{"meta":{"query_hash":"7b2a3eba743a","filters":{"venue":"International Journal of Intelligent Systems"},"cohort_total":33,"direct_labels_cover":0,"predictions_cover":33,"exported":33,"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/7b2a3eba743a","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Intelligent+Systems"},"results":[{"id":"W13093665","doi":"10.1042/bj0550204","title":"Toward better scoring metrics for pseudo-independent models: Research Articles","year":2004,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of Guelph","funders":"","keywords":"Computer science; Heuristic; Dimension (graph theory); Artificial intelligence; Hypercube; Domain (mathematical analysis); Perspective (graphical); Machine learning; Theoretical computer science; Algorithm; Mathematics; Combinatorics","score_opus":0.26153605443513944,"score_gpt":0.38425387352950996,"score_spread":0.12271781909437052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W13093665","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":[0.039395027,0.00073760335,0.95379275,0.0023413813,0.0032841256,0.00016343231,0.0000038963703,0.000026560458,0.00025524333],"genre_scores_gemma":[0.9639361,0.00015598569,0.034990612,0.00012793516,0.00069365755,0.000014690848,7.712219e-7,0.000012705577,0.00006755998],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99680054,0.00009810087,0.0008465746,0.0002537417,0.0016739463,0.00032707298],"domain_scores_gemma":[0.9960805,0.0002733268,0.0003303455,0.00023704389,0.0029097644,0.00016902716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002627813,0.00013444395,0.00022727314,0.0008051769,0.000083043866,0.00065256655,0.002016794,0.000081859056,0.0000035130058],"category_scores_gemma":[0.00026126098,0.00011191163,0.0001828443,0.00034284478,0.00004025988,0.000818276,0.00019749749,0.00037719266,0.000031200358],"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":[0.00011032981,0.0003719674,0.00029472198,0.00006459553,0.00040836426,0.00023255947,0.0034893185,0.30444908,0.0029619858,0.63474894,0.0009136976,0.051954478],"study_design_scores_gemma":[0.0010571539,0.000549073,0.00003942816,0.00094068743,0.000018824614,0.0010207266,0.0010222236,0.65822077,0.033838496,0.30133486,0.0016164727,0.0003412667],"about_ca_topic_score_codex":0.00012831212,"about_ca_topic_score_gemma":0.0000036545766,"teacher_disagreement_score":0.92454106,"about_ca_system_score_codex":0.00047889093,"about_ca_system_score_gemma":0.00025669602,"threshold_uncertainty_score":0.62927145},"labels":[],"label_agreement":null},{"id":"W1979980829","doi":"10.1002/int.20476","title":"Alternative approach for learning and improving the MCDA method PROAFTN","year":2011,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"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 New Brunswick; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Machine learning; Artificial intelligence; Preprocessor; Data pre-processing; Multiple-criteria decision analysis; Data mining; Construct (python library); Measure (data warehouse); Mathematics; Mathematical optimization","score_opus":0.07620748980444986,"score_gpt":0.32182830837970583,"score_spread":0.24562081857525597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979980829","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":[0.0013842133,0.0005942166,0.995446,0.00011847611,0.0013463212,0.00017755652,0.000001500213,0.000017723085,0.00091398216],"genre_scores_gemma":[0.8329241,0.00006235044,0.16640316,0.000058984093,0.00034851464,0.000018105466,6.1564623e-7,0.000008450871,0.00017571815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858624,0.00015981378,0.0005125483,0.00018837294,0.00040542247,0.00014762573],"domain_scores_gemma":[0.99816585,0.0002162418,0.0005711706,0.00011265849,0.0008624274,0.0000716303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016595495,0.00011779279,0.00017516856,0.00013774046,0.00008475004,0.00026275057,0.0011295471,0.000043781823,0.0000027887597],"category_scores_gemma":[0.00017680471,0.00007441062,0.00010152707,0.000068238885,0.00003571717,0.0003447012,0.00014127801,0.0002605941,0.0000019079127],"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":[0.00023748678,0.00029635127,0.0017801535,0.000113524286,0.0010758648,0.00005036287,0.02013205,0.01907557,0.0029111346,0.58439875,0.00032282274,0.36960593],"study_design_scores_gemma":[0.00023907733,0.0002546233,0.000036891022,0.000099654644,0.000019000292,0.00066330924,0.0009505843,0.983683,0.0060741846,0.006741145,0.0011015706,0.00013697358],"about_ca_topic_score_codex":0.00010832042,"about_ca_topic_score_gemma":5.867509e-7,"teacher_disagreement_score":0.9646074,"about_ca_system_score_codex":0.000057992194,"about_ca_system_score_gemma":0.00006553434,"threshold_uncertainty_score":0.3034377},"labels":[],"label_agreement":null},{"id":"W2002910581","doi":"10.1002/int.1031","title":"Conceptual design of fuzzy object-oriented databases using extended entity-relationship model","year":2001,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":79,"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 Saskatchewan","funders":"","keywords":"Computer science; Conceptual schema; Entity–relationship model; Fuzzy logic; Database schema; Database design; Data mining; Conceptual model; Relational database; Schema (genetic algorithms); Database; Fuzzy set; Imperfect; Object (grammar); Database model; Information retrieval; Artificial intelligence","score_opus":0.1350884247380204,"score_gpt":0.3359440521008348,"score_spread":0.20085562736281437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002910581","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":[0.013110762,0.00047246643,0.9826805,0.000085482505,0.0029871834,0.00017070962,0.000027298092,0.000022572534,0.0004430332],"genre_scores_gemma":[0.93212396,0.00016534519,0.06691825,0.000043851618,0.00032380925,0.0000027283074,0.000016956794,0.00001082684,0.0003942605],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732345,0.00016459986,0.0010248574,0.00021649126,0.0010837284,0.00018684824],"domain_scores_gemma":[0.99742544,0.0002325167,0.0009450636,0.00032007095,0.0009828712,0.000094027506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010186465,0.0001557628,0.00026846703,0.0005415427,0.000060080412,0.00017553555,0.0015186322,0.00003730745,0.000019658879],"category_scores_gemma":[0.0002432729,0.00013791888,0.00013364662,0.00030124758,0.00008022217,0.0016870605,0.00026515528,0.00016802477,0.000016972235],"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":[0.00013780087,0.0004969589,0.005372109,0.000022361532,0.0005721092,0.00025434163,0.0010348072,0.4702771,0.0018117676,0.5139348,0.002125836,0.0039600097],"study_design_scores_gemma":[0.00053190807,0.000110635556,0.00021967074,0.00034646076,0.00004079677,0.0003515149,0.00065015483,0.9914162,0.0015820102,0.0016567656,0.0028963161,0.00019754868],"about_ca_topic_score_codex":0.00007856329,"about_ca_topic_score_gemma":0.0000021079327,"teacher_disagreement_score":0.9190132,"about_ca_system_score_codex":0.00015298238,"about_ca_system_score_gemma":0.00014863655,"threshold_uncertainty_score":0.56241685},"labels":[],"label_agreement":null},{"id":"W2041926928","doi":"10.1002/1098-111x(200011)15:11<1015::aid-int3>3.0.co;2-9","title":"Granular worlds: Representation and communication problems","year":2000,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency; University of Alberta","funders":"","keywords":"Granular computing; Granularity; Computer science; Rough set; Possible world; Theoretical computer science; Probabilistic logic; Representation (politics); Interoperability; Artificial intelligence; Epistemology; World Wide