0.00022723907,0.000009788829,0.0000012734164,0.000052757412,0.0011776272,0.020388963,0.7164259,0.000085322565,0.08884312,0.000117654905,0.1726364],"study_design_scores_gemma":[0.000027188824,0.00031921023,0.000007832313,0.000003990516,0.000031676027,0.0024529549,0.023001995,0.94683,0.00006790537,0.025413029,0.0017681916,0.00007602562],"about_ca_topic_score_codex":0.000065824446,"about_ca_topic_score_gemma":0.000043931002,"teacher_disagreement_score":0.9345417,"about_ca_system_score_codex":0.0000191163,"about_ca_system_score_gemma":0.00019155929,"threshold_uncertainty_score":0.2976678},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93535465,0.022659658,0.032986496,0.0017149674,0.00283479,0.0007410879,0.000007030549,0.00007352439,0.0036278162],"genre_scores_gemma":[0.98917294,0.010591925,0.00007686354,0.0000016163467,0.00012947332,0.000011223291,3.7810713e-7,0.000007100629,0.000008498826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886703,0.00011961282,0.0004916599,0.00007128823,0.00025355536,0.00019683105],"domain_scores_gemma":[0.9982701,0.0007762744,0.0001521974,0.00008171913,0.0006625635,0.0000571749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015642426,0.000056818906,0.00009392916,0.0002959673,0.00008994418,0.00005056483,0.00010818998,0.00003386953,0.0000029375392],"category_scores_gemma":[0.0012002288,0.00004378448,0.000027692091,0.0010402232,0.00007486521,0.00038172325,0.00001991106,0.0005388302,0.00003276911],"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":[0.00008646829,0.00005145709,0.00034173942,0.000042183172,0.000015233789,0.00000219974,0.0016208213,0.0014071667,0.0055995486,0.06104754,0.00021811387,0.9295675],"study_design_scores_gemma":[0.0002230243,0.0006664797,0.037210856,0.0005433096,0.00008696253,0.0001961362,0.016108692,0.3303649,0.23854367,0.18521504,0.19035764,0.00048325013],"about_ca_topic_score_codex":0.000014879628,"about_ca_topic_score_gemma":0.00012195644,"teacher_disagreement_score":0.9290843,"about_ca_system_score_codex":0.00006325821,"about_ca_system_score_gemma":0.000085306245,"threshold_uncertainty_score":0.2340979},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15522128,0.00034445897,0.8222805,0.018853,0.0020931726,0.0003222051,0.000019387566,0.000024967905,0.0008410198],"genre_scores_gemma":[0.9963476,0.0007783961,0.0021730943,0.00020147309,0.0004383964,0.000004384954,0.0000018544812,0.00001084462,0.00004396631],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99445486,0.00043042752,0.0026436883,0.00027206136,0.0018156427,0.0003833168],"domain_scores_gemma":[0.98629624,0.008776921,0.0025338414,0.00048630085,0.0017877206,0.000118983866],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.017132172,0.00015877551,0.0003837021,0.00041678466,0.00033583102,0.00049547333,0.0013131032,0.00011248155,0.00005874902],"category_scores_gemma":[0.03228321,0.00009992442,0.0002421413,0.003085938,0.00028110662,0.0016312395,0.00018303505,0.00053557975,0.00029947402],"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":[0.0004172402,0.00021869793,0.0003206076,0.000006009164,0.000022630524,0.000030305471,0.0024407466,0.0034469373,0.0032527756,0.020082098,0.0006777849,0.96908414],"study_design_scores_gemma":[0.000075351876,0.0007193531,0.0033595772,0.0002174177,0.00010460908,0.00019496006,0.17467164,0.2273805,0.049642827,0.47896615,0.064106,0.00056161935],"about_ca_topic_score_codex":0.00011467877,"about_ca_topic_score_gemma":0.00010265994,"teacher_disagreement_score":0.96852255,"about_ca_system_score_codex":0.000066298206,"about_ca_system_score_gemma":0.0002573746,"threshold_uncertainty_score":0.9758683},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.939315,0.00063839194,0.05548235,0.00008855767,0.0030449084,0.00047133723,0.0000027022886,0.000047366913,0.00090937316],"genre_scores_gemma":[0.99860376,0.0004478938,0.00075275503,0.000002878365,0.00014698172,0.000019836614,3.3605997e-7,0.00001623829,0.00000933282],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9972496,0.00023551901,0.0017097746,0.00013679489,0.00045291212,0.00021541875],"domain_scores_gemma":[0.99773574,0.000718177,0.0007539516,0.00023471333,0.00050015084,0.000057265686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001549439,0.0001521622,0.0002908109,0.0005258253,0.00012368438,0.00011146965,0.0002378787,0.0000901853,0.000016575566],"category_scores_gemma":[0.00035999727,0.0001264181,0.00008940827,0.0014507534,0.00013650475,0.0005988754,0.00003701829,0.0004703875,0.000021681473],"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":[0.00047277802,0.0002901637,0.00043516292,0.00006532099,0.00025293248,0.0005123972,0.010938095,0.12864289,0.00087724923,0.03737004,0.000022155215,0.8201208],"study_design_scores_gemma":[0.000039833045,0.00036517475,0.000046254816,0.0001541624,0.00014209187,0.0020690137,0.7924999,0.18625589,0.010466535,0.0071830475,0.00062675605,0.00015132269],"about_ca_topic_score_codex":0.00022191244,"about_ca_topic_score_gemma":0.0003979639,"teacher_disagreement_score":0.8199695,"about_ca_system_score_codex":0.00007658643,"about_ca_system_score_gemma":0.000039245806,"threshold_uncertainty_score":0.51551807},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9614243,0.00014935009,0.0030764616,0.029007055,0.001569823,0.00019330444,0.000001986584,0.00017704556,0.0044007045],"genre_scores_gemma":[0.997211,0.00006473646,0.0006060895,0.0008776012,0.0011315461,0.0000019914457,0.000004421728,0.0000014899816,0.00010115217],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99839157,0.0002099998,0.00048221857,0.00016011372,0.00048922846,0.00026688757],"domain_scores_gemma":[0.9970243,0.001999069,0.0004833241,0.0000401469,0.0003361449,0.00011699657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012316605,0.00012713164,0.0001844562,0.000048442194,0.00028067612,0.00017306577,0.00026810187,0.0000838956,0.00030211086],"category_scores_gemma":[0.0021601461,0.000047364465,0.00015747946,0.0009252825,0.000029816061,0.0004613319,0.000033446115,0.0004741066,0.00048697507],"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":[0.00042790745,0.0004264211,0.0014930904,0.000007887367,0.000048686103,0.00034513348,0.00068957085,0.063315995,0.15223736,0.0013557791,0.0015704826,0.7780817],"study_design_scores_gemma":[0.0002054491,0.009268681,0.05100642,0.0006364058,0.00037802412,0.0007142707,0.05948659,0.113895856,0.24022588,0.0072953305,0.51514065,0.0017464564],"about_ca_topic_score_codex":0.000034167228,"about_ca_topic_score_gemma":0.00006261363,"teacher_disagreement_score":0.77633524,"about_ca_system_score_codex":0.000030824653,"about_ca_system_score_gemma":0.0000146312295,"threshold_uncertainty_score":0.62592417},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71447736,0.000022452221,0.28017253,0.004607472,0.00011631779,0.000233688,0.000005446982,0.00002198069,0.00034274772],"genre_scores_gemma":[0.99531466,0.00005449258,0.0044917674,0.000063821855,0.000052212963,0.0000051977745,0.0000010962202,0.000010864668,0.00000587812],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99795127,0.00009231169,0.00087513676,0.0001961738,0.0006311988,0.00025391686],"domain_scores_gemma":[0.99740124,0.0014617696,0.00056667754,0.00016223425,0.0003438775,0.00006417769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024618765,0.00011460746,0.00020636432,0.00026462067,0.00007215449,0.000023133069,0.00027866574,0.00010249157,0.00042580455],"category_scores_gemma":[0.0017645512,0.00010654661,0.000035255307,0.002111387,0.0003935179,0.00037646093,0.000115458926,0.0005339135,0.000034410165],"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":[0.00028829178,0.00022280459,0.00038629855,0.000036685888,0.000022672766,0.000022664752,0.00085390866,0.042189267,0.72313863,0.0009623336,0.000092547765,0.23178387],"study_design_scores_gemma":[0.000021488502,0.00056432915,0.00012845066,0.00007046961,0.000011814509,0.000015386267,0.009889493,0.014490845,0.96424323,0.010320233,0.00015326626,0.00009100862],"about_ca_topic_score_codex":0.000068327405,"about_ca_topic_score_gemma":0.0000050101844,"teacher_disagreement_score":0.2808373,"about_ca_system_score_codex":0.00008653017,"about_ca_system_score_gemma":0.000053447387,"threshold_uncertainty_score":0.46622613},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09086842,0.000027492555,0.8979136,0.010340252,0.00036741406,0.00029486135,0.0000033095303,0.000030093322,0.00015457752],"genre_scores_gemma":[0.98377633,0.00006835994,0.01582726,0.0002159807,0.00008567513,0.0000085734955,7.674075e-7,0.000011352081,0.0000056947883],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967213,0.0003283631,0.001313162,0.0002889742,0.0009924322,0.00035576543],"domain_scores_gemma":[0.9923036,0.003536139,0.0010564975,0.00029614047,0.0027166184,0.00009100976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004902885,0.00014766084,0.0003117135,0.0014339313,0.000109684624,0.00013275509,0.0007314215,0.00009901981,0.00002898697],"category_scores_gemma":[0.0033214542,0.00012960794,0.000049572063,0.0042845774,0.00036645058,0.0015245805,0.00020594368,0.0005428659,0.000048461356],"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":[0.00028138282,0.00046839242,0.00022890505,0.000038420378,0.000040823063,0.000055602228,0.0027378274,0.0017577655,0.02532618,0.08326065,0.00012936363,0.8856747],"study_design_scores_gemma":[0.000046990786,0.005502197,0.00024001283,0.0002072226,0.000015037317,0.00004008962,0.019960472,0.042091057,0.7968139,0.13451304,0.00038612273,0.00018384408],"about_ca_topic_score_codex":0.0000456484,"about_ca_topic_score_gemma":0.00000787716,"teacher_disagreement_score":0.8929079,"about_ca_system_score_codex":0.00004897145,"about_ca_system_score_gemma":0.00034193302,"threshold_uncertainty_score":0.5285258},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27356172,0.0006053972,0.6855774,0.022647751,0.002403949,0.0014683588,0.000010443656,0.00007844453,0.013646573],"genre_scores_gemma":[0.99342626,0.0015303082,0.004620166,0.00008774303,0.00024350933,0.000022546898,6.820126e-7,0.000017626462,0.00005114056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961254,0.0003983111,0.0015719701,0.00026236402,0.0011568278,0.00048510343],"domain_scores_gemma":[0.9944837,0.0035757907,0.0012442253,0.00037128327,0.00021079303,0.00011422749],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007004306,0.00015908713,0.00027045055,0.00018428334,0.00032427264,0.00010146032,0.00094174506,0.000073892996,0.00014616954],"category_scores_gemma":[0.012324285,0.000119869714,0.00013495944,0.0017941383,0.0006441723,0.000870127,0.00045689242,0.00068750023,0.0007351399],"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":[0.00046805895,0.00028207243,0.0011208333,0.000013569092,0.000036534682,0.00006289904,0.002804748,0.02776128,0.011854227,0.005628283,0.0005715337,0.94939595],"study_design_scores_gemma":[0.0000945721,0.0015671984,0.00579082,0.00031809224,0.00014598163,0.0002423411,0.0977881,0.4395539,0.14768626,0.2446193,0.06130239,0.00089107745],"about_ca_topic_score_codex":0.00020140281,"about_ca_topic_score_gemma":0.00030504566,"teacher_disagreement_score":0.94850487,"about_ca_system_score_codex":0.0002676687,"about_ca_system_score_gemma":0.00006457556,"threshold_uncertainty_score":0.99599534},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.939315,0.00063839194,0.05548235,0.00008855767,0.0030449084,0.00047133723,0.0000027022886,0.000047366913,0.00090937316],"genre_scores_gemma":[0.99860376,0.0004478938,0.00075275503,0.000002878365,0.00014698172,0.000019836614,3.3605997e-7,0.00001623829,0.00000933282],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9972496,0.00023551901,0.0017097746,0.00013679489,0.00045291212,0.00021541875],"domain_scores_gemma":[0.99773574,0.000718177,0.0007539516,0.00023471333,0.00050015084,0.000057265686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001549439,0.0001521622,0.0002908109,0.0005258253,0.00012368438,0.00011146965,0.0002378787,0.0000901853,0.000016575566],"category_scores_gemma":[0.00035999727,0.0001264181,0.00008940827,0.0014507534,0.00013650475,0.0005988754,0.00003701829,0.0004703875,0.000021681473],"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":[0.00047277802,0.0002901637,0.00043516292,0.00006532099,0.00025293248,0.0005123972,0.010938095,0.12864289,0.00087724923,0.03737004,0.000022155215,0.8201208],"study_design_scores_gemma":[0.000039833045,0.00036517475,0.000046254816,0.0001541624,0.00014209187,0.0020690137,0.7924999,0.18625589,0.010466535,0.0071830475,0.00062675605,0.00015132269],"about_ca_topic_score_codex":0.00022191244,"about_ca_topic_score_gemma":0.0003979639,"teacher_disagreement_score":0.8199695,"about_ca_system_score_codex":0.00007658643,"about_ca_system_score_gemma":0.000039245806,"threshold_uncertainty_score":0.51551807},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9614243,0.00014935009,0.0030764616,0.029007055,0.001569823,0.00019330444,0.000001986584,0.00017704556,0.0044007045],"genre_scores_gemma":[0.997211,0.00006473646,0.0006060895,0.0008776012,0.0011315461,0.0000019914457,0.000004421728,0.0000014899816,0.00010115217],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99839157,0.0002099998,0.00048221857,0.00016011372,0.00048922846,0.00026688757],"domain_scores_gemma":[0.9970243,0.001999069,0.0004833241,0.0000401469,0.0003361449,0.00011699657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012316605,0.00012713164,0.0001844562,0.000048442194,0.00028067612,0.00017306577,0.00026810187,0.0000838956,0.00030211086],"category_scores_gemma":[0.0021601461,0.000047364465,0.00015747946,0.0009252825,0.000029816061,0.0004613319,0.000033446115,0.0004741066,0.00048697507],"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":[0.00042790745,0.0004264211,0.0014930904,0.000007887367,0.000048686103,0.00034513348,0.00068957085,0.063315995,0.15223736,0.0013557791,0.0015704826,0.7780817],"study_design_scores_gemma":[0.0002054491,0.009268681,0.05100642,0.0006364058,0.00037802412,0.0007142707,0.05948659,0.113895856,0.24022588,0.0072953305,0.51514065,0.0017464564],"about_ca_topic_score_codex":0.000034167228,"about_ca_topic_score_gemma":0.00006261363,"teacher_disagreement_score":0.77633524,"about_ca_system_score_codex":0.000030824653,"about_ca_system_score_gemma":0.0000146312295,"threshold_uncertainty_score":0.62592417},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020571108,0.00024406049,0.99221927,0.0059785466,0.0009902297,0.00022902947,0.000021327074,0.000062193234,0.000049652383],"genre_scores_gemma":[0.06395738,0.00023017808,0.9338561,0.0010851974,0.00069934997,0.000012580104,0.00006418719,0.000010826658,0.00008418792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980592,0.0001230048,0.00075742695,0.00028550174,0.0006347874,0.00014007374],"domain_scores_gemma":[0.9977015,0.0008662459,0.00039444416,0.00032788288,0.0006103981,0.00009952244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023296005,0.00010257018,0.00012602481,0.00020793415,0.00012401927,0.0006172793,0.00079893693,0.000058501406,0.00004494639],"category_scores_gemma":[0.0019325374,0.00008842594,0.000063898864,0.0002690649,0.000048989627,0.0062276116,0.00013690189,0.00023974513,0.000029693736],"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":[0.000057730547,0.00025903393,6.6989264e-7,0.000038721468,0.00006794366,0.000049458155,0.00165345,0.010328281,0.1642597,0.059634868,0.01765651,0.7459936],"study_design_scores_gemma":[0.000024825425,0.0001368705,5.895899e-7,0.00003348316,0.000043636766,0.00009011301,0.00021236128,0.8925573,0.08230149,0.023102045,0.0014127219,0.00008451247],"about_ca_topic_score_codex":0.000004010966,"about_ca_topic_score_gemma":0.0000030441283,"teacher_disagreement_score":0.8822291,"about_ca_system_score_codex":0.000090041794,"about_ca_system_score_gemma":0.00030961892,"threshold_uncertainty_score":0.5952439},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048207395,0.0041020303,0.9467061,0.00054564554,0.00028381668,0.00008327136,3.036923e-7,0.00001702615,0.000054402248],"genre_scores_gemma":[0.95096236,0.000032744087,0.04875783,0.00003559533,0.00016677736,0.0000032531652,0.0000018549473,0.0000060323196,0.000033572032],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819934,0.000097096345,0.00090709573,0.00022579862,0.0004039831,0.00016668647],"domain_scores_gemma":[0.997931,0.00068620144,0.0007297509,0.00013094963,0.00047943878,0.000042633215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022892363,0.00009738083,0.00025941886,0.00069484947,0.000077261335,0.00045635275,0.0003706438,0.000043950397,0.00001144142],"category_scores_gemma":[0.00069856504,0.00008494418,0.00025482092,0.001806466,0.000032563177,0.0014039603,0.000054938362,0.00030797886,0.0000061953037],"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":[0.00007628477,0.00013215073,0.0014020843,0.000047323534,0.00031331662,0.00002621453,0.008718441,0.07823909,0.006201164,0.0031220189,0.0000024699418,0.90171945],"study_design_scores_gemma":[0.000025918725,0.00007671904,0.00022536055,0.00004690575,0.00022614992,0.000016639771,0.0033747088,0.96589327,0.02908874,0.00040635827,0.00053131045,0.000087933295],"about_ca_topic_score_codex":0.00002959881,"about_ca_topic_score_gemma":0.000020562411,"teacher_disagreement_score":0.90275496,"about_ca_system_score_codex":0.0000636084,"about_ca_system_score_gemma":0.00008165907,"threshold_uncertainty_score":0.440062},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017332674,0.00070216693,0.9869691,0.009597979,0.00060754287,0.0002653985,0.000010222487,0.000017395192,0.00009690726],"genre_scores_gemma":[0.87648994,0.00035980763,0.1223745,0.00019956681,0.00054648565,0.000010515428,0.000009574217,0.000008260112,0.0000013314746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99702275,0.00024961313,0.0016027798,0.0003774202,0.0004896347,0.00025781748],"domain_scores_gemma":[0.9960774,0.0016784173,0.00074930064,0.0011609802,0.00029793644,0.00003597117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041989707,0.00014250723,0.00030993758,0.00055644085,0.000055826615,0.00015663677,0.0034130635,0.00012987878,0.0000043705845],"category_scores_gemma":[0.00065434515,0.00010851377,0.00006456552,0.003591668,0.00023256891,0.0012760136,0.00052684906,0.00072203105,0.000030447824],"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":[0.000037776597,0.00032596092,0.000052294672,0.000023230703,0.000017749793,0.000060448518,0.0009309298,0.005017401,0.001361828,0.270415,0.00013960355,0.72161776],"study_design_scores_gemma":[0.000017244545,0.00033301348,0.00014212506,0.00023629844,0.00005341535,0.00027473323,0.0015815176,0.67532146,0.007431645,0.29999518,0.014417773,0.00019555422],"about_ca_topic_score_codex":0.0001408782,"about_ca_topic_score_gemma":0.0002502276,"teacher_disagreement_score":0.8747567,"about_ca_system_score_codex":0.000046342066,"about_ca_system_score_gemma":0.0002388971,"threshold_uncertainty_score":0.63423777},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18650453,0.0023491727,0.7889199,0.015536981,0.0038679265,0.00026593282,0.0000046813384,0.000021599655,0.0025292654],"genre_scores_gemma":[0.9542424,0.00012865565,0.045124028,0.00013779406,0.00033770606,0.0000061393425,0.0000015430481,0.0000065667105,0.000015148107],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9973256,0.00011550843,0.0015874953,0.00018945688,0.000623771,0.0001581794],"domain_scores_gemma":[0.9956247,0.0015592832,0.00074611924,0.00026438624,0.001761098,0.00004439058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002401897,0.0001255004,0.00020425684,0.0004450867,0.00006883968,0.00033186836,0.0009227443,0.000063366555,0.000022578999],"category_scores_gemma":[0.0013353957,0.000095331656,0.00008996325,0.0012487071,0.000111087036,0.0022295984,0.00007195693,0.00041170412,0.000020748454],"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":[0.00002263533,0.00060821295,0.0000059971208,0.000027603888,0.0000240425,0.0000041943863,0.028459312,0.00008056547,0.00045119438,0.44188982,0.000082787745,0.5283436],"study_design_scores_gemma":[0.000024315015,0.0005736788,0.00022994349,0.0008471216,0.00007261033,0.00026907216,0.59054273,0.010462283,0.14877388,0.20659576,0.041150097,0.00045852008],"about_ca_topic_score_codex":0.00001494861,"about_ca_topic_score_gemma":0.00003502545,"teacher_disagreement_score":0.76773787,"about_ca_system_score_codex":0.00008472309,"about_ca_system_score_gemma":0.0021242464,"threshold_uncertainty_score":0.3887512},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031190782,0.0004538803,0.9945555,0.00042399002,0.0035434482,0.00017107964,0.0000033857868,0.00012450185,0.00041231324],"genre_scores_gemma":[0.21541813,0.000056661134,0.7832579,0.00016424344,0.000994825,0.0000010766088,5.676759e-7,0.000052396048,0.000054197415],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986917,0.000104273706,0.00055415835,0.0001564247,0.0002757866,0.00021766729],"domain_scores_gemma":[0.9981886,0.0010411275,0.00016054051,0.00012349235,0.0004143501,0.00007184781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016979427,0.00015747602,0.00018271014,0.00026503755,0.00010172072,0.00019017419,0.00007849025,0.00008554616,0.000018878782],"category_scores_gemma":[0.0013778599,0.00014455065,0.00016066193,0.000304916,0.000025048488,0.00080196897,0.0000065591976,0.00050541706,0.000025431538],"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":[0.0002069941,0.000012952622,3.5023618e-8,0.00003384666,0.00003227977,0.000017060998,0.000078676974,0.055633243,0.21467586,0.0000052032524,0.000032272415,0.7292716],"study_design_scores_gemma":[0.000026326512,0.0002127549,3.1001798e-7,0.00005318731,0.000041503885,0.00003102947,0.00020818606,0.5282549,0.4560498,0.0018386348,0.013199891,0.000083457184],"about_ca_topic_score_codex":0.0000046054433,"about_ca_topic_score_gemma":0.0000019079043,"teacher_disagreement_score":0.72918814,"about_ca_system_score_codex":0.000152465,"about_ca_system_score_gemma":0.00004429819,"threshold_uncertainty_score":0.58946043},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08934169,0.011339874,0.8797889,0.017421817,0.0004102316,0.0004506007,0.000026657593,0.000009913808,0.0012103261],"genre_scores_gemma":[0.991919,0.0012826357,0.006418243,0.000037015147,0.0002914174,0.0000019668967,0.00003073227,0.0000059821946,0.000013003388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973042,0.0005037225,0.00062079966,0.00016320523,0.0013068259,0.0001012266],"domain_scores_gemma":[0.9939293,0.0034484759,0.00048618173,0.0002836728,0.0017549446,0.000097403776],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0039915633,0.000070095,0.00025265943,0.00020881106,0.000047958052,0.00004055123,0.00020066886,0.000045831286,0.00055146724],"category_scores_gemma":[0.035134126,0.000042299373,0.00013320104,0.00079930335,0.000107643566,0.0010501639,0.000053250693,0.0004827209,0.0000212483],"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":[0.0028107404,0.0009808514,0.00089064066,0.00003422823,0.006616382,0.00015416091,0.015697395,0.004752538,0.0071968366,0.0028226578,0.000118425254,0.95792514],"study_design_scores_gemma":[0.0001707492,0.0012621775,0.0013200644,0.0002751316,0.008008423,0.00003118104,0.06128013,0.8916228,0.021421837,0.013610649,0.00092417997,0.000072672025],"about_ca_topic_score_codex":0.0011970962,"about_ca_topic_score_gemma":0.00013687716,"teacher_disagreement_score":0.9578525,"about_ca_system_score_codex":0.00011266269,"about_ca_system_score_gemma":0.00039219813,"threshold_uncertainty_score":0.9729934},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15693854,0.03031947,0.0043727895,0.7373556,0.0055621704,0.0007237867,0.000010520316,0.00007407626,0.06464304],"genre_scores_gemma":[0.9879127,0.010246761,0.00010541577,0.00035372208,0.0012107278,0.0000034971952,5.0302685e-8,0.000016361217,0.0001507328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967354,0.0009304457,0.000877783,0.00018553941,0.00090672413,0.0003641403],"domain_scores_gemma":[0.9872174,0.011072436,0.0005416396,0.0002516138,0.0007581154,0.0001587941],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00866257,0.00016353882,0.0002241261,0.00012985282,0.00080419594,0.0007071651,0.0006671797,0.00022849438,0.00019474006],"category_scores_gemma":[0.0120469425,0.00008666536,0.00019823076,0.00051975704,0.0013724627,0.0013527086,0.00006434335,0.0018396107,0.00007206641],"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":[0.00019934753,0.00009146257,0.000012705753,0.000007542877,0.000102452155,0.000044575998,0.0100093335,0.0002920095,0.00020484808,0.77074206,0.0009531348,0.2173405],"study_design_scores_gemma":[0.000012360535,0.001056568,0.00005526838,0.0003780494,0.00014908402,0.00017847033,0.19534282,0.0020376313,0.021273663,0.2496889,0.52956456,0.0002626557],"about_ca_topic_score_codex":0.0010107106,"about_ca_topic_score_gemma":0.00056796166,"teacher_disagreement_score":0.8309742,"about_ca_system_score_codex":0.00012915001,"about_ca_system_score_gemma":0.00076029345,"threshold_uncertainty_score":0.996275},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005157462,0.0006729374,0.99680674,0.0004753452,0.00051432266,0.00006116613,7.871164e-7,0.00004039599,0.00091255945],"genre_scores_gemma":[0.94736534,0.00041150782,0.051441446,0.00008912902,0.00061305385,0.0000022728502,0.0000029191763,0.000029954748,0.000044357967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988715,0.00003346009,0.0005686345,0.00009186858,0.00026757058,0.00016694999],"domain_scores_gemma":[0.9988984,0.0005214815,0.00021758783,0.00007001398,0.00022882989,0.00006372648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045331984,0.00012061567,0.00016881463,0.00022487559,0.000055553053,0.0002029637,0.0001195209,0.00007231322,0.00006936482],"category_scores_gemma":[0.00023977866,0.0001066737,0.00010314669,0.00030041876,0.00001602015,0.0009522259,0.0000066437174,0.00032825008,0.0000019974555],"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":[0.00009555426,0.000011837621,3.3752468e-7,0.000049915834,0.00004853348,0.0000069797434,0.00038188996,0.80229807,0.004102576,0.01183246,0.00024173598,0.18093014],"study_design_scores_gemma":[0.00003153144,0.0005013947,4.949858e-7,0.00015596271,0.000056107718,0.00001913065,0.0014995991,0.92747015,0.057632416,0.0026087787,0.009928899,0.00009551783],"about_ca_topic_score_codex":0.000025688076,"about_ca_topic_score_gemma":0.0000014163879,"teacher_disagreement_score":0.9468496,"about_ca_system_score_codex":0.00007786672,"about_ca_system_score_gemma":0.00022813836,"threshold_uncertainty_score":0.43500274},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6477385,0.010945649,0.005339208,0.31584883,0.002338289,0.00034081168,0.0000023248663,0.00006217767,0.017384175],"genre_scores_gemma":[0.9982938,0.0009729