{"meta":{"query_hash":"9b3ecc510556","filters":{"venue":"Cybernetics & Systems"},"cohort_total":11,"direct_labels_cover":0,"predictions_cover":11,"exported":11,"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/9b3ecc510556","api":"https://metacan.xera.ac/api/v1/cohort?venue=Cybernetics+%26+Systems"},"results":[{"id":"W1519178882","doi":"10.1080/01969722.2015.1038480","title":"On Belief and the Making of All Things Beautiful and Sublime: Creation by Ordinance and Destruction by Chaos","year":2015,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Visual Culture and Art Theory","field":"Arts and Humanities","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":"New York Institute of Technology","funders":"","keywords":"Beauty; Aesthetics; Epistemology; Sublime; Order (exchange); Semiotics; Taste; Style (visual arts); Meaning (existential); Sophistication; Philosophy; Sociology; Art; Literature; Psychology","score_opus":0.022327562698427762,"score_gpt":0.24052112926454067,"score_spread":0.2181935665661129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519178882","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15920681,0.025958734,0.0407164,0.06794631,0.0009996832,0.000055361303,0.00009624431,0.00005401026,0.70496637],"genre_scores_gemma":[0.97728264,0.0048508025,0.0028581563,0.0018913009,0.00030296968,0.000055767418,0.000021802001,0.000041229832,0.012695379],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99878496,0.00067070365,0.000022904562,0.00015698351,0.00018326541,0.00018129639],"domain_scores_gemma":[0.996869,0.002248876,0.0002822379,0.00020065914,0.00023673748,0.00016245936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018002789,0.00037994448,0.00030295146,0.0012953818,0.00390525,0.007492784,0.00082369335,0.0018271819,0.0044764327],"category_scores_gemma":[0.0037450627,0.0001861528,0.00045706445,0.0012350249,0.03436603,0.0077688983,0.003080246,0.0025326912,0.00040909782],"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":[0.000009970872,0.0000097591255,0.00025047307,0.000023237868,0.000003270388,0.000054145934,0.012272111,0.00021596352,0.000082346756,0.98252493,0.0011219272,0.0034320084],"study_design_scores_gemma":[0.0000073568394,0.000024392974,0.0011534112,0.00015065966,0.000007982553,0.00009651132,0.010204396,0.00073476724,0.00024258546,0.95064473,0.03671846,0.000014759121],"about_ca_topic_score_codex":0.004538705,"about_ca_topic_score_gemma":0.0037716348,"teacher_disagreement_score":0.007492784,"about_ca_system_score_codex":0.0040487847,"about_ca_system_score_gemma":0.0014901352,"threshold_uncertainty_score":0.02937609},"labels":[],"label_agreement":null},{"id":"W1985171145","doi":"10.1080/01969720802069831","title":"INFLUENCE OF TEMPERATURE ON SWARMBOTS THAT LEARN","year":2008,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ethogram; Ethology; Artificial intelligence; Computer science; Swarm behaviour; Cybernetics; Control (management); Swarm robotics; Duration (music); Machine learning; Ecology","score_opus":0.017008710101454404,"score_gpt":0.2303582216459071,"score_spread":0.2133495115444527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985171145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.735604,0.0007033266,0.23313491,0.0023648622,0.00025631842,0.00008600203,0.0000839057,0.000596272,0.027170412],"genre_scores_gemma":[0.98990357,0.0001324291,0.0071895337,0.00010936832,0.000031220243,0.000034953748,0.000024868028,0.000040140112,0.0025339471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996518,0.0001356401,0.00001532761,0.00007133036,0.00005882351,0.000066991364],"domain_scores_gemma":[0.9973334,0.0014154268,0.0003960302,0.0002771813,0.00030132855,0.00027669192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010933669,0.0004427776,0.0005575784,0.00023137162,0.00051363645,0.0008850115,0.000670005,0.00086200674,0.0019907723],"category_scores_gemma":[0.0076267626,0.0002934257,0.00039108974,0.00011050618,0.00135523,0.0012419546,0.0011254878,0.0009561891,0.