Web","score_opus":0.027624800744125867,"score_gpt":0.28333172015030306,"score_spread":0.2557069194061772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041926928","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":[0.17832938,0.016708035,0.76510537,0.009969264,0.0057678963,0.0006961923,0.000008652698,0.00012026334,0.023294983],"genre_scores_gemma":[0.9908277,0.0025436971,0.0059094047,0.00012986184,0.00019284582,0.0000049249174,0.0000035703665,0.00000578075,0.0003822564],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985178,0.00012829687,0.0006045163,0.00013370262,0.00051581144,0.00009985348],"domain_scores_gemma":[0.9988512,0.0000911594,0.00034285666,0.00025334483,0.0003909791,0.00007047455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005515637,0.00008884323,0.00015412245,0.00017335285,0.000053455966,0.00037667525,0.0010483371,0.000038247977,0.000046818568],"category_scores_gemma":[0.00003236849,0.00007002728,0.00007354019,0.00013248099,0.000037321242,0.0006177848,0.00006925421,0.0001363884,0.000037814],"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":[0.00014071149,0.00048815587,0.013024425,0.00005374599,0.0004318064,0.00024838166,0.008565867,0.03944254,0.00062305474,0.10923675,0.0087174745,0.81902707],"study_design_scores_gemma":[0.003000254,0.0009450621,0.017657485,0.0023363654,0.000083733365,0.010087797,0.001675306,0.3589409,0.0026432655,0.062121138,0.5393589,0.0011497802],"about_ca_topic_score_codex":0.00010594223,"about_ca_topic_score_gemma":0.0000043596983,"teacher_disagreement_score":0.8178773,"about_ca_system_score_codex":0.00006196164,"about_ca_system_score_gemma":0.000024830168,"threshold_uncertainty_score":0.3632288},"labels":[],"label_agreement":null},{"id":"W2079838695","doi":"10.1002/int.20072","title":"An approach to measure the robustness of fuzzy reasoning","year":2005,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":49,"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 Alberta","funders":"","keywords":"Robustness (evolution); Fuzzy logic; Computer science; Artificial intelligence; Fuzzy control system; Machine learning","score_opus":0.18220406777295975,"score_gpt":0.427996754052197,"score_spread":0.24579268627923725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079838695","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":[0.24478938,0.00067010085,0.74328667,0.0010404484,0.0058396123,0.00028768397,0.000016988244,0.000012282352,0.0040568328],"genre_scores_gemma":[0.98499584,0.000011851398,0.012454729,0.00014013395,0.0020125129,0.0000060034463,0.0000011098158,0.000018370165,0.0003594637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9912313,0.00062303717,0.0024569063,0.0003154977,0.005159585,0.0002136431],"domain_scores_gemma":[0.9913022,0.0010033102,0.0016906944,0.000626704,0.0051604444,0.00021666063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010505485,0.00018671254,0.00048502404,0.00084660563,0.000083401064,0.00069422857,0.0041363863,0.000085649765,0.00011665767],"category_scores_gemma":[0.0042528547,0.000106127176,0.00028874248,0.000514374,0.00006938812,0.0006740643,0.00016791702,0.00028513567,0.00008378092],"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":[0.0003436146,0.00034488944,0.0023953917,0.000005058244,0.00018597844,0.000014967847,0.0028702223,0.8834457,0.0028080512,0.0061965175,0.007074727,0.09431489],"study_design_scores_gemma":[0.0015680435,0.0004708895,0.0076594935,0.0016754061,0.000086402084,0.0038917677,0.031034626,0.77346385,0.009007644,0.001356608,0.16898496,0.0008002808],"about_ca_topic_score_codex":0.000035524634,"about_ca_topic_score_gemma":0.000009566717,"teacher_disagreement_score":0.7402065,"about_ca_system_score_codex":0.0001976707,"about_ca_system_score_gemma":0.00013007356,"threshold_uncertainty_score":0.76865035},"labels":[],"label_agreement":null},{"id":"W2086262313","doi":"10.1002/int.20491","title":"Evidential reasoning using extended fuzzy Dempster-Shafer theory for handling various facets of information deficiency","year":2011,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vagueness; Dempster–Shafer theory; Fuzzy logic; Data mining; Artificial intelligence; Computer science; Ambiguity; Belief structure; Ignorance; Fuzzy set operations; Fuzzy set; Machine learning; Mathematics","score_opus":0.2264374948042466,"score_gpt":0.4315268378480013,"score_spread":0.20508934304375467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086262313","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":[0.26973152,0.00054577494,0.7220637,0.000016555921,0.006657039,0.00026844858,0.000026854575,0.000009248987,0.0006808347],"genre_scores_gemma":[0.9797205,0.000024339526,0.019739194,0.000047693717,0.00035294075,0.000005431842,0.0000025245974,0.000016328551,0.00009109938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926717,0.00039431718,0.003542782,0.0002422065,0.002881599,0.00026738632],"domain_scores_gemma":[0.9887777,0.0016026886,0.003693181,0.00034452783,0.0054487884,0.00013310589],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009629663,0.00021558086,0.00055822637,0.0014657872,0.000115870134,0.0005680451,0.0018935764,0.00012779447,0.00022613938],"category_scores_gemma":[0.008720362,0.00016101162,0.00043364492,0.00031213614,0.00008176961,0.001943969,0.00020775432,0.00019951438,0.000046940597],"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":[0.0134294685,0.0017359221,0.034380216,0.0003654872,0.0024522704,0.00025887135,0.08261095,0.13284822,0.05144923,0.20364137,0.0028824753,0.47394553],"study_design_scores_gemma":[0.008183031,0.002049804,0.015670745,0.0099266535,0.00064247864,0.007221229,0.053413544,0.626083,0.074913695,0.18182792,0.017821774,0.0022461268],"about_ca_topic_score_codex":0.000092010494,"about_ca_topic_score_gemma":0.000005122274,"teacher_disagreement_score":0.7099889,"about_ca_system_score_codex":0.00021892168,"about_ca_system_score_gemma":0.00021741087,"threshold_uncertainty_score":0.9996296},"labels":[],"label_agreement":null},{"id":"W2126397666","doi":"10.1002/int.20247","title":"Pyramid collaborative filtering technique for an intelligent autonomous guide agent","year":2007,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Collaborative filtering; Credibility; Pyramid (geometry); The Internet; Task (project management); Filter (signal processing); Artificial intelligence; Human–computer interaction; Domain (mathematical analysis); Intelligent agent; World Wide Web; Recommender system; Computer vision","score_opus":0.04121726241789809,"score_gpt":0.34761656320555295,"score_spread":0.30639930078765487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126397666","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":[0.0011964252,0.000650249,0.9872154,0.00033883352,0.0072939126,0.0009400821,0.000016953765,0.00011240747,0.002235763],"genre_scores_gemma":[0.8736864,0.00017528996,0.123931296,0.00020755024,0.0013269732,0.00011075995,0.00000862296,0.000038922462,0.00051421096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99638087,0.00012875554,0.0019588938,0.0003527386,0.0008099929,0.0003687421],"domain_scores_gemma":[0.99527293,0.000261121,0.0013079081,0.00043200175,0.0024732582,0.00025279977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037922736,0.0002825773,0.00045626753,0.0007156567,0.0000916962,0.00054492307,0.0024088097,0.00014587484,0.000010019813],"category_scores_gemma":[0.0001422708,0.00023925828,0.00026902344,0.0002528035,0.00004288789,0.0008658272,0.0002065944,0.00024473085,0.000010051079],"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":[0.0006655358,0.0019374217,0.0014132396,0.00032716905,0.0025791498,0.0011027101,0.011728058,0.005191119,0.16394924,0.44889855,0.048499614,0.31370822],"study_design_scores_gemma":[0.0003564181,0.0013532706,0.00006408403,0.00063453725,0.00001783656,0.001667061,0.0013055208,0.0070244255,0.5106214,0.0026738902,0.47382003,0.00046148105],"about_ca_topic_score_codex":0.00016166977,"about_ca_topic_score_gemma":0.00003335159,"teacher_disagreement_score":0.8724899,"about_ca_system_score_codex":0.0007991602,"about_ca_system_score_gemma":0.00022870438,"threshold_uncertainty_score":0.975667},"labels":[],"label_agreement":null},{"id":"W2800627592","doi":"10.1002/int.21995","title":"Generating