473,0.00049637666,0.00003055139,0.000102988415,0.000003898513,9.284762e-8,0.0000038713597,0.000095450654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988179,0.000108340966,0.00059194234,0.000085128035,0.000243911,0.00015276996],"domain_scores_gemma":[0.9983044,0.0009887385,0.00027581287,0.00009296453,0.0003115132,0.000026534128],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001918109,0.000052111598,0.000107975204,0.00044356225,0.000090145695,0.0001636955,0.00032827607,0.00013124352,0.000025563815],"category_scores_gemma":[0.009420762,0.000039297636,0.000042209125,0.0013599956,0.00028891512,0.0010529073,0.000016528098,0.0007362979,0.000043794626],"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":[0.000025276768,0.000112174486,0.004457999,0.0000020523853,0.000009842433,0.000015469559,0.0049636634,0.000029983648,0.0009279238,0.6143313,0.000034891324,0.3750894],"study_design_scores_gemma":[0.000012152393,0.00008695094,0.00009162434,0.00011662569,0.000008286559,0.000023525872,0.25301874,0.00037853513,0.01378426,0.6103817,0.122040145,0.000057400848],"about_ca_topic_score_codex":0.00023450068,"about_ca_topic_score_gemma":0.0007443876,"teacher_disagreement_score":0.375032,"about_ca_system_score_codex":0.000104237304,"about_ca_system_score_gemma":0.00047978546,"threshold_uncertainty_score":0.9989233},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005802372,0.00017130036,0.97117555,0.023401594,0.0017806725,0.00013470952,0.000004168199,0.000038677157,0.002713075],"genre_scores_gemma":[0.9888718,0.0002614732,0.009891127,0.00032907652,0.000449949,0.000008162813,0.0000018056962,0.000009978973,0.00017663767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687266,0.00064793805,0.0005692423,0.00027610446,0.0013655394,0.00026852934],"domain_scores_gemma":[0.99371797,0.004737122,0.00021551174,0.0002918026,0.0009300088,0.000107596905],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005873824,0.00012647176,0.00015582678,0.0009927428,0.000543025,0.0013767642,0.0005582002,0.00007671645,0.000047454505],"category_scores_gemma":[0.001818355,0.00008192464,0.0002877319,0.0026654184,0.00009764051,0.0011285119,0.000026707288,0.0011416724,0.0001234951],"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":[0.00017408558,0.00022273211,2.431854e-7,0.0000078556595,0.00010815603,0.00005094227,0.00102213,0.0015768181,0.00009210652,0.02419971,0.0004175386,0.9721277],"study_design_scores_gemma":[0.000029605766,0.0010170693,0.00008785605,0.000108960594,0.0002119858,0.00006068631,0.0012306786,0.91000843,0.03319925,0.039944235,0.013964683,0.00013654314],"about_ca_topic_score_codex":0.00003226656,"about_ca_topic_score_gemma":0.00006296281,"teacher_disagreement_score":0.98829156,"about_ca_system_score_codex":0.000168448,"about_ca_system_score_gemma":0.00019628757,"threshold_uncertainty_score":0.9996599},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064058095,0.0039918497,0.9055773,0.005507441,0.010262707,0.0007630695,0.000041942774,0.00019278946,0.009604796],"genre_scores_gemma":[0.98943406,0.0015117745,0.0042176163,0.00025625838,0.004136471,0.00003254229,0.000003341294,0.0000674376,0.00034051205],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9946107,0.0010504305,0.002326268,0.00037627143,0.00096337515,0.0006729321],"domain_scores_gemma":[0.9923326,0.004338339,0.0009920431,0.00029438914,0.0017049657,0.00033770892],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0049714623,0.00031050402,0.00075380475,0.00047943895,0.0006933111,0.00019882208,0.0005022924,0.00037636276,0.0008413275],"category_scores_gemma":[0.005677733,0.00025188117,0.00032810803,0.00087437296,0.00020127605,0.0014161277,0.0001337842,0.0024978272,0.0011127824],"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":[0.002062293,0.00086300453,0.00009206455,0.0005078245,0.0006508701,0.00070303463,0.17088483,0.012570641,0.001972776,0.34858808,0.0009784495,0.46012613],"study_design_scores_gemma":[0.000053171334,0.000968922,0.000025650155,0.0012590587,0.00074812653,0.000034009725,0.42328605,0.22579792,0.0055112075,0.3038888,0.037841123,0.0005859686],"about_ca_topic_score_codex":0.00016452465,"about_ca_topic_score_gemma":0.00021281293,"teacher_disagreement_score":0.92537594,"about_ca_system_score_codex":0.00045748675,"about_ca_system_score_gemma":0.0013018743,"threshold_uncertainty_score":0.9999933},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67563367,0.0043716202,0.0073654423,0.29995552,0.0030661472,0.0012688335,0.0000028027719,0.0000115052335,0.008324468],"genre_scores_gemma":[0.99765295,0.00039704508,0.00033174854,0.00060084113,0.0008333571,0.000030693995,0.0000014383319,0.000011650763,0.00014029395],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99712205,0.0005552123,0.0011574152,0.00015782965,0.00082881085,0.00017869408],"domain_scores_gemma":[0.9925726,0.0050908993,0.00046248265,0.00037351498,0.0014350462,0.0000654444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075594154,0.00009160995,0.00019653876,0.0005198426,0.00009337129,0.000052967895,0.00033683583,0.00008269162,0.00011275312],"category_scores_gemma":[0.0021209049,0.000051866988,0.00011489837,0.0014305456,0.00024630743,0.00032798838,0.000029742592,0.0010334764,0.000070090304],"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":[0.0016064511,0.0018298897,0.00034285596,0.00030909927,0.000057792724,0.000019366871,0.022595981,0.00045150353,0.0048094294,0.41020098,0.00299909,0.55477756],"study_design_scores_gemma":[0.000026696125,0.004029917,0.0047092414,0.0042048427,0.00035364792,0.00034218212,0.32574993,0.007886424,0.18441449,0.36064515,0.10737542,0.00026205505],"about_ca_topic_score_codex":0.00033372745,"about_ca_topic_score_gemma":0.000033333792,"teacher_disagreement_score":0.5545155,"about_ca_system_score_codex":0.00015061341,"about_ca_system_score_gemma":0.00095301674,"threshold_uncertainty_score":0.44899985},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044942356,0.0006700155,0.9917274,0.0045946487,0.0019530498,0.00009093417,8.554095e-7,0.00008439927,0.000429271],"genre_scores_gemma":[0.58214515,0.000013233994,0.41698834,0.00045713817,0.00035940018,9.175499e-7,4.1248748e-7,0.000014283,0.000021133194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782586,0.00023807051,0.00071763544,0.00029978153,0.0006582649,0.000260411],"domain_scores_gemma":[0.9937382,0.005142229,0.00039586803,0.000264199,0.00030850054,0.0001510365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019058078,0.00018396303,0.00023510987,0.0004034308,0.00013748475,0.000810364,0.00045262178,0.00010017259,0.000008778724],"category_scores_gemma":[0.0024984938,0.00016155119,0.000088098896,0.00054531044,0.000056615532,0.0019345916,0.000043060198,0.00068985496,0.000053779746],"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":[0.0000654957,0.00008451513,0.00001316194,0.00002207737,0.000027751345,0.0006023977,0.0011099325,0.8958161,0.0003509292,0.004475355,0.00005990783,0.09737239],"study_design_scores_gemma":[0.000028304354,0.00049163465,0.00008332389,0.00033329724,0.000050148785,0.00021544832,0.00035020898,0.9936284,0.0015295878,0.002190142,0.0009275847,0.00017191216],"about_ca_topic_score_codex":0.0000062730237,"about_ca_topic_score_gemma":2.2572036e-7,"teacher_disagreement_score":0.58169574,"about_ca_system_score_codex":0.00010341881,"about_ca_system_score_gemma":0.0002453285,"threshold_uncertainty_score":0.7814359},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045422066,0.0019196889,0.98958755,0.0061085923,0.0016202007,0.00009570879,0.0000020252803,0.00003496052,0.00017706412],"genre_scores_gemma":[0.8230857,0.00011907354,0.17518702,0.00064297364,0.00084354746,0.000007366522,0.0000056446884,0.000012602406,0.00009607482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772334,0.00015224061,0.000941848,0.00030857392,0.0006088328,0.00026514035],"domain_scores_gemma":[0.99650866,0.0018239619,0.0005672486,0.00025069542,0.0007195455,0.00012989398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025764753,0.00015027562,0.00031403734,0.00086841715,0.00016987817,0.0008237534,0.00056711625,0.00006595951,0.00013228566],"category_scores_gemma":[0.000401244,0.00012831221,0.0007644026,0.002340095,0.000052720283,0.0012974769,0.00005237574,0.0002829903,0.000034330227],"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":[0.0002069081,0.00049718795,0.00017259704,0.000023944807,0.0041889395,0.00013941353,0.0006381692,0.5357764,0.0006029563,0.35624686,0.0038045445,0.09770208],"study_design_scores_gemma":[0.000031157288,0.00018950291,0.000029498482,0.000038934082,0.001090411,0.000027983375,0.0003068407,0.9758495,0.003505582,0.0060730944,0.01270979,0.0001477245],"about_ca_topic_score_codex":0.0000116723795,"about_ca_topic_score_gemma":0.0000063116436,"teacher_disagreement_score":0.8226315,"about_ca_system_score_codex":0.000088365916,"about_ca_system_score_gemma":0.00021385385,"threshold_uncertainty_score":0.7943473},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004227861,0.00039394706,0.94936305,0.04447118,0.0012415774,0.00013808247,0.0000011424397,0.00001773113,0.00014541247],"genre_scores_gemma":[0.5035627,0.00071544247,0.4953916,0.000120361365,0.00013782125,0.0000014268138,1.480035e-7,0.000004332474,0.00006619433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981693,0.0007198103,0.0004835203,0.00011554618,0.00041805374,0.00009379062],"domain_scores_gemma":[0.97289616,0.024906935,0.0008328477,0.00032483618,0.0010095438,0.000029693938],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0078113168,0.00006484798,0.00011229768,0.00011470092,0.0002321431,0.00017466156,0.0011983372,0.000052533716,0.0000057242232],"category_scores_gemma":[0.029842941,0.00003771955,0.00010022636,0.00061947276,0.00025193105,0.0012513174,0.00012489625,0.0003640341,0.0000028570055],"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":[0.00020968547,0.0001987568,0.000036074704,0.0000122853835,0.00010660605,0.0000046052455,0.0018513751,0.00019818632,0.0013712283,0.5474709,0.00021924188,0.44832104],"study_design_scores_gemma":[0.000084613006,0.00077700324,0.0003435164,0.00017373147,0.00042565193,0.0005475418,0.077505775,0.016900396,0.09001878,0.06927722,0.74376684,0.00017893502],"about_ca_topic_score_codex":0.000023681056,"about_ca_topic_score_gemma":0.0000020288976,"teacher_disagreement_score":0.7435476,"about_ca_system_score_codex":0.00005556639,"about_ca_system_score_gemma":0.00023046196,"threshold_uncertainty_score":0.9783291},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021825056,0.0025443991,0.99056906,0.0041269423,0.00021312425,0.00004687729,1.4112791e-7,0.000029994768,0.00028693708],"genre_scores_gemma":[0.8697875,0.00006963879,0.12993489,0.000118157,0.0000732629,0.0000016477036,5.792488e-7,0.000004018208,0.0000103077855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920744,0.00007931627,0.00030826105,0.00012678429,0.00020222508,0.00007600094],"domain_scores_gemma":[0.9986648,0.00062138564,0.0003022854,0.00009047236,0.00029385436,0.00002719883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007529507,0.000060264843,0.00008718258,0.0002213496,0.00007292975,0.000105129984,0.00021108423,0.000055361597,0.0000045507127],"category_scores_gemma":[0.0005567971,0.000052372365,0.000028268068,0.00036185337,0.000048350845,0.0012382277,0.000022799639,0.00038944822,0.0000035288053],"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":[0.000027482138,0.0000508769,0.0000046445134,0.000021425809,0.000006504347,0.00000600736,0.0016088246,0.4851842,0.00075863575,0.0346295,0.0000023223033,0.4776996],"study_design_scores_gemma":[0.000009261697,0.00011714844,0.000005651284,0.00006000466,0.000015496655,0.00007369695,0.0011736809,0.98580843,0.00588042,0.0066321245,0.00017507834,0.000049002334],"about_ca_topic_score_codex":0.000008014255,"about_ca_topic_score_gemma":0.0000015984873,"teacher_disagreement_score":0.867605,"about_ca_system_score_codex":0.000025782976,"about_ca_system_score_gemma":0.00012683429,"threshold_uncertainty_score":0.2135683},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9407779,0.00048792464,0.009819059,0.015430786,0.0052659838,0.000364389,0.00004614058,0.00006041738,0.02774739],"genre_scores_gemma":[0.9974558,0.00012495795,0.0011245771,0.00012070312,0.0010085849,5.6053292e-8,0.0000147749115,0.000004783811,0.00014579053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978311,0.0005038558,0.00044911445,0.0002532228,0.0006918266,0.00027087022],"domain_scores_gemma":[0.996995,0.00226686,0.0001381135,0.0002537508,0.00024136752,0.00010490788],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004246951,0.000114536844,0.00015550532,0.00037498784,0.00016171188,0.00053680816,0.00033980006,0.00007887569,0.0003019726],"category_scores_gemma":[0.0016557616,0.00007412106,0.000051473813,0.00078359724,0.00008217656,0.0015472078,0.000015264168,0.0010886901,0.0012420213],"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":[0.0031712565,0.00020700086,0.00017334978,0.000045329954,0.000041257805,0.0019363515,0.0007892698,0.056146745,0.002253593,0.000051955056,0.0023494244,0.93283445],"study_design_scores_gemma":[0.00006993821,0.0030800418,0.012190668,0.0007345658,0.000057207526,0.00073513284,0.005178773,0.9337024,0.030177705,0.0009811558,0.012769744,0.00032263203],"about_ca_topic_score_codex":0.00065415644,"about_ca_topic_score_gemma":0.0013205791,"teacher_disagreement_score":0.9325118,"about_ca_system_score_codex":0.000026042655,"about_ca_system_score_gemma":0.00012138059,"threshold_uncertainty_score":0.9995356},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44749436,0.0010022608,0.4869671,0.041217938,0.0009697117,0.00045187614,0.0000018168516,0.00008028049,0.021814665],"genre_scores_gemma":[0.9967177,0.00018440203,0.0025383523,0.00013582986,0.0003818049,0.0000019270508,2.8084258e-7,0.0000074376594,0.000032245287],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99886256,0.000037957187,0.00038707716,0.00010505744,0.00047543301,0.00013190361],"domain_scores_gemma":[0.99726725,0.001861295,0.00026333422,0.00014827155,0.00045482823,0.000005049725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030890347,0.000064053966,0.00010930782,0.00074316486,0.00009872347,0.00020897038,0.00040973347,0.000058753132,0.000040774612],"category_scores_gemma":[0.0010302032,0.000042321597,0.000052811964,0.0012235395,0.00013634036,0.0014117229,0.00011351147,0.0005615591,0.00014461146],"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":[0.00013353913,0.000074690244,0.00001438548,0.000016250182,0.000011752393,0.000042582837,0.00014085279,0.00044516343,0.00041904624,0.4358644,0.000434464,0.5624029],"study_design_scores_gemma":[0.00004620721,0.00022668175,0.00026565674,0.0006140459,0.0000740524,0.000029382601,0.10531846,0.1293259,0.0152413715,0.5421577,0.20652257,0.00017797886],"about_ca_topic_score_codex":0.00016426353,"about_ca_topic_score_gemma":0.00004529603,"teacher_disagreement_score":0.5622249,"about_ca_system_score_codex":0.000074209194,"about_ca_system_score_gemma":0.000056712564,"threshold_uncertainty_score":0.24397263},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11885938,0.0006082846,0.87831527,0.0011284507,0.0006506126,0.00013634628,0.0000070572178,0.0000889328,0.00020564943],"genre_scores_gemma":[0.83650726,0.00005561549,0.16296557,0.00018938955,0.00020436037,0.000010654488,0.0000027749384,0.000041476746,0.000022904778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875784,0.000047404203,0.00060147326,0.00013263566,0.00026118653,0.00019945156],"domain_scores_gemma":[0.99827486,0.001134892,0.00015735289,0.00009729182,0.00022814934,0.00010744068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012707838,0.00014783342,0.00017940656,0.00022997365,0.00011302438,0.00043642745,0.00013841639,0.00008959055,0.000023738397],"category_scores_gemma":[0.0005499737,0.00014433142,0.00011450385,0.00017264715,0.000030026635,0.000924404,0.000012085429,0.0004940479,0.000010288834],"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":[0.00014978873,0.000061336184,0.000018739336,0.00017342498,0.000049266324,0.000059346574,0.0010464059,0.6219289,0.011048601,0.0026600042,0.00009272644,0.3627115],"study_design_scores_gemma":[0.00004296659,0.000068855064,0.000008547711,0.00017902175,0.00018578701,0.000053838758,0.0008913719,0.9865763,0.0075360145,0.0035661384,0.000731955,0.00015917582],"about_ca_topic_score_codex":0.000006617483,"about_ca_topic_score_gemma":0.000007724801,"teacher_disagreement_score":0.71764785,"about_ca_system_score_codex":0.00011148131,"about_ca_system_score_gemma":0.00017450115,"threshold_uncertainty_score":0.5885665},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043110166,0.012580648,0.9044708,0.03763663,0.0013823467,0.00023473913,0.000007019345,0.000025982601,0.00055165036],"genre_scores_gemma":[0.9948896,0.0033298107,0.0016198354,0.000026691132,0.0001136079,0.0000036183974,2.9847294e-7,0.000008165845,0.000008358155],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992259,0.00003862234,0.00045402706,0.00004996212,0.00016399457,0.00006749932],"domain_scores_gemma":[0.9983158,0.0011304126,0.00020737883,0.00012157061,0.00019682155,0.000027988099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001118659,0.000056318546,0.000081737555,0.0000442313,0.00009880161,0.00008383616,0.00013743746,0.000025929017,0.000012348828],"category_scores_gemma":[0.0003216067,0.000032783348,0.000042309934,0.00021233116,0.000057430887,0.00048447773,0.000014866909,0.00022967368,0.0000031498753],"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":[0.00007850283,0.00016613552,0.000008850212,0.00011738349,0.00015409874,0.0000024344997,0.0072910106,0.045167174,0.029475205,0.46381173,0.0019611388,0.45176634],"study_design_scores_gemma":[0.000020550384,0.00016454971,0.000022749062,0.00018828074,0.00014215292,0.000068528716,0.011135977,0.69419295,0.10041397,0.036821757,0.15673369,0.000094828036],"about_ca_topic_score_codex":0.0000068829922,"about_ca_topic_score_gemma":0.000013882772,"teacher_disagreement_score":0.9517794,"about_ca_system_score_codex":0.000021179965,"about_ca_system_score_gemma":0.000052189407,"threshold_uncertainty_score":0.1336866},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027957456,0.0010645672,0.959906,0.008664232,0.0017871119,0.00024228674,0.0000014285389,0.000084897954,0.00029206186],"genre_scores_gemma":[0.8683555,0.00013955186,0.12977716,0.00007931079,0.0013319482,0.0000123440095,0.0000013197537,0.00002770615,0.00027511537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963219,0.0006127059,0.0011110287,0.00037284166,0.0011574336,0.00042408562],"domain_scores_gemma":[0.9918293,0.0053733494,0.0005173792,0.00026206175,0.0018631371,0.00015479683],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009054149,0.00017648058,0.00029498164,0.0006832111,0.00045161016,0.0017221273,0.0009842075,0.00010540057,0.0000026284233],"category_scores_gemma":[0.004297295,0.00014253998,0.00019283243,0.0009790262,0.000059623086,0.0019822626,0.00018105058,0.0022922377,0.000031156942],"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":[0.000064602136,0.00054017996,0.0000114750155,0.00008360099,0.00008157469,0.00022613636,0.0056127366,0.3226736,0.0011056159,0.28272316,0.00013790681,0.38673943],"study_design_scores_gemma":[0.000024726583,0.0013683903,0.0000025697377,0.00066489883,0.00006275403,0.00016072356,0.025707327,0.9485832,0.00077812315,0.014276219,0.008219504,0.00015158147],"about_ca_topic_score_codex":0.00007039011,"about_ca_topic_score_gemma":0.0000109197645,"teacher_disagreement_score":0.8403981,"about_ca_system_score_codex":0.00023873137,"about_ca_system_score_gemma":0.0003277412,"threshold_uncertainty_score":0.9993142},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7269272,0.00067626516,0.031002952,0.2147966,0.0027498954,0.0008557697,0.000019376064,0.000020948,0.022950979],"genre_scores_gemma":[0.99765146,0.00022844374,0.0005389379,0.00078952,0.0007282799,0.000007955236,4.6698625e-7,0.000007554712,0.00004737018],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9927649,0.0038936534,0.0012665454,0.00019631782,0.0014644862,0.00041409367],"domain_scores_gemma":[0.9753961,0.02322992,0.00040571007,0.0002160974,0.0006485918,0.00010360602],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.021692201,0.000117177326,0.00026866852,0.00026344287,0.0004012409,0.00023607023,0.00089886255,0.000104528684,0.00036981315],"category_scores_gemma":[0.023810225,0.00006637964,0.00018033962,0.0007880373,0.000978531,0.0005477275,0.00006574715,0.0018420662,0.00008948709],"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":[0.00023682295,0.00064755685,0.0000036611245,0.000014916301,0.00002259413,0.000024471052,0.025326062,0.00016005896,0.0011461264,0.8619591,0.00029567062,0.11016293],"study_design_scores_gemma":[0.000007859039,0.0007408649,0.000009301659,0.00025484376,0.000043531225,0.000029165438,0.36443603,0.0031520328,0.051846687,0.56700915,0.01237168,0.00009887014],"about_ca_topic_score_codex":0.0037056406,"about_ca_topic_score_gemma":0.0005225723,"teacher_disagreement_score":0.33910996,"about_ca_system_score_codex":0.0001727073,"about_ca_system_score_gemma":0.00058215496,"threshold_uncertainty_score":0.9844126},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041417954,0.0017459296,0.9774255,0.0068840263,0.0030277662,0.00034723428,0.0000122932215,0.000091729904,0.0063237324],"genre_scores_gemma":[0.98821867,0.000046418794,0.006751414,0.0019475403,0.0021398773,0.000008873645,0.0000010814485,0.00004101175,0.0008451212],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980194,0.000372064,0.0006623696,0.00019577029,0.0004914675,0.00025893308],"domain_scores_gemma":[0.99677324,0.0019625402,0.00037624166,0.00013634682,0.00061218056,0.00013947424],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011781293,0.00020869162,0.00032104654,0.00027721573,0.00040023538,0.00049988093,0.00026007614,0.000068472604,0.0010708515],"category_scores_gemma":[0.00069250737,0.00014591483,0.00019103209,0.00007776052,0.00046761177,0.00084294874,0.00001715516,0.0007609897,0.00014004679],"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":[0.01243692,0.0011179164,0.000008885199,0.00018671753,0.0007934073,0.0024760596,0.3317968,0.0066835205,0.0055231755,0.191159,0.009503196,0.4383144],"study_design_scores_gemma":[0.00023138885,0.0035746929,0.0000035818034,0.0006021142,0.00063086074,0.00026193808,0.026855802,0.062519476,0.0044879653,0.017168563,0.8830501,0.00061355205],"about_ca_topic_score_codex":0.00019124962,"about_ca_topic_score_gemma":0.00033260917,"teacher_disagreement_score":0.98407686,"about_ca_system_score_codex":0.00013714329,"about_ca_system_score_gemma":0.0005394685,"threshold_uncertainty_score":0.9998423},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94146883,0.00068729726,0.0013326198,0.013983218,0.0018756534,0.001293866,0.0000031252596,0.000026333211,0.039329045],"genre_scores_gemma":[0.99543065,0.0018478011,0.0024065329,0.000074885334,0.00013117606,0.00000753178,2.9519055e-7,0.000008167017,0.00009295076],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973957,0.00030223164,0.00088865747,0.00014602528,0.0010067219,0.00026066988],"domain_scores_gemma":[0.99873763,0.0005775898,0.0004222756,0.00009424834,0.00006301241,0.000105246225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002420598,0.00009951734,0.0002273595,0.00030018098,0.00006209347,0.00006517646,0.0002315157,0.000047877566,0.00006627338],"category_scores_gemma":[0.0005193539,0.00008434489,0.000097974975,0.00054302294,0.00017050323,0.000577991,0.000031375515,0.00028440336,0.000018785553],"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":[0.0011301572,0.008923558,0.005225347,0.000088589164,0.0004886265,0.00076522876,0.11804465,0.003845891,0.00016583473,0.08909852,0.0013017996,0.7709218],"study_design_scores_gemma":[0.00019929832,0.004447348,0.030563217,0.0006786215,0.00029192393,0.000018936826,0.46817175,0.0003898367,0.0024160428,0.008114291,0.48431867,0.0003900615],"about_ca_topic_score_codex":0.00025028578,"about_ca_topic_score_gemma":0.00015763904,"teacher_disagreement_score":0.7705317,"about_ca_system_score_codex":0.00020146907,"about_ca_system_score_gemma":0.00022237119,"threshold_uncertainty_score":0.34394845},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8423271,0.00071343774,0.15392129,0.002272764,0.0002655316,0.00023278438,0.0000015246914,0.000016628086,0.0002489085],"genre_scores_gemma":[0.9932302,0.00033031413,0.006308797,0.000030322033,0.00007438787,0.000005991037,5.067856e-7,0.0000091282145,0.000010347315],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984661,0.00013032285,0.0007286612,0.00021028196,0.000279864,0.00018480896],"domain_scores_gemma":[0.9991752,0.000346033,0.00028289866,0.000100089535,0.00003173733,0.00006402235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001377804,0.0001316266,0.00020176373,0.00020624477,0.00007070115,0.00008275475,0.0001281222,0.00013112494,0.0001599723],"category_scores_gemma":[0.0010771647,0.000114486764,0.000043185937,0.00045235004,0.00039422084,0.0011452468,0.00011585069,0.00056662946,0.000016184207],"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":[0.0002892473,0.000192528,0.0011218581,0.000038378483,0.00001762275,0.00009767658,0.0038702765,0.0028158738,0.032579783,0.0039225877,0.000008129734,0.95504606],"study_design_scores_gemma":[0.00007644317,0.0028651615,0.008381644,0.0007076509,0.00018154291,0.0017067462,0.13694112,0.2507265,0.28523743,0.31054187,0.0019802593,0.0006536359],"about_ca_topic_score_codex":0.00035205393,"about_ca_topic_score_gemma":0.00009787376,"teacher_disagreement_score":0.9543924,"about_ca_system_score_codex":0.00013833861,"about_ca_system_score_gemma":0.000019855142,"threshold_uncertainty_score":0.46686345},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1049769,0.00017538118,0.74669176,0.13522525,0.003459258,0.00059396494,0.0000067960473,0.0001987955,0.0086719105],"genre_scores_gemma":[0.99074036,0.000049291186,0.006340159,0.0020264613,0.00074185326,0.000007701331,0.000014132041,0.000025862757,0.000054189015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983385,0.000045384328,0.00075785816,0.00021844625,0.000473539,0.00016625861],"domain_scores_gemma":[0.99734545,0.00053535856,0.00084585696,0.00026398868,0.0009948518,0.000014492957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012964662,0.00017483745,0.00020438545,0.00096939073,0.00014765006,0.0002800508,0.00029516293,0.00016187783,0.00014801256],"category_scores_gemma":[0.0021644298,0.00014151262,0.0001387345,0.0012647248,0.00013663698,0.0024628958,0.000042477604,0.00079208106,0.00040368552],"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":[0.0011827586,0.0022803738,0.00010829281,0.00021041992,0.00011872978,0.