00029959148],"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":[0.0003262299,0.00017687403,0.008929873,0.000118039796,0.00014506493,0.00037390727,0.0005258931,0.8771305,0.016498037,0.059238184,0.0017225539,0.034814876],"study_design_scores_gemma":[0.000035317487,0.00014500179,0.001957289,0.000011495834,0.000026253314,0.0000692136,0.000068937516,0.95350784,0.0021523866,0.040947806,0.0010605361,0.00001790918],"about_ca_topic_score_codex":0.0014185295,"about_ca_topic_score_gemma":0.0010084055,"teacher_disagreement_score":0.0019907723,"about_ca_system_score_codex":0.00057408755,"about_ca_system_score_gemma":0.0004847705,"threshold_uncertainty_score":0.006659746},"labels":[],"label_agreement":null},{"id":"W2006655378","doi":"10.1080/01969722.2012.732797","title":"QUANTIFYING NEARNESS IN VISUAL SPACES","year":2012,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Measure (data warehouse); Similarity (geometry); Similarity measure; Computer science; Image (mathematics); Matching (statistics); Set (abstract data type); Artificial intelligence; Cybernetics; Earth mover's distance; Pattern recognition (psychology); Mathematics; Data mining; Statistics","score_opus":0.04681242619161964,"score_gpt":0.3129967717192166,"score_spread":0.26618434552759695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006655378","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28819737,0.0014119628,0.7016462,0.00023292644,0.0000491966,0.000086720414,0.0001913375,0.00033009556,0.007854126],"genre_scores_gemma":[0.89223784,0.0004972228,0.105826564,0.00006618846,0.00005896116,0.00009056646,0.0002477266,0.00006208919,0.0009129149],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968539,0.00089905004,0.0002455809,0.00056537683,0.0013118493,0.00012430536],"domain_scores_gemma":[0.9921726,0.0044054664,0.0011987159,0.0009692132,0.00091304805,0.0003408676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027905963,0.00056770485,0.0007414042,0.006178114,0.0008265648,0.0035479327,0.0007911296,0.0011435737,0.0015728237],"category_scores_gemma":[0.019569037,0.00031670823,0.00053868245,0.0021587014,0.0026997481,0.008270245,0.0045669028,0.00082007697,0.00028828526],"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":[0.0006876588,0.00018671773,0.024026837,0.0007403481,0.00036803706,0.00037980996,0.0030514007,0.15355262,0.03548933,0.34679854,0.0016337527,0.433085],"study_design_scores_gemma":[0.0000306091,0.00050245813,0.024776788,0.00013674774,0.00009388331,0.0010280435,0.0018798325,0.32872564,0.015970754,0.619995,0.006716318,0.00014387556],"about_ca_topic_score_codex":0.00086635316,"about_ca_topic_score_gemma":0.0005329669,"teacher_disagreement_score":0.006178114,"about_ca_system_score_codex":0.0010609734,"about_ca_system_score_gemma":0.00037427738,"threshold_uncertainty_score":0.014758289},"labels":[],"label_agreement":null},{"id":"W2042206577","doi":"10.1080/01969722.2015.1012892","title":"Uplift Random Forests","year":2015,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Outcome (game theory); Random forest; Machine learning; Observational study; Artificial intelligence; Action (physics); Simple (philosophy); Range (aeronautics); Data mining; Statistics; Mathematics","score_opus":0.03610241145063336,"score_gpt":0.2406608925201367,"score_spread":0.20455848106950333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042206577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030145124,0.0012044786,0.9578324,0.0004838143,0.00026456357,0.00033813066,0.002294606,0.004205907,0.0032309578],"genre_scores_gemma":[0.44649395,0.0007561184,0.53821373,0.00063347816,0.00044498622,0.0006482114,0.007076247,0.00046405918,0.0052692406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979335,0.0010056495,0.000088834575,0.0004422784,0.0003465058,0.00018316507],"domain_scores_gemma":[0.9953402,0.0031413515,0.0002960118,0.0006041676,0.00050947483,0.00010874535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044143884,0.0011964923,0.0018080057,0.0018508