Z-number based on OWA weights using maximum entropy","year":2018,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Decision maker; Principle of maximum entropy; Entropy (arrow of time); Mathematics; Preference; Mathematical optimization; Computer science; Operations research; Statistics","score_opus":0.17793564038136858,"score_gpt":0.4494769802438753,"score_spread":0.2715413398625067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800627592","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":[0.48113215,0.0000836211,0.49670196,0.00036461675,0.019762777,0.00015105146,0.000018117447,0.00001635249,0.0017693513],"genre_scores_gemma":[0.97648746,0.0000060562393,0.017336786,0.00056661863,0.0050542587,0.0000020449409,0.0000017484226,0.000029572602,0.0005154396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9908159,0.00055173854,0.0026511955,0.0004297203,0.0052403947,0.00031107716],"domain_scores_gemma":[0.99035674,0.0015613487,0.0020829872,0.00050829514,0.0052632485,0.00022736886],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0047148536,0.00026771912,0.0005105505,0.0010638221,0.00017551857,0.0013135861,0.0021013909,0.00011978244,0.00255445],"category_scores_gemma":[0.0036400435,0.00018288971,0.00039191695,0.00039958398,0.00011903135,0.0005014275,0.00015544926,0.00029433603,0.0012740178],"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":[0.0041950257,0.0019071263,0.099136665,0.000039439412,0.00146743,0.0029516546,0.0045634042,0.49667665,0.12588233,0.020382378,0.064243495,0.1785544],"study_design_scores_gemma":[0.0008249406,0.00024236988,0.0003606319,0.0007379454,0.000022085751,0.00094339624,0.0006488643,0.8998063,0.013408763,0.0043846425,0.078301735,0.00031831095],"about_ca_topic_score_codex":0.000051711715,"about_ca_topic_score_gemma":0.0000058506216,"teacher_disagreement_score":0.4953553,"about_ca_system_score_codex":0.00044078167,"about_ca_system_score_gemma":0.00020696713,"threshold_uncertainty_score":0.99972314},"labels":[],"label_agreement":null},{"id":"W2972580158","doi":"10.1002/int.22120","title":"Synthetic minority oversampling for function approximation problems","year":2019,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Imbalanced Data 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 Alberta","funders":"","keywords":"Oversampling; Categorical variable; Computer science; Artificial intelligence; Machine learning; Function approximation; Preprocessor; Benchmark (surveying); Function (biology); Data mining; Mathematics; Algorithm; Artificial neural network","score_opus":0.030057870587504226,"score_gpt":0.27872686520023826,"score_spread":0.24866899461273403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972580158","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":[0.003937944,0.00017858592,0.98908114,0.00035717085,0.005241145,0.00047819017,0.00001185615,0.00006210843,0.0006518819],"genre_scores_gemma":[0.976511,0.00004417958,0.022590633,0.000072804636,0.0003740405,0.000033796347,0.000016181753,0.000009475591,0.000347873],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824136,0.000052138374,0.0007381464,0.0002116129,0.0006214133,0.00013530752],"domain_scores_gemma":[0.99732137,0.00018636303,0.0008842282,0.0003019397,0.0012545493,0.0000515823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096514693,0.00011278925,0.00019005212,0.00031054686,0.000031870648,0.0002851987,0.001129527,0.000066997796,0.000013348239],"category_scores_gemma":[0.00015200068,0.000098407814,0.00013233162,0.000115876,0.000015050023,0.00091700646,0.00007020243,0.00012446281,0.000053127995],"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":[0.00025460275,0.00047049372,0.0043790033,0.0004139423,0.0006090858,0.000006639592,0.00094696815,0.015349237,0.05842667,0.84957105,0.006617675,0.06295463],"study_design_scores_gemma":[0.0014619984,0.0010856931,0.0013198141,0.0013073912,0.000059973885,0.00062169565,0.0005282423,0.6593852,0.066203505,0.024253527,0.24308151,0.0006914231],"about_ca_topic_score_codex":0.000014527159,"about_ca_topic_score_gemma":5.564505e-7,"teacher_disagreement_score":0.97257304,"about_ca_system_score_codex":0.00026764037,"about_ca_system_score_gemma":0.00007169138,"threshold_uncertainty_score":0.4012954},"labels":[],"label_agreement":null},{"id":"W2998229105","doi":"10.1002/int.22213","title":"A normal wiggly hesitant fuzzy linguistic projection‐based multiattributive border approximation area comparison method","year":2020,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":22,"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 Alberta","funders":"National Natural Science Foundation of China","keywords":"Measure (data warehouse); Projection (relational algebra); Representation (politics); Set (abstract data type); Computer science; Rule-based machine translation; Term (time); Fuzzy logic; Artificial intelligence; Score; Fuzzy set; Scale (ratio); Function (biology); Mathematics; Algorithm; Data mining; Machine learning","score_opus":0.25908008157913953,"score_gpt":0.4883926337511811,"score_spread":0.22931255217204155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998229105","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":[0.02387319,0.00036630794,0.9661987,0.0016851602,0.006347637,0.00047255127,0.000076092605,0.000041416697,0.00093896675],"genre_scores_gemma":[0.96025497,0.0000070157193,0.037711956,0.00042982434,0.0014234395,0.000013999768,0.000014107839,0.000027198783,0.00011751118],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9900192,0.00088074483,0.0035836347,0.00052570284,0.0046837404,0.0003069483],"domain_scores_gemma":[0.985776,0.0028939478,0.0031622269,0.00031782777,0.0075297044,0.00032030212],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005207316,0.0003196079,0.0008381175,0.0010117575,0.00013424558,0.0010310634,0.0019139205,0.00014522085,0.00041942138],"category_scores_gemma":[0.018841524,0.00023755405,0.00042308358,0.0008142741,0.00006238516,0.0005384782,0.00018306474,0.0005232501,0.00026375454],"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":[0.013046712,0.0032200872,0.083708726,0.00023060657,0.0029535017,0.0017406422,0.04174665,0.50233203,0.032684714,0.011639457,0.047264528,0.25943235],"study_design_scores_gemma":[0.0011861037,0.00042477058,0.0010905226,0.00035415497,0.000055333792,0.00027318037,0.003668149,0.9477713,0.0070977365,0.0009175914,0.036839716,0.00032141132],"about_ca_topic_score_codex":0.00007345465,"about_ca_topic_score_gemma":0.000008432156,"teacher_disagreement_score":0.93638176,"about_ca_system_score_codex":0.00040621907,"about_ca_system_score_gemma":0.00032201776,"threshold_uncertainty_score":0.9942568},"labels":[],"label_agreement":null},{"id":"W3167071550","doi":"10.1002/int.22531","title":"Intelligent optimization for charging scheduling of electric vehicle using exponential Harris Hawks technique","year":2021,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Scheduling (production processes); Schedule; Exponential function; Renewable energy; Electric vehicle; Mathematical optimization; Automotive engineering; Electrical