000120972385,0.00068728486,0.004271239,0.07988521,0.4596115,0.011003695,0.4405195],"study_design_scores_gemma":[0.0002272594,0.0009634114,0.0006316088,0.0016119333,0.00058385124,0.000044531418,0.023976987,0.6467045,0.17890099,0.043427438,0.10224222,0.0006852818],"about_ca_topic_score_codex":0.000051587394,"about_ca_topic_score_gemma":0.000045696746,"teacher_disagreement_score":0.88576347,"about_ca_system_score_codex":0.000073708514,"about_ca_system_score_gemma":0.00007166833,"threshold_uncertainty_score":0.5770717},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2465494,0.0026276615,0.64035416,0.090543486,0.00069846836,0.0013219097,0.00006364455,0.0001058465,0.017735457],"genre_scores_gemma":[0.9958173,0.0007618254,0.0023533604,0.00044573093,0.00033013817,0.000021659213,0.000023367134,0.000024733476,0.00022189048],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99577516,0.00064195495,0.0010615184,0.00046604357,0.0015646431,0.00049067545],"domain_scores_gemma":[0.9917527,0.0060755867,0.0002483607,0.0003693522,0.0010409319,0.00051309],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004952332,0.000207795,0.00043415822,0.0042952974,0.00017081121,0.00039574152,0.00020670176,0.00015301823,0.0005277137],"category_scores_gemma":[0.01079743,0.00016005535,0.00020999461,0.0065018525,0.00022756707,0.0005845136,0.000040593684,0.0013150574,0.00012359266],"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":[0.00500273,0.0022659993,0.00095147506,0.00008189193,0.00044253678,0.0011332353,0.0009820935,0.0022204723,0.0006336439,0.11903644,0.00028870202,0.86696076],"study_design_scores_gemma":[0.00032705648,0.013771997,0.016989142,0.0027358115,0.002266271,0.000444617,0.007055028,0.8203177,0.03856784,0.08419329,0.012650204,0.0006810838],"about_ca_topic_score_codex":0.00009560539,"about_ca_topic_score_gemma":0.00004968415,"teacher_disagreement_score":0.8662797,"about_ca_system_score_codex":0.00035877907,"about_ca_system_score_gemma":0.00050995324,"threshold_uncertainty_score":0.99753505},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034062265,0.00008437227,0.99792916,0.0008439737,0.00015140513,0.00014927174,0.0000043945415,0.00004122014,0.00045557535],"genre_scores_gemma":[0.51476526,0.00006689867,0.48502344,0.000085909676,0.00003935647,0.00001004348,0.0000051817165,0.000002251082,0.0000016195264],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985,0.000052437867,0.0008777129,0.00012620659,0.0003537485,0.00008990702],"domain_scores_gemma":[0.9984776,0.00036749826,0.00050511275,0.0002407298,0.00037082465,0.000038241596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097298564,0.000072325565,0.0001648988,0.00092759205,0.000031962958,0.00014125872,0.00045410587,0.000027873073,0.0000023428158],"category_scores_gemma":[0.000038248218,0.00006625581,0.00009277714,0.002562817,0.00002045911,0.0028107457,0.00009422475,0.00016065981,0.000020083253],"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":[0.000010594963,0.00005143644,0.0000032013345,0.0000401766,0.0000971966,0.00001112814,0.0002648096,0.08719124,0.000085022904,0.58549464,0.000028167866,0.3267224],"study_design_scores_gemma":[0.000010583567,0.00004566823,0.00007341659,0.00008346653,0.00015023579,0.000035339508,0.00036392783,0.97945803,0.0012238252,0.010143644,0.008347261,0.000064611],"about_ca_topic_score_codex":0.000018633895,"about_ca_topic_score_gemma":0.0000024565163,"teacher_disagreement_score":0.8922668,"about_ca_system_score_codex":0.00010741752,"about_ca_system_score_gemma":0.000045317,"threshold_uncertainty_score":0.27018335},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17362384,0.0060579027,0.58557713,0.08324827,0.0034098094,0.002628516,0.0000040678324,0.00038111256,0.14506936],"genre_scores_gemma":[0.99721104,0.0003151201,0.001157114,0.00011475367,0.0010648239,0.000018863906,0.0000011304236,0.000022083143,0.0000950921],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99708414,0.00009633351,0.0012916223,0.0002638764,0.0008632867,0.0004007514],"domain_scores_gemma":[0.9970796,0.0012307531,0.00062954903,0.00036836247,0.0006747869,0.000016908247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008131743,0.00014989702,0.00022176938,0.0010074428,0.00042130827,0.0006472118,0.0009706647,0.00010965354,0.000036095436],"category_scores_gemma":[0.001705405,0.00011125689,0.0001167074,0.0021464827,0.0004295279,0.0015070615,0.00032528065,0.0009719692,0.00020002053],"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":[0.00014772417,0.00013422054,0.000025491965,0.000082907005,0.000029977053,0.000052531217,0.00025126222,0.0004991002,0.0010659832,0.56138736,0.0004235527,0.43589988],"study_design_scores_gemma":[0.000017085227,0.00013624743,0.00009576807,0.00040018983,0.000107798405,0.000026487958,0.06357047,0.01756209,0.011291254,0.62583584,0.2807326,0.00022417204],"about_ca_topic_score_codex":0.00040802144,"about_ca_topic_score_gemma":0.0003787927,"teacher_disagreement_score":0.8235872,"about_ca_system_score_codex":0.00015294421,"about_ca_system_score_gemma":0.000072680356,"threshold_uncertainty_score":0.6241078},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9530105,0.0004034179,0.0022753412,0.043455493,0.00013018171,0.00032889078,0.0000019310116,0.0000025423149,0.000391677],"genre_scores_gemma":[0.99881524,0.00019795976,0.00037984477,0.0004407473,0.000109548404,0.0000060035404,0.0000011969836,0.0000073279593,0.00004215186],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9974103,0.00048575288,0.0007758271,0.0001601549,0.0009792896,0.00018868268],"domain_scores_gemma":[0.99742126,0.0016437378,0.00036034783,0.00020216142,0.0002888737,0.00008362877],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0050783404,0.000086507614,0.00024068661,0.00019720495,0.000027719183,0.000038651713,0.00016078372,0.00005670018,0.000107518885],"category_scores_gemma":[0.012527325,0.000049378403,0.00006106411,0.00079147326,0.00007611976,0.00030260967,0.000039534163,0.00078077347,0.0000042161955],"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":[0.0073501677,0.007915177,0.053391326,0.00027417764,0.000573495,0.012529617,0.23127504,0.0007094853,0.027209649,0.125493,0.00005500963,0.53322387],"study_design_scores_gemma":[0.0005738469,0.0066654133,0.16206588,0.003264734,0.00043313895,0.0024659229,0.6709952,0.010636983,0.04349699,0.09816599,0.0010113665,0.00022456143],"about_ca_topic_score_codex":0.0033014247,"about_ca_topic_score_gemma":0.0077538188,"teacher_disagreement_score":0.5329993,"about_ca_system_score_codex":0.0002771719,"about_ca_system_score_gemma":0.00027182788,"threshold_uncertainty_score":0.9957906},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049896515,0.00026001243,0.9935563,0.00036332035,0.00030669477,0.00006659997,0.0000021871983,0.000033380962,0.0004218488],"genre_scores_gemma":[0.88138837,0.0004397016,0.11774521,0.000027175598,0.00036615928,0.0000029257853,0.0000070973147,0.000020275556,0.0000030906176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992443,0.00004558575,0.0003897829,0.00007890822,0.00016511459,0.000076310745],"domain_scores_gemma":[0.99900144,0.0005736912,0.00015397757,0.000043604236,0.00018389786,0.000043360942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004072902,0.00007171034,0.00010085814,0.00016642411,0.000064407046,0.00008903933,0.000041607127,0.0000616339,0.000041879197],"category_scores_gemma":[0.00046878762,0.00006897605,0.000037373542,0.00027327088,0.000023675771,0.00066901144,0.0000061383485,0.00039985235,0.000009204564],"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":[0.00003696732,0.00002467645,8.574699e-7,0.000009133957,0.000009958432,0.000004813929,0.000100178935,0.6567503,0.0013374478,0.00020869337,0.000005266749,0.3415117],"study_design_scores_gemma":[0.000026694584,0.00025989016,0.0000021969975,0.0002777108,0.000050002483,0.000035253855,0.0004530004,0.98121625,0.015538412,0.0016320545,0.00043847132,0.00007003695],"about_ca_topic_score_codex":0.0000033370486,"about_ca_topic_score_gemma":6.2918417e-7,"teacher_disagreement_score":0.8763987,"about_ca_system_score_codex":0.000041094296,"about_ca_system_score_gemma":0.000020515161,"threshold_uncertainty_score":0.28127617},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83754826,0.005622847,0.07322924,0.05385757,0.0023231613,0.0017679777,0.000019300862,0.00004047196,0.02559116],"genre_scores_gemma":[0.9955506,0.0013234778,0.00182592,0.0004114536,0.0007775385,0.000012178272,0.0000029303098,0.000019965497,0.00007594624],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9937474,0.0011336165,0.001369467,0.0003015811,0.0028843144,0.0005636269],"domain_scores_gemma":[0.98879325,0.009308949,0.00025687902,0.00039250613,0.00096956216,0.00027885867],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.012224134,0.00018184881,0.00041551443,0.00097413483,0.00012660887,0.00019505914,0.0005277211,0.00014944104,0.0004610956],"category_scores_gemma":[0.01989306,0.0001098255,0.0002279586,0.0017017648,0.00055613753,0.0004295707,0.000069885005,0.002862729,0.00044972633],"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":[0.007593841,0.0040002544,0.00008672421,0.0003078823,0.0002684406,0.019168215,0.0074757966,0.00022375057,0.0043453635,0.09424765,0.0009264908,0.8613556],"study_design_scores_gemma":[0.00039385466,0.04278118,0.003106804,0.008699605,0.0017070302,0.007004214,0.23786938,0.020543033,0.3153652,0.31201088,0.049674727,0.00084406644],"about_ca_topic_score_codex":0.000453566,"about_ca_topic_score_gemma":0.00007621351,"teacher_disagreement_score":0.86051154,"about_ca_system_score_codex":0.00021468058,"about_ca_system_score_gemma":0.0009090923,"threshold_uncertainty_score":0.9994377},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008500049,0.00011172359,0.88801676,0.08715198,0.0028325873,0.0004112428,0.0000018132479,0.00018215519,0.012791675],"genre_scores_gemma":[0.98036975,0.000030215713,0.0076087303,0.0056230896,0.006278763,0.000019480973,0.0000069497432,0.000037463407,0.000025590089],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971689,0.00010233973,0.0010448445,0.00036682884,0.0008752649,0.00044184932],"domain_scores_gemma":[0.9954208,0.002206589,0.00051399064,0.00040044644,0.00143063,0.000027543738],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005177032,0.00020630188,0.00024166974,0.001359242,0.0004569947,0.00088139676,0.0006326503,0.0001964718,0.00018009871],"category_scores_gemma":[0.0012161063,0.00018071308,0.00013778434,0.003480396,0.000279932,0.0015284439,0.00010770737,0.0018175418,0.0029777335],"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":[0.00026440702,0.00035477657,0.000016648532,0.000028451284,0.000032532193,0.000072544986,0.00003538701,0.03988515,0.00012696846,0.6130121,0.0054121655,0.3407589],"study_design_scores_gemma":[0.00001862315,0.00042469523,0.00003723021,0.00042999402,0.00009958864,0.00003005532,0.0019449049,0.5425389,0.003382163,0.3170793,0.1337228,0.00029174634],"about_ca_topic_score_codex":0.00011488864,"about_ca_topic_score_gemma":0.000050413353,"teacher_disagreement_score":0.97186965,"about_ca_system_score_codex":0.000115950694,"about_ca_system_score_gemma":0.00012569567,"threshold_uncertainty_score":0.99779856},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7875246,0.005737136,0.17900996,0.0013549066,0.011493165,0.000698866,0.000010618122,0.00021138866,0.0139593715],"genre_scores_gemma":[0.9977069,0.0011175367,0.00034385064,0.000014802235,0.0007455464,0.000003991585,2.7680875e-7,0.000021575508,0.000045541456],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979342,0.00011772494,0.00089501514,0.00015313976,0.0006705238,0.00022939613],"domain_scores_gemma":[0.99819034,0.000865502,0.0001693054,0.00013839509,0.00053515285,0.000101326674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035988016,0.00013019524,0.00025300906,0.00064755767,0.0001129442,0.0002023191,0.000121342644,0.00013569508,0.000051607145],"category_scores_gemma":[0.0007536244,0.00010438476,0.00009838455,0.00068198185,0.00010336576,0.0006355585,0.000029745705,0.001014517,0.000071191396],"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":[0.0014052273,0.0002202593,0.000012066001,0.00036295815,0.00027924436,0.00025173416,0.0065612677,0.031066263,0.025978481,0.03846862,0.0045218137,0.89087206],"study_design_scores_gemma":[0.00013426802,0.005509035,0.00006862644,0.0031984472,0.00014711406,0.0015838152,0.064659536,0.14263794,0.6152478,0.00997922,0.15632123,0.0005129893],"about_ca_topic_score_codex":0.00004608699,"about_ca_topic_score_gemma":0.000012688996,"teacher_disagreement_score":0.89035904,"about_ca_system_score_codex":0.00027642166,"about_ca_system_score_gemma":0.000088579494,"threshold_uncertainty_score":0.44076282},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039249337,0.00033242814,0.99278575,0.00039501674,0.0015856538,0.00013826857,0.0000042617153,0.00005077436,0.00078294054],"genre_scores_gemma":[0.9023847,0.000058108235,0.097290106,0.000008565647,0.00020751337,0.000003125791,0.0000022568863,0.000021659689,0.000023961757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756694,0.00027795692,0.0008207388,0.00021201541,0.0008576822,0.0002646427],"domain_scores_gemma":[0.9946761,0.0037978804,0.00021099942,0.00022522203,0.0009993108,0.00009049697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005080466,0.00012597295,0.00028766054,0.0015655751,0.00016092374,0.0006789673,0.00018228737,0.00008469411,0.000024731635],"category_scores_gemma":[0.007232839,0.00010873683,0.00011985479,0.0023485548,0.00012240668,0.00084811973,0.000023812834,0.00087473774,0.000013025376],"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":[0.00005906844,0.0000948253,0.0000026346763,0.00011185366,0.00024988284,0.00009167593,0.00045516348,0.9775511,0.0053475364,0.0117644435,0.0001344589,0.0041373293],"study_design_scores_gemma":[0.000011720351,0.00017732731,0.0000025156128,0.00023743173,0.00035427246,0.000025901949,0.0015352013,0.953346,0.04293953,0.00027910402,0.0009933879,0.00009759911],"about_ca_topic_score_codex":0.00001878583,"about_ca_topic_score_gemma":0.0000041992635,"teacher_disagreement_score":0.8984598,"about_ca_system_score_codex":0.00019246881,"about_ca_system_score_gemma":0.0002195156,"threshold_uncertainty_score":0.8658906},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26287937,0.0014366765,0.72579235,0.0069674854,0.002281246,0.0003832936,0.0000033586646,0.000012770787,0.00024345056],"genre_scores_gemma":[0.9798683,0.00021157184,0.019684462,0.00005080234,0.00016667673,0.0000067113497,1.6970571e-7,0.0000057445786,0.000005539414],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99737376,0.00063264417,0.0011847881,0.00020184147,0.0004620666,0.0001449105],"domain_scores_gemma":[0.99490255,0.0030742807,0.000982014,0.0003518715,0.00065290526,0.000036361394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028794152,0.00012961598,0.00028062667,0.00023940229,0.000082048035,0.000057332138,0.00094387983,0.00010387667,0.0000063121256],"category_scores_gemma":[0.0011626479,0.0000853488,0.00011938276,0.0011835127,0.00040297597,0.0007509761,0.0000762862,0.0006800702,0.000005003633],"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":[0.00009852296,0.00043735272,0.000041276533,0.000095965166,0.000039209302,0.000004666743,0.006983001,0.0015393771,0.0042534308,0.69336987,0.000028030721,0.2931093],"study_design_scores_gemma":[0.000016839263,0.00065880944,0.0003057626,0.0009292965,0.00014949388,0.0014227651,0.05424208,0.07210925,0.57037485,0.2992224,0.00039368,0.00017476067],"about_ca_topic_score_codex":0.000068240224,"about_ca_topic_score_gemma":0.000016847469,"teacher_disagreement_score":0.7169889,"about_ca_system_score_codex":0.00007655669,"about_ca_system_score_gemma":0.0013286832,"threshold_uncertainty_score":0.3480423},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03386783,0.00010842205,0.9604725,0.003553244,0.0006511205,0.00013260724,0.0000012377645,0.00004905399,0.0011639361],"genre_scores_gemma":[0.9292082,0.00012900762,0.07039446,0.00017495881,0.00007928553,0.0000023114146,1.4520269e-7,0.00000754011,0.000004062514],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99817777,0.0003058006,0.000518926,0.00024110412,0.00055420515,0.00020221634],"domain_scores_gemma":[0.9952427,0.0036811403,0.00025180937,0.00023394517,0.00055077765,0.00003964197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003082064,0.00009277748,0.00014423257,0.00090214505,0.000085851585,0.00017133975,0.00035920023,0.00012471029,0.000010253258],"category_scores_gemma":[0.006747865,0.0000836792,0.000044270455,0.0011814565,0.00014897739,0.0014678424,0.0000972456,0.0014863544,0.000027880904],"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":[0.00029429074,0.00020034926,0.0000056316576,0.00004361596,0.000011405943,0.00030988027,0.0011598874,0.004723708,0.019771516,0.0092354305,0.0000301814,0.9642141],"study_design_scores_gemma":[0.00005021433,0.0021534418,0.000037675643,0.0006378864,0.000011164017,0.000116607196,0.0019118771,0.27308726,0.61102116,0.11006561,0.0007731119,0.00013399227],"about_ca_topic_score_codex":0.000029828636,"about_ca_topic_score_gemma":0.00009222811,"teacher_disagreement_score":0.9640801,"about_ca_system_score_codex":0.00011099598,"about_ca_system_score_gemma":0.00012256336,"threshold_uncertainty_score":0.8078312},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90790933,0.000017809614,0.019824972,0.07082479,0.00027135774,0.0002787151,0.0000018042623,0.000050167157,0.00082102645],"genre_scores_gemma":[0.99720865,0.000041790256,0.0005428253,0.0019837706,0.00016258095,0.000019484341,0.000010360984,0.000015989064,0.000014528051],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99790275,0.00006702697,0.0011155256,0.00025995256,0.00046532755,0.0001894185],"domain_scores_gemma":[0.9929199,0.0010181686,0.0011546807,0.00029754013,0.0046030465,0.000006617597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007620519,0.00013628944,0.00019477698,0.004625019,0.00023953163,0.00015808688,0.00049780525,0.00026931593,0.000048072725],"category_scores_gemma":[0.0058577904,0.000110594374,0.000026396781,0.007336673,0.00046355207,0.0020703077,0.00016313634,0.0013137192,0.000047477293],"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":[0.00035335147,0.00014654417,0.00021247209,0.00004592801,0.00005174618,0.0000069112752,0.000102614955,0.00017850916,0.06365103,0.9055677,0.00016326302,0.029519893],"study_design_scores_gemma":[0.00008323624,0.0003671635,0.000105610336,0.00036529894,0.00004803188,0.000038432372,0.01426993,0.008658289,0.23914124,0.72512305,0.011661929,0.00013777752],"about_ca_topic_score_codex":0.00019330626,"about_ca_topic_score_gemma":0.0000206747,"teacher_disagreement_score":0.18044466,"about_ca_system_score_codex":0.000065728724,"about_ca_system_score_gemma":0.00011756453,"threshold_uncertainty_score":0.7012746},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49813163,0.021828318,0.31443906,0.13804734,0.012207086,0.003823751,0.000038705046,0.00021943521,0.011264666],"genre_scores_gemma":[0.9915568,0.004241572,0.002185466,0.00021770729,0.0015695547,0.0000964501,0.000002928701,0.00004149545,0.00008802392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9900144,0.003735021,0.003134844,0.0003509068,0.0020378355,0.0007270204],"domain_scores_gemma":[0.9713171,0.024657108,0.0009033581,0.0002855519,0.0025014062,0.00033543463],"candidate_categories":["metaresearch","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.026916008,0.00017446523,0.00048280085,0.0010558226,0.00045868405,0.000039409806,0.0006423885,0.0004446008,0.0010568111],"category_scores_gemma":[0.04223669,0.00015066587,0.00014136605,0.0021609731,0.00032914986,0.0009063943,0.0001896534,0.005974023,0.00065848825],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062499737,0.00056633557,0.028656384,0.00064549514,0.00006611517,0.00047675794,0.027299285,0.0014842915,0.00063585467,0.03890835,0.0003832153,0.90025294],"study_design_scores_gemma":[0.00008966869,0.0018270419,0.0006551498,0.006070034,0.00011425335,0.00025600367,0.1412073,0.5945455,0.021240711,0.055830445,0.17767775,0.0004861515],"about_ca_topic_score_codex":0.011584152,"about_ca_topic_score_gemma":0.004587985,"teacher_disagreement_score":0.89976674,"about_ca_system_score_codex":0.0007570129,"about_ca_system_score_gemma":0.0027671074,"threshold_uncertainty_score":0.99985635},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8582747,0.000179292,0.018052466,0.08668633,0.0014627236,0.00047119815,0.00000580246,0.000039725168,0.034827743],"genre_scores_gemma":[0.9967125,0.0001950912,0.0021781456,0.00010746445,0.00031304036,0.0000034383238,0.000001212038,0.000012466434,0.00047666507],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996884,0.0004820156,0.00071918627,0.00032482715,0.0011653301,0.00042466322],"domain_scores_gemma":[0.9969077,0.0018725374,0.00014923184,0.00023346972,0.0007256829,0.00011135765],"candidate_categories":["research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.009487616,0.00011101853,0.00014244136,0.0015828027,0.00039427582,0.0002282932,0.00056660105,0.0003141963,0.0014491202],"category_scores_gemma":[0.004619079,0.000097590986,0.000045870263,0.007272203,0.00065907545,0.001649871,0.00017548742,0.0040727863,0.001686876],"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":[0.00031368912,0.0007722249,0.0002325339,0.000008840612,0.00001768484,0.00024146166,0.0013991682,0.0120329745,0.007081521,0.7224006,0.001984677,0.25351465],"study_design_scores_gemma":[0.000036242993,0.0025091255,0.0008747222,0.0005992367,0.000030185689,0.00030045267,0.14789987,0.020957833,0.11238195,0.47707647,0.23683694,0.0004969943],"about_ca_topic_score_codex":0.00037294326,"about_ca_topic_score_gemma":0.0003151429,"teacher_disagreement_score":0.25301766,"about_ca_system_score_codex":0.0011388191,"about_ca_system_score_gemma":0.0005044097,"threshold_uncertainty_score":0.9994637},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25744778,0.0010249913,0.7354846,0.00469148,0.0003853722,0.00051045965,0.000005602518,0.000007691076,0.0004420512],"genre_scores_gemma":[0.9963967,0.0010595997,0.002290129,0.00005765764,0.0001613817,0.0000073798697,0.0000030508827,0.0000083650775,0.00001571632],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978659,0.00037654882,0.00090678706,0.00013981931,0.0005855472,0.00012538127],"domain_scores_gemma":[0.9972724,0.0015205282,0.00044310943,0.00013975914,0.0005313273,0.000092923285],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0032274371,0.00008617575,0.00021210096,0.00026320442,0.000036994075,0.000029392175,0.00009340137,0.00008070658,0.00006292936],"category_scores_gemma":[0.008477042,0.00005717342,0.00008581883,0.00061589014,0.00019908631,0.00076010526,0.000028721832,0.00042562195,0.000021282327],"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":[0.0017411136,0.00053051894,0.000086935834,0.00015180846,0.000065427834,0.00003027215,0.0018303178,0.00007025319,0.017307883,0.02491129,0.000009602349,0.9532646],"study_design_scores_gemma":[0.00008091104,0.0016302535,0.00063629483,0.0014988363,0.00037876324,0.00028000135,0.013965094,0.024258588,0.44484785,0.51191014,0.00039763734,0.00011561684],"about_ca_topic_score_codex":0.000057675552,"about_ca_topic_score_gemma":0.000026101774,"teacher_disagreement_score":0.95314896,"about_ca_system_score_codex":0.00007333969,"about_ca_system_score_gemma":0.00023323201,"threshold_uncertainty_score":0.99987495},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08790951,0.0006420724,0.73951364,0.17033039,0.000502149,0.00026014165,9.220656e-7,0.00007444761,0.0007667028],"genre_scores_gemma":[0.9885859,0.00020533647,0.010430382,0.00052296853,0.00023038065,0.0000069237653,0.000002450341,0.00001055379,0.0000050956123],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99857265,0.000026164886,0.00092957687,0.00015885898,0.00017710951,0.00013566417],"domain_scores_gemma":[0.9982862,0.00045182617,0.00058406283,0.00023632862,0.00043334908,0.000008265592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011487218,0.00010890794,0.00020146683,0.0010950122,0.0000906385,0.00014705908,0.00034500292,0.0001645361,0.000028198363],"category_scores_gemma":[0.0008844131,0.00008384185,0.000045784174,0.0016504069,0.0002165846,0.0016154015,0.00016877393,0.00062899454,0.000047263984],"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":[0.000051088053,0.0001328053,0.00031539577,0.000036420985,0.000013403595,0.000008504275,0.00013581643,0.000163146,0.001964473,0.4967737,0.00009591544,0.50030935],"study_design_scores_gemma":[0.000022716178,0.00014360246,0.00014401558,0.00045775514,0.00010740913,0.00008307535,0.008096048,0.08629456,0.024367977,0.8688627,0.0112147955,0.0002053744],"about_ca_topic_score_codex":0.00018463444,"about_ca_topic_score_gemma":0.00012666851,"teacher_disagreement_score":0.9006764,"about_ca_system_score_codex":0.000027465314,"about_ca_system_score_gemma":0.00003450586,"threshold_uncertainty_score":0.34189713},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08259336,0.001944735,0.9117876,0.001781251,0.0011549608,0.00014786307,0.0000068645613,0.0003579803,0.00022532843],"genre_scores_gemma":[0.99495924,0.0003462593,0.004499631,0.000010489604,0.0001536165,0.000005697987,9.881826e-7,0.000020615065,0.0000034705752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977069,0.00006456936,0.0014159569,0.0001687489,0.00034318396,0.00030063247],"domain_scores_gemma":[0.9981624,0.001050078,0.00024651614,0.00020092256,0.00029046275,0.000049619488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013585885,0.00018220923,0.00052413094,0.0032880693,0.000029501372,0.00009078134,0.0003484258,0.00033967628,0.00004442473],"category_scores_gemma":[0.003300233,0.00017163565,0.0002011574,0.0055587944,0.00010033644,0.00055840943,0.000021828419,0.0011193096,0.000027787915],"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":[0.00008180719,0.000085558044,0.000023916587,0.00002405567,0.0005828549,0.0001126732,0.00024346377,0.5396749,0.009768606,0.05345785,0.000019242842,0.3959251],"study_design_scores_gemma":[0.00001421221,0.0002208306,0.000020848422,0.00009092828,0.00060022954,0.00006527939,0.0010444004,0.89749855,0.086138465,0.013595013,0.000552644,0.00015862996],"about_ca_topic_score_codex":0.000025437394,"about_ca_topic_score_gemma":0.000017946137,"teacher_disagreement_score":0.91236585,"about_ca_system_score_codex":0.00020725088,"about_ca_system_score_gemma":0.000103310325,"threshold_uncertainty_score":0.69990987},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024755849,0.008991492,0.87191564,0.07702347,0.001615364,0.00032711122,0.000072258415,0.00005235044,0.015246448],"genre_scores_gemma":[0.99289566,0.0022240432,0.0044754697,0.00021168108,0.0001253033,0.0000068