417,0.00079478155,0.0011270398,0.0018696915,0.0014859876,0.0036286558],"category_scores_gemma":[0.008143941,0.0006309899,0.001961459,0.0014606112,0.00048422188,0.0011738703,0.0009396457,0.0016298759,0.0019839571],"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":[0.00038454853,0.000261654,0.006204402,0.00026855557,0.00036290806,0.00027908082,0.000089581714,0.6482788,0.0014071406,0.01730286,0.025951587,0.29920888],"study_design_scores_gemma":[0.000033274304,0.000036974394,0.0004381509,0.000020575208,0.0000288617,0.00005703232,0.000012503822,0.9818657,0.00040692536,0.014321322,0.002767178,0.0000114504965],"about_ca_topic_score_codex":0.00610239,"about_ca_topic_score_gemma":0.014395307,"teacher_disagreement_score":0.00610239,"about_ca_system_score_codex":0.000578146,"about_ca_system_score_gemma":0.0010826591,"threshold_uncertainty_score":0.023345828},"labels":[],"label_agreement":null},{"id":"W2133005637","doi":"10.1080/01969722.2015.1038481","title":"Integration | Innovation | Inclusion: Values, Variables and the Design of Human Environments","year":2015,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Sustainable Development and Environmental Policy","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Ingenuity; Compassion; Civilization; Contradiction; Environmental ethics; Sociology; Inclusion (mineral); Politics; Epistemology; Computer science; Social science; Political science; Law","score_opus":0.0250969595422031,"score_gpt":0.2382898171196655,"score_spread":0.2131928575774624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133005637","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07410445,0.015667137,0.19743185,0.05178079,0.0014724581,0.00040223487,0.00010102984,0.00046787193,0.6585722],"genre_scores_gemma":[0.9442351,0.003984443,0.031214185,0.0012678406,0.00017444258,0.0005374655,0.000052359483,0.0001792354,0.018354878],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9760016,0.01888644,0.00046865272,0.001097963,0.002513442,0.0010318942],"domain_scores_gemma":[0.98999715,0.0068133217,0.00066173455,0.0010016415,0.0007811616,0.0007449915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012914094,0.0010349262,0.00094073097,0.0021813337,0.006617468,0.026763482,0.0022482707,0.0038109242,0.0060122977],"category_scores_gemma":[0.013319895,0.00052414474,0.00069483405,0.0032001007,0.0686307,0.015740441,0.012041906,0.0035871647,0.000888005],"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":[0.000021230686,0.000029444895,0.000996885,0.00023703171,0.000014126873,0.000090169204,0.016698074,0.00086516264,0.00014543062,0.9537293,0.0018066979,0.02536646],"study_design_scores_gemma":[0.000018370702,0.00007403351,0.00048939115,0.00047492553,0.00002155466,0.0001409527,0.016088458,0.0010054868,0.00033520017,0.8662184,0.11510994,0.000023275736],"about_ca_topic_score_codex":0.0030505238,"about_ca_topic_score_gemma":0.0025063537,"teacher_disagreement_score":0.026763482,"about_ca_system_score_codex":0.0072696884,"about_ca_system_score_gemma":0.0076264627,"threshold_uncertainty_score":0.06829709},"labels":[],"label_agreement":null},{"id":"W4224083579","doi":"10.1080/01969722.2022.2062850","title":"Unsupervised Learning Using Expectation Propagation Inference of Inverted Beta-Liouville Mixture Models for Pattern Recognition Applications","year":2022,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Inference; Artificial intelligence; Pattern recognition (psychology); Categorization; Generative model; Machine learning; Mixture model; Unsupervised learning; Scheme (mathematics); Generative