{"meta":{"query_hash":"30b426b009ec","filters":{"venue":"Human-Centric Intelligent Systems"},"cohort_total":4,"direct_labels_cover":0,"predictions_cover":4,"exported":4,"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/30b426b009ec","api":"https://metacan.xera.ac/api/v1/cohort?venue=Human-Centric+Intelligent+Systems"},"results":[{"id":"W4388907242","doi":"10.1007/s44230-023-00050-2","title":"Artificial Intelligence and Sensor Innovations: Enhancing Livestock Welfare with a Human-Centric Approach","year":2023,"lang":"en","type":"article","venue":"Human-Centric Intelligent Systems","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":121,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Bioethics Society","funders":"Dalhousie University","keywords":"Animal welfare; Transformative learning; Safeguarding; Responsible Research and Innovation; Computer science; Knowledge management; Business; Data science; Engineering ethics; Engineering; Sociology; Medicine","score_opus":0.14513782252606366,"score_gpt":0.34571985672118444,"score_spread":0.20058203419512077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388907242","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":[0.9719721,0.0019615483,0.014523092,0.00016747256,0.0008194637,0.0030863436,0.00006005671,0.0010571877,0.006352719],"genre_scores_gemma":[0.9972441,0.00013737418,0.00016023255,0.00002274594,0.00057348906,0.00047659513,0.00014269585,0.00011743432,0.0011253349],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.99485666,0.00037604163,0.0014126414,0.0011760266,0.0009617388,0.0012169022],"domain_scores_gemma":[0.99783415,0.00020673354,0.00040119057,0.00064770813,0.0006998423,0.00021036172],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0012102788,0.00063618843,0.0007802004,0.0013323984,0.0023277577,0.00034791927,0.00043590693,0.00025955608,0.00014898383],"category_scores_gemma":[0.00012624364,0.00054798817,0.00013449384,0.0033164239,0.0003686016,0.0002479086,0.00037951334,0.00068927906,0.000650431],"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":[0.0011036654,0.0035047608,0.064234145,0.00450938,0.001377673,0.002861002,0.03603089,0.0016492514,0.02054509,0.8443958,0.005413717,0.014374625],"study_design_scores_gemma":[0.0028349306,0.019946605,0.2438339,0.0053073457,0.0022939292,0.006238957,0.61021864,0.016220354,0.008466782,0.0054173227,0.0652812,0.013940012],"about_ca_topic_score_codex":0.0005978768,"about_ca_topic_score_gemma":0.000028494658,"teacher_disagreement_score":0.83897847,"about_ca_system_score_codex":0.0003260588,"about_ca_system_score_gemma":0.000047771624,"threshold_uncertainty_score":0.99969715},"labels":[],"label_agreement":null},{"id":"W4411931746","doi":"10.1007/s44230-025-00105-6","title":"Empowering Africa’s Disfranchised SMEs: Machine Learning-Based Credit Scoring for Informal African Merchants","year":2025,"lang":"en","type":"article","venue":"Human-Centric Intelligent Systems","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"","keywords":"Business; Small and medium-sized enterprises; Economic growth; Economics; Finance","score_opus":0.0357097551633336,"score_gpt":0.2748065292146771,"score_spread":0.23909677405134347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411931746","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":[0.21916942,0.0070497096,0.54378784,0.00039974588,0.01566331,0.00878753,0.00015894332,0.0029982976,0.20198518],"genre_scores_gemma":[0.99049926,0.000016399683,0.000050456252,0.00014796568,0.0009766008,0.00035681223,0.00016448185,0.000089761794,0.0076982887],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9963998,0.000024097451,0.0011566251,0.0007136842,0.00057082437,0.0011349431],"domain_scores_gemma":[0.9981778,0.00019663107,0.00070506166,0.00047792733,0.0003927344,0.000049823284],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008727197,0.0005827378,0.0007033263,0.0014484372,0.00079026574,0.001057751,0.0007889857,0.00018030831,0.00010904873],"category_scores_gemma":[0.0006372032,0.0005778026,0.00033386983,0.0016099232,0.00010465123,0.001102605,0.00033157045,0.00044173788,0.00022452092],"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":[0.0028984558,0.0031521786,0.24248838,0.039666463,0.001693141,0.0002671259,0.0034813096,0.10086095,0.0035771586,0.5135426,0.053797755,0.03457449],"study_design_scores_gemma":[0.0022665504,0.00013720276,0.0018083374,0.002469958,0.00018565406,0.0000036608478,0.0012707609,0.2026974,0.0010950812,0.000551043,0.7862156,0.0012987491],"about_ca_topic_score_codex":0.0006184128,"about_ca_topic_score_gemma":0.00007423353,"teacher_disagreement_score":0.7713298,"about_ca_system_score_codex":0.00031206268,"about_ca_system_score_gemma":0.00008054366,"threshold_uncertainty_score":0.99997926},"labels":[],"label_agreement":null},{"id":"W4412676402","doi":"10.1007/s44230-025-00108-3","title":"Agency