278996,0.0000017300408,0.000012389871,0.000046872752],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99636537,0.00013696274,0.0027422959,0.00026291105,0.00022247258,0.00027000424],"domain_scores_gemma":[0.995812,0.0026514032,0.0008906999,0.00025345237,0.00032244038,0.000070028625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059113475,0.00016207052,0.0005628505,0.0010041269,0.00010861568,0.00049533905,0.0005653089,0.00026850487,0.000228647],"category_scores_gemma":[0.0059821946,0.00012757738,0.0003505822,0.0029236649,0.0005480737,0.001233287,0.00005475658,0.0012541161,0.00022338642],"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":[0.00012648675,0.00016286797,0.000042966876,0.000012350275,0.00039867326,0.000034705256,0.0012992448,0.007786782,0.000094977986,0.90409946,0.000044055778,0.08589743],"study_design_scores_gemma":[0.00001123394,0.00027098774,0.00007603862,0.000071047914,0.00017657795,0.000038858932,0.0059551164,0.08184107,0.014519316,0.844411,0.052396614,0.00023213623],"about_ca_topic_score_codex":0.00005391306,"about_ca_topic_score_gemma":0.000100924655,"teacher_disagreement_score":0.9681398,"about_ca_system_score_codex":0.00012984095,"about_ca_system_score_gemma":0.00007800623,"threshold_uncertainty_score":0.7161678},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019344108,0.09461844,0.88055104,0.0006274183,0.0038000569,0.00025562424,0.0000016114476,0.000029600988,0.000772076],"genre_scores_gemma":[0.9856244,0.0042705266,0.00994993,0.000022005737,0.00010257553,0.0000017990976,9.6371686e-8,0.000013591933,0.000015080393],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985843,0.00012861569,0.00075717486,0.00007347799,0.0003083433,0.00014809398],"domain_scores_gemma":[0.9988588,0.0005190045,0.00024929797,0.000092922885,0.00020425013,0.00007573456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008409326,0.00009815794,0.0001996516,0.000098311306,0.000022678467,0.000024737252,0.00013635404,0.00003627934,0.00006187243],"category_scores_gemma":[0.00050813984,0.0000710437,0.00008006031,0.00014652743,0.000060396484,0.00035663252,0.00002510966,0.0003115027,0.000007546634],"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":[0.00021059955,0.00020778342,0.00004012125,0.00044991486,0.00023114616,0.000015389047,0.007074477,0.08161457,0.01676687,0.010463335,0.00068449863,0.8822413],"study_design_scores_gemma":[0.00026134207,0.00091537763,0.002211431,0.0026676506,0.0008860642,0.00042151168,0.0056817355,0.42482415,0.19944268,0.0035976644,0.35836706,0.00072336243],"about_ca_topic_score_codex":0.00000829751,"about_ca_topic_score_gemma":0.000006464749,"teacher_disagreement_score":0.9662803,"about_ca_system_score_codex":0.00019520264,"about_ca_system_score_gemma":0.00035958752,"threshold_uncertainty_score":0.2897078},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7065711,0.0061554145,0.14439994,0.13388225,0.0008965394,0.0020842226,0.000019721632,0.000031855827,0.005958931],"genre_scores_gemma":[0.9973108,0.00096912717,0.00070052996,0.00020824982,0.0007202066,0.00002673782,0.0000014662016,0.000015749634,0.00004709745],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961537,0.0007358155,0.0008976778,0.00026210875,0.0016041884,0.0003464808],"domain_scores_gemma":[0.9935933,0.0047802916,0.00026930685,0.00018686567,0.0009819868,0.00018829147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007440516,0.00014316945,0.0003227266,0.0005055257,0.0002321213,0.0001437352,0.00021802887,0.00013050518,0.00014956189],"category_scores_gemma":[0.007475739,0.00008945503,0.000103724225,0.0014882948,0.00058686134,0.00028892118,0.000059258055,0.0016068551,0.000100294004],"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":[0.005169979,0.0012205711,0.00015967482,0.00010422236,0.00026296225,0.0005016999,0.004815332,0.000101410646,0.0046890923,0.18353029,0.00068638416,0.7987584],"study_design_scores_gemma":[0.00033024867,0.011011935,0.0044000647,0.0043836944,0.000721868,0.00081868086,0.073901914,0.043910604,0.37984148,0.45511055,0.024865787,0.0007031475],"about_ca_topic_score_codex":0.000103521525,"about_ca_topic_score_gemma":0.000038508082,"teacher_disagreement_score":0.79805523,"about_ca_system_score_codex":0.0001963154,"about_ca_system_score_gemma":0.0005267138,"threshold_uncertainty_score":0.8949698},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5021339,0.0072956025,0.40295988,0.046522517,0.025232686,0.0015387848,0.000023277693,0.00031198797,0.013981387],"genre_scores_gemma":[0.98829144,0.0001479932,0.0028021333,0.005089423,0.0035221581,0.000004397697,0.0000036100205,0.000046613997,0.00009224396],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99720895,0.000047181766,0.001141144,0.0003922334,0.00074498815,0.00046547435],"domain_scores_gemma":[0.99776953,0.00036012358,0.0005923576,0.00023921583,0.0009885106,0.00005023188],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020326807,0.00027569913,0.00037641614,0.0013703328,0.00014179923,0.0016767586,0.00069511076,0.00012605684,0.00062155176],"category_scores_gemma":[0.0011050507,0.00023875457,0.00016319602,0.0029314477,0.000074626776,0.007720425,0.00022880957,0.0007261315,0.0011110428],"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":[0.00309558,0.0016176875,0.0010847729,0.0023079806,0.00037351425,0.003640108,0.0041292496,0.015446519,0.057240378,0.15262572,0.003387087,0.7550514],"study_design_scores_gemma":[0.00011681455,0.00009689287,0.00015162007,0.0017818788,0.00053802837,0.00041211682,0.008886652,0.21909171,0.0060893926,0.017682407,0.74428236,0.0008701196],"about_ca_topic_score_codex":0.0010359739,"about_ca_topic_score_gemma":0.00008489695,"teacher_disagreement_score":0.75418127,"about_ca_system_score_codex":0.00016170488,"about_ca_system_score_gemma":0.00017712323,"threshold_uncertainty_score":0.9996667},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028828825,0.00025682687,0.9890324,0.0041165566,0.0037932878,0.00013459919,0.0000026802247,0.0001056108,0.0022697505],"genre_scores_gemma":[0.41061524,0.000027724376,0.5870815,0.00064445165,0.0015051437,0.000008056917,9.2026613e-7,0.00003464272,0.000082318744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99543256,0.00088424265,0.00080576463,0.00047835926,0.0018767572,0.0005223383],"domain_scores_gemma":[0.9928124,0.005278348,0.0003700979,0.00046065223,0.00085071934,0.00022776355],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007920823,0.00021525216,0.0002552039,0.0011413022,0.0003482639,0.0010525963,0.0009896241,0.00014319285,0.00002286052],"category_scores_gemma":[0.0027346115,0.00018250516,0.00014669506,0.001733248,0.00009031045,0.0016823423,0.00009153313,0.0022098962,0.00049619406],"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":[0.00016747443,0.00031063418,0.0000020810073,0.00001736221,0.000040680316,0.0025003108,0.0013514878,0.29621834,0.0008371509,0.0040096105,0.0007918511,0.693753],"study_design_scores_gemma":[0.00002967381,0.0020674684,0.000015644926,0.0005173519,0.000015867186,0.00038706372,0.0012551787,0.9543221,0.02762691,0.0051964517,0.008385512,0.00018078633],"about_ca_topic_score_codex":0.00002290725,"about_ca_topic_score_gemma":2.4661293e-7,"teacher_disagreement_score":0.6935722,"about_ca_system_score_codex":0.00031013292,"about_ca_system_score_gemma":0.00045084677,"threshold_uncertainty_score":0.9999844},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92858994,0.0004860329,0.0009844137,0.06759201,0.0000934194,0.00026436694,0.000001403688,0.0000015190479,0.001986873],"genre_scores_gemma":[0.9988186,0.00069934793,0.00010021468,0.00027132285,0.000087727305,0.0000035444264,3.195416e-7,0.000004190473,0.000014755713],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99775773,0.00039904678,0.0005249155,0.00007295721,0.0010856936,0.00015965727],"domain_scores_gemma":[0.9957583,0.003731074,0.00011711742,0.00010930099,0.00020442397,0.000079791644],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0063957116,0.000058144968,0.00014233199,0.00022661514,0.000057272064,0.000049953273,0.00012476352,0.00009439201,0.0000988712],"category_scores_gemma":[0.0026092576,0.000027728387,0.00005232665,0.00034198875,0.00015666173,0.0002899577,0.000014844606,0.0030517825,0.000007199105],"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":[0.0052004145,0.0020632239,0.00038240023,0.0006751859,0.00049715786,0.0012901627,0.038344927,0.00021423897,0.0075314282,0.06374182,0.0010972578,0.8789618],"study_design_scores_gemma":[0.00027729388,0.0054781456,0.0028129618,0.0043511833,0.00020919758,0.0008864373,0.097353816,0.003361183,0.8504358,0.028232643,0.006499983,0.00010133595],"about_ca_topic_score_codex":0.000081519494,"about_ca_topic_score_gemma":0.000007539477,"teacher_disagreement_score":0.8788605,"about_ca_system_score_codex":0.00006353178,"about_ca_system_score_gemma":0.00024683794,"threshold_uncertainty_score":0.9992482},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10337209,0.00092307467,0.8936636,0.0014536122,0.00035228935,0.00016451819,0.0000021580631,0.00003287934,0.000035810932],"genre_scores_gemma":[0.9215508,0.00036583137,0.077992864,0.000020883299,0.000060214184,0.000004495242,0.0000025706418,0.0000021443836,1.9065115e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984454,0.0001534463,0.0007299088,0.00015733199,0.00040379426,0.00011011457],"domain_scores_gemma":[0.99824065,0.00022471607,0.0004553725,0.00033966143,0.0007149572,0.000024661584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00517674,0.00007607063,0.00014567215,0.0006324982,0.00005378712,0.00011871383,0.0006111327,0.00013692296,0.0000020200002],"category_scores_gemma":[0.0005870144,0.000070051225,0.000017799115,0.00090314145,0.00012277094,0.0067734364,0.0001906627,0.00037388274,0.000013557244],"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":[0.000014269865,0.000053608408,0.00013252153,0.00004107353,0.000014418202,0.0000026505413,0.002325051,0.00044745696,0.0001550107,0.02652992,0.00001140344,0.9702726],"study_design_scores_gemma":[0.000029818963,0.00018825386,0.000284505,0.000118465054,0.000027260843,0.00031483342,0.00061730214,0.94648397,0.0034038879,0.047592804,0.0008774969,0.00006138567],"about_ca_topic_score_codex":0.000036331967,"about_ca_topic_score_gemma":0.000019409013,"teacher_disagreement_score":0.9702112,"about_ca_system_score_codex":0.00007554489,"about_ca_system_score_gemma":0.00013540368,"threshold_uncertainty_score":0.49105778},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002603716,0.004287551,0.98859304,0.0011349827,0.0025571785,0.0003233124,0.0000031087015,0.0002493508,0.00024775424],"genre_scores_gemma":[0.987537,0.00027675703,0.011862624,0.000007142096,0.00026520007,0.000015555834,2.1452391e-7,0.000020747597,0.000014727413],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987633,0.000073172036,0.0007083098,0.000095679505,0.00017112104,0.00018837673],"domain_scores_gemma":[0.99714756,0.002251776,0.00021713469,0.00013391863,0.00022574945,0.000023840517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001670202,0.00012444472,0.00029008073,0.00030408864,0.00004252703,0.000059134443,0.00027533522,0.00027191054,0.000014619411],"category_scores_gemma":[0.0025563976,0.000103542596,0.00011928064,0.00035225772,0.000086946966,0.00047160228,0.000015342574,0.0005387967,0.00002080662],"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":[0.0005307877,0.00007553261,0.0000038605444,0.00018899032,0.0005384371,0.00008894483,0.0005694509,0.14594305,0.17974375,0.029893074,0.00090275373,0.6415214],"study_design_scores_gemma":[0.00005996047,0.0008799807,6.825145e-7,0.00031193945,0.00025149283,0.00026248064,0.0031892287,0.12483353,0.8354581,0.017861119,0.016706986,0.00018449305],"about_ca_topic_score_codex":0.0000060705056,"about_ca_topic_score_gemma":0.0000014857228,"teacher_disagreement_score":0.9849333,"about_ca_system_score_codex":0.0000645495,"about_ca_system_score_gemma":0.00007073476,"threshold_uncertainty_score":0.42223445},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82132953,0.0010805432,0.073700875,0.072056495,0.0069141877,0.0008563048,0.000007936684,0.00006221169,0.02399189],"genre_scores_gemma":[0.99329424,0.0012519705,0.004187806,0.00014925499,0.0008312959,0.000010165608,8.213875e-7,0.000018344626,0.00025610157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948822,0.0011355155,0.0015379085,0.00030730743,0.0016633234,0.0004737084],"domain_scores_gemma":[0.99278986,0.005862699,0.0005697958,0.00023696,0.00036751362,0.00017317437],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.019574478,0.00017656687,0.0002906683,0.00034380198,0.00068865303,0.00038638187,0.0007762936,0.00012953719,0.0007608139],"category_scores_gemma":[0.002423863,0.00013043694,0.00015492982,0.00058181304,0.0006809385,0.00082208763,0.00012990765,0.00094307266,0.0007925643],"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":[0.0003630038,0.00026022372,0.000023076993,0.00002373321,0.00012759947,0.000060261144,0.022295885,0.002746392,0.00097645546,0.7401883,0.00066247344,0.23227261],"study_design_scores_gemma":[0.000026236245,0.000691954,0.0007856759,0.0008455406,0.00008625882,0.00003942776,0.22527152,0.0012580425,0.1316572,0.24838734,0.3902622,0.0006886218],"about_ca_topic_score_codex":0.0005510048,"about_ca_topic_score_gemma":0.00024746047,"teacher_disagreement_score":0.49180096,"about_ca_system_score_codex":0.00075198506,"about_ca_system_score_gemma":0.0013542882,"threshold_uncertainty_score":0.99998546},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00079121266,0.00042738565,0.99219745,0.0034202887,0.0023380439,0.00017221419,0.0000044011636,0.000059672468,0.00058932963],"genre_scores_gemma":[0.34988135,0.000048645114,0.64917403,0.00029621605,0.0005291862,0.000004287342,8.9016606e-7,0.000025658424,0.00003971267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949085,0.0009692798,0.0011230193,0.00046606737,0.0020351359,0.0004979884],"domain_scores_gemma":[0.99091023,0.0065631266,0.00061002496,0.0005615938,0.001134482,0.00022051705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008356362,0.00022773861,0.00036412498,0.0011369773,0.00022591211,0.00045676754,0.0012213892,0.00015454803,0.000021557766],"category_scores_gemma":[0.0032809346,0.00018614877,0.00019051839,0.0015651428,0.00012201719,0.0012023534,0.00014247574,0.0018711266,0.00018392784],"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":[0.00027541057,0.0009100869,0.000014242192,0.000047725203,0.00005745628,0.002311669,0.0023471618,0.46814832,0.0034602012,0.0046514384,0.0006320536,0.5171442],"study_design_scores_gemma":[0.0000655126,0.0022915807,0.00006449475,0.0011451837,0.000026132799,0.00018245747,0.0012785569,0.96970195,0.0225988,0.0012564342,0.0012135324,0.00017538723],"about_ca_topic_score_codex":0.00004021966,"about_ca_topic_score_gemma":3.0710197e-7,"teacher_disagreement_score":0.51696885,"about_ca_system_score_codex":0.00019601974,"about_ca_system_score_gemma":0.0005828929,"threshold_uncertainty_score":0.81292176},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003662559,0.0014540454,0.9899263,0.0019566661,0.0013198432,0.00014950598,5.0430737e-7,0.000030235731,0.0015003325],"genre_scores_gemma":[0.5817627,0.00020923627,0.4170171,0.00018977215,0.00065228116,0.000005884219,2.5817167e-7,0.000018079763,0.00014468048],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862355,0.00022012816,0.0005025828,0.00021411112,0.00019651893,0.00024310571],"domain_scores_gemma":[0.9961992,0.002794348,0.00027559727,0.00017380595,0.00043247585,0.00012458728],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004998968,0.000112573594,0.00014948647,0.00013628746,0.00032918018,0.002302051,0.000605478,0.00003637678,0.0000026148846],"category_scores_gemma":[0.0019089018,0.000069232294,0.00008026696,0.00040483449,0.000091881615,0.0052885283,0.00008357082,0.0002492012,0.0000037397751],"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":[0.000041847175,0.00007677134,2.2575925e-7,0.000027764501,0.000035593308,0.000015497244,0.0074004936,0.00013791698,0.00036314214,0.25473887,0.000020219139,0.73714167],"study_design_scores_gemma":[0.000016461183,0.00029203956,6.20663e-7,0.000069614325,0.000063389605,0.00012666601,0.0038591812,0.68244344,0.006958501,0.2252326,0.0808207,0.00011677441],"about_ca_topic_score_codex":0.000003871163,"about_ca_topic_score_gemma":0.00001150231,"teacher_disagreement_score":0.7370249,"about_ca_system_score_codex":0.000033014367,"about_ca_system_score_gemma":0.0002335119,"threshold_uncertainty_score":0.99873364},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21523431,0.0055754906,0.7177134,0.03088999,0.0040262914,0.0012205254,0.000076671175,0.000039827875,0.025223447],"genre_scores_gemma":[0.99646103,0.0014097891,0.0011401162,0.00030710152,0.00058939273,0.000027922975,0.0000010693974,0.000024107083,0.00003948823],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99651617,0.00022684662,0.0021426305,0.00036466663,0.00034942885,0.00040028087],"domain_scores_gemma":[0.9960554,0.0021341552,0.00088720984,0.0004135838,0.00044599935,0.000063642714],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010646079,0.00017625575,0.0004421376,0.0009575893,0.00018430433,0.00022677073,0.0007571291,0.000139069,0.000113117974],"category_scores_gemma":[0.0026631588,0.00015330547,0.00019074621,0.0021453917,0.00028071183,0.00084418687,0.00009103157,0.001334437,0.0009303914],"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":[0.00032396242,0.00036005568,0.00022781265,0.00003519713,0.000031059473,0.000033459783,0.0015279801,0.0023828452,0.00010321213,0.7238753,0.00019918295,0.27089992],"study_design_scores_gemma":[0.000037958038,0.00096120883,0.00398161,0.00039303084,0.00002331322,0.000030248217,0.00763467,0.055150885,0.008019511,0.8311003,0.09227006,0.00039719546],"about_ca_topic_score_codex":0.0005558817,"about_ca_topic_score_gemma":0.0001819236,"teacher_disagreement_score":0.7812267,"about_ca_system_score_codex":0.00023507341,"about_ca_system_score_gemma":0.00012670657,"threshold_uncertainty_score":0.9998475},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005238756,0.0032928244,0.98717475,0.0020226063,0.0018843822,0.000053918797,7.0229515e-7,0.00004047708,0.0002916002],"genre_scores_gemma":[0.88191044,0.00022585211,0.117011905,0.0001829772,0.00058004167,0.0000025894224,0.0000010026141,0.000009634855,0.000075532276],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735487,0.00041754785,0.000981314,0.00034063234,0.00068526535,0.00022036953],"domain_scores_gemma":[0.9943983,0.0042529944,0.00058463105,0.0002505396,0.00037703078,0.00013650405],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0041960524,0.00017141433,0.0003429834,0.00075561024,0.0001734349,0.0012889907,0.00039683116,0.00008543364,0.00012470609],"category_scores_gemma":[0.0019419813,0.00013361323,0.00035024315,0.0019113442,0.000065415545,0.0015681721,0.00013404309,0.00059234403,0.00005622514],"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":[0.00007864114,0.000115228744,0.00022204894,0.000026353651,0.0021687474,0.00015059616,0.004093829,0.0035509178,0.0010013253,0.7607732,0.00015401587,0.2276651],"study_design_scores_gemma":[0.000037121714,0.00030236488,0.00010243117,0.00052005815,0.0026769897,0.001189087,0.016037269,0.90198565,0.0037978902,0.06656106,0.006376333,0.000413741],"about_ca_topic_score_codex":0.000012940671,"about_ca_topic_score_gemma":0.0000024518329,"teacher_disagreement_score":0.89843476,"about_ca_system_score_codex":0.00006546601,"about_ca_system_score_gemma":0.00012653002,"threshold_uncertainty_score":0.99974775},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011378087,0.0007415956,0.9944271,0.0028921152,0.00034603194,0.000098496654,5.7780454e-7,0.000026799962,0.00032950638],"genre_scores_gemma":[0.4739509,0.00054860924,0.52531123,0.000081906095,0.00009411911,0.000003829541,4.1385485e-7,0.000004347103,0.0000046084892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987684,0.000088766676,0.0005207503,0.00013398714,0.00035084152,0.00013724084],"domain_scores_gemma":[0.9984221,0.0009335288,0.00022540867,0.00011642179,0.00027189264,0.00003064931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020896124,0.00007164765,0.00009947388,0.00025421768,0.000067995155,0.00023070774,0.00038140544,0.00003720035,0.0000039595516],"category_scores_gemma":[0.0007271193,0.000053234726,0.000040161198,0.0012443082,0.000066284236,0.002360563,0.00005990298,0.0002981688,0.000006696421],"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":[0.000008694432,0.000049614984,0.0000023909151,0.00001812029,0.000007259329,0.000023479568,0.0022965963,0.60731375,0.0004925519,0.022922836,0.000034804634,0.3668299],"study_design_scores_gemma":[0.000011660169,0.00016079671,0.000015747837,0.00029494066,0.000013896404,0.00046533934,0.0006121972,0.8326403,0.0021430617,0.16277403,0.0007837762,0.000084264975],"about_ca_topic_score_codex":0.000008426882,"about_ca_topic_score_gemma":0.0000016236002,"teacher_disagreement_score":0.4728131,"about_ca_system_score_codex":0.000042526943,"about_ca_system_score_gemma":0.00009159997,"threshold_uncertainty_score":0.222472},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2168347,0.004429506,0.6858212,0.08641347,0.0053901277,0.00044354238,0.000048003945,0.000025342428,0.00059415423],"genre_scores_gemma":[0.9906574,0.00068660843,0.0051400564,0.0031052572,0.00033492697,0.000002259132,0.0000017351435,0.00001093238,0.000060828723],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.995853,0.00030216368,0.0018901378,0.00025372737,0.0014590728,0.0002419056],"domain_scores_gemma":[0.99362093,0.0032857088,0.00090684777,0.00028318434,0.0017589908,0.00014431158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009104154,0.00017733348,0.00042392692,0.0002383226,0.00023320132,0.0009581019,0.0008323423,0.000092149596,0.00023697359],"category_scores_gemma":[0.008315885,0.00009570452,0.00022280197,0.001033029,0.00024045982,0.0019443794,0.0002275326,0.0005051004,0.000086651504],"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":[0.002196935,0.0003329077,0.00007932179,0.00006658115,0.0001754079,0.00007480643,0.049503256,0.00021145563,0.0012112776,0.011464233,0.005407287,0.9292765],"study_design_scores_gemma":[0.0005847768,0.00066706823,0.0018347196,0.0015018398,0.0005671133,0.0010325371,0.10397899,0.035009656,0.051131945,0.06460761,0.738216,0.00086774526],"about_ca_topic_score_codex":0.000065015935,"about_ca_topic_score_gemma":0.000042278447,"teacher_disagreement_score":0.9284088,"about_ca_system_score_codex":0.000055957487,"about_ca_system_score_gemma":0.0006578057,"threshold_uncertainty_score":0.99554926},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001952521,0.9650878,0.014627478,0.018449252,0.0005270334,0.0012226095,0.000011054425,0.00001515764,0.000040142422],"genre_scores_gemma":[0.00013449269,0.99182,0.0017348567,0.005742243,0.00046305786,0.000047448306,0.0000046468585,0.0000476885,0.0000056090043],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99529654,0.0006477613,0.0026286729,0.00043509522,0.0007477852,0.00024412],"domain_scores_gemma":[0.9925529,0.0036689555,0.0019300729,0.00039800786,0.0011405222,0.0003095511],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0035836427,0.0003767697,0.0022616836,0.0008297622,0.000037025355,0.000051965042,0.0003048977,0.00035940352,0.00007754774],"category_scores_gemma":[0.016664224,0.00026777733,0.00042279414,0.0014640951,0.00014193845,0.00030058896,0.00016216957,0.0017019992,0.00013459042],"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":[0.00014957326,0.0003265516,2.5205026e-7,0.063821785,0.00021176056,0.00027348215,0.00031075726,0.0000013065448,0.00012364813,0.00006197414,0.0006397375,0.9340792],"study_design_scores_gemma":[0.000038572314,0.0005260168,0.0000021723204,0.1839581,0.0024144629,0.0033467708,0.00030486338,0.0002235119,0.0002463918,0.00021799073,0.8085409,0.00018029122],"about_ca_topic_score_codex":0.00013962916,"about_ca_topic_score_gemma":0.00003378589,"teacher_disagreement_score":0.93389887,"about_ca_system_score_codex":0.00037287784,"about_ca_system_score_gemma":0.0012435921,"threshold_uncertainty_score":0.99997747},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039400123,0.00057845894,0.92582345,0.032940242,0.0009783373,0.00009228843,0.000003916486,0.000010946665,0.00017223635],"genre_scores_gemma":[0.96946317,0.000059516744,0.029475495,0.00057500374,0.0003669992,0.000003287632,6.341816e-7,0.0000071637796,0.00004873842],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973058,0.0002063983,0.0011592719,0.00023071376,0.0008706704,0.0002271518],"domain_scores_gemma":[0.99435395,0.0043034647,0.00047150865,0.00026562196,0.00050226686,0.00010320715],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0054508825,0.00010229886,0.00028854547,0.00046178038,0.00008491769,0.00015435691,0.00090435595,0.00009414627,0.000099891346],"category_scores_gemma":[0.008776666,0.00006972937,0.00028102857,0.001251666,0.00007603456,0.0009897483,0.00019199557,0.0005138008,0.000011954849],"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":[0.00019671142,0.0002595793,0.00017317687,0.000046136425,0.0007539709,0.00029852102,0.008410393,0.039684076,0.0017037794,0.11824009,0.0002390006,0.82999456],"study_design_scores_gemma":[0.000022853628,0.00013797551,0.0001271879,0.000092868126,0.00028414346,0.0001672383,0.0015724809,0.98494864,0.0047277496,0.0052579343,0.002574491,0.00008643284],"about_ca_topic_score_codex":0.00010463898,"about_ca_topic_score_gemma":0.00049434364,"teacher_disagreement_score":0.9452646,"about_ca_system_score_codex":0.0001067978,"about_ca_system_score_gemma":0.00037386178,"threshold_uncertainty_score":0.9995728},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56963205,0.0016753788,0.03661507,0.38064292,0.0019777536,0.00041898858,0.000008614653,0.00003890448,0.008990308],"genre_scores_gemma":[0.99751055,0.00035505954,0.0010316272,0.0007470437,0.000316619,0.0000017212838,2.9514914e-7,0.0000063607126,0.000030738116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99742085,0.00081703695,0.0010208879,0.00013587691,0.0004292091,0.00017616597],"domain_scores_gemma":[0.992825,0.004673727,0.0004433101,0.00010420002,0.0018723315,0.00008145744],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.016015187,0.00007085441,0.00020980333,0.00035296398,0.00019085196,0.00036910226,0.0001646124,0.00028605456,0.000014575768],"category_scores_gemma":[0.037396666,0.00006683964,0.000046046203,0.0012082161,0.00034537134,0.0013311934,0.000028872933,0.0014626653,0.000004417377],"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":[0.00009665642,0.00007833445,0.00024834523,0.000041715193,0.000022003253,0.000015720956,0.022980912,0.00014644099,0.003070721,0.9520718,0.00011760883,0.021109737],"study