engineering; Engineering; Mathematics","score_opus":0.0159643605403183,"score_gpt":0.25662334842883283,"score_spread":0.24065898788851453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167071550","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":[0.17639206,0.0029911322,0.81811357,0.00003559136,0.0021591238,0.00019873994,0.000012483335,0.00002649083,0.000070823764],"genre_scores_gemma":[0.9740536,0.0005953565,0.024466032,0.00001666223,0.00078293367,0.000010692467,0.0000128760785,0.000043346074,0.000018489676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980247,0.000046846126,0.0010873066,0.00013886017,0.00048109322,0.00022121235],"domain_scores_gemma":[0.99779844,0.00008974197,0.00046668792,0.00011997927,0.0014455953,0.00007956414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040775104,0.00017566832,0.00034532484,0.00042394892,0.000048848953,0.00009716072,0.00034958724,0.00017090152,0.00007332018],"category_scores_gemma":[0.00012817615,0.0001727403,0.00024673587,0.00028765132,0.00001445711,0.00024211372,0.000031635496,0.00034380244,0.0000012649969],"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":[0.00003872278,0.000032952623,0.00023755351,0.000091313865,0.0003033457,0.000018432653,0.00012137639,0.7467278,0.24862267,0.00045087756,0.00006168042,0.0032932768],"study_design_scores_gemma":[0.00015653658,0.000042725864,0.000007816097,0.0003108724,0.000029354804,0.00036955363,0.00015800516,0.5528522,0.44536114,0.00007333356,0.0005389316,0.00009950017],"about_ca_topic_score_codex":0.000016295488,"about_ca_topic_score_gemma":6.9311363e-7,"teacher_disagreement_score":0.79766154,"about_ca_system_score_codex":0.00037168778,"about_ca_system_score_gemma":0.000107484266,"threshold_uncertainty_score":0.70441455},"labels":[],"label_agreement":null},{"id":"W3170466384","doi":"10.1002/int.22473","title":"A novel method based on probabilistic linguistic term sets and its application in ranking products through online ratings","year":2021,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Ranking (information retrieval); Probabilistic logic; Term (time); Computer science; Decision maker; Selection (genetic algorithm); Prospect theory; Artificial intelligence; Machine learning; Data mining; Mathematics; Operations research","score_opus":0.190004953955526,"score_gpt":0.471015128163082,"score_spread":0.28101017420755603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170466384","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":[0.20726429,0.00074891787,0.7840549,0.0020451962,0.0047190706,0.00071473944,0.000059311413,0.00001787958,0.00037569413],"genre_scores_gemma":[0.9625366,0.000021241673,0.036277995,0.000336126,0.00065555074,0.000017144044,0.0000118881635,0.000021357577,0.00012210259],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934008,0.00054139516,0.0024166736,0.0005878933,0.0028474992,0.00020574924],"domain_scores_gemma":[0.9879361,0.0045942347,0.001610253,0.00037601285,0.005387623,0.00009582452],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005766486,0.0002257208,0.0005641529,0.0006731276,0.000059536298,0.00054314215,0.0009304529,0.00009322069,0.000044608336],"category_scores_gemma":[0.04235842,0.00017208428,0.00013064353,0.0006291252,0.00003387584,0.0002887209,0.00014688639,0.00036515296,0.000023501125],"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":[0.0020346025,0.00467309,0.018040586,0.00045952355,0.00045321058,0.0019773284,0.012298984,0.47303936,0.2536412,0.032049134,0.0007320226,0.20060095],"study_design_scores_gemma":[0.0017180975,0.00015430208,0.0040755295,0.0022643982,0.000030270769,0.0016540274,0.000864964,0.9649087,0.009328423,0.0057590874,0.00889182,0.00035039012],"about_ca_topic_score_codex":0.000036393718,"about_ca_topic_score_gemma":0.00003220185,"teacher_disagreement_score":0.7552723,"about_ca_system_score_codex":0.00028607057,"about_ca_system_score_gemma":0.00030847942,"threshold_uncertainty_score":0.9657082},"labels":[],"label_agreement":null},{"id":"W3171436672","doi":"10.1002/int.22493","title":"Digital‐twin assisted: Fault diagnosis using deep transfer learning for machining tool condition","year":2021,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":104,"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":"Automation; Cloud computing; Computer science; Process (computing); Fault (geology); Software deployment; Manufacturing engineering; Machining; Systems engineering; Engineering; Software engineering; Mechanical engineering","score_opus":0.02158188550714101,"score_gpt":0.28915211068924546,"score_spread":0.26757022518210444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171436672","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":[0.064463645,0.0017600668,0.93055826,0.000038411486,0.0026056706,0.0000985939,0.000025774869,0.00004817801,0.0004013754],"genre_scores_gemma":[0.9949397,0.0004875722,0.003754748,0.000026844915,0.00051911565,0.000015099645,0.00008179111,0.000043531472,0.00013162229],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986315,0.000026511916,0.00070377026,0.00012242483,0.0003636826,0.00015214665],"domain_scores_gemma":[0.9986349,0.00021988104,0.00016314942,0.000058984842,0.00086541014,0.0000576861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018626239,0.00014908936,0.00023688975,0.00017336015,0.00006458815,0.00030499505,0.00017874462,0.00007465263,0.000045917775],"category_scores_gemma":[0.00027517678,0.00014785555,0.00017128131,0.000105091865,0.000013166928,0.0005863186,0.000014582905,0.00022176439,0.0000039071497],"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":[0.00002930782,0.000034378212,0.0011934536,0.000086408414,0.00026009383,0.000039495488,0.0002471107,0.9640624,0.0010237655,0.00040239136,0.00004228377,0.032578908],"study_design_scores_gemma":[0.0006284188,0.00008511831,0.00008785063,0.00084636325,0.0000730959,0.00076404307,0.0011768043,0.95808834,0.011749761,0.00015882713,0.026058108,0.00028326717],"about_ca_topic_score_codex":0.0000023225282,"about_ca_topic_score_gemma":0.0000017338383,"teacher_disagreement_score":0.930476,"about_ca_system_score_codex":0.00022167656,"about_ca_system_score_gemma":0.000042069074,"threshold_uncertainty_score":0.60293746},"labels":[],"label_agreement":null},{"id":"W3182719754","doi":"10.1002/int.22548","title":"Trusted audit with untrusted auditors: A decentralized data integrity Crowdauditing approach based on blockchain","year":2021,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":24,"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 Victoria","funders":"National University of Defense Technology; National Natural Science Foundation of China","keywords":"Computer science; Audit; Cloud computing; Computer security; Enhanced Data Rates for GSM Evolution; Blockchain; Incentive; Credibility; Crowdsourcing; External auditor; Accounting; Internal audit; Business; Operating system","score_opus":0.034155547205424186,"score_gpt":0.2848747199434247,"score_spread":0.25071917273800054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182719754","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":[0.016124366,0.00045590993,0.97441256,0.005400197,0.0025007203,0.00025714748,0.000057357473,0.000121514095,0.0006702147],"genre_scores_gemma":[0.948826,0.000054045933,0.04988122,0.00043501423,0.0006265278,0.000020533073,0.000058689802,0.000020607862,0.00007735341],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996554,0.00027967492,0.0010571111,0.00058158784,0.0012127819,0.0003148291],"domain_scores_gemma":[0.9946875,0.00037416798,0.0010693693,0.0012798301,0.0024236578,0.0001654489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013511769,0.000267535,0.00045037697,0.0004384404,0.00012319059,0.00037292688,0.003929975,0.0002025811,0.000040369232],"category_scores_gemma":[0.00070761656,0.00021045977,0.00013327981,0.00066950056,0.00010230352,0.00023933164,0.00040394187,0.00082923484,0.000017733633],"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":[0.0017471246,0.010889187,0.01090913,0.00045712185,0.0060808277,0.0053748903,0.0026361744,0.21301375,0.0034448456,0.57196677,0.07848668,0.094993524],"study_design_scores_gemma":[0.0015626606,0.00020898887,0.00021374201,0.0006307731,0.000048475904,0.0017597545,0.00074916804,0.9525015,0.0057667587,0.0007240305,0.035467025,0.00036713592],"about_ca_topic_score_codex":0.00005484344,"about_ca_topic_score_gemma":0.000015030482,"teacher_disagreement_score":0.93270165,"about_ca_system_score_codex":0.00030939645,"about_ca_system_score_gemma":0.00061626855,"threshold_uncertainty_score":0.85823005},"labels":[],"label_agreement":null},{"id":"W3185287204","doi":"10.1002/int.22562","title":"A