in Livestock Farming—A Perspective on Human–Animal–Computer Interactions","year":2025,"lang":"en","type":"article","venue":"Human-Centric Intelligent Systems","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Spasmodic Dysphonia Association; Department of Agriculture, Nova Scotia","keywords":"Livestock; Perspective (graphical); Agency (philosophy); Agriculture; Animal production; Business; Environmental planning; Geography; Environmental resource management; Agricultural economics; Agricultural science; Agroforestry; Natural resource economics; Economics; Sociology; Environmental science; Computer science; Biology; Forestry; Social science; Archaeology; Animal science; Artificial intelligence","score_opus":0.11969300810459593,"score_gpt":0.41532194524623095,"score_spread":0.295628937141635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412676402","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":[0.8741327,0.0017441752,0.002631186,0.00017231765,0.0028239246,0.0017388097,0.000025426883,0.0003225756,0.11640891],"genre_scores_gemma":[0.99223113,0.000048389087,0.000027260488,0.00009358574,0.00043585247,0.00022487695,0.000015724037,0.00004171289,0.0068814913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9971503,0.000257561,0.0008723188,0.00079610245,0.00032414106,0.00059958483],"domain_scores_gemma":[0.99882144,0.00015340529,0.00020839424,0.00046979968,0.0002588928,0.00008808338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002512001,0.00045121522,0.0005789878,0.0008996232,0.0006025597,0.00013171043,0.00040584107,0.00013920662,0.00042581788],"category_scores_gemma":[0.00004137519,0.00043033375,0.00027164284,0.0006085658,0.000093823706,0.00014722774,0.0002498186,0.0005749246,0.0007213794],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000932979,0.004413144,0.08810926,0.0005987366,0.00076706224,0.0015472287,0.024146326,0.00033604342,0.010989624,0.8386343,0.026863484,0.0026618168],"study_design_scores_gemma":[0.0029841699,0.007821961,0.8730079,0.003941707,0.0005030847,0.00029340622,0.04197667,0.0021138398,0.0008586907,0.0023649659,0.061262954,0.0028706358],"about_ca_topic_score_codex":0.0021357746,"about_ca_topic_score_gemma":0.00008819568,"teacher_disagreement_score":0.8362693,"about_ca_system_score_codex":0.0013506905,"about_ca_system_score_gemma":0.0000394961,"threshold_uncertainty_score":0.99981487},"labels":[],"label_agreement":null},{"id":"W4416574723","doi":"10.1007/s44230-025-00118-1","title":"Prediction of Risk Factors from Gastric Cancer Genetic Data Using Machine Learning","year":2025,"lang":"en","type":"article","venue":"Human-Centric Intelligent Systems","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Science and Engineering Research Board; Department of Biotechnology, Ministry of Science and Technology, India","keywords":"Random forest; Feature selection; Interpretability; Undersampling; Preprocessor; Precision and recall; Classifier (UML); Matthews correlation coefficient; Overfitting","score_opus":0.07674531389958915,"score_gpt":0.3141559396423136,"score_spread":0.23741062574272442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416574723","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":[0.916326,0.01621736,0.06540321,0.0000036673955,0.0010991929,0.00030070983,0.0005123979,0.00002113295,0.00011636515],"genre_scores_gemma":[0.995101,0.0027421962,0.00010310753,0.000007869891,0.0002671833,0.000016205877,0.0012650186,0.000019112891,0.00047833938],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983735,0.00020741507,0.00050183444,0.00051014085,0.00020614394,0.0002009647],"domain_scores_gemma":[0.9987264,0.00002390286,0.0003770982,0.0006910059,0.00012306293,0.00005851563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016607971,0.00017574274,0.00021283998,0.0001810114,0.00016685684,0.000034260604,0.00040657257,0.00014721678,0.000069331174],"category_scores_gemma":[0.00009038691,0.00016031427,0.000066637665,0.00028851902,0.00003853688,0.0000063004854,0.0001988612,0.00014627978,0.0000029187793],"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":[0.000051742878,0.00012160124,0.5595368,0.00008760892,0.00024701247,6.9084564e-7,0.00015224938,0.017492957,0.41825917,0.00003478223,0.001764071,0.0022513368],"study_design_scores_gemma":[0.0021694081,0.0003059559,0.14336301,0.0008704026,0.0009721356,0.00000719418,0.0042237346,0.28397602,0.41142255,0.00007142324,0.15173364,0.00088452565],"about_ca_topic_score_codex":0.0039099194,"about_ca_topic_score_gemma":0.00006577696,"teacher_disagreement_score":0.4161738,"about_ca_system_score_codex":0.00007685086,"about_ca_system_score_gemma":0.00010058795,"threshold_uncertainty_score":0.6537426},"labels":[],"label_agreement":null}]}