_design_scores_gemma":[0.000053789812,0.00046770746,0.0035792673,0.00073114614,0.000119145676,0.00008117729,0.08351471,0.0032558339,0.0023724262,0.8652343,0.040306307,0.00028418677],"about_ca_topic_score_codex":0.002141313,"about_ca_topic_score_gemma":0.0009031315,"teacher_disagreement_score":0.4278785,"about_ca_system_score_codex":0.00022966483,"about_ca_system_score_gemma":0.0016460185,"threshold_uncertainty_score":0.97071177},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009001866,0.002061047,0.9817089,0.013600132,0.0008412897,0.00014084249,0.0000010573353,0.00017011471,0.001386623],"genre_scores_gemma":[0.16317017,0.00040173242,0.83447474,0.00066737994,0.0011420818,0.0000071116883,5.5081125e-7,0.000025645053,0.000110559144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954332,0.0008846021,0.0010964278,0.00045032785,0.0015420261,0.00059340143],"domain_scores_gemma":[0.99184346,0.004737862,0.00045462963,0.000660498,0.002073685,0.00022985078],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01045457,0.00020230147,0.00034262374,0.00071982684,0.00029471374,0.0011702677,0.0017079846,0.00014522547,0.00007081413],"category_scores_gemma":[0.0039020088,0.00017367605,0.00027661028,0.0032222325,0.00021924957,0.003955394,0.00027934494,0.0019286873,0.0002149293],"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":[0.000049746108,0.00024279783,0.000004265883,0.000015611591,0.00008336032,0.00092216127,0.00041020874,0.008079293,0.00076578796,0.086889215,0.0047946176,0.8977429],"study_design_scores_gemma":[0.000016331438,0.0004784322,0.0000034605325,0.00017483684,0.000057602338,0.0002538169,0.00032162401,0.63560236,0.029881284,0.22175188,0.11123624,0.00022213138],"about_ca_topic_score_codex":0.000036568614,"about_ca_topic_score_gemma":0.000011993218,"teacher_disagreement_score":0.8975208,"about_ca_system_score_codex":0.00026364252,"about_ca_system_score_gemma":0.0006844997,"threshold_uncertainty_score":0.9998666},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9658606,0.0055530737,0.0058117374,0.018408738,0.00048679582,0.0008645542,0.0000054482293,0.000013914624,0.0029951322],"genre_scores_gemma":[0.9948658,0.003228447,0.0012707629,0.00016798495,0.000375856,0.000016161097,0.0000020115003,0.0000138458,0.000059168495],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972156,0.00041620078,0.0008241691,0.00026010134,0.0009968618,0.0002870674],"domain_scores_gemma":[0.9969252,0.0016590197,0.00020811157,0.00017178676,0.0006954108,0.00034052215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004066665,0.00012993498,0.00029376373,0.00096523477,0.000090395166,0.00012442241,0.000109358225,0.000108762586,0.00008940268],"category_scores_gemma":[0.007975216,0.0001019061,0.00005742654,0.0008154646,0.0003746182,0.0006331487,0.0000789621,0.0015530569,0.000024760451],"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":[0.0036147966,0.0014606654,0.000120829274,0.0006795615,0.00008123755,0.00026869285,0.0038468668,0.00000851753,0.0046360116,0.022761982,0.00010863238,0.9624122],"study_design_scores_gemma":[0.00040044615,0.021813804,0.015541155,0.012751309,0.00056771527,0.0037825333,0.06788203,0.021221317,0.2494729,0.58053356,0.025259139,0.00077407644],"about_ca_topic_score_codex":0.00031733356,"about_ca_topic_score_gemma":0.000028343193,"teacher_disagreement_score":0.96163815,"about_ca_system_score_codex":0.00023332609,"about_ca_system_score_gemma":0.0018411638,"threshold_uncertainty_score":0.95476544},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11094856,0.0006028103,0.87907714,0.0005331187,0.0029915455,0.00010437303,0.0000016706256,0.000097836026,0.005642925],"genre_scores_gemma":[0.99180895,0.00018831108,0.002721053,0.00020467036,0.0049731755,0.0000025801073,0.0000044787766,0.000030446043,0.0000663207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817204,0.000029243007,0.00078267854,0.00022741717,0.00053055235,0.0002580833],"domain_scores_gemma":[0.9983085,0.000160098,0.0006322255,0.00010776963,0.00076688017,0.00002454108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011170128,0.00017103097,0.00021158776,0.00038404038,0.00028283108,0.0008342052,0.00018983419,0.000114848735,0.00010697569],"category_scores_gemma":[0.0016167305,0.00015602041,0.00016191625,0.000635775,0.000054880195,0.0059086597,0.00006560693,0.0006683555,0.00013010796],"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":[0.00060799276,0.00024519576,0.00006297326,0.00024179231,0.0001053228,0.00015331742,0.0007624085,0.4892578,0.06350114,0.057979837,0.0004159994,0.3866662],"study_design_scores_gemma":[0.000029346596,0.000066214765,0.00003265149,0.000322878,0.00028133186,0.000062357794,0.0016166647,0.9690324,0.005702789,0.015832365,0.006866597,0.0001544216],"about_ca_topic_score_codex":0.0001244251,"about_ca_topic_score_gemma":0.000024181087,"teacher_disagreement_score":0.8808604,"about_ca_system_score_codex":0.00010574647,"about_ca_system_score_gemma":0.000105479296,"threshold_uncertainty_score":0.80442595},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065392025,0.0003085313,0.9292968,0.0020966881,0.0008584688,0.00010879049,0.000011259369,0.000028261977,0.0018992048],"genre_scores_gemma":[0.99698913,0.00004282258,0.0028199237,0.000038249746,0.000072026174,9.657128e-7,3.4758315e-7,0.0000075605635,0.000028999953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801415,0.00010535566,0.00084967224,0.00019403415,0.0006404808,0.00019632779],"domain_scores_gemma":[0.99589336,0.002410511,0.00067312643,0.00039706405,0.0005365162,0.00008941578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009347737,0.00014022342,0.00037089232,0.00088765746,0.00003634122,0.00026934143,0.0010646852,0.000080683094,0.00001946832],"category_scores_gemma":[0.003936578,0.000083634666,0.0004915215,0.0032017136,0.00018044193,0.0020272995,0.00014730247,0.0003890007,0.00004826676],"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":[0.00027988167,0.0009194583,0.00024246269,0.000013928652,0.0017842356,0.00010850117,0.0035659652,0.048354924,0.006128671,0.043343116,0.00038745097,0.8948714],"study_design_scores_gemma":[0.000042419768,0.0029691346,0.0004065619,0.00032691867,0.00087511074,0.00027513597,0.00084920455,0.67674637,0.1452922,0.17080548,0.0011092643,0.00030223606],"about_ca_topic_score_codex":0.000006110507,"about_ca_topic_score_gemma":0.000002257823,"teacher_disagreement_score":0.93159705,"about_ca_system_score_codex":0.00006673384,"about_ca_system_score_gemma":0.00033207826,"threshold_uncertainty_score":0.4712736},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064665303,0.0022850689,0.9420202,0.03841935,0.006254681,0.00017172744,0.0000016476306,0.00007351127,0.0043073003],"genre_scores_gemma":[0.8670409,0.0006018552,0.13126501,0.00031463234,0.00046909653,0.0000049724677,0.00000132254,0.000011304712,0.00029095644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982291,0.0002187635,0.000706015,0.00027921272,0.00039912527,0.00016780039],"domain_scores_gemma":[0.9976788,0.0011130362,0.00053247064,0.00020762101,0.00038802586,0.00008004522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013060587,0.00014741802,0.00018826265,0.00055681384,0.00013140214,0.00018459764,0.0003802271,0.00011064038,0.00013066662],"category_scores_gemma":[0.00049425074,0.0001355867,0.00007773869,0.0007131893,0.00018322206,0.000806212,0.00007086136,0.0006873911,0.0000498013],"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":[0.000093451454,0.00021011557,0.00003600787,0.00004121747,0.00003491739,0.000018262877,0.00029266672,0.0011554951,0.00036758074,0.6372902,0.00006418213,0.36039594],"study_design_scores_gemma":[0.000031513984,0.0014232534,0.00014666811,0.0006608805,0.00022042982,0.00069008046,0.005096155,0.14226569,0.06522903,0.6858746,0.09792257,0.00043911912],"about_ca_topic_score_codex":0.000026540314,"about_ca_topic_score_gemma":0.0000033438214,"teacher_disagreement_score":0.8605743,"about_ca_system_score_codex":0.000075961245,"about_ca_system_score_gemma":0.00031892536,"threshold_uncertainty_score":0.5529065},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010314763,0.0017641627,0.9580379,0.02754868,0.0011444698,0.00062653626,0.000055266166,0.000056916324,0.00045129572],"genre_scores_gemma":[0.9240611,0.001262281,0.07400685,0.00010076073,0.0004246329,0.000011281645,0.000014576044,0.000022705142,0.000095815165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9907938,0.0026952743,0.0008763254,0.0013297314,0.0035271482,0.0007777026],"domain_scores_gemma":[0.9567501,0.037792936,0.00036056855,0.002351286,0.0023767445,0.0003683576],"candidate_categories":["metaresearch","scholarly_communication","research_integrity"],"consensus_categories":["metaresearch","scholarly_communication"],"category_scores_codex":[0.08778841,0.00019764663,0.00036772157,0.0060768593,0.0009946496,0.015459174,0.0038228314,0.00015333209,0.000009949297],"category_scores_gemma":[0.038897797,0.00016158597,0.0001073203,0.011259867,0.00068003114,0.017050324,0.0013833004,0.0026045782,0.00003411591],"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":[0.0037689358,0.0008351512,0.000055470307,0.00063807337,0.0011163029,0.0012384825,0.0007689448,0.004114102,0.0011441305,0.8318742,0.0026297555,0.15181649],"study_design_scores_gemma":[0.000073601674,0.0024882979,0.00014523047,0.00073433755,0.00033755534,0.0000811201,0.0035821535,0.9322063,0.004171273,0.010226373,0.045706406,0.00024739013],"about_ca_topic_score_codex":0.000034673078,"about_ca_topic_score_gemma":0.00001373102,"teacher_disagreement_score":0.9280922,"about_ca_system_score_codex":0.00017275129,"about_ca_system_score_gemma":0.000485235,"threshold_uncertainty_score":0.99969643},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008817098,0.000106891035,0.97807956,0.015430567,0.0005587009,0.00010954804,0.0000012935982,0.00015723972,0.005468006],"genre_scores_gemma":[0.7264911,0.000022725366,0.2727494,0.00045408157,0.00024237137,0.0000091677075,5.567512e-7,0.000008264995,0.000022359603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984816,0.000100117526,0.00058985635,0.00022188612,0.00047486718,0.00013168035],"domain_scores_gemma":[0.997475,0.0012671979,0.0003735348,0.00028342826,0.0005181534,0.000082656865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092272705,0.000104320636,0.00012093139,0.00025207552,0.00011767097,0.00039615482,0.00057952607,0.000050523613,0.000008978487],"category_scores_gemma":[0.00035356852,0.00008798846,0.00010338482,0.00073140353,0.000033499637,0.0012213942,0.000033227567,0.0003493036,0.00020073088],"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":[0.000020717984,0.000118716634,2.504724e-7,0.000014143906,0.000008893627,0.000031226613,0.00008840066,0.015630903,0.000932114,0.77478915,0.00031658483,0.2080489],"study_design_scores_gemma":[0.000008925086,0.0002755314,0.0000063607467,0.00015194147,0.000017557419,0.00018190936,0.00035484816,0.91036797,0.009316208,0.037880518,0.041339327,0.00009893419],"about_ca_topic_score_codex":0.0000026823868,"about_ca_topic_score_gemma":2.8454338e-7,"teacher_disagreement_score":0.89473706,"about_ca_system_score_codex":0.00017249446,"about_ca_system_score_gemma":0.0002133541,"threshold_uncertainty_score":0.382013},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8159503,0.0003940775,0.16311795,0.01608884,0.0013881355,0.0003465341,0.000006463895,0.00008203974,0.0026256933],"genre_scores_gemma":[0.99577326,0.00004646098,0.0031053375,0.00065979006,0.00034936285,0.0000073383344,0.000010322449,0.00001959991,0.000028513381],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976004,0.000030892264,0.0015176631,0.00023596382,0.0003925915,0.0002225417],"domain_scores_gemma":[0.9972025,0.0003721441,0.0011254906,0.00014193323,0.0011406221,0.000017319242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001218612,0.000198946,0.00026630436,0.0009759939,0.00014273978,0.00038840566,0.00029659644,0.00022638142,0.00017834321],"category_scores_gemma":[0.0011539579,0.0001819994,0.000049036105,0.0015749416,0.00022761349,0.002977814,0.00014182179,0.00112348,0.00008329747],"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":[0.00021924316,0.00048774813,0.00028790144,0.00010246098,0.00023831196,0.00005541516,0.00079938286,0.00033477572,0.009682671,0.31786847,0.0008407062,0.66908294],"study_design_scores_gemma":[0.00014176976,0.0006528108,0.00046561935,0.0017588856,0.0006417023,0.00037205813,0.1331437,0.02172188,0.47318318,0.24163519,0.12498006,0.0013031504],"about_ca_topic_score_codex":0.00010904291,"about_ca_topic_score_gemma":0.00003415911,"teacher_disagreement_score":0.66777974,"about_ca_system_score_codex":0.00009047354,"about_ca_system_score_gemma":0.00016094698,"threshold_uncertainty_score":0.74217206},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025089001,0.0019342995,0.97958916,0.014429527,0.0012588167,0.00015885764,0.000014975407,0.000010615048,0.000094832576],"genre_scores_gemma":[0.3147059,0.00013358032,0.6848376,0.00006685796,0.0001603001,0.000010421328,0.000012231024,0.000005441426,0.00006765365],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984638,0.00010054251,0.00083207106,0.00019963467,0.00029150443,0.00011242518],"domain_scores_gemma":[0.9951927,0.0027484102,0.00066786865,0.00045668596,0.00090437214,0.000029944917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022242675,0.00009499532,0.00014872361,0.00016333998,0.00013379233,0.000079467485,0.0014076688,0.000047791767,0.000014547505],"category_scores_gemma":[0.002170328,0.00006551203,0.00006251851,0.00053761585,0.0001487368,0.0018882159,0.00020659927,0.0002792204,0.0000081973985],"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":[0.00005700993,0.0013619809,0.0000093935,0.00008280746,0.00018864556,0.0000013979429,0.013371814,0.00019538966,0.012899677,0.66948175,0.0024805597,0.29986957],"study_design_scores_gemma":[0.00011151779,0.00042342924,0.00012869472,0.0011493444,0.0003357639,0.00032975542,0.041336752,0.17241299,0.39773417,0.11296979,0.2726837,0.00038407816],"about_ca_topic_score_codex":0.000014629652,"about_ca_topic_score_gemma":0.00004044,"teacher_disagreement_score":0.556512,"about_ca_system_score_codex":0.000045373352,"about_ca_system_score_gemma":0.0017165722,"threshold_uncertainty_score":0.30451253},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4380272,0.010033803,0.48740602,0.04186161,0.0065630823,0.0006717822,0.000012986283,0.00014767023,0.015275835],"genre_scores_gemma":[0.9912485,0.0006536439,0.0057276995,0.00032730185,0.0019804994,0.0000030812132,8.0588194e-7,0.000017939847,0.000040539304],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99516666,0.0015513558,0.0014076048,0.0003413745,0.0010401057,0.0004928887],"domain_scores_gemma":[0.9873649,0.011219524,0.00042970554,0.0001249878,0.0005695694,0.0002913525],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008204594,0.0002023349,0.0004123973,0.00087986275,0.00036415548,0.00080325746,0.00028725472,0.00016166092,0.00018156519],"category_scores_gemma":[0.017654432,0.00017815671,0.00021355954,0.0013771015,0.00037683823,0.0011399193,0.00005526017,0.0007763563,0.00010064027],"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":[0.0001837204,0.00019644549,0.000043861826,0.000024734218,0.00006200287,0.00029979227,0.0090733245,0.0016987213,0.00023597974,0.57060736,0.000027075035,0.417547],"study_design_scores_gemma":[0.000033827502,0.0005344807,0.000083373496,0.0011714747,0.00043187715,0.00056129036,0.13684198,0.045436077,0.0059232283,0.79363805,0.014734142,0.0006101707],"about_ca_topic_score_codex":0.00079432776,"about_ca_topic_score_gemma":0.0004223483,"teacher_disagreement_score":0.5532213,"about_ca_system_score_codex":0.00032990106,"about_ca_system_score_gemma":0.00075379945,"threshold_uncertainty_score":0.99062026},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91816914,0.031290036,0.026044033,0.019793142,0.0019220997,0.0016577397,0.000033624732,0.000014823012,0.0010753557],"genre_scores_gemma":[0.99608403,0.0022092685,0.0011113674,0.00003174085,0.00050397083,0.000014082101,0.000002145996,0.000012464159,0.000030924766],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99689853,0.00034818824,0.0011086978,0.0002325142,0.0011567363,0.00025533515],"domain_scores_gemma":[0.995593,0.0022883278,0.0007052561,0.00027838355,0.0009978394,0.00013718463],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002336997,0.00015386968,0.0003178926,0.00010311413,0.00014213489,0.00006575077,0.00031747334,0.00006664292,0.000040296934],"category_scores_gemma":[0.009063596,0.00007494544,0.0001285349,0.00038111475,0.0004072829,0.00022839215,0.00015347106,0.0011619694,0.000005314051],"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":[0.0018528644,0.0007230149,0.0021857107,0.000110005276,0.0002977439,0.00002546962,0.04206145,0.000018945964,0.0025569187,0.031381913,0.000107250686,0.9186787],"study_design_scores_gemma":[0.00049748743,0.0032669962,0.031939782,0.005502309,0.0012611913,0.00026793403,0.2639968,0.019874498,0.44479367,0.06920701,0.15880434,0.0005879754],"about_ca_topic_score_codex":0.0003467816,"about_ca_topic_score_gemma":0.0004568534,"teacher_disagreement_score":0.91809076,"about_ca_system_score_codex":0.00035946764,"about_ca_system_score_gemma":0.00081993575,"threshold_uncertainty_score":0.9992835},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23734096,0.000039604587,0.76161474,0.0005345956,0.00014455919,0.000046863177,0.0000030095484,0.000036408383,0.00023924116],"genre_scores_gemma":[0.9904255,0.000028187433,0.009271165,0.000069962596,0.00017998414,0.00000141792,0.000001375143,0.00001625849,0.000006122039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999038,0.000028190612,0.00049272046,0.000110949564,0.00023589704,0.00009423216],"domain_scores_gemma":[0.9991831,0.00044816197,0.000072440984,0.00007198305,0.00014783019,0.00007644573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034751612,0.000096112264,0.00013220053,0.00024274536,0.000039605646,0.00014411825,0.00007756975,0.000068884605,0.000021724783],"category_scores_gemma":[0.00012062826,0.00008670411,0.00007995303,0.00016615303,0.000061882805,0.0004556839,0.0000135770215,0.00042411961,0.000014705418],"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":[0.000071323026,0.00011359736,0.000004667306,0.000024367147,0.000023863957,0.0000032144255,0.00051883946,0.90004575,0.012157992,0.0032881878,0.0000602119,0.08368797],"study_design_scores_gemma":[0.00002274759,0.00019477152,0.000015471262,0.00008201186,0.00003195778,0.000020303658,0.00018541704,0.9928168,0.002693437,0.0035951347,0.00026298012,0.00007898408],"about_ca_topic_score_codex":0.0000017256946,"about_ca_topic_score_gemma":5.953633e-7,"teacher_disagreement_score":0.7530846,"about_ca_system_score_codex":0.000034585057,"about_ca_system_score_gemma":0.000031385614,"threshold_uncertainty_score":0.3535691},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58441263,0.00034177245,0.37968877,0.034832835,0.00048665714,0.000097003445,4.6012462e-7,0.000009031344,0.00013084052],"genre_scores_gemma":[0.9799913,0.00005007054,0.019804634,0.00011124863,0.000016457418,0.0000018698111,3.9432865e-7,0.0000016734382,0.00002231935],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992143,0.00008390913,0.00042560045,0.0000924642,0.00013851741,0.000045193083],"domain_scores_gemma":[0.9986111,0.0004296273,0.00034641766,0.00009365533,0.00051060854,0.000008614906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007310656,0.000049203198,0.00007880623,0.00074578845,0.00004583546,0.000030446605,0.00016129501,0.00006627924,0.0000024181834],"category_scores_gemma":[0.0006883252,0.000038683913,0.000009845781,0.0011085342,0.0000985307,0.0009683938,0.000031644988,0.00021631575,5.4001356e-7],"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":[0.000046646725,0.00013129876,0.0004150224,0.000021893316,0.000007491076,9.130491e-7,0.0036051492,0.00033502976,0.004639082,0.6894927,0.000019476545,0.3012853],"study_design_scores_gemma":[0.00005364383,0.0004142973,0.0023844696,0.0007624665,0.000029773233,0.00006941412,0.04283198,0.02351264,0.70584536,0.2215687,0.0024004767,0.0001267779],"about_ca_topic_score_codex":0.000008556589,"about_ca_topic_score_gemma":0.000023224246,"teacher_disagreement_score":0.70120627,"about_ca_system_score_codex":0.000056114088,"about_ca_system_score_gemma":0.0013923245,"threshold_uncertainty_score":0.24699238},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43198186,0.005244155,0.45889014,0.09991976,0.0004634275,0.0010650926,0.0000041079134,0.00002135028,0.0024101138],"genre_scores_gemma":[0.99834603,0.0005850166,0.0008587433,0.000059154932,0.00013296897,0.0000071944255,8.837339e-7,0.000004048361,0.0000059798285],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865323,0.00012845121,0.0005894752,0.0000849103,0.00044954033,0.00009440991],"domain_scores_gemma":[0.9985994,0.0005910536,0.0002622007,0.000120421035,0.00037192117,0.000054973414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015795428,0.00005570727,0.00019578842,0.0002871792,0.000021796952,0.00001803152,0.000098137796,0.00006261785,0.000009570725],"category_scores_gemma":[0.0026303413,0.000033341523,0.000041160878,0.00062276574,0.00012181451,0.0003285928,0.00001745,0.0004318749,0.000011208099],"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":[0.0009953583,0.0015534097,0.0030711254,0.000832421,0.00019511643,0.0004642569,0.0032148755,0.000049896727,0.010352053,0.22921748,0.00033887813,0.74971515],"study_design_scores_gemma":[0.0010989954,0.013963563,0.003954191,0.007909214,0.0020600175,0.0076535605,0.2799521,0.11479391,0.366313,0.16765794,0.034163795,0.00047970307],"about_ca_topic_score_codex":0.00011662761,"about_ca_topic_score_gemma":0.000024552344,"teacher_disagreement_score":0.74923545,"about_ca_system_score_codex":0.00005436968,"about_ca_system_score_gemma":0.00015342326,"threshold_uncertainty_score":0.31489542},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007652076,0.00009380865,0.98224735,0.008349409,0.0022814258,0.00013495727,8.3042386e-7,0.000066688466,0.006060295],"genre_scores_gemma":[0.8786627,0.00008734087,0.11857018,0.0019171451,0.00044747416,0.000006590502,4.7273628e-7,0.000021345366,0.0002867375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783385,0.0001772299,0.00066147646,0.00027861696,0.00078368536,0.00026512562],"domain_scores_gemma":[0.99610424,0.0027873013,0.00027851562,0.0002911439,0.00033199653,0.0002067779],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017101057,0.00017160721,0.00020647953,0.00034123016,0.000089087654,0.00091914297,0.0006955714,0.00007259577,0.00003288463],"category_scores_gemma":[0.002246719,0.00013280907,0.00015722032,0.00055669073,0.000052123294,0.0030824982,0.000078084544,0.0005030512,0.00082976144],"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":[0.000495846,0.0010937259,0.0000023214768,0.000034718203,0.000045827586,0.00074557925,0.0015963351,0.11402415,0.007673216,0.18527968,0.0046034055,0.6844052],"study_design_scores_gemma":[0.000021431108,0.00095778797,0.0000012229259,0.00018182657,0.00002593371,0.00011038273,0.00009905801,0.92928535,0.028677586,0.026427355,0.01406613,0.0001459253],"about_ca_topic_score_codex":0.0000023028836,"about_ca_topic_score_gemma":7.021893e-7,"teacher_disagreement_score":0.8778975,"about_ca_system_score_codex":0.00008211323,"about_ca_system_score_gemma":0.00036261155,"threshold_uncertainty_score":0.9999482},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19897957,0.005518452,0.56984705,0.21933174,0.002329656,0.0021223142,0.000038492817,0.00004444271,0.0017883083],"genre_scores_gemma":[0.9858355,0.0010254851,0.011448653,0.0009510975,0.0006248245,0.000019596044,0.00000455777,0.000024227724,0.000066049826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952235,0.00083303073,0.0010738608,0.00038546865,0.0021808513,0.0003032897],"domain_scores_gemma":[0.98706084,0.009577553,0.000513663,0.00033491824,0.002231691,0.00028132813],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0057936953,0.00022790366,0.0003451965,0.00020824147,0.00023954838,0.00017582873,0.0001730208,0.0001499763,0.00042881997],"category_scores_gemma":[0.03727684,0.000120374,0.0002122584,0.0005505338,0.0004930169,0.00052436476,0.00008360732,0.0016102092,0.00012147868],"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":[0.018281866,0.005635759,0.00004582565,0.0000844669,0.0025030351,0.00089550816,0.014668222,0.009607652,0.031120973,0.2545389,0.0029138275,0.65970397],"study_design_scores_gemma":[0.00007683916,0.0019504278,0.000058015834,0.00053169916,0.000992465,0.00052238046,0.017519997,0.8263053,0.050947648,0.100154154,0.0008055925,0.0001354593],"about_ca_topic_score_codex":0.00022385662,"about_ca_topic_score_gemma":0.00007892327,"teacher_disagreement_score":0.81669766,"about_ca_system_score_codex":0.0004455054,"about_ca_system_score_gemma":0.0014013656,"threshold_uncertainty_score":0.9708326},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004741204,0.00096934615,0.99205315,0.0013425174,0.0004968004,0.00023165438,0.000010760723,0.000058892187,0.000095660005],"genre_scores_gemma":[0.7113525,0.0005395032,0.28731325,0.00014990261,0.0005864543,0.00001717064,0.0000017295944,0.000029466513,0.0000099721265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989352,0.00006016383,0.0005370716,0.00015204439,0.00016027325,0.00015521978],"domain_scores_gemma":[0.99845845,0.0009835319,0.00011417432,0.00010817209,0.00021595146,0.000119747434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010373449,0.00012522796,0.00017588015,0.00013119617,0.00008490848,0.00023689048,0.00011474939,0.00006547496,0.000027675855],"category_scores_gemma":[0.00023866627,0.00010844695,0.0000766797,0.0001763127,0.00002823828,0.0013584818,0.00001090388,0.00030253147,0.000021016212],"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":[0.00010482368,0.00007375307,8.0803653e-7,0.00006446064,0.00006335868,0.0000073677534,0.00158364,0.024049392,0.025929617,0.0089774905,0.00007414073,0.9390712],"study_design_scores_gemma":[0.00003766919,0.00051026134,0.000018761164,0.000098341676,0.00017969782,0.00019320642,0.0036800716,0.89798224,0.0099888025,0.025183728,0.061953995,0.00017324764],"about_ca_topic_score_codex":0.000010786485,"about_ca_topic_score_gemma":0.000016162932,"teacher_disagreement_score":0.9388979,"about_ca_system_score_codex":0.00006488695,"about_ca_system_score_gemma":0.000029523822,"threshold_uncertainty_score":0.44223383},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9623959,0.0008734079,0.025766855,0.008374239,0.0010634684,0.0005248183,0.0000019537479,0.000020264255,0.0009790958],"genre_scores_gemma":[0.9967353,0.0018091982,0.0009806644,0.000048670037,0.0003446781,0.0000142808585,8.8837913e-7,0.000013510807,0.000052852374],