grammar; Mathematics","score_opus":0.05525624368380263,"score_gpt":0.28159580137212853,"score_spread":0.2263395576883259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224083579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016823009,0.00005014258,0.997898,0.000043268043,0.0000046538476,0.000009496909,0.000010376133,0.00015680463,0.00014501641],"genre_scores_gemma":[0.23060039,0.00043325993,0.76468706,0.00026229784,0.00010432721,0.00028763418,0.0005733408,0.00028951812,0.0027620993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988944,0.00050134497,0.000054491862,0.00021645238,0.00026717497,0.00006613347],"domain_scores_gemma":[0.9972652,0.0019373535,0.00019806785,0.00023321099,0.00028560258,0.00008059587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003192143,0.0010753856,0.0013193274,0.0013521172,0.0005812603,0.0014005756,0.0025810539,0.001370606,0.0017301999],"category_scores_gemma":[0.008750818,0.0010489663,0.0014714838,0.0011985261,0.0013223176,0.0023613046,0.0017872731,0.0027554282,0.0010057895],"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":[0.00013789622,0.00015157103,0.0014265033,0.00011072119,0.00014746902,0.000095327276,0.00011773974,0.7356774,0.006697508,0.05033524,0.0021782685,0.20292436],"study_design_scores_gemma":[0.0000021652334,0.0000064204055,0.000042606178,0.0000023370308,0.0000030248702,0.0000074362815,0.0000018720153,0.991879,0.00041838622,0.007461782,0.00017073283,0.0000042358374],"about_ca_topic_score_codex":0.0036754482,"about_ca_topic_score_gemma":0.004485281,"teacher_disagreement_score":0.0036754482,"about_ca_system_score_codex":0.001024874,"about_ca_system_score_gemma":0.0012527735,"threshold_uncertainty_score":0.016881883},"labels":[],"label_agreement":null},{"id":"W4320916154","doi":"10.1080/01969722.2023.2175134","title":"A Detection of Intrusions Based on Deep Learning","year":2023,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Intrusion detection system; Artificial intelligence; False positive paradox; Machine learning; Deep learning; Convolutional neural network; False positive rate; Support vector machine; Data mining; Network security; Pattern recognition (psychology); Computer security","score_opus":0.012604887968794389,"score_gpt":0.224558189888463,"score_spread":0.2119533019196686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320916154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27470645,0.0014800446,0.70591855,0.0011582604,0.0003230933,0.00022717132,0.0020867607,0.0072972625,0.006802348],"genre_scores_gemma":[0.9204429,0.00050333055,0.07205138,0.00023879521,0.000042863998,0.000098497396,0.0018722375,0.000039512724,0.0047104913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951696,0.000060079914,0.000038303428,0.0001329033,0.00015395778,0.00009783483],"domain_scores_gemma":[0.9994438,0.00013120311,0.00006907558,0.0000609294,0.00025732897,0.0000375967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005710503,0.001010751,0.00071779575,0.0018268775,0.00039596154,0.00070077856,0.0011747467,0.00085433177,0.001194078],"category_scores_gemma":[0.0014021291,0.00038269756,0.00075228827,0.0009552435,0.0003194849,0.0012326778,0.00083079207,0.0010505769,0.00040272067],"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":[0.0004767433,0.0009186857,0.022511242,0.0002183273,0.0003415749,0.00041056826,0.000086639026,0.33765006,0.022013938,0.004048587,0.010647488,0.60067624],"study_design_scores_gemma":[0.000004744436,0.00005832899,0.0012056462,0.0000075547955,0.000013987681,0.000041223895,0.000006474105,0.99309397,0.00433382,0.00075842097,0.00046789393,0.000008004217],"about_ca_topic_score_codex":0.014249399,"about_ca_topic_score_gemma":0.013378813,"teacher_disagreement_score":0.014249399,"about_ca_system_score_codex":0.0012473844,"about_ca_system_score_gemma":0.0010191401,"threshold_uncertainty_score":0.02833289},"labels":[],"label_agreement":null},{"id":"W4321780243","doi":"10.1080/01969722.2023.2175118","title":"Fractional-Sea Lion Optimization Based Routing and Charge Scheduling in Internet of Electric Vehicles","year":2023,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Scheduling (production processes); Mathematical optimization; Schedule; Simulation; Real-time