new method for deriving priority from dual hesitant fuzzy preference relations","year":2021,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":6,"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 Alberta","funders":"","keywords":"Consistency (knowledge bases); Preference; Dual (grammatical number); Preference relation; Computer science; Group decision-making; Fuzzy logic; Probabilistic logic; Property (philosophy); Basis (linear algebra); Data mining; Mathematical optimization; Artificial intelligence; Mathematics; Statistics","score_opus":0.2587212012644264,"score_gpt":0.4737449613594993,"score_spread":0.2150237600950729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185287204","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":[0.05025265,0.0012778133,0.9355228,0.0013587759,0.010552381,0.00021190307,0.00010967098,0.000015993031,0.00069802115],"genre_scores_gemma":[0.72636485,0.00004132793,0.2687466,0.00013334781,0.0018843049,0.000006980648,0.000010544855,0.000022110056,0.0027899542],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9927076,0.00060127344,0.0027053624,0.00048319853,0.0032640183,0.00023854515],"domain_scores_gemma":[0.9843639,0.007528713,0.0017831438,0.00047181253,0.005578425,0.00027399015],"candidate_categories":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004445973,0.00021150222,0.0005907013,0.0005427096,0.000116287265,0.0012619129,0.0014387777,0.0001380626,0.00096890697],"category_scores_gemma":[0.015386706,0.0001644571,0.00046335915,0.00038186702,0.000023389397,0.0007021474,0.00026442172,0.00031662072,0.00017909809],"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":[0.0018399367,0.0007961037,0.0312599,0.00003232156,0.002705542,0.0015713333,0.0111539345,0.031840477,0.130452,0.06331425,0.12044652,0.6045877],"study_design_scores_gemma":[0.0037234924,0.0004516122,0.026362134,0.0026526118,0.00026923785,0.003852376,0.012665818,0.14165874,0.049089305,0.24818854,0.5098765,0.0012096588],"about_ca_topic_score_codex":0.00020676332,"about_ca_topic_score_gemma":0.00009593282,"teacher_disagreement_score":0.6761122,"about_ca_system_score_codex":0.0003387275,"about_ca_system_score_gemma":0.00076363026,"threshold_uncertainty_score":0.9999443},"labels":[],"label_agreement":null},{"id":"W3201288682","doi":"10.1002/int.22676","title":"EviChain: A scalable blockchain for accountable intelligent surveillance systems","year":2021,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Blockchain Technology Applications and Security","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 Victoria","funders":"","keywords":"Computer science; Scalability; Computer security; Byzantine fault tolerance; Context (archaeology); Overhead (engineering); Cryptography; Block (permutation group theory); Blockchain; Authentication (law); Distributed computing; Tamper resistance; Process (computing); Exploit; Vulnerability (computing); Embedded system; Fault tolerance; Database; Operating system","score_opus":0.021129659900494775,"score_gpt":0.280248707412957,"score_spread":0.25911904751246223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201288682","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":[0.010733085,0.010377827,0.96676034,0.0030984858,0.007957009,0.00045774857,0.00004033177,0.00009382588,0.000481373],"genre_scores_gemma":[0.99099845,0.00059597444,0.005948133,0.00023909615,0.0008461803,0.00010646275,0.000010499146,0.00002430473,0.0012309304],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99679714,0.00015544164,0.0013954663,0.0004311546,0.0008437228,0.00037704702],"domain_scores_gemma":[0.9937501,0.00046497112,0.0009489358,0.0006429042,0.0040048785,0.00018820832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019011355,0.00024475087,0.00052770926,0.00038947767,0.0001322298,0.0005367037,0.0023852803,0.00020209917,0.000024607658],"category_scores_gemma":[0.0004955041,0.00022190854,0.00028668958,0.00046998577,0.000064444364,0.00021995728,0.00029575746,0.00033014527,0.000038871814],"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":[0.00012162844,0.0010642126,0.0033060182,0.00025548454,0.0015569835,0.00035239686,0.0010368812,0.04636655,0.0035284453,0.89809644,0.025520304,0.018794626],"study_design_scores_gemma":[0.00094728277,0.00027253476,0.00008111173,0.00064308406,0.000033179844,0.00394274,0.001687465,0.41698486,0.024881043,0.00711514,0.54279387,0.00061771995],"about_ca_topic_score_codex":0.000093015224,"about_ca_topic_score_gemma":0.000021032425,"teacher_disagreement_score":0.9802653,"about_ca_system_score_codex":0.00037528694,"about_ca_system_score_gemma":0.00041962764,"threshold_uncertainty_score":0.90491676},"labels":[],"label_agreement":null},{"id":"W4200120261","doi":"10.1002/int.22477","title":"Issue Information","year":2021,"lang":"en","type":"paratext","venue":"International Journal of Intelligent Systems","topic":"Corporate Taxation and Avoidance","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":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.020679507946288245,"score_gpt":0.26071741615497185,"score_spread":0.2400379082086836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200120261","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002202636,0.008565973,0.04941161,0.004734194,0.30881283,0.00061958306,0.00012519705,0.00005803155,0.6254699],"genre_scores_gemma":[0.37035194,0.005153841,0.00033497575,0.012880246,0.13886969,0.000049790367,0.0034323395,0.00018377275,0.4687434],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970084,0.000024252671,0.0014976051,0.00013582666,0.0011686463,0.00016530004],"domain_scores_gemma":[0.9908904,0.00004725648,0.004242527,0.00017354557,0.0046228194,0.000023442713],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00048982096,0.0002659511,0.0004649813,0.00095746655,0.000051424875,0.0015558945,0.0008737678,0.0001856615,0.00752375],"category_scores_gemma":[0.00026867093,0.00023357404,0.00029229475,0.00026782698,0.000028904327,0.002185698,0.00015180111,0.0004311425,0.030829374],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055827866,0.00005939955,0.000085023086,0.0002645185,0.0002963575,0.00004942616,0.00006328704,0.0039731236,0.00005357382,0.002779348,0.9849714,0.007348723],"study_design_scores_gemma":[0.00023980872,0.000009360082,0.000024190116,0.0010987862,0.000036479683,0.00008976534,0.0006189305,0.0008891483,0.00014633761,0.00005321124,0.99657124,0.00022275985],"about_ca_topic_score_codex":0.00026187717,"about_ca_topic_score_gemma":0.000008665312,"teacher_disagreement_score":0.3681493,"about_ca_system_score_codex":0.00021672323,"about_ca_system_score_gemma":0.00019269921,"threshold_uncertainty_score":0.9994806},"labels":[],"label_agreement":null},{"id":"W4229799740","doi":"10.1002/int.20018","title":"Belief, plausibility, and probability measures on interval-valued type 2 fuzzy sets","year":2004,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":5,"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 Toronto","funders":"","keywords":"Mathematics; Interval (graph theory); Axiom; Type (biology); Measure (data warehouse); Fuzzy set; Fuzzy logic; Discrete mathematics; Probability measure; Set (abstract data type); Fuzzy measure theory; Representation (politics); Analogy; Fuzzy number; Combinatorics; Artificial intelligence; Computer science; Data