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99733496,0.00050867564,0.00084621477,0.0002078533,0.00085065357,0.00025167016],"domain_scores_gemma":[0.9980379,0.0007188173,0.00076436024,0.00013935844,0.00024868752,0.00009092524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004105558,0.00014593475,0.00020514595,0.00011340728,0.0005432402,0.00031053074,0.00036237005,0.000069488764,0.00010684022],"category_scores_gemma":[0.0011667114,0.00008909037,0.00010227739,0.0006158205,0.0005694744,0.0023300315,0.00006213942,0.0005057441,0.000009790907],"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":[0.00048427918,0.0002821699,0.000038566755,0.00007149943,0.00020597645,0.000033835735,0.27051035,0.00078575435,0.044235535,0.076008834,0.00007494954,0.6072683],"study_design_scores_gemma":[0.000028369343,0.00080776535,0.00020261103,0.0005174568,0.00021438267,0.00012624112,0.75687635,0.0042239926,0.15838817,0.048972838,0.029284835,0.00035697775],"about_ca_topic_score_codex":0.0028878704,"about_ca_topic_score_gemma":0.00026565487,"teacher_disagreement_score":0.60691124,"about_ca_system_score_codex":0.0001255348,"about_ca_system_score_gemma":0.00019163176,"threshold_uncertainty_score":0.43656155},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9767898,0.0027115415,0.012048778,0.0032189959,0.000266207,0.00042346705,0.000043155862,0.000011939084,0.0044861673],"genre_scores_gemma":[0.9926172,0.0011778976,0.005133499,0.000028972001,0.00027084237,0.0000040805016,0.000006348,0.000021961527,0.0007391932],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9884918,0.0065942816,0.0010482435,0.00030817703,0.0030613297,0.0004961828],"domain_scores_gemma":[0.97192675,0.023843141,0.00042735215,0.00057065574,0.0027210584,0.0005110528],"candidate_categories":["metaresearch","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.030982127,0.00014699712,0.0006296968,0.0015895427,0.00019514262,0.000113998925,0.0005047263,0.0001279241,0.0038245202],"category_scores_gemma":[0.0526259,0.000080428,0.000552138,0.0045523643,0.00047229658,0.00041021022,0.00015789902,0.0032153085,0.000030125157],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.022881526,0.0052554663,0.008972891,0.00019040896,0.016793942,0.0013467361,0.010073075,0.002432792,0.078282334,0.0057854126,0.0017536447,0.84623176],"study_design_scores_gemma":[0.0009455384,0.03742487,0.05653047,0.0035643142,0.007231508,0.0003300238,0.034314327,0.41418853,0.36417606,0.055042867,0.025722997,0.00052847055],"about_ca_topic_score_codex":0.021454802,"about_ca_topic_score_gemma":0.00021811231,"teacher_disagreement_score":0.8457033,"about_ca_system_score_codex":0.00033512607,"about_ca_system_score_gemma":0.0012943249,"threshold_uncertainty_score":0.9990843},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014643248,0.000492327,0.9725092,0.008847967,0.002670209,0.00024024046,0.0000044040676,0.00008958293,0.0005027992],"genre_scores_gemma":[0.894778,0.00011650083,0.10436254,0.000088394176,0.00050923345,0.000016948428,0.0000022921265,0.000014932051,0.00011111656],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971183,0.00033477152,0.0013831344,0.0003155933,0.00057435525,0.0002738485],"domain_scores_gemma":[0.9950888,0.0027765671,0.0008786562,0.00024090306,0.0009313338,0.000083704974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004041213,0.00018272453,0.00030188638,0.00055433536,0.00030212072,0.00043027097,0.0009558238,0.00016074044,0.000029342855],"category_scores_gemma":[0.0033053462,0.00017105097,0.00017618269,0.0006545967,0.00014000625,0.0052263723,0.00012069223,0.0009978873,0.000037099624],"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":[0.0001382511,0.0003632982,0.000010057343,0.00003796652,0.000099409815,0.00002923481,0.0081991535,0.0076128384,0.0019103762,0.835268,0.00010878712,0.14622259],"study_design_scores_gemma":[0.000024266566,0.0016217442,0.000009243103,0.00021398159,0.00012605422,0.0002697196,0.08176438,0.10510371,0.038498547,0.7471404,0.024943,0.00028494533],"about_ca_topic_score_codex":0.00003302022,"about_ca_topic_score_gemma":0.000019631712,"teacher_disagreement_score":0.8801348,"about_ca_system_score_codex":0.00011529868,"about_ca_system_score_gemma":0.0009650746,"threshold_uncertainty_score":0.6975256},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04677963,0.00025840438,0.94386697,0.00446763,0.002053582,0.00020539697,0.000018726048,0.000032702337,0.002316967],"genre_scores_gemma":[0.99425936,0.00008189172,0.005204572,0.00013478399,0.0002716384,0.0000036492831,0.000002118342,0.00001249971,0.000029485596],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960677,0.0001457456,0.0018905713,0.00037535402,0.0011907277,0.00032989174],"domain_scores_gemma":[0.99279773,0.004431627,0.0009261821,0.00049489894,0.0011447968,0.00020476233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024806522,0.00021701251,0.00051270425,0.0009938586,0.00009149212,0.0007885149,0.0013313622,0.00008185302,0.00004244815],"category_scores_gemma":[0.007590845,0.00015340325,0.000441924,0.0044826944,0.00024268446,0.0032628635,0.00024921895,0.00041657456,0.00013516162],"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":[0.00020713275,0.0006067672,0.000035663998,0.000034344423,0.0005137426,0.00005593015,0.00913207,0.004181973,0.008105094,0.2256709,0.00008448077,0.7513719],"study_design_scores_gemma":[0.000018265964,0.0022230726,0.00008576751,0.00035860922,0.0014853575,0.00012490358,0.013098196,0.4076999,0.492003,0.07712674,0.005268353,0.00050781964],"about_ca_topic_score_codex":0.000023652132,"about_ca_topic_score_gemma":0.00002222606,"teacher_disagreement_score":0.9474797,"about_ca_system_score_codex":0.00006998643,"about_ca_system_score_gemma":0.00031987045,"threshold_uncertainty_score":0.90874994},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3858205,0.012478158,0.20898642,0.37658215,0.004311675,0.0027274522,0.000019741305,0.00006042262,0.009013462],"genre_scores_gemma":[0.995031,0.0024895687,0.0009982814,0.00019770638,0.001062771,0.000010993825,0.0000022259662,0.000014903635,0.00019256015],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964815,0.00059623545,0.0008883686,0.00023177016,0.0015335843,0.0002685366],"domain_scores_gemma":[0.9931892,0.004517849,0.0002801033,0.00026512827,0.0012827661,0.0004649529],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00577427,0.0001274011,0.00024806111,0.00045284512,0.00014486686,0.00020045119,0.00019029385,0.00008800276,0.00028059806],"category_scores_gemma":[0.017287262,0.00007876207,0.00010270139,0.0008363381,0.00018391621,0.00071952824,0.000051151976,0.0011394044,0.00021073325],"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":[0.002442432,0.0010474352,0.000012680392,0.00022315836,0.00016993002,0.00021517949,0.0038175047,0.000046957197,0.0016778592,0.23469043,0.0034243912,0.752232],"study_design_scores_gemma":[0.000060009465,0.005460288,0.00004286041,0.0038393224,0.00038512776,0.00062132074,0.04545908,0.022128291,0.18957336,0.683123,0.04909131,0.00021604856],"about_ca_topic_score_codex":0.000104786566,"about_ca_topic_score_gemma":0.000041280156,"teacher_disagreement_score":0.752016,"about_ca_system_score_codex":0.00016528739,"about_ca_system_score_gemma":0.0022224956,"threshold_uncertainty_score":0.9909905},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006043076,0.0004727817,0.99093807,0.0001650791,0.001788943,0.0003406171,0.000001534956,0.00010948467,0.00014043097],"genre_scores_gemma":[0.9553994,0.000091930946,0.04405665,0.000017469709,0.00038135325,0.000006108616,4.7455117e-7,0.0000360938,0.000010516764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987148,0.000103169514,0.0006009302,0.00016166034,0.00023236708,0.00018704002],"domain_scores_gemma":[0.99723387,0.0022221028,0.0001710248,0.00011178014,0.0001785101,0.00008270489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016701927,0.00015719804,0.00021157037,0.00017960843,0.00012503851,0.00032367167,0.000102797945,0.00005288296,0.000002601272],"category_scores_gemma":[0.001257946,0.00013696631,0.00005126934,0.0001298072,0.000025299549,0.0004951259,0.000016821728,0.00035188688,0.000010399408],"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":[0.00017588983,0.00007047014,0.0000016968202,0.00011423146,0.00008780848,0.00014035442,0.003021642,0.9371235,0.00022050743,0.0031833665,0.000037853326,0.055822723],"study_design_scores_gemma":[0.00004963143,0.0010902003,0.0000065281715,0.0005200973,0.00028801797,0.00026540394,0.025992773,0.9287749,0.037741136,0.0036134485,0.0013759644,0.0002819196],"about_ca_topic_score_codex":0.00000268566,"about_ca_topic_score_gemma":0.0000017320745,"teacher_disagreement_score":0.9493563,"about_ca_system_score_codex":0.00015160807,"about_ca_system_score_gemma":0.000026724334,"threshold_uncertainty_score":0.5585324},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72614866,0.008348526,0.02055711,0.23145461,0.006248104,0.0006701015,0.000027084305,0.00031501846,0.0062307906],"genre_scores_gemma":[0.99227,0.0002823971,0.0019040615,0.0021551761,0.0031216992,0.000004809908,0.000005712301,0.0000024828983,0.0002536524],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99809843,0.00016480507,0.0006712956,0.00026059189,0.00049369375,0.0003111644],"domain_scores_gemma":[0.99662495,0.0023801662,0.00034603005,0.000066078595,0.0004169384,0.00016584902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011120307,0.00018664444,0.0002516708,0.000052239764,0.00018380546,0.0003748662,0.0003929936,0.00014499412,0.0005969034],"category_scores_gemma":[0.0011895953,0.00006970894,0.00028220203,0.00094748917,0.00004777778,0.0006643201,0.000039346774,0.00058707624,0.00029479087],"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":[0.00052921474,0.0004838505,0.000021757367,0.00003155661,0.000109416,0.00075785635,0.0004507006,0.00042428463,0.54134405,0.0027613454,0.009098902,0.44398707],"study_design_scores_gemma":[0.00005306631,0.0025421584,0.00041789043,0.0003414217,0.00031175892,0.001805751,0.006033608,0.0052813794,0.2703369,0.0045990585,0.70767516,0.0006018726],"about_ca_topic_score_codex":0.00019902723,"about_ca_topic_score_gemma":0.00016695786,"teacher_disagreement_score":0.6985762,"about_ca_system_score_codex":0.00005220798,"about_ca_system_score_gemma":0.00007397327,"threshold_uncertainty_score":0.6535673},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07122184,0.018898673,0.8107151,0.05187632,0.0101085715,0.0016707865,0.00007166816,0.0002778255,0.035159204],"genre_scores_gemma":[0.9916202,0.0005421379,0.0024004427,0.0019258233,0.0034595258,0.0000027974893,0.000015369676,0.000019375755,0.000014356327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983463,0.000031565174,0.00073563325,0.00031856116,0.00035894633,0.00020896998],"domain_scores_gemma":[0.99837446,0.0005108796,0.00042180135,0.00035420255,0.00031178753,0.000026886608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022356345,0.00014457626,0.00019788185,0.0003673914,0.00011055165,0.0009115736,0.0006158886,0.00006645242,0.00010458409],"category_scores_gemma":[0.0012760108,0.0001274375,0.00003801804,0.0009354792,0.00011486801,0.0054115863,0.0004862036,0.0004960584,0.00014515643],"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":[0.00022230874,0.0004786706,0.0004533066,0.0004438583,0.00008294689,0.00024497323,0.00055970164,0.0004762623,0.000334472,0.66954875,0.005049234,0.3221055],"study_design_scores_gemma":[0.000030653653,0.00002618446,0.000081885875,0.0003405099,0.00016372178,0.0001819075,0.0033096827,0.12941243,0.00030388756,0.10254175,0.76336664,0.00024075675],"about_ca_topic_score_codex":0.00030426422,"about_ca_topic_score_gemma":0.00012931458,"teacher_disagreement_score":0.92039835,"about_ca_system_score_codex":0.00004536905,"about_ca_system_score_gemma":0.00009331185,"threshold_uncertainty_score":0.8790325},"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":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024848621,0.1434528,0.8510718,0.0024741841,0.0024412381,0.00028273728,0.00000820718,0.000043497723,0.00020068356],"genre_scores_gemma":[0.002897318,0.019080386,0.9761168,0.0008676292,0.00090442464,0.00002270082,0.0000035265643,0.000026479025,0.00008076584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717027,0.00020854533,0.0013186622,0.0003200534,0.00067548035,0.000306968],"domain_scores_gemma":[0.99489915,0.0023363526,0.0009294819,0.0003762801,0.0013152357,0.00014350456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037840437,0.00020431202,0.00046607363,0.00028059527,0.00008766517,0.00038920884,0.0009776672,0.00010783568,0.000010131822],"category_scores_gemma":[0.003088342,0.00016516351,0.00028753528,0.0011000269,0.000052622716,0.002429286,0.00010215413,0.0006417581,0.000021335727],"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":[0.00011196162,0.00038689122,0.0000042838287,0.0056207688,0.0002895247,0.002032227,0.0073379874,0.030219866,0.0017310396,0.023135701,0.009871466,0.9192583],"study_design_scores_gemma":[0.00008059473,0.0024793914,0.0000068913264,0.07772955,0.00036513238,0.0058876793,0.0011415133,0.79742783,0.016967135,0.014943544,0.08235809,0.00061266957],"about_ca_topic_score_codex":0.0000028698278,"about_ca_topic_score_gemma":4.0213983e-8,"teacher_disagreement_score":0.9186456,"about_ca_system_score_codex":0.000067960194,"about_ca_system_score_gemma":0.00034830277,"threshold_uncertainty_score":0.6735172},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086404,0.0012389957,0.83961135,0.057296738,0.004840235,0.0015199197,0.000025168722,0.000096996824,0.00896662],"genre_scores_gemma":[0.97939616,0.00011623479,0.018771736,0.0010423583,0.00030667704,0.000004919737,0.000009184808,0.0000010486034,0.00035167448],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99826485,0.0002380808,0.0006703096,0.00018447734,0.00046500962,0.00017726494],"domain_scores_gemma":[0.99759084,0.0011715936,0.0005717032,0.000056525707,0.0005399444,0.00006937085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009315483,0.00015702713,0.00023184101,0.000059868107,0.00016183245,0.00007663876,0.00028050516,0.00008476968,0.00020894359],"category_scores_gemma":[0.00037948778,0.000054098033,0.00016835317,0.00067084323,0.000048657803,0.00037649495,0.00004253998,0.00028303108,0.000030903622],"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":[0.0029951867,0.00371902,0.00023358864,0.000033252527,0.00023244137,0.000070868,0.00013492096,0.104913354,0.37467024,0.0051541035,0.0055506225,0.5022924],"study_design_scores_gemma":[0.00097133213,0.011254862,0.13989504,0.00174266,0.0014313452,0.00025424888,0.015965506,0.09743538,0.63281524,0.00620411,0.09041715,0.0016131407],"about_ca_topic_score_codex":0.000029458002,"about_ca_topic_score_gemma":0.000022772061,"teacher_disagreement_score":0.89299214,"about_ca_system_score_codex":0.00004861526,"about_ca_system_score_gemma":0.000018756442,"threshold_uncertainty_score":0.22877857},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030048795,0.000046355275,0.9737223,0.02190957,0.0005076881,0.00032605312,8.8449355e-7,0.000014994256,0.0004673042],"genre_scores_gemma":[0.96116805,0.00014583892,0.037365396,0.0010931568,0.00011775835,0.000015666365,0.0000017496606,0.000007315453,0.00008504703],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981843,0.0003564202,0.0007369711,0.00022181815,0.00026285002,0.0002376343],"domain_scores_gemma":[0.99540186,0.0025426284,0.00075412006,0.00017056453,0.001042535,0.00008826946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017342024,0.0001305954,0.00022386087,0.00048326526,0.00017438401,0.00030679523,0.00043320694,0.00009867226,0.000009192263],"category_scores_gemma":[0.0052578948,0.0001287021,0.00010010604,0.00089613657,0.000042953954,0.002783749,0.000072553215,0.0006241741,0.000028125009],"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":[0.0018868799,0.0010481277,0.000011764488,0.000024515435,0.00018313045,0.00006182977,0.006492984,0.2655705,0.08845768,0.18827525,0.0025433043,0.44544405],"study_design_scores_gemma":[0.00024820035,0.0017464457,0.00006227445,0.00028707023,0.00012086697,0.00011618656,0.012307318,0.713153,0.21427672,0.043080397,0.014328991,0.0002725293],"about_ca_topic_score_codex":0.000055240744,"about_ca_topic_score_gemma":0.000018253138,"teacher_disagreement_score":0.9581632,"about_ca_system_score_codex":0.00019268632,"about_ca_system_score_gemma":0.0003655349,"threshold_uncertainty_score":0.6294571},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041919153,0.000067775785,0.95192224,0.0051279175,0.00032751603,0.00010271132,1.9496073e-7,0.000005480723,0.00052700774],"genre_scores_gemma":[0.990338,0.0001239687,0.009265786,0.00020842678,0.000050379695,0.000005474711,1.29058e-7,0.0000021338876,0.0000056765107],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99902433,0.000064508,0.0004917448,0.00009939933,0.00022938913,0.00009062702],"domain_scores_gemma":[0.9969478,0.0022797936,0.0002911978,0.00013365487,0.00031197694,0.00003552891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010393504,0.00005950538,0.00011652492,0.00017253037,0.000037291124,0.00022374003,0.00034026327,0.000019094436,4.742729e-7],"category_scores_gemma":[0.00296719,0.000042603944,0.000028712202,0.00054042484,0.000067103225,0.004197035,0.00007192077,0.00018797534,0.0000052047976],"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":[0.00009174719,0.0001430406,0.00012509538,0.0000060456705,0.000011243989,0.0000050248677,0.002248334,0.00073899614,0.0010832805,0.16216725,0.000021451522,0.83335847],"study_design_scores_gemma":[0.00014744782,0.00079757883,0.0038655412,0.00036230142,0.00007395375,0.00014364405,0.007092858,0.2399451,0.20705038,0.5304809,0.009742342,0.0002979143],"about_ca_topic_score_codex":0.000016104977,"about_ca_topic_score_gemma":0.000004936692,"teacher_disagreement_score":0.94841886,"about_ca_system_score_codex":0.000024026269,"about_ca_system_score_gemma":0.00009806919,"threshold_uncertainty_score":0.35522178},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99469554,0.00021958712,0.0015128327,0.0024607237,0.00028319246,0.00031123954,0.0000014100178,0.0000013513823,0.00051413197],"genre_scores_gemma":[0.9981746,0.0005484077,0.0010909942,0.0001041354,0.000057904115,0.0000056039767,7.6769834e-7,0.0000033117328,0.000014277214],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9971104,0.000989733,0.0011319272,0.000101723206,0.000540051,0.00012611017],"domain_scores_gemma":[0.99535453,0.0030137585,0.00106362,0.000116959854,0.00040986983,0.000041232026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043877778,0.000093443756,0.00025667372,0.00023848985,0.000105683466,0.000073254625,0.0002134392,0.00006626622,0.000014335836],"category_scores_gemma":[0.0059074364,0.00007263669,0.00003753964,0.00054753677,0.00032933633,0.0020223388,0.000032441472,0.00025421276,9.988623e-7],"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":[0.0024919568,0.004205064,0.005944183,0.000032395325,0.000082948056,0.00074798684,0.84557676,0.0014117142,0.0055116047,0.016504046,0.000035732995,0.11745562],"study_design_scores_gemma":[0.00017766685,0.00049457495,0.002136556,0.00010549025,0.00006592678,0.00012710557,0.9882897,0.00015778934,0.004107386,0.003829854,0.00043522433,0.00007272543],"about_ca_topic_score_codex":0.005682117,"about_ca_topic_score_gemma":0.01700056,"teacher_disagreement_score":0.14271295,"about_ca_system_score_codex":0.000082451865,"about_ca_system_score_gemma":0.00023177959,"threshold_uncertainty_score":0.94867116},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065670437,0.0003593866,0.99171054,0.00021468743,0.00030044804,0.00021851163,0.0000025001311,0.000023161356,0.00060372223],"genre_scores_gemma":[0.9634037,0.00096889766,0.035468757,0.000029393199,0.000073425195,0.000014626838,0.00000668615,0.000009316542,0.000025171124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990111,0.00002526131,0.0006505053,0.000061034254,0.00015577076,0.00009634472],"domain_scores_gemma":[0.99894446,0.00022304346,0.0003180718,0.000059750575,0.00043805438,0.000016625674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066403474,0.000074883246,0.00014818883,0.0010440244,0.000036772904,0.000054143246,0.00009366658,0.00007178806,0.000009620762],"category_scores_gemma":[0.00075453834,0.000073294206,0.000041386258,0.00074046146,0.000016546413,0.002006196,0.000006796749,0.0001224745,7.5194276e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005050212,0.00027356998,0.000105802865,0.00093721435,0.000073186995,0.0000036914657,0.0013928504,0.6105373,0.0036438392,0.0183777,0.00029293637,0.3638569],"study_design_scores_gemma":[0.00005482457,0.00024591677,0.000064262254,0.00028894248,0.000065580294,0.00000764674,0.0016215338,0.06683382,0.9090498,0.019200336,0.0024517693,0.0001155909],"about_ca_topic_score_codex":0.000012065233,"about_ca_topic_score_gemma":0.000010319233,"teacher_disagreement_score":0.9568367,"about_ca_system_score_codex":0.00007726803,"about_ca_system_score_gemma":0.000039325314,"threshold_uncertainty_score":0.2988851},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93177027,0.006262601,0.0110925445,0.04227694,0.0026588284,0.00087757315,0.00000465798,0.000011231901,0.005045347],"genre_scores_gemma":[0.996359,0.0017509899,0.0006518817,0.0004887807,0.00034787387,0.00001037773,0.000002212487,0.00000689512,0.00038200518],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99755555,0.00034044025,0.0009628776,0.00019089851,0.00069590955,0.00025431823],"domain_scores_gemma":[0.9964965,0.0015442221,0.00053085794,0.00021883484,0.00095723534,0.00025236068],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001953374,0.00014749165,0.0002968292,0.00033199126,0.00012584217,0.000090873444,0.00019219557,0.00010514739,0.00017733187],"category_scores_gemma":[0.009958257,0.0000885598,0.00011735094,0.0004303097,0.00032509523,0.00028948803,0.00004168287,0.0006964974,0.000020950953],"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":[0.009001517,0.003629229,0.003095242,0.000046353878,0.0001555905,0.00006618654,0.00014273747,0.00018170978,0.0014408935,0.038281035,0.0007196911,0.9432398],"study_design_scores_gemma":[0.00051799277,0.010613409,0.27908581,0.0042690397,0.0012374861,0.000765033,0.015872186,0.007509839,0.05919239,0.6058311,0.01447006,0.0006356429],"about_ca_topic_score_codex":0.0003684196,"about_ca_topic_score_gemma":0.000037138838,"teacher_disagreement_score":0.9426042,"about_ca_system_score_codex":0.00021197296,"about_ca_system_score_gemma":0.0028010516,"threshold_uncertainty_score":0.99838126},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040330873,0.00038463954,0.9423788,0.043247186,0.0058963806,0.0008325762,0.000017241151,0.00015949729,0.0030505552],"genre_scores_gemma":[0.86816835,0.00018599846,0.11999525,0.009882152,0.0013650728,0.00009088223,0.000009793621,0.00009151934,0.00021096841],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99266267,0.0004911322,0.0034686695,0.0009965965,0.0013042559,0.0010766982],"domain_scores_gemma":[0.9809355,0.008048252,0.002561037,0.0012237711,0.006802143,0.00042926634],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0050569773,0.00068209495,0.0008356964,0.0011234389,0.0007180105,0.0020153841,0.0036201337,0.00033008854,0.00012856515],"category_scores_gemma":[0.0062013017,0.00066423655,0.0005306615,0.004138029,0.00014084198,0.0046541924,0.00091103447,0.0013870236,0.00096569123],"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":[0.0023119585,0.002467772,0.00001704076,0.0003410463,0.0005444437,0.00012001167,0.0060866093,0.07166721,0.0046819802,0.13727006,0.0007533864,0.7737385],"study_design_scores_gemma":[0.00009981258,0.00047426106,0.000018121502,0.00087822205,0.0003878005,0.00084063975,0.017355219,0.75800914,0.086678855,0.06749478,0.0666172,0.0011459637],"about_ca_topic_score_codex":0.00014193261,"about_ca_topic_score_gemma":0.00015762141,"teacher_disagreement_score":0.86413527,"about_ca_system_score_codex":0.00055173243,"about_ca_system_score_gemma":0.00083490706,"threshold_uncertainty_score":0.9998122},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037890119,0.00006068633,0.9821464,0.008126792,0.0025841093,0.00033774375,8.1804245e-7,0.000040699197,0.002913733],"genre_scores_gemma":[0.9854121,0.00010036218,0.013595419,0.00047944876,0.00037747648,0.000010528645,3.0402063e-7,0.00001045712,0.0000138748355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947444,0.0017786948,0.0013985394,0.0003634299,0.0013842363,0.00033070415],"domain_scores_gemma":[0.9902441,0.0056110113,0.0010904378,0.0006664561,0.002297352,0.00009064116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009390333,0.0001916616,0.0002922241,0.0011052863,0.00067905063,0.00037990173,0.001200509,0.00019226463,0.00004910806],"category_scores_gemma":[0.0046732402,0.00014163443,0.00017471358,0.0032539328,0.00020097864,0.000993727,0.00016505961,0.0013884719,0.000061881496],"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":[0.0010362365,0.00035909473,4.8169056e-7,0.000026240357,0.00002200078,0.000013020471,0.00031187377,0.47612554,0.0018500665,0.25974464,0.000082537954,0.26042825],"study_design_scores_gemma":[0.00001597544,0.0011116425,0.0000026751213,0.00038149633,0.000021387494,0.000022498623,0.002467379,0.69942874,0.28265873,0.013099169,0.00070155464,0.00008871417],"about_ca_topic_score_codex":0.00006493514,"about_ca_topic_score_gemma":0.000022982122,"teacher_disagreement_score":0.9816231,"about_ca_system_score_codex":0.00033828436,"about_ca_system_score_gemma":0.00031959257,"threshold_uncertainty_score":0.60322964},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13223472,0.001174037,0.65534645,0.16887806,0.009146703,0.0013687673,0.0000054292204,0.00007925782,0.031766552],"genre_scores_gemma":[0.98861194,0.000042007836,0.0013577966,0.008217759,0.0014490177,0.000012659985,0.0000012790758,0.00001793487,0.00028961294],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979688,0.00003168186,0.0010741987,0.00022136445,0.00036677686,0.00033716817],"domain_scores_gemma":[0.99716425,0.00083145354,0.0010284851,0.00020399857,0.0007591561,0.0000126649165],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0029482488,0.0001896826,0.00030614733,0.00048307044,0.0005192763,0.00057853817,0.0006994579,0.00010726166,0.000036807374],"category_scores_gemma":[0.015043124,0.00014631216,0.00017939815,0.0010668071,0.000111963345,0.0036009203,0.00052911777,0.00068073306,0.000060532926],"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":[0.002582493,0.0007203507,0.0008563396,0.0002189326,0.000058881797,0.000111971705,0.0013873675,0.0030971372,0.002217995,0.6471715,0.011931531,0.3296455],"study_design_scores_gemma":[0.00019893236,0.000098711855,0.0008622401,0.00057045685,0.00014170396,0.000023527908