computing; Mathematics","score_opus":0.007556296912056153,"score_gpt":0.20496056848225755,"score_spread":0.1974042715702014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321780243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11847225,0.00045966866,0.8715783,0.00036619438,0.00009918654,0.000057072113,0.00009235395,0.00016586971,0.008709159],"genre_scores_gemma":[0.9574267,0.00019701007,0.03944688,0.000052559597,0.000015248844,0.000051939875,0.00005068287,0.000023379233,0.0027354422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979955,0.00007008092,0.000007278972,0.000033047167,0.000041535986,0.000048588692],"domain_scores_gemma":[0.9998122,0.000099436715,0.00003381409,0.000009475056,0.000029095825,0.00001593929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047940967,0.0004930771,0.0006163433,0.0004285486,0.00046445732,0.00079127436,0.0005855779,0.0006149345,0.0010003948],"category_scores_gemma":[0.0008314077,0.00029459147,0.00056272926,0.0004804202,0.0005283572,0.0006418689,0.0004559359,0.00044507353,0.00006655239],"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":[0.00000845977,0.0000051727693,0.00011804479,0.000006028313,0.000004080999,0.000011322679,0.000006483102,0.995535,0.00014263588,0.0019373934,0.00007636554,0.0021490217],"study_design_scores_gemma":[0.0000013198849,0.000004916442,0.000028187407,7.6809704e-7,0.0000013911266,0.0000018256201,0.0000036620663,0.99909616,0.000042223408,0.0007173022,0.00010137818,9.528604e-7],"about_ca_topic_score_codex":0.019679666,"about_ca_topic_score_gemma":0.010751821,"teacher_disagreement_score":0.019679666,"about_ca_system_score_codex":0.0014461833,"about_ca_system_score_gemma":0.001366205,"threshold_uncertainty_score":0.03913021},"labels":[],"label_agreement":null},{"id":"W4414883160","doi":"10.1080/01969722.2025.2566662","title":"Image Steganography with Security Using Massive Threefold Attentional Residual GAN Optimized By Chaotic PSO Algorithm","year":2025,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Residual; Steganography; Chaotic; Image (mathematics); Particle swarm optimization; Key (lock)","score_opus":0.008184494544106196,"score_gpt":0.24276121479401236,"score_spread":0.23457672024990617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414883160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08815458,0.00037254157,0.90444356,0.00019319225,0.000044613444,0.000033178218,0.000030788135,0.00037805657,0.0063495026],"genre_scores_gemma":[0.9307677,0.00017587646,0.06595603,0.00007398043,0.000014583707,0.000051234096,0.00004727727,0.000036138423,0.0028772661],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999279,0.000018322184,0.0000026114228,0.00001607361,0.00002255766,0.000012569365],"domain_scores_gemma":[0.99992025,0.000030793817,0.000013189224,0.000009243335,0.000019759551,0.000006681564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020510974,0.00046564254,0.00032977524,0.00015999566,0.00010494601,0.0002535924,0.00035867523,0.000320243,0.0007430331],"category_scores_gemma":[0.00036996667,0.0001534835,0.0003505253,0.00012555343,0.00030278484,0.0003384447,0.00034663096,0.0004064543,0.00013846505],"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":[0.000057186713,0.000019986821,0.0006618451,0.000040986597,0.000034496337,0.00008025995,0.00002981841,0.9382518,0.021398734,0.006068195,0.0006098217,0.03274691],"study_design_scores_gemma":[0.0000021171054,0.000012214546,0.00004981166,0.0000013245475,0.000002288161,0.000010155866,0.0000013495478,0.9986386,0.0008565133,0.0003040389,0.000119689605,0.0000018496654],"about_ca_topic_score_codex":0.001835804,"about_ca_topic_score_gemma":0.001986078,"teacher_disagreement_score":0.001835804,"about_ca_system_score_codex":0.0002595383,"about_ca_system_score_gemma":0.00028790015,"threshold_uncertainty_score":0.003650248},"labels":[],"label_agreement":null},{"id":"W4415644281","doi":"10.1080/01969722.2025.2573330","title":"Enhancing Medical Diagnosis through Multimodal Image Fusion: A Novel Approach Using Modified Swin-Based Cross Attention Fusion","year":2025,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Advanced Image Fusion Techniques","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Medical