mining","score_opus":0.2532592522639635,"score_gpt":0.4500281764192226,"score_spread":0.1967689241552591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229799740","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":[0.9541972,0.00081905327,0.029823197,0.0014655199,0.011568314,0.00044508846,0.000033144486,0.000027309927,0.0016211423],"genre_scores_gemma":[0.9966126,0.000054268814,0.0023546016,0.00023106854,0.0004987703,0.000004139043,0.0000017504135,0.000019706295,0.00022308214],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9914304,0.00051979895,0.0026020212,0.0005414822,0.004652019,0.00025427583],"domain_scores_gemma":[0.9920757,0.0011954402,0.0013882247,0.00056404877,0.004515412,0.00026121948],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008419464,0.00027628292,0.00061987014,0.0008259832,0.00009079275,0.00089171325,0.001827077,0.00013516247,0.00017130775],"category_scores_gemma":[0.011522293,0.00018565828,0.00027624963,0.00035508416,0.00015450355,0.0005220127,0.00031775437,0.00040612894,0.0002825346],"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":[0.0132204825,0.0054916204,0.102342345,0.00019088556,0.0030581977,0.002357653,0.020327484,0.15628617,0.02743182,0.13163102,0.02470816,0.5129542],"study_design_scores_gemma":[0.009919491,0.0059643085,0.053377226,0.0073241186,0.00019932925,0.009568668,0.010498288,0.010571846,0.032846127,0.7226929,0.13454066,0.002497007],"about_ca_topic_score_codex":0.000103130486,"about_ca_topic_score_gemma":0.00004459755,"teacher_disagreement_score":0.5910619,"about_ca_system_score_codex":0.00056325074,"about_ca_system_score_gemma":0.00022986675,"threshold_uncertainty_score":0.99680406},"labels":[],"label_agreement":null},{"id":"W4251464076","doi":"10.1002/int.22002","title":"Issue Information","year":2018,"lang":"en","type":"paratext","venue":"International Journal of Intelligent Systems","topic":"Human auditory perception and evaluation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Alberta; University of Calgary","funders":"","keywords":"Computer science; Data science","score_opus":0.024531415310985006,"score_gpt":0.30293360298314215,"score_spread":0.27840218767215713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251464076","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002554413,0.002994438,0.1367413,0.00021331206,0.5223054,0.00048202608,0.00025823098,0.00010879803,0.3343421],"genre_scores_gemma":[0.22033957,0.019288782,0.00081606436,0.0009508218,0.18678075,0.00007929199,0.0024317852,0.00035289582,0.5689601],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973058,0.00006305915,0.0013419241,0.00007616865,0.0010669196,0.0001461022],"domain_scores_gemma":[0.9972181,0.00003769475,0.00068320957,0.00014660115,0.0018154286,0.00009896442],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00051466754,0.00023594014,0.0003452006,0.0007525193,0.000029016483,0.00031290154,0.0006434807,0.0002646979,0.071396425],"category_scores_gemma":[0.0000619173,0.00021481316,0.00020238348,0.000073911455,0.000036688296,0.00063990906,0.000027610145,0.00038326925,0.2673572],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018447567,0.000011749724,0.0000022645652,0.00009506322,0.00032764953,0.0000028911904,0.0006722089,0.04720924,0.000092387316,0.000006914306,0.9475965,0.003964721],"study_design_scores_gemma":[0.00016882391,0.0000644617,0.000012391495,0.00060478377,0.000025886131,0.00011700439,0.00034833944,0.008395607,0.00036925133,0.000008891521,0.9896958,0.00018877644],"about_ca_topic_score_codex":0.0000032735572,"about_ca_topic_score_gemma":0.0000015376033,"teacher_disagreement_score":0.33552465,"about_ca_system_score_codex":0.000657697,"about_ca_system_score_gemma":0.00009428485,"threshold_uncertainty_score":0.9294524},"labels":[],"label_agreement":null},{"id":"W4251659863","doi":"10.1002/int.20016","title":"Associations and rules in data mining: A link analysis","year":2004,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":11,"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 Alberta","funders":"","keywords":"Data mining; Computer science; Consistency (knowledge bases); Set (abstract data type); Cluster analysis; Relevance (law); Quality (philosophy); Rough set; Association rule learning; Fuzzy rule; Fuzzy logic; Rule-based system; Block (permutation group theory); Fuzzy set; Artificial intelligence; Mathematics","score_opus":0.06573665888699667,"score_gpt":0.32312337699832433,"score_spread":0.25738671811132763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251659863","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":[0.12137647,0.0031426216,0.8621826,0.008932039,0.0029500197,0.0001308297,0.000084385174,0.000027611317,0.0011734009],"genre_scores_gemma":[0.9807867,0.00035369847,0.018470632,0.00010003331,0.0002345733,0.000001022861,0.00002027274,0.0000036150677,0.000029451787],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984263,0.00005720732,0.00068871764,0.00018132183,0.0005305462,0.000115916446],"domain_scores_gemma":[0.9987308,0.00012999358,0.0004885061,0.00027309865,0.00030929703,0.000068307025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008866712,0.00008479963,0.00024609533,0.0006214157,0.00002804998,0.00033436317,0.0016468964,0.000048059606,0.0000036548954],"category_scores_gemma":[0.0001922791,0.00006793719,0.00007885272,0.0003415809,0.000020025354,0.0005527444,0.00026285456,0.00012651026,0.000008076823],"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":[0.00008838647,0.0013224217,0.24560605,0.00004119262,0.010262792,0.002100815,0.01711436,0.20687564,0.0002395639,0.31037733,0.0030099477,0.2029615],"study_design_scores_gemma":[0.00419765,0.0006122367,0.22461605,0.0012285648,0.00065047445,0.002043337,0.0028599265,0.6859928,0.00036353164,0.036599085,0.0394738,0.0013625869],"about_ca_topic_score_codex":0.00024120645,"about_ca_topic_score_gemma":0.00008063022,"teacher_disagreement_score":0.8594102,"about_ca_system_score_codex":0.00016604245,"about_ca_system_score_gemma":0.00009390955,"threshold_uncertainty_score":0.32242715},"labels":[],"label_agreement":null},{"id":"W4283710515","doi":"10.1002/int.22947","title":"A data variability index: Quantifying complexity of models and analyzing adversarial data","year":2022,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Data mining; Transformation (genetics); Algorithm; Lipschitz continuity; Piecewise; Mathematical optimization; Mathematics","score_opus":0.24637303769046864,"score_gpt":0.3822583595158197,"score_spread":0.13588532182535107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283710515","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":[0.009627529,0.00040543132,0.98231745,0.00047627147,0.00664019,0.00013497382,0.00022556055,0.000019578352,0.00015304722],"genre_scores_gemma":[0.9824995,0.00004085262,0.016959948,0.000029253059,0.0003789606,0.0000015022922,0.00006457126,0.00001086478,0.000014553513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959131,0.00073788455,0.0012420487,0.00048047563,0.0014478742,0.00017860594],"domain_scores_gemma":[0.99595153,0.00058617763,0.0014762372,0.0013348813,0.0005569923,0.00009418155],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.006336932,0.00015529383,0.00041688242,0.00037606843,0.00015896728,0.00019481004,0.008197879,0.00004230212,0.000032746197],"category_scores_gemma":[0.00085453404,0.00014936754,0.00007102151,0.00027635254,0.00012375307,0.0019028245,0.0077204695,0.0006077849,8.233522e-7],"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":[0.00026553485,0.0002565545,0.02078684,0.00006440338,0.000776378,0.0001130214,0.0013288679,0.8468767,0.00021709455,0.121014215,0.00056106516,0.007739324],"study_design_scores_gemma":[0.00038981545,0.000060670536,0.00034066674,0.000080063546,0.000028212238,0.00038968204,0.00047903176,0.99082804,0.000019162766,0.004308801,0.0029467628,0.00012910739],"about_ca_topic_score_codex":0.0005925301,"about_ca_topic_score_gemma":0.000018120243,"teacher_disagreement_score":0.97287196,"about_ca_system_score_codex":0.00021344516,"about_ca_system_score_gemma":0.0002684577,"threshold_uncertainty_score":0.99716824},"labels":[],"label_agreement":null},{"id":"W4322488172","doi":"10.1155/2023/2467539","title":"CNFRD: A Few‐Shot Rumor Detection Framework via Capsule Network for COVID‐19","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Sichuan Province Science and Technology Support Program; Xihua University; National Natural Science Foundation of China","keywords":"Rumor; Computer science; Artificial intelligence; Class (philosophy); Metric (unit); Data mining","score_opus":0.09118930593706516,"score_gpt":0.40680201849015324,"score_spread":0.3156127125530881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322488172","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":[0.07337432,0.0005194689,0.87846166,0.0071094204,0.03246258,0.00095698255,0.000058380137,0.00018530259,0.0068718633],"genre_scores_gemma":[0.99194676,0.00038399402,0.0003632476,0.00072882767,0.0044991625,0.000009714424,0.000012814964,0.000015834039,0.002039628],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974541,0.00016029789,0.00085525884,0.000109773326,0.0010896818,0.00033090034],"domain_scores_gemma":[0.9973491,0.00046310743,0.00078047236,0.00010245854,0.0009202764,0.0003845873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029031837,0.00012131939,0.00022401028,0.00036854242,0.00036835484,0.0003363957,0.00056643813,0.00014445622,0.00034187766],"category_scores_gemma":[0.0027024979,0.0001073666,0.00022212502,0.00040549453,0.00006845212,0.00043199363,0.000031420757,0.00020994744,0.00022438775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014201022,0.00036040324,0.0026876696,0.0002926602,0.0020517374,0.00020079211,0.16000055,0.20046195,0.0018995606,0.2048606,0.31645358,0.10931041],"study_design_scores_gemma":[0.0006036635,0.00025318903,0.00044003164,0.00040794667,0.000037297952,0.00019570872,0.028308628,0.0122802155,0.00093268004,0.0135138165,0.9427177,0.0003091255],"about_ca_topic_score_codex":0.000598876,"about_ca_topic_score_gemma":0.00018948555,"teacher_disagreement_score":0.9185724,"about_ca_system_score_codex":0.0006165682,"about_ca_system_score_gemma":0.00037102308,"threshold_uncertainty_score":0.4378283},"labels":[],"label_agreement":null},{"id":"W4362575694","doi":"10.1155/2023/8616939","title":"Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Gastrointestinal Bleeding Diagnosis and Treatment","field":"Medicine","cited_by":20,"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":"Ministry of Education – Kingdom of Saudi Arabi","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Principal component analysis; Artificial neural network; Support vector machine; Histogram; Dimensionality reduction; Discrete wavelet transform; Wavelet transform; Wavelet; Image (mathematics)","score_opus":0.02506109929127648,"score_gpt":0.3188664433560617,"score_spread":0.2938053440647852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362575694","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":[0.7397646,0.00032514756,0.25333378,0.0021953497,0.001975579,0.0018887615,0.00033271266,0.00010866583,0.000075462835],"genre_scores_gemma":[0.99259347,0.000055251796,0.0062099025,0.00006028239,0.00074766757,0.00016084599,0.000048406484,0.00003310276,0.00009106537],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982378,0.00003467781,0.00070468074,0.00019202399,0.00062781177,0.00020304539],"domain_scores_gemma":[0.9965063,0.0010044872,0.0006481318,0.00012241733,0.0015496267,0.00016905846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005167198,0.00018786747,0.00035309844,0.0006411104,0.000055242508,0.00006351831,0.00017972635,0.000025367455,0.0000062602535],"category_scores_gemma":[0.0013975817,0.00014532782,0.00042379875,0.00009314387,0.00004216651,0.000087130444,0.00002041196,0.00013150467,0.000003819995],"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":[0.03699249,0.0051030642,0.41873646,0.0022209664,0.0013832281,0.0018910619,0.00022635955,0.011829334,0.4353688,0.0010995428,0.027727375,0.057421304],"study_design_scores_gemma":[0.0033536735,0.012985742,0.079644255,0.016538896,0.0004549862,0.0013345706,0.00020921521,0.012538537,0.8705503,0.00040315976,0.001738401,0.0002483045],"about_ca_topic_score_codex":0.000042337775,"about_ca_topic_score_gemma":6.993082e-7,"teacher_disagreement_score":0.43518144,"about_ca_system_score_codex":0.00024766652,"about_ca_system_score_gemma":0.000104737155,"threshold_uncertainty_score":0.5926297},"labels":[],"label_agreement":null},{"id":"W4378904793","doi":"10.1155/2023/2662719","title":"Analysis of Histopathological Images for Early Diagnosis of Oral Squamous Cell Carcinoma by Hybrid Systems Based on CNN Fusion Features","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"AI in cancer detection","field":"Computer Science","cited_by":33,"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":"Najran University","keywords":"Basal cell; Stage (stratigraphy); Computer science; Cancer; Medicine; Segmentation; Artificial intelligence; Pathology; Internal medicine","score_opus":0.022708683803372168,"score_gpt":0.2799947814244216,"score_spread":0.2572860976210494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378904793","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":[0.5799267,0.00092108577,0.41319916,0.00025270024,0.005004136,0.0003288652,0.00020881309,0.000036851536,0.00012167414],"genre_scores_gemma":[0.99900275,0.00006226484,0.00044218785,0.000029378592,0.0001617777,0.000044991284,0.000018569253,0.0000141725295,0.0002239245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689543,0.00021671863,0.0011533095,0.00030019536,0.001237331,0.00019700977],"domain_scores_gemma":[0.9959949,0.000790552,0.0014733238,0.00031324071,0.0013377608,0.00009021429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001169311,0.00018718667,0.00058302307,0.0013430761,0.000045948847,0.00011379513,0.0012631962,0.00008784637,0.000010462877],"category_scores_gemma":[0.00021126562,0.00015171534,0.00051889854,0.0006660739,0.000057029738,0.00021269552,0.0000939421,0.00017577814,0.000005458423],"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":[0.0014162319,0.0014723853,0.18572865,0.00061430794,0.0013025125,0.0005040768,0.0014768214,0.6962149,0.028255984,0.0012336441,0.066680774,0.015099696],"study_design_scores_gemma":[0.001672338,0.0041483855,0.037109956,0.000495935,0.00059574534,0.00016464831,0.0003914664,0.653331,0.29649,0.00011619073,0.0049476074,0.00053673604],"about_ca_topic_score_codex":0.0004099389,"about_ca_topic_score_gemma":0.0000026588232,"teacher_disagreement_score":0.41907603,"about_ca_system_score_codex":0.00039244007,"about_ca_system_score_gemma":0.00007403755,"threshold_uncertainty_score":0.6186772},"labels":[],"label_agreement":null},{"id":"W4385693906","doi":"10.1155/2023/6442756","title":"Towards Diagnostic Aided Systems in Coronary Artery Disease Detection: A Comprehensive Multiview Survey of the State of the Art","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":61,"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":"CAD; Support vector machine; Computer science; Machine learning; Artificial intelligence; Field (mathematics); Random forest; Artificial neural network; Coronary artery disease; Feature extraction; Data mining; Data extraction; Pattern recognition (psychology); Medicine; MEDLINE; Mathematics; Internal medicine; Engineering