,0.001542861,0.019654771,0.005878584,0.40218872,0.5685126,0.0003268417],"about_ca_topic_score_codex":0.00015532263,"about_ca_topic_score_gemma":0.00038745996,"teacher_disagreement_score":0.8563772,"about_ca_system_score_codex":0.00012164324,"about_ca_system_score_gemma":0.00021933985,"threshold_uncertainty_score":0.9932536},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003572954,0.0021978107,0.91899216,0.07396812,0.000096159776,0.0001416733,0.000014174824,0.00000863917,0.0010082852],"genre_scores_gemma":[0.9743489,0.0028822676,0.02245688,0.00020489458,0.000028073358,0.0000036589988,0.0000035784337,0.0000028772572,0.000068844296],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973516,0.00036270477,0.0013060268,0.00025575387,0.00060726755,0.000116673255],"domain_scores_gemma":[0.98069185,0.015504455,0.0018532815,0.0009472626,0.0009717471,0.00003138864],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013151901,0.00007099716,0.00023807804,0.00048136653,0.000214277,0.00026291312,0.0016451791,0.00006395016,0.000008123819],"category_scores_gemma":[0.0856035,0.000044723303,0.000050870487,0.0036243529,0.00020939899,0.0013646205,0.0004897413,0.00052976795,0.000009234403],"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":[0.00019757957,0.00014955767,0.0014181383,0.0000018712984,0.00013222772,0.0000028577615,0.0001563092,0.0025194925,0.0005324294,0.07059322,0.00032119264,0.9239751],"study_design_scores_gemma":[0.00004003791,0.00008487726,0.0011615409,0.000015490035,0.00058689073,0.00003610939,0.027651642,0.27227044,0.0023006923,0.086424366,0.60933465,0.000093284194],"about_ca_topic_score_codex":0.00031404398,"about_ca_topic_score_gemma":0.00071518985,"teacher_disagreement_score":0.97077596,"about_ca_system_score_codex":0.000023233744,"about_ca_system_score_gemma":0.000105134146,"threshold_uncertainty_score":0.9220989},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050729908,0.00091909844,0.93583214,0.0111802835,0.00095908286,0.000113546754,1.5422926e-7,0.000026436252,0.00023937257],"genre_scores_gemma":[0.96604764,0.000553765,0.033212114,0.00007032407,0.000069373265,0.000003256777,8.463013e-8,0.000005383628,0.000038064845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813634,0.00024350414,0.00092921115,0.00018630481,0.00027644626,0.00022821236],"domain_scores_gemma":[0.9972592,0.0013546682,0.00066838256,0.0002029213,0.00046432787,0.000050487764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027527711,0.000113895534,0.00021533751,0.00036971373,0.00018196335,0.00019853826,0.00067238894,0.00009229718,0.000006823105],"category_scores_gemma":[0.0061130547,0.00009192104,0.00006901833,0.00088235503,0.00019627584,0.00059886987,0.00015045224,0.0007957302,0.000004719794],"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":[0.0000812984,0.0001499591,0.00007573619,0.000018640354,0.000033375472,0.00002078063,0.002238285,0.0039279447,0.0042932285,0.3377227,0.000009183517,0.6514289],"study_design_scores_gemma":[0.00007248907,0.0005904634,0.0005267025,0.0003543026,0.00008394583,0.00042497006,0.021682994,0.6793461,0.099591404,0.190217,0.0067830808,0.00032654335],"about_ca_topic_score_codex":0.000049976796,"about_ca_topic_score_gemma":0.00001928103,"teacher_disagreement_score":0.9153177,"about_ca_system_score_codex":0.000053002557,"about_ca_system_score_gemma":0.00022594906,"threshold_uncertainty_score":0.73183393},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010688844,0.0003398215,0.98663133,0.0007938332,0.00089195784,0.00020809044,0.0000036574118,0.000039658375,0.00040279602],"genre_scores_gemma":[0.8735207,0.00039266935,0.12587734,0.00011834471,0.00005617184,0.0000063463285,7.0177623e-7,0.000017038265,0.000010718434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977581,0.00015834173,0.0012885579,0.00019337492,0.00039307534,0.00020856813],"domain_scores_gemma":[0.9976014,0.0010602805,0.00048722778,0.0001754382,0.000588281,0.00008737837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010523064,0.00021797647,0.0003065728,0.0006262535,0.0001049482,0.000107124644,0.0002280868,0.0001685259,0.000028618795],"category_scores_gemma":[0.0011325926,0.00021025178,0.000114123824,0.0005442196,0.00013588753,0.0006631864,0.000019422632,0.0006295241,0.0000070823894],"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":[0.0003276695,0.000069613096,0.000002151864,0.000029225479,0.000039058497,0.000002449973,0.00037691323,0.6299579,0.011568758,0.043169875,0.000012371622,0.31444404],"study_design_scores_gemma":[0.000014528472,0.00016825365,0.0000032384255,0.00016783996,0.00008496032,0.00001560968,0.0022937965,0.7448275,0.2033126,0.04890966,0.00008146351,0.00012056399],"about_ca_topic_score_codex":0.00001087439,"about_ca_topic_score_gemma":0.000010356085,"teacher_disagreement_score":0.86283183,"about_ca_system_score_codex":0.00016810356,"about_ca_system_score_gemma":0.00019843783,"threshold_uncertainty_score":0.8573818},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6726153,0.0015777475,0.31532565,0.006723007,0.0011070372,0.00017582189,0.000011729162,0.000027323236,0.002436378],"genre_scores_gemma":[0.98747426,0.00055536936,0.011664468,0.00017770084,0.000075279044,0.00000490529,0.0000017087596,0.000011155192,0.00003516887],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977085,0.000037891485,0.001729762,0.00024794388,0.000072681265,0.00020323333],"domain_scores_gemma":[0.9973099,0.00019745002,0.0015860226,0.00022771422,0.0006390149,0.00003985567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011289722,0.00014603298,0.0003931772,0.0017320473,0.00012645761,0.00008421738,0.00027000337,0.0002848742,0.000023405368],"category_scores_gemma":[0.003193153,0.0001659039,0.000059509115,0.0017302098,0.00026065228,0.0011938029,0.000055557743,0.000567107,0.000020523072],"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":[0.00006959226,0.00027799103,0.00044372675,0.00000978812,0.00006406576,9.445117e-7,0.0004845933,0.00021510717,0.001171906,0.56610155,0.000055741748,0.431105],"study_design_scores_gemma":[0.000031504293,0.00034866144,0.0002142663,0.00011860821,0.000040149735,0.00013012982,0.003589468,0.002357128,0.05502079,0.9374474,0.0005520801,0.00014977837],"about_ca_topic_score_codex":0.000083127496,"about_ca_topic_score_gemma":0.000004584472,"teacher_disagreement_score":0.43095523,"about_ca_system_score_codex":0.00009768179,"about_ca_system_score_gemma":0.00024038104,"threshold_uncertainty_score":0.67653644},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12838311,0.0032132163,0.23547497,0.45277366,0.0025891939,0.003708049,0.000050711657,0.000059760638,0.17374733],"genre_scores_gemma":[0.9900494,0.0015523479,0.0044014184,0.0029640254,0.00042028533,0.0000243758,0.0000048895895,0.000024937084,0.0005582934],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99263066,0.0017134334,0.0018567389,0.00038437982,0.0027592154,0.0006555833],"domain_scores_gemma":[0.96822727,0.02194139,0.001205689,0.0006389336,0.007615636,0.00037108982],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.009359348,0.00025542712,0.0005877225,0.00080392946,0.0003469102,0.0001565292,0.00070271315,0.00020919986,0.0005719757],"category_scores_gemma":[0.11963919,0.00016368706,0.000193751,0.0037613367,0.00088207354,0.00078332797,0.00019184593,0.0029374566,0.0002648939],"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":[0.017947841,0.006721096,0.00016034931,0.00011447341,0.0013841282,0.00091596664,0.0022039334,0.00015358564,0.0131108,0.3368437,0.024605896,0.59583825],"study_design_scores_gemma":[0.0003378962,0.010766675,0.00010717404,0.002064043,0.0006921193,0.0004454586,0.120414644,0.002665435,0.6560121,0.075624734,0.13048689,0.00038279986],"about_ca_topic_score_codex":0.00045411018,"about_ca_topic_score_gemma":0.000025434168,"teacher_disagreement_score":0.8616663,"about_ca_system_score_codex":0.00035408602,"about_ca_system_score_gemma":0.0017218835,"threshold_uncertainty_score":0.9993628},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022283921,0.000046712245,0.96707267,0.0009259253,0.0005569787,0.00011137486,0.0000016143614,0.000021082504,0.0089797005],"genre_scores_gemma":[0.9924229,0.0001114493,0.006882733,0.00021769736,0.00009173489,0.0000041941225,0.0000012628725,0.000013734117,0.0002543085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775124,0.00031838895,0.0008936862,0.00013233912,0.00073717535,0.00016719713],"domain_scores_gemma":[0.9966072,0.0014669963,0.00034397512,0.00017773856,0.0013447102,0.00005937349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003465747,0.00010535326,0.00017876469,0.00077081454,0.000110837274,0.000095501055,0.0002598928,0.00009637656,0.000072977375],"category_scores_gemma":[0.0058709774,0.00010513422,0.000095010124,0.00054312794,0.00005085056,0.00043855485,0.000020235968,0.0009002056,0.00004713873],"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":[0.0005674561,0.00019948916,0.000010911787,0.00002452381,0.000016156857,0.000004223512,0.0004695409,0.9564095,0.007593164,0.009761968,0.0005821214,0.024360923],"study_design_scores_gemma":[0.00005948674,0.00019683126,0.0000140756565,0.00017289264,0.000024309069,0.0000019069057,0.00058935746,0.89452267,0.09972186,0.004217034,0.00041472557,0.00006484353],"about_ca_topic_score_codex":0.000005442789,"about_ca_topic_score_gemma":0.0000038809885,"teacher_disagreement_score":0.97013897,"about_ca_system_score_codex":0.00015972061,"about_ca_system_score_gemma":0.00024094994,"threshold_uncertainty_score":0.70285326},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33972383,0.00015255016,0.65637004,0.002911705,0.0002555432,0.00036443738,0.0000019512104,0.000040278414,0.00017964048],"genre_scores_gemma":[0.9904447,0.00040914142,0.00899284,0.00004278361,0.00007869663,0.000016017257,5.875743e-7,0.0000123693635,0.0000028370735],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982154,0.0002130064,0.00084606075,0.00018276418,0.0003180506,0.00022477022],"domain_scores_gemma":[0.9980393,0.0009376065,0.00025384728,0.000219968,0.0004980738,0.00005117288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024727508,0.00012791636,0.00022473937,0.00095530454,0.00015983643,0.00007378515,0.00027636255,0.0001531638,0.00001258825],"category_scores_gemma":[0.0013897199,0.000104314866,0.000043553515,0.0018409179,0.00013997182,0.00033959182,0.00006115629,0.0008934412,0.000024955303],"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":[0.00022092464,0.000224854,0.00003391559,0.00004642693,0.000017422126,0.000005805659,0.00028434096,0.018644119,0.30667394,0.0032660575,0.00003223834,0.6705499],"study_design_scores_gemma":[0.000027800344,0.00036073374,0.00051389664,0.00024339487,0.00003357566,0.000031340132,0.013989196,0.3311139,0.6444357,0.0072425813,0.00187772,0.00013016985],"about_ca_topic_score_codex":0.00018478642,"about_ca_topic_score_gemma":0.001104446,"teacher_disagreement_score":0.6704198,"about_ca_system_score_codex":0.00015976645,"about_ca_system_score_gemma":0.000049203365,"threshold_uncertainty_score":0.4253837},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014464371,0.000028049353,0.98770565,0.009596163,0.00032662638,0.00022166339,0.0000014694928,0.00008809965,0.0005858324],"genre_scores_gemma":[0.8656411,0.000012231609,0.1334655,0.00063778646,0.0001791041,0.00001748319,0.0000013588312,0.000007801965,0.000037596375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823517,0.00021707323,0.000665323,0.00029203467,0.00037620615,0.00021421861],"domain_scores_gemma":[0.9969186,0.0004256255,0.00085575896,0.00044309258,0.0012308665,0.00012603085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00131122,0.00015211309,0.00020913835,0.0002461733,0.00029652903,0.00032370613,0.00075071317,0.00010019638,0.000017519094],"category_scores_gemma":[0.0005844627,0.00012553662,0.000101764905,0.00082174514,0.00008880468,0.0015232022,0.00003517917,0.00045396324,0.0000074480104],"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":[0.0011938147,0.0007881214,0.000024413992,0.000018658058,0.00012234102,0.00002602021,0.0016650722,0.015412599,0.034390192,0.14858957,0.00025693662,0.79751223],"study_design_scores_gemma":[0.00022401434,0.0014892819,0.00006393486,0.00013456219,0.00017813557,0.00011638976,0.005468964,0.8385878,0.10742637,0.015321682,0.030621476,0.0003673658],"about_ca_topic_score_codex":0.00005993579,"about_ca_topic_score_gemma":0.00004640469,"teacher_disagreement_score":0.8641947,"about_ca_system_score_codex":0.00014642757,"about_ca_system_score_gemma":0.00082124065,"threshold_uncertainty_score":0.5119235},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009599844,0.0017067972,0.95467055,0.02392098,0.005998169,0.000663605,0.0022812097,0.00007171899,0.00972697],"genre_scores_gemma":[0.7962666,0.011725878,0.15744531,0.018229537,0.0113093825,0.000024801413,0.003558129,0.00019371558,0.001246632],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99368596,0.000082650644,0.0023483844,0.0015852047,0.0015762447,0.0007215393],"domain_scores_gemma":[0.9896632,0.0010106124,0.0020744458,0.0061619235,0.0009984629,0.000091343936],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.004520149,0.00055132434,0.00071697775,0.0003546942,0.00036830147,0.0017122629,0.016446777,0.0001824052,0.0008910886],"category_scores_gemma":[0.007273198,0.00051351415,0.0000993592,0.0022491715,0.00018413173,0.022921283,0.00871668,0.0009665448,0.0008775813],"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":[0.0012255609,0.0007757034,0.00016254997,0.00022771044,0.00044861445,0.00027398323,0.00013383527,0.00045897166,0.00031819736,0.032884393,0.10113602,0.86195445],"study_design_scores_gemma":[0.00009358794,0.000035134017,0.0003862874,0.0012987822,0.0009485494,0.0000426933,0.0033522649,0.05823531,0.0015724136,0.0110901985,0.922287,0.00065777765],"about_ca_topic_score_codex":0.0026156877,"about_ca_topic_score_gemma":0.0010521321,"teacher_disagreement_score":0.86129665,"about_ca_system_score_codex":0.00015692798,"about_ca_system_score_gemma":0.00028193844,"threshold_uncertainty_score":0.99990034},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006232187,0.00009685001,0.9827237,0.0066241585,0.001170438,0.0001194064,0.0000010958164,0.000038765094,0.0029933657],"genre_scores_gemma":[0.8809064,0.000074159325,0.1165115,0.002280135,0.00013108963,0.000005041651,9.202991e-8,0.000008863027,0.00008273865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998018,0.00033952942,0.0006673311,0.00033492505,0.000395116,0.00024507777],"domain_scores_gemma":[0.9973961,0.0010247731,0.0006234126,0.00036080426,0.00048936106,0.000105522326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016039801,0.00016296022,0.00022669048,0.0004264276,0.00019445016,0.00021776218,0.0004964727,0.00012549461,0.000011866146],"category_scores_gemma":[0.0008858676,0.0001530328,0.0000965336,0.0006212576,0.00009059241,0.0011498948,0.000106376734,0.0007451166,0.000021268994],"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":[0.0012522844,0.0005749025,0.00003112569,0.000048726644,0.000032188993,0.00019918995,0.0010855045,0.4989956,0.02237387,0.030907875,0.00008287223,0.44441584],"study_design_scores_gemma":[0.00009446467,0.00058687764,0.000017255294,0.000031082862,0.000038315065,0.00009565983,0.00040295714,0.8776072,0.10672971,0.012881523,0.0013974973,0.00011743101],"about_ca_topic_score_codex":0.000036957907,"about_ca_topic_score_gemma":0.000040675983,"teacher_disagreement_score":0.8746742,"about_ca_system_score_codex":0.00019503737,"about_ca_system_score_gemma":0.00029057433,"threshold_uncertainty_score":0.62404966},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017716616,0.0002830034,0.9588971,0.016902234,0.0012713576,0.00028160017,0.000005092771,0.00005359722,0.004589412],"genre_scores_gemma":[0.8910298,0.00030094266,0.10735011,0.00095485646,0.00031805664,0.000006295408,0.000008750472,0.000017570608,0.000013653829],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750686,0.00008957632,0.0015399018,0.00024530347,0.0004057588,0.00021258421],"domain_scores_gemma":[0.99344176,0.0017266524,0.002211127,0.00023957258,0.0023614329,0.000019467881],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002266944,0.00018939575,0.00035166,0.00089082244,0.00028493875,0.0002953841,0.00034528814,0.00020418056,0.00004696777],"category_scores_gemma":[0.010023266,0.00018299167,0.000080713864,0.0017040024,0.00036795606,0.00357733,0.0001422939,0.00051630766,0.00003374437],"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":[0.0010124395,0.0004063159,0.0002906514,0.00011217077,0.00014205497,0.00002310517,0.00015489261,0.013541312,0.0028139043,0.63655066,0.0008359412,0.34411654],"study_design_scores_gemma":[0.00046108826,0.00073804025,0.0001069391,0.0030268575,0.003093905,0.001480899,0.25363845,0.42346844,0.08253313,0.15233907,0.077663384,0.0014497963],"about_ca_topic_score_codex":0.00023648357,"about_ca_topic_score_gemma":0.000026511829,"teacher_disagreement_score":0.8733131,"about_ca_system_score_codex":0.00008059479,"about_ca_system_score_gemma":0.000094288764,"threshold_uncertainty_score":0.99831575},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05055348,0.001220476,0.85930294,0.084870465,0.0014943841,0.00007617505,2.9261585e-7,0.000015603955,0.0024662036],"genre_scores_gemma":[0.96307755,0.00014860817,0.03618351,0.00036484053,0.00009922785,3.0760188e-7,4.970309e-8,0.0000031620161,0.00012272004],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829787,0.00035646698,0.0007314655,0.000111916575,0.0003396884,0.00016258922],"domain_scores_gemma":[0.9955638,0.0030648548,0.00070036354,0.00025305664,0.00038192872,0.000036011137],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0037203894,0.000077661716,0.000174562,0.00021690606,0.00014437689,0.0001876584,0.0009511989,0.000083230574,0.0000015589865],"category_scores_gemma":[0.008631114,0.0000534999,0.00009298425,0.0005991438,0.000059493465,0.0007387847,0.000104832,0.0015603418,0.0000050069148],"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":[0.00009120066,0.0002881984,0.00072960946,0.000011964014,0.00005539072,0.000057908874,0.0017205304,0.007895669,0.0012428577,0.5905897,0.00021160247,0.39710534],"study_design_scores_gemma":[0.00015955586,0.0006821165,0.0011715369,0.00065903657,0.00012922399,0.00021372558,0.015756669,0.49045727,0.03079661,0.3830314,0.07659928,0.00034357427],"about_ca_topic_score_codex":0.0000309973,"about_ca_topic_score_gemma":0.00003940227,"teacher_disagreement_score":0.9125241,"about_ca_system_score_codex":0.00004174273,"about_ca_system_score_gemma":0.000253659,"threshold_uncertainty_score":0.9997196},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000024523508,0.95374125,0.040842492,0.002810275,0.0007708285,0.0008272852,0.000033047356,0.000003344264,0.00096902886],"genre_scores_gemma":[0.00013865074,0.9974431,0.0018422643,0.00010746555,0.0003304547,0.000020849056,0.000004764941,0.00001530553,0.000097177974],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9807034,0.005542389,0.008351322,0.00054814975,0.00431894,0.00053580943],"domain_scores_gemma":[0.91370624,0.06681552,0.009007898,0.0013865709,0.008929746,0.00015404452],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.10739557,0.00033580457,0.0023139957,0.0010373594,0.00029587027,0.00034894675,0.0034249527,0.00034894294,0.000110484754],"category_scores_gemma":[0.20291439,0.00019180334,0.0009907477,0.0074313334,0.0007202295,0.0009147341,0.00045383585,0.0019162435,0.00006321887],"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":[0.00022182317,0.0003321196,0.000001234636,0.010715457,0.000058977115,0.000011850662,0.00016454233,0.000009881235,0.0000038650874,0.0094307,0.0014142427,0.9776353],"study_design_scores_gemma":[0.000008629179,0.00017391292,0.0000023934526,0.07031386,0.00029607286,0.00006828674,0.0041561145,0.0002651487,0.00009216991,0.017756412,0.9066797,0.00018731075],"about_ca_topic_score_codex":0.00010691262,"about_ca_topic_score_gemma":0.0000439698,"teacher_disagreement_score":0.977448,"about_ca_system_score_codex":0.00017251902,"about_ca_system_score_gemma":0.0027429983,"threshold_uncertainty_score":0.9191241},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9541324,0.00030825238,0.043339264,0.0017611466,0.00013116488,0.00008731661,0.0000011772665,0.000002865082,0.00023645388],"genre_scores_gemma":[0.9892172,0.000068299334,0.010628051,0.000062266794,0.000010220745,0.0000013859801,3.7301024e-7,0.0000016643414,0.000010519408],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99812376,0.00046231813,0.00083675317,0.00013971743,0.00031911745,0.00011834567],"domain_scores_gemma":[0.9975059,0.0011659698,0.00081898505,0.00018695991,0.00030283447,0.000019353258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021010702,0.00008679406,0.00027311605,0.0007732208,0.000111348265,0.000029510633,0.0008642272,0.000073700765,0.000018653971],"category_scores_gemma":[0.00050724845,0.00006407251,0.000077984965,0.0021724054,0.0003333977,0.0005544818,0.0001170745,0.00039852777,3.3992418e-7],"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":[0.00038570218,0.00092420616,0.019297142,0.000026311207,0.00030618123,0.0000071728828,0.09293148,0.0101848,0.008989389,0.63215274,0.0000016880762,0.2347932],"study_design_scores_gemma":[0.000053858737,0.0003112891,0.022478953,0.00018145096,0.00028256886,0.00003231442,0.7068679,0.069566965,0.16271372,0.03679657,0.0005599914,0.0001544204],"about_ca_topic_score_codex":0.00019972291,"about_ca_topic_score_gemma":0.0007119568,"teacher_disagreement_score":0.6139364,"about_ca_system_score_codex":0.000077356286,"about_ca_system_score_gemma":0.00079677976,"threshold_uncertainty_score":0.26128012},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030300602,0.00000961575,0.9376737,0.02046648,0.00045389024,0.00043605577,0.0000019018742,0.000016623972,0.010641143],"genre_scores_gemma":[0.97633064,0.000051621668,0.022462081,0.0009432621,0.00013964175,0.000021927954,0.0000022365357,0.0000070514834,0.000041529565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973643,0.00042558528,0.0008651103,0.00020533457,0.00090523623,0.00023445318],"domain_scores_gemma":[0.9976928,0.0010695745,0.0004433669,0.00014162183,0.00055355026,0.00009905916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055670356,0.00013639763,0.00014929785,0.00061333505,0.0005431549,0.00021361302,0.00019540681,0.00013423582,0.00039988648],"category_scores_gemma":[0.0027283675,0.00011067886,0.00006113078,0.0019147235,0.00012449098,0.0019500543,0.00003358517,0.0010541715,0.0001483737],"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":[0.0011887782,0.0006434379,0.000031406766,0.000020459589,0.00001450543,0.0000014370866,0.0009428822,0.05696385,0.0033400815,0.07049955,0.00034581753,0.8660078],"study_design_scores_gemma":[0.000056198187,0.0024203835,0.0010334586,0.00028002827,0.00005425854,0.000049649596,0.009446156,0.45364416,0.21756072,0.25039843,0.064769894,0.00028664974],"about_ca_topic_score_codex":0.00030112677,"about_ca_topic_score_gemma":0.00003801933,"teacher_disagreement_score":0.94603,"about_ca_system_score_codex":0.00044490278,"about_ca_system_score_gemma":0.00020028815,"threshold_uncertainty_score":0.4579909},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7436903,0.0012337315,0.23139314,0.01877081,0.0013114781,0.0009226673,0.0000075396465,0.000034133747,0.0026361838],"genre_scores_gemma":[0.9933433,0.00038502275,0.004447615,0.0013871999,0.00030710726,0.000022579467,0.0000020308148,0.00001349962,0.0000916563],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99571514,0.00096923654,0.0023602245,0.00022871066,0.00043177884,0.0002949077],"domain_scores_gemma":[0.99054134,0.0039526643,0.0022558128,0.0003041343,0.0027805145,0.00016551228],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003283561,0.00015840385,0.00043121964,0.0004954505,0.00043408637,0.000028647273,0.00029974594,0.00025087022,0.00008423045],"category_scores_gemma":[0.009103116,0.00014146887,0.00009155955,0.00090640824,0.00036033362,0.0010920364,0.00009941416,0.0012273389,0.000038008766],"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":[0.0020914124,0.0004302928,0.33419636,0.0012300905,0.00051432353,0.000030033327,0.02597964,0.0036972233,0.10129402,0.23837668,0.0031802277,0.2889797],"study_design_scores_gemma":[0.00012672445,0.000758366,0.011567691,0.0019550181,0.00082493894,0.000025418727,0.11811085,0.055237703,0.39655071,0.40377873,0.0105809495,0.00048289535],"about_ca_topic_score_codex":0.0038928082,"about_ca_topic_score_gemma":0.000988615,"teacher_disagreement_score":0.32262865,"about_ca_system_score_codex":0.00016224843,"about_ca_system_score_gemma":0.0007915795,"threshold_uncertainty_score":0.9992436},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14025155,0.0001171011,0.7916141,0.06711986,0.00029987496,0.0004073668,5.7144615e-7,0.000017471546,0.00017211561],"genre_scores_gemma":[0.97876394,0.00010650522,0.019502956,0.0014889229,0.00010155409,0.000017124496,0.0000013963863,0.0000058725477,0.000011749308],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991918,0.00001926873,0.0004958124,0.00009845397,0.00010406706,0.000090584464],"domain_scores_gemma":[0.99773586,0.0007308664,0.0006589755,0.00009942278,0.0007712075,0.0000036625893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012700598,0.000075173695,0.0001245836,0.0006198385,0.00021928088,0.00009070024,0.00009670095,0.00007879219,0.000021915155],"category_scores_gemma":[0.0013112491,0.000058035657,0.00002643165,0.00060157623,0.00016932732,0.0007828563,0.000029493129,0.0001835627,0.000002244093],"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":[0.0011317048,0.0002491999,0.0019231947,0.00008756899,0.00012847803,0.0000031465192,0.00013324666,0.0013935295,0.02532094,0.23248208,0.0013078683,0.73583907],"study_design_scores_gemma":[0.0018130766,0.0019553138,0.0043085613,0.0004480888,0.0028112698,0.0001460524,0.18603502,0.25496784,0.17195348,0.2719985,0.10290754,0.0006552682],"about_ca_topic_score_codex":0.000098808945,"about_ca_topic_score_gemma":0.000091233785,"teacher_disagreement_score":0.83851236,"about_ca_system_score_codex":0.000018752526,"about_ca_system_score_gemma":0.000042687716,"threshold_uncertainty_score":0.23666254},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01656397,0.0002894913,0.9673498,0.00010773043,0.0005968604,0.0001831895,3.2062385e-8,0.00005526824,0.014853603],"genre_scores_gemma":[0.9909361,0.0001445337,0.008632265,0.000049388207,0.00013616103,0.0000045729353,0.0000021229407,0.000017213144,0.00007761794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981671,0.00011400335,0.0011067234,0.00010533616,0.00033302413,0.00017379821],"domain_scores_gemma":[0.9983763,0.0005185366,0.00060515053,0.00013476537,0.00031139338,0.0000538199