diagnosis; Image fusion; Medical imaging; Image (mathematics); Fusion; Pattern recognition (psychology)","score_opus":0.017775713452138176,"score_gpt":0.29235679000763304,"score_spread":0.27458107655549485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415644281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022487186,0.0007164646,0.97369856,0.00024177626,0.00006353996,0.00005907086,0.00004068574,0.00059074775,0.0021019082],"genre_scores_gemma":[0.7302089,0.0007535929,0.26299673,0.0005870745,0.00017015256,0.00010056259,0.00021608252,0.00017734268,0.004789662],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993717,0.00009987198,0.00003831474,0.00016024604,0.00024486321,0.00008501144],"domain_scores_gemma":[0.999526,0.0001258149,0.000062837564,0.000054329077,0.00019153475,0.0000395793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011758596,0.0010134658,0.00090281863,0.0015177188,0.00043478547,0.0008603942,0.0012044619,0.0012207598,0.0017505968],"category_scores_gemma":[0.0018315467,0.00042479797,0.0012869665,0.00071775785,0.00059351383,0.0015916653,0.0018261264,0.00091933506,0.0004068821],"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":[0.0007247854,0.0003241037,0.0030858018,0.00029748635,0.0004323001,0.00073696964,0.0003979355,0.20252433,0.14897679,0.012978462,0.003511002,0.6260101],"study_design_scores_gemma":[0.000012047027,0.00015213591,0.0013293533,0.000014743373,0.00010839575,0.00035102206,0.000028575678,0.96750534,0.024278088,0.004120535,0.0020706817,0.000029055924],"about_ca_topic_score_codex":0.0031164733,"about_ca_topic_score_gemma":0.003061354,"teacher_disagreement_score":0.0031164733,"about_ca_system_score_codex":0.0008502861,"about_ca_system_score_gemma":0.00069061917,"threshold_uncertainty_score":0.006218672},"labels":[],"label_agreement":null},{"id":"W4417280194","doi":"10.1080/01969722.2025.2590761","title":"Mobile-Le Harmonic Fusion Network for Object Recognition and SiamMoT Based Multi-Object Tracking Using Video Surveillance","year":2025,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Tracking (education); Video tracking; Object (grammar); Cognitive neuroscience of visual object recognition; Sensor fusion; Artificial neural network; Pattern recognition (psychology)","score_opus":0.05726045120970017,"score_gpt":0.30516595167892735,"score_spread":0.24790550046922719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417280194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12636748,0.0013452079,0.8591767,0.0004028341,0.00027467858,0.0001351922,0.0005022144,0.0053479443,0.006447737],"genre_scores_gemma":[0.7849898,0.0007120012,0.19914706,0.00028386916,0.00010837754,0.00012607867,0.0023744071,0.0001389472,0.012119567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996592,0.000039215964,0.000015302106,0.00013241966,0.000103463666,0.000050523708],"domain_scores_gemma":[0.9997825,0.000037688067,0.000024137531,0.000036821348,0.00009952018,0.000019278412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006738003,0.00082241185,0.0007544556,0.0012497475,0.00043850596,0.0007366152,0.0010521184,0.0006944163,0.0013752681],"category_scores_gemma":[0.0008861619,0.0002441362,0.00087724876,0.00081913633,0.00032304172,0.0011226337,0.0008777647,0.00075333385,0.00060074904],"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":[0.0003711787,0.0003025381,0.0048957434,0.00008125752,0.00019581393,0.00019317563,0.000078039964,0.20144199,0.023937546,0.004037856,0.0067982264,0.7576666],"study_design_scores_gemma":[0.0000053982185,0.00007834519,0.0010239043,0.0000047677336,0.000020472558,0.000054306798,0.000011872577,0.99093926,0.0057830657,0.0008670103,0.001202126,0.000009521949],"about_ca_topic_score_codex":0.012297305,"about_ca_topic_score_gemma":0.012225181,"teacher_disagreement_score":0.012297305,"about_ca_system_score_codex":0.00092242094,"about_ca_system_score_gemma":0.0008994888,"threshold_uncertainty_score":0.024451435},"labels":[],"label_agreement":null}]}