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013340667,0.00013162031,0.00027541374,0.00032397534,0.000113699796,0.00007709005,0.00016226855,0.00009047412,0.00011620057],"category_scores_gemma":[0.0010629093,0.00013609215,0.00009890024,0.0003820009,0.000026905753,0.0005449604,0.00002969144,0.0007041091,0.000020812038],"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":[0.0001755388,0.000029623312,0.000053173862,0.000054481363,0.00007263948,0.0000054667157,0.0016839458,0.88586164,0.016689265,0.006967347,0.000024951814,0.08838194],"study_design_scores_gemma":[0.00012518869,0.0001505768,0.00015169778,0.00017547495,0.00015560568,0.000030042182,0.007934671,0.9464296,0.0348606,0.0014097294,0.008374316,0.00020247538],"about_ca_topic_score_codex":0.00001620144,"about_ca_topic_score_gemma":0.00000481351,"teacher_disagreement_score":0.97437215,"about_ca_system_score_codex":0.00013544413,"about_ca_system_score_gemma":0.000040821353,"threshold_uncertainty_score":0.5549677},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5244343,0.00008999458,0.27201265,0.16679946,0.0009026612,0.00069282216,0.0000010392633,0.00002962708,0.0350374],"genre_scores_gemma":[0.99562573,0.000015610374,0.0019076159,0.0016963545,0.00068945996,0.000028058055,0.000001787104,0.0000085424335,0.000026821743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981562,0.00017340267,0.0008515736,0.00018589415,0.00043167276,0.00020124015],"domain_scores_gemma":[0.9956383,0.0017703628,0.0007733659,0.00029426985,0.0015156408,0.000008023374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044947225,0.00011638211,0.00020247459,0.001161667,0.0003233119,0.00016682381,0.0005624041,0.00009293242,0.000018868976],"category_scores_gemma":[0.005627868,0.000088757704,0.00007186281,0.0023469909,0.0003028705,0.0011125527,0.00011183939,0.0014177072,0.0000974012],"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":[0.0001140492,0.0008832192,0.000082252474,0.000012981705,0.00001887916,0.000001404908,0.0003117619,0.0008282821,0.003319917,0.73166853,0.0004120763,0.26234666],"study_design_scores_gemma":[0.000015866974,0.00009337585,0.00017107747,0.00019468112,0.00005957343,0.000004478382,0.05356088,0.027604366,0.04569246,0.8637681,0.008709574,0.00012558195],"about_ca_topic_score_codex":0.000593522,"about_ca_topic_score_gemma":0.00018525463,"teacher_disagreement_score":0.4711914,"about_ca_system_score_codex":0.000094206276,"about_ca_system_score_gemma":0.00022010188,"threshold_uncertainty_score":0.673749},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8513851,0.00037891915,0.1449912,0.0009617882,0.0008695064,0.0003645173,0.0000074510576,0.000011022006,0.001030463],"genre_scores_gemma":[0.970922,0.00006941977,0.028622067,0.00021649091,0.00007948254,0.000008112552,3.425627e-7,0.0000059761715,0.00007610708],"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","domain_scores_codex":[0.9940892,0.0031401564,0.0017579765,0.00025804667,0.00054723525,0.00020740555],"domain_scores_gemma":[0.9932461,0.0034241164,0.0014217931,0.00029811004,0.0015448182,0.00006510053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009772637,0.00016766744,0.0006869686,0.0007213215,0.00013345317,0.00018041277,0.00031253955,0.0000800152,0.00021922344],"category_scores_gemma":[0.008292009,0.00013254084,0.0001567148,0.0018077032,0.00012714973,0.0021716666,0.00008923697,0.00053549587,0.000023209512],"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":[0.022381779,0.018353943,0.003118667,0.0001991184,0.0055298214,0.0049231877,0.19608793,0.16281572,0.52025646,0.009393675,0.0004594959,0.056480233],"study_design_scores_gemma":[0.00031971978,0.0033053015,0.00027122174,0.00013139818,0.006105794,0.0016514092,0.49702883,0.028152145,0.45727846,0.0020418756,0.00339397,0.00031989175],"about_ca_topic_score_codex":0.0010944819,"about_ca_topic_score_gemma":0.0003246661,"teacher_disagreement_score":0.3009409,"about_ca_system_score_codex":0.00009363442,"about_ca_system_score_gemma":0.000274929,"threshold_uncertainty_score":0.99269086},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00762534,0.00003371929,0.92268944,0.030784428,0.006039977,0.00082532887,0.0000051774464,0.00003426334,0.03196233],"genre_scores_gemma":[0.9615686,0.0005336135,0.02968991,0.0045769173,0.00068314956,0.00002376304,0.0000013549652,0.00005451013,0.0028681834],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.987241,0.005032737,0.0025795272,0.001117437,0.0030793082,0.0009499808],"domain_scores_gemma":[0.97924507,0.013999075,0.0026097146,0.0010050833,0.0027691764,0.00037189282],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.017933642,0.00051750906,0.0006694966,0.0023379864,0.0015896171,0.0011265232,0.0012556973,0.00049073924,0.0003920692],"category_scores_gemma":[0.019899318,0.0005020513,0.00046036713,0.004126022,0.00042742203,0.0017948623,0.00013161758,0.0042574373,0.0007607985],"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":[0.012304129,0.0035639566,3.3676625e-7,0.000103672435,0.00009560576,0.00032753233,0.00060529285,0.036829084,0.45365584,0.32732376,0.00051996013,0.16467084],"study_design_scores_gemma":[0.00029586977,0.0042144815,0.00002921828,0.0007266246,0.00017926717,0.00022081063,0.012101793,0.3021949,0.6189872,0.055309016,0.0053792926,0.00036153616],"about_ca_topic_score_codex":0.000055086653,"about_ca_topic_score_gemma":0.000031506577,"teacher_disagreement_score":0.95394325,"about_ca_system_score_codex":0.0013212443,"about_ca_system_score_gemma":0.0015306394,"threshold_uncertainty_score":0.9999104},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.9287025e-7,0.50716287,0.48414534,0.006630712,0.0003686308,0.0014338203,0.0000131566085,0.000003686977,0.0002410196],"genre_scores_gemma":[0.00015741361,0.95542544,0.042903196,0.001155882,0.00019931511,0.000106890206,0.0000047585945,0.000019440522,0.000027684375],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9885808,0.0020778861,0.006513207,0.00056919525,0.001913743,0.0003451348],"domain_scores_gemma":[0.9762344,0.008035718,0.011674799,0.0013631516,0.0024977308,0.00019420558],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0067926887,0.00044644758,0.0018475802,0.00054265535,0.00017988558,0.0000730575,0.003779533,0.0002703474,0.00021460002],"category_scores_gemma":[0.010690506,0.0003231653,0.00085289596,0.006527846,0.000495534,0.0015978942,0.0008094871,0.0021042896,0.00002827769],"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":[0.000040756047,0.0008895343,0.0000013244911,0.0223184,0.00006718358,0.000010052234,0.00013723243,0.00019575703,0.000100517565,0.008327805,0.0002484597,0.967663],"study_design_scores_gemma":[0.0001628663,0.00066119496,0.0000077637615,0.45245963,0.0024768512,0.0011457345,0.0003810521,0.016291292,0.004333776,0.016929505,0.50441694,0.0007333904],"about_ca_topic_score_codex":0.00003385258,"about_ca_topic_score_gemma":0.000031062766,"teacher_disagreement_score":0.9669296,"about_ca_system_score_codex":0.00027204907,"about_ca_system_score_gemma":0.002567768,"threshold_uncertainty_score":0.99992204},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009696431,0.92349374,0.0678776,0.00019697857,0.0045919637,0.0030691063,0.000043614113,0.000010641939,0.00061940687],"genre_scores_gemma":[0.026341643,0.9724852,0.00031020847,0.000039770544,0.00071394414,0.0000311642,0.0000021317726,0.00004390035,0.000032034783],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.988125,0.0027515728,0.007311753,0.00037831918,0.0010903709,0.00034296082],"domain_scores_gemma":[0.9835316,0.0050058668,0.008668219,0.0008072452,0.0018696332,0.00011745579],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01108953,0.0005596668,0.0027394907,0.0008223347,0.00015654901,0.000074211704,0.00087405456,0.0009362842,0.00011888718],"category_scores_gemma":[0.014552333,0.0004152284,0.001862477,0.005483371,0.00013219424,0.00070647465,0.00017387119,0.0034429792,0.000021718166],"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":[0.00029948534,0.00033423706,0.0000030836227,0.036567014,0.0002967287,0.0000044614276,0.00010249241,0.0050637377,0.0005419933,0.00012637296,0.00011964096,0.95654076],"study_design_scores_gemma":[0.00021734106,0.0010138383,0.0000018265678,0.38476095,0.003905923,0.00044245785,0.0005570113,0.0017314097,0.016435713,0.00026500723,0.59014225,0.0005263003],"about_ca_topic_score_codex":0.00046684372,"about_ca_topic_score_gemma":0.00009027118,"teacher_disagreement_score":0.95601445,"about_ca_system_score_codex":0.0005781729,"about_ca_system_score_gemma":0.0012025565,"threshold_uncertainty_score":0.99982995},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02843233,0.0020436766,0.96443206,0.003011958,0.00083680014,0.0003753957,0.000014478317,0.00011584773,0.0007374302],"genre_scores_gemma":[0.9745681,0.001659748,0.023583129,0.00007076942,0.000054625376,0.000009817346,5.853112e-7,0.000015788879,0.00003738154],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9956504,0.000660477,0.002087326,0.0005352386,0.00068853074,0.00037801045],"domain_scores_gemma":[0.9900094,0.0018688561,0.003320371,0.00087470055,0.003833781,0.000092850314],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0052401293,0.00034933788,0.0006993792,0.0007797218,0.00039959987,0.0003711103,0.0014256579,0.00029244315,0.00002346615],"category_scores_gemma":[0.0037216747,0.0003063885,0.00019916747,0.0042546014,0.0005861856,0.001987162,0.00039941797,0.0012330774,0.00000975312],"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":[0.0025742047,0.0015755577,0.001848233,0.00017593298,0.00038120183,0.000080041005,0.0011924473,0.0016044003,0.009860495,0.012082092,0.00009515763,0.96853024],"study_design_scores_gemma":[0.00020726466,0.006174804,0.006112234,0.0009392738,0.00025010196,0.0012789045,0.0048790043,0.18395872,0.7692911,0.017427096,0.008621839,0.00085962063],"about_ca_topic_score_codex":0.0012068978,"about_ca_topic_score_gemma":0.0010264792,"teacher_disagreement_score":0.9676706,"about_ca_system_score_codex":0.00018557861,"about_ca_system_score_gemma":0.0005539606,"threshold_uncertainty_score":0.99993885},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004964447,0.00072313874,0.98183334,0.00265719,0.007095341,0.0005377993,0.000014320286,0.00002605143,0.002148385],"genre_scores_gemma":[0.8869727,0.0006707612,0.10998869,0.00078968075,0.0014122662,0.000016964143,9.348159e-7,0.000052414394,0.00009558949],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99684113,0.00024769828,0.0015773042,0.00034792337,0.0004217709,0.00056414795],"domain_scores_gemma":[0.99438334,0.0034393067,0.0007131062,0.00024829688,0.0010352845,0.00018067143],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031248908,0.0004232873,0.00062876026,0.00059361954,0.00034348454,0.00033896297,0.00026550514,0.00024491525,0.000057003923],"category_scores_gemma":[0.0036666938,0.00040709716,0.00034389697,0.00037845547,0.00009849116,0.0011149397,0.000016283999,0.0013388274,0.000009129175],"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":[0.004599238,0.00004533369,0.000005345435,0.00034991006,0.000358136,0.00006334156,0.0008043829,0.08791031,0.074311964,0.002929315,0.00018868256,0.82843405],"study_design_scores_gemma":[0.00021668359,0.00096729444,0.00001167869,0.00055074727,0.0006603895,0.00012585177,0.0021517233,0.35644212,0.5954504,0.006750136,0.03637539,0.00029755442],"about_ca_topic_score_codex":0.00004041748,"about_ca_topic_score_gemma":0.0000075271205,"teacher_disagreement_score":0.88200825,"about_ca_system_score_codex":0.0003199664,"about_ca_system_score_gemma":0.0002711279,"threshold_uncertainty_score":0.9998381},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001707215,0.005755124,0.98757094,0.0022977898,0.0024218308,0.00016357788,0.000040004958,0.00005787258,0.0015221599],"genre_scores_gemma":[0.68028843,0.007237239,0.3083527,0.0005001172,0.0025482383,0.00006853102,0.00002297622,0.00011544027,0.00086633675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967214,0.00013825833,0.0018547339,0.00036891014,0.00043379518,0.0004829216],"domain_scores_gemma":[0.9948031,0.0020342967,0.0010798142,0.0002937662,0.0015801949,0.00020887906],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008639868,0.00039167813,0.0005538599,0.00040016498,0.00046946527,0.00029076633,0.00044988457,0.00029920082,0.0000442158],"category_scores_gemma":[0.0013782162,0.0004263164,0.00029535065,0.0009174203,0.00011815226,0.000752534,0.00007008216,0.00084857066,0.000016185808],"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":[0.00022207286,0.0004686383,0.000002767157,0.000036209996,0.0002773379,0.000017166334,0.00048718613,0.38937885,0.009266947,0.0073032384,0.0060699163,0.58646965],"study_design_scores_gemma":[0.00013333427,0.00046334896,0.0000044137955,0.0004498001,0.0006405369,0.000073459094,0.003820881,0.9069742,0.0408096,0.0036251522,0.042660147,0.00034512847],"about_ca_topic_score_codex":0.00017069287,"about_ca_topic_score_gemma":0.0000098514665,"teacher_disagreement_score":0.6801177,"about_ca_system_score_codex":0.00038427784,"about_ca_system_score_gemma":0.0002557725,"threshold_uncertainty_score":0.99981886},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012359246,0.0015555213,0.96423674,0.012930202,0.007730447,0.00029817564,0.000003147517,0.0000370078,0.0008494937],"genre_scores_gemma":[0.8021227,0.00096172147,0.19298886,0.0031382863,0.0006494133,0.0000034238292,9.424889e-7,0.000029616811,0.00010504287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99307936,0.001974238,0.0022084927,0.0007454878,0.0011770991,0.0008152995],"domain_scores_gemma":[0.9893938,0.0053608385,0.002673918,0.00065751106,0.0016757884,0.0002381813],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.014705126,0.00048378028,0.00076652813,0.00043737845,0.0010918778,0.0023829804,0.0008478993,0.0003290963,0.000054065345],"category_scores_gemma":[0.0060869553,0.00048227241,0.00033633696,0.0017767606,0.00028862501,0.0025234201,0.00026067323,0.0017459651,0.00005902476],"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":[0.0018893176,0.0008102068,0.0003228472,0.00014034647,0.00032739618,0.0005534579,0.0008491306,0.097179525,0.0020260361,0.076702334,0.00020880398,0.8189906],"study_design_scores_gemma":[0.00017357574,0.0011503734,0.0011812111,0.0010286635,0.00031262785,0.00027418512,0.0006244919,0.93106,0.0024570262,0.05652663,0.004768572,0.00044263783],"about_ca_topic_score_codex":0.00007499547,"about_ca_topic_score_gemma":0.00006254192,"teacher_disagreement_score":0.8338805,"about_ca_system_score_codex":0.0002345292,"about_ca_system_score_gemma":0.0010074953,"threshold_uncertainty_score":0.9997629},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019171402,0.0024362977,0.92408454,0.04674976,0.0055580195,0.00088519097,0.00005171963,0.000031416876,0.0010316761],"genre_scores_gemma":[0.98347574,0.0032031296,0.0107253855,0.00037406344,0.002164608,0.0000035694763,0.000004921768,0.000020517129,0.000028046516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9905459,0.0031421832,0.0022408792,0.0012170436,0.0019696984,0.0008842672],"domain_scores_gemma":[0.9889194,0.0051557394,0.0017708574,0.0024694211,0.0013558762,0.00032872002],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.018243805,0.00041197275,0.0005783553,0.0010909513,0.001720896,0.0033944044,0.004000983,0.00032320974,0.000032291762],"category_scores_gemma":[0.012937698,0.0004265687,0.000121865785,0.0022778357,0.0010027725,0.0058449553,0.0046138847,0.004389157,0.00014326505],"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":[0.0011677087,0.0024611305,0.00000899743,0.00019101905,0.00029448103,0.00020623246,0.013332071,0.0029908887,0.00094242365,0.26467824,0.001655366,0.7120714],"study_design_scores_gemma":[0.00019381565,0.0027253125,0.00006380256,0.001362647,0.00029095617,0.00049109716,0.034550697,0.5713818,0.0056393538,0.2757284,0.10697796,0.0005941165],"about_ca_topic_score_codex":0.0005368249,"about_ca_topic_score_gemma":0.000096934426,"teacher_disagreement_score":0.9643043,"about_ca_system_score_codex":0.00048324093,"about_ca_system_score_gemma":0.0015785819,"threshold_uncertainty_score":0.9998186},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24977006,0.00018107942,0.7474868,0.0012635717,0.00009397699,0.000055189026,0.0000065957624,0.000003893034,0.0011388077],"genre_scores_gemma":[0.9944849,0.0000030520516,0.0052100606,0.0000854642,0.00019777722,6.0386486e-7,0.00001152768,0.000003665372,0.0000029498888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988728,0.00017736122,0.0005759514,0.00010150973,0.00017934415,0.000093009396],"domain_scores_gemma":[0.99860567,0.00065833016,0.0003514937,0.00007639412,0.00025552945,0.00005257389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048183807,0.000081629514,0.00023371997,0.00013761429,0.000092702656,0.00005062191,0.0000988383,0.000027540747,0.00007618501],"category_scores_gemma":[0.000019395902,0.00007117677,0.00014104425,0.00067742827,0.00007841105,0.00046078616,0.0000069592693,0.00020134106,6.530921e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034390067,0.0004190827,0.005013708,0.0000057157085,0.0006005619,0.000001133097,0.0006760087,0.20344502,0.001110318,0.28373367,0.00000778066,0.50464314],"study_design_scores_gemma":[0.00007187786,0.00027097788,0.0039706207,0.000019041101,0.0018456214,0.0000013740714,0.0007588879,0.61958504,0.0016460624,0.37144178,0.00028073683,0.0001079601],"about_ca_topic_score_codex":0.000047451973,"about_ca_topic_score_gemma":0.000008008791,"teacher_disagreement_score":0.74471486,"about_ca_system_score_codex":0.000006551834,"about_ca_system_score_gemma":0.000033923763,"threshold_uncertainty_score":0.29025045},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035264283,0.00053848064,0.9248127,0.029629497,0.00043220705,0.00023776838,8.526621e-7,0.0000116646,0.009072546],"genre_scores_gemma":[0.99847645,0.00020902668,0.0009670193,0.0002486122,0.000049275262,0.0000016589022,1.3341646e-7,0.0000018913456,0.000045912115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982465,0.00029082043,0.00082649937,0.00014824889,0.00031457338,0.00017338504],"domain_scores_gemma":[0.9943526,0.0026704655,0.0012092347,0.00032034892,0.0014069069,0.00004044294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024278774,0.00012424863,0.0002229989,0.00009761763,0.00032572125,0.00017117336,0.00089367473,0.00006257531,0.000009899366],"category_scores_gemma":[0.007032104,0.00007033414,0.00010897844,0.0006307404,0.0002587659,0.000580722,0.00012642358,0.0004030145,0.0000015820934],"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":[0.00077035924,0.00061290973,0.00060652255,0.00011703105,0.00026072538,0.000013508227,0.007990104,0.16399845,0.008100775,0.5773973,0.0005604032,0.2395719],"study_design_scores_gemma":[0.00004954596,0.0008646216,0.00061681704,0.0005810072,0.000090456735,0.00011321593,0.008475271,0.14170027,0.44985607,0.39649653,0.000943961,0.00021223135],"about_ca_topic_score_codex":0.000054430857,"about_ca_topic_score_gemma":0.0000063337375,"teacher_disagreement_score":0.9632122,"about_ca_system_score_codex":0.00003429286,"about_ca_system_score_gemma":0.00028689852,"threshold_uncertainty_score":0.8418593},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041122427,0.0029128017,0.9806986,0.0027233248,0.008563779,0.00024033431,0.000010883368,0.00007048934,0.00066754204],"genre_scores_gemma":[0.9051677,0.0015061606,0.09242953,0.0002278285,0.0005190464,0.000003772237,0.0000016581763,0.000082822145,0.00006150394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952328,0.00021215307,0.0030001237,0.00034967394,0.0005865665,0.0006186473],"domain_scores_gemma":[0.99471617,0.0017738701,0.0013222351,0.0005585862,0.0013892215,0.00023989096],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012073084,0.00055355614,0.0010007377,0.001622254,0.00012852436,0.00014540841,0.00084915827,0.0005025007,0.00040014272],"category_scores_gemma":[0.0053918636,0.00062007504,0.00039353955,0.0018247928,0.00024234607,0.0015303805,0.00012646886,0.001660367,0.000027712542],"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":[0.00024905417,0.00012470955,0.000020052505,0.00035977305,0.0009907912,0.00010744491,0.0009640556,0.9095379,0.047153607,0.02543758,0.0002940776,0.014760934],"study_design_scores_gemma":[0.00013124946,0.00027936284,0.00003557196,0.000649457,0.0009083466,0.00036817483,0.0015192333,0.7586035,0.22572558,0.0029063057,0.008388446,0.00048478224],"about_ca_topic_score_codex":0.00007190022,"about_ca_topic_score_gemma":0.000006872451,"teacher_disagreement_score":0.90105546,"about_ca_system_score_codex":0.00037436336,"about_ca_system_score_gemma":0.00032654285,"threshold_uncertainty_score":0.9996251},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016547473,0.0028159136,0.8962426,0.08263525,0.01338398,0.0014519558,0.000041813277,0.00007589687,0.0016978044],"genre_scores_gemma":[0.77008617,0.0013025235,0.20935455,0.01687307,0.001827229,0.00006547385,0.0000017429123,0.00008641375,0.0004028195],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9871398,0.0014596537,0.005358282,0.0015790955,0.002447862,0.00201528],"domain_scores_gemma":[0.9696145,0.013014865,0.0060698395,0.0020673897,0.0084731905,0.00076021755],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0096005155,0.0011131702,0.0016498169,0.0009012452,0.0013385761,0.0028797023,0.005245182,0.0009773074,0.00047971058],"category_scores_gemma":[0.04782487,0.0011679406,0.0014301707,0.004406812,0.0007301675,0.007394792,0.00069281773,0.0036785246,0.00043884382],"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":[0.003621709,0.0039120703,0.00005736707,0.0003628593,0.00058478874,0.0005484509,0.0039482075,0.021736728,0.00072610023,0.70469695,0.0071868887,0.2526179],"study_design_scores_gemma":[0.00014830266,0.001829091,0.000020546768,0.0015615015,0.00055257726,0.00012488957,0.0024227356,0.35853213,0.12249458,0.42192248,0.08959388,0.00079729996],"about_ca_topic_score_codex":0.00027329882,"about_ca_topic_score_gemma":0.00012975253,"teacher_disagreement_score":0.7684314,"about_ca_system_score_codex":0.001216779,"about_ca_system_score_gemma":0.0045334995,"threshold_uncertainty_score":0.99996156},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010492197,0.0012177594,0.9733264,0.012026407,0.0013728571,0.0011159575,0.000015956619,0.000023333634,0.0004091398],"genre_scores_gemma":[0.96811074,0.000891867,0.03053731,0.00015387434,0.00023308114,0.000045678353,0.0000019751271,0.000015458778,0.000010001255],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99275064,0.0005820169,0.0045229155,0.000704786,0.00085387053,0.00058578013],"domain_scores_gemma":[0.9895805,0.0010752028,0.00518998,0.0012010653,0.002816181,0.00013706267],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0048519294,0.0003888355,0.0009030354,0.0014971344,0.00022493229,0.00016349467,0.0023257532,0.0005397087,0.000047667],"category_scores_gemma":[0.0037107859,0.00040205976,0.00028646612,0.0070778746,0.0007079478,0.002065593,0.00063457986,0.0016649601,0.00004430849],"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":[0.0010286012,0.002496295,0.00020609079,0.000119998855,0.00012495699,0.00001586237,0.001989267,0.007889047,0.011534683,0.3444514,0.00007172265,0.63007206],"study_design_scores_gemma":[0.00005955909,0.0012534737,0.00012995426,0.00085369765,0.00023936298,0.00013072997,0.0053361924,0.24654545,0.21891361,0.52152145,0.004618603,0.00039794843],"about_ca_topic_score_codex":0.00030221746,"about_ca_topic_score_gemma":0.00014702955,"teacher_disagreement_score":0.95761853,"about_ca_system_score_codex":0.00027154727,"about_ca_system_score_gemma":0.0009794607,"threshold_uncertainty_score":0.9998431},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.954932,0.0003713123,0.0023223576,0.021308353,0.0029154622,0.0014976434,0.0000049245855,0.000011518155,0.016636463],"genre_scores_gemma":[0.99709713,0.0001882281,0.0008182197,0.00074737443,0.00083491346,0.000032413234,0.0000022950774,0.00001879772,0.00026063868],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99025613,0.0045077633,0.0026711551,0.00045932995,0.0015055502,0.0006000628],"domain_scores_gemma":[0.98439384,0.0056545874,0.00370329,0.00044274933,0.0056500738,0.00015547923],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.017885419,0.00034633887,0.0007142427,0.00044035315,0.0011978016,0.00071597466,0.0013405567,0.00020711122,0.00018657542],"category_scores_gemma":[0.005954588,0.00027191127,0.00012554102,0.004456089,0.0007389365,0.00346087,0.00015239533,0.0019916324,0.00001526217],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001130414,0.015250914,0.0039042332,0.00003937184,0.00037098242,0.00008780976,0.32971916,0.00090706587,0.000256144,0.56231767,0.00012794808,0.085888274],"study_design_scores_gemma":[0.00020348238,0.0019144665,0.0023864063,0.00028329197,0.00031755242,0.000028711209,0.9180909,0.00025072714,0.00043477432,0.055815767,0.020001916,0.00027202966],"about_ca_topic_score_codex":0.078771554,"about_ca_topic_score_gemma":0.051426157,"teacher_disagreement_score":0.5883717,"about_ca_system_score_codex":0.0003114145,"about_ca_system_score_gemma":0.0059986115,"threshold_uncertainty_score":0.9999733},"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":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8163597,0.001889964,0.010509812,0.15837106,0.0049663223,0.00034413833,0.0000017738257,0.000010107953,0.0075470963],"genre_scores_gemma":[0.99301416,0.0011320255,0.0031607165,0.0009678009,0.00031189524,0.000009698724,1.4580358e-7,0.000007845076,0.0013957331],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9949118,0.0016041303,0.0015323993,0.00045184838,0.0010488596,0.00045097322],"domain_scores_gemma":[0.98707634,0.0065934374,0.0015026021,0.0007524038,0.003933112,0.00014212451],"candidate_categories":["metaresearch","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008262989,0.00021307863,0.00042221477,0.0022116317,0.0002305142,0.00015576836,0.00180815,0.00033623065,0.00013016525],"category_scores_gemma":[0.010989325,0.0002173493,0.00012486396,0.0035146493,0.0006254837,0.0024737543,0.0002737427,0.002923133,0.000101624006],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013848171,0.0037786942,0.0009876769,0.00008879598,0.000059048234,0.000029400926,0.0012733284,0.013220116,0.004511939,0.71767676,0.0004555774,0.25653383],"study_design_scores_gemma":[0.00012666544,0.002214971,0.099257976,0.0032900553,0.00012126719,0.0000836988,0.0035038157,0.004075162,0.15940955,0.6828135,0.044622295,0.00048102564],"about_ca_topic_score_codex":0.00031156468,"about_ca_topic_score_gemma":0.000035848287,"teacher_disagreement_score":0.25605282,"about_ca_system_score_codex":0.0004256812,"about_ca_system_score_gemma":0.0072491886,"threshold_uncertainty_score":0.9993772},"labels":[],"label_agreement":null}]}