drawing","score_opus":0.17777504142200737,"score_gpt":0.44229123227928785,"score_spread":0.2645161908572805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385693906","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":[0.9723242,0.0024279875,0.0008793735,0.0008835783,0.021303324,0.0017484524,0.00035801425,0.000018667815,0.000056423818],"genre_scores_gemma":[0.99817,0.0008714113,0.0000032895873,0.000121009165,0.00031300154,0.00008821185,0.0000099031395,0.000030102976,0.00039303582],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99161226,0.0032774208,0.0031193446,0.0002142854,0.0014200424,0.00035667967],"domain_scores_gemma":[0.9876502,0.00542558,0.0025233193,0.0004493939,0.0037921823,0.00015931237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026578067,0.0002201653,0.0006284567,0.0003805762,0.00016467138,0.000021271297,0.0011796929,0.00011541633,0.0000384916],"category_scores_gemma":[0.0049087345,0.0001348439,0.0002853156,0.00078233477,0.0002013614,0.00015808515,0.0003055274,0.0008268937,0.00011279989],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007350236,0.00022000419,0.9160132,0.0011992824,0.0003803982,0.00014497773,0.004772768,0.06827187,0.00066546083,0.0002599203,0.0018347256,0.0055023683],"study_design_scores_gemma":[0.00032291035,0.00011557089,0.96153146,0.009507188,0.00003602101,0.000071132345,0.0056713247,0.01919356,0.0009559675,0.00036081334,0.002069757,0.00016430659],"about_ca_topic_score_codex":0.009146815,"about_ca_topic_score_gemma":0.0042860904,"teacher_disagreement_score":0.049078308,"about_ca_system_score_codex":0.0005982253,"about_ca_system_score_gemma":0.0009484946,"threshold_uncertainty_score":0.99745136},"labels":[],"label_agreement":null},{"id":"W4388405697","doi":"10.1155/2023/3044155","title":"A New Multinetwork Mean Distillation Loss Function for Open‐World Domain Incremental Object Detection","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Program of Guizhou Province; Petroleum Technology Research Centre; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Pascal (unit); Computer science; Distillation; Object detection; Artificial intelligence; Benchmark (surveying); Detector; Pattern recognition (psychology); Computer vision; Chromatography","score_opus":0.03736207295549617,"score_gpt":0.32318409551757526,"score_spread":0.2858220225620791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388405697","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":[0.0063513163,0.00014267018,0.9847224,0.00075271534,0.007066423,0.00067789876,0.000008362148,0.000094281495,0.00018396894],"genre_scores_gemma":[0.9797234,0.000052847543,0.016842967,0.000094118644,0.0022953192,0.000061912426,0.000024526009,0.000022458127,0.00088242715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785215,0.00007332341,0.00090973097,0.0002888983,0.00064914825,0.0002267285],"domain_scores_gemma":[0.9978077,0.00030274174,0.00088235614,0.00024150805,0.00062793784,0.0001377887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080802006,0.00016683339,0.00023810066,0.00046798395,0.000145296,0.0004268912,0.0015266615,0.000049303886,0.000011049151],"category_scores_gemma":[0.000061691324,0.00015142589,0.0001627519,0.0007672934,0.00001923772,0.0008573989,0.00027560003,0.0001626122,0.00006153211],"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":[0.0018802644,0.0002619367,0.0040459866,0.000058924106,0.0011455057,0.00008882968,0.0015629799,0.40098667,0.017212676,0.1495328,0.04052016,0.38270324],"study_design_scores_gemma":[0.0034979098,0.0009683933,0.0043044416,0.0007496591,0.0000804928,0.00088016764,0.0006620802,0.5843806,0.02159339,0.065369405,0.31662267,0.00089078624],"about_ca_topic_score_codex":0.0000762453,"about_ca_topic_score_gemma":0.00013536795,"teacher_disagreement_score":0.9733721,"about_ca_system_score_codex":0.00038921027,"about_ca_system_score_gemma":0.00007844014,"threshold_uncertainty_score":0.6174969},"labels":[],"label_agreement":null},{"id":"W4388445625","doi":"10.1155/2023/3578867","title":"Hybrid Time‐Series Prediction Method Based on Entropy Fusion Feature","year":2023,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"Major Science and Technology Projects in Yunnan Province","keywords":"Hilbert–Huang transform; Computer science; Subsequence; Pattern recognition (psychology); Artificial intelligence; Entropy (arrow of time); Feature (linguistics); Time series; Series (stratigraphy); Algorithm; Data mining; Machine learning; Mathematics","score_opus":0.009320707029338618,"score_gpt":0.29155278498199394,"score_spread":0.2822320779526553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388445625","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":[0.13028252,0.001026312,0.82008135,0.004660534,0.030511929,0.0012303117,0.0006244632,0.0027888399,0.008793711],"genre_scores_gemma":[0.9916101,0.0006611871,0.0045043854,0.000119600314,0.0018832468,0.000041903993,0.00012155009,0.000078090816,0.0009799115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818325,0.00010226339,0.0005748203,0.00012776045,0.0008476426,0.00016426413],"domain_scores_gemma":[0.99892443,0.00022802579,0.0002136829,0.00015498012,0.00039156317,0.00008734172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007321573,0.00018520937,0.00026305811,0.00070445536,0.00003149226,0.000112652844,0.00044371342,0.00008068738,0.0001226784],"category_scores_gemma":[0.00018568094,0.00015623114,0.0001833565,0.00015409784,0.000013919301,0.000208349,0.000029694254,0.00032113784,0.00017853474],"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":[0.00019227999,0.000105203406,0.0014253578,0.000084969186,0.00035502302,0.00032236247,0.00013778213,0.6306156,0.019318752,0.00085068354,0.33687082,0.009721206],"study_design_scores_gemma":[0.0002775892,0.00027713136,0.00060694054,0.0007787228,0.000025622317,0.00030746113,0.000047791757,0.7634899,0.109309465,0.00024615155,0.124454886,0.00017833443],"about_ca_topic_score_codex":0.000010101718,"about_ca_topic_score_gemma":5.5773876e-7,"teacher_disagreement_score":0.8613276,"about_ca_system_score_codex":0.0003082181,"about_ca_system_score_gemma":0.000025595213,"threshold_uncertainty_score":0.63709205},"labels":[],"label_agreement":null},{"id":"W4391162811","doi":"10.1155/2024/5780186","title":"Constructing Perturbation Matrices of Prototypes for Enhancing the Performance of Fuzzy Decoding Mechanism","year":2024,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":2,"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 Alberta","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Decoding methods; Perturbation (astronomy); Fuzzy logic; Mechanism (biology); Computer science; Algebra over a field; Algorithm; Theoretical computer science; Mathematics; Artificial intelligence; Pure mathematics; Physics","score_opus":0.022396438670897793,"score_gpt":0.28117350499639704,"score_spread":0.25877706632549924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391162811","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":[0.1416453,0.0020939605,0.8496209,0.00041415828,0.0052310447,0.0003717649,0.0000071732134,0.000015013601,0.0006007369],"genre_scores_gemma":[0.98904115,0.00019117368,0.010361108,0.000012194619,0.00033772495,0.000011388986,7.341953e-7,0.000005621031,0.00003889811],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984568,0.000041847255,0.00083203067,0.00010482556,0.00046990332,0.00009462229],"domain_scores_gemma":[0.99809307,0.00043470965,0.0006503243,0.00009952176,0.00069937797,0.00002297577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011387089,0.00008199399,0.00018050108,0.00022818508,0.00003845888,0.00016252593,0.00079358247,0.000034973622,0.0000038508583],"category_scores_gemma":[0.00010823789,0.00004977425,0.00013836248,0.00013925723,0.000027221147,0.00039500467,0.000060512855,0.00010105243,0.000001786097],"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":[0.00007273925,0.000052409727,0.0006341712,0.00075892144,0.00049150194,0.000013092178,0.00384439,0.004780952,0.01311766,0.8880396,0.0001340015,0.08806055],"study_design_scores_gemma":[0.00022730797,0.0006066763,0.000038844817,0.003250661,0.000044022745,0.0008294884,0.0025164767,0.7325737,0.24575919,0.012575246,0.0014074579,0.00017092998],"about_ca_topic_score_codex":0.000014165125,"about_ca_topic_score_gemma":0.000002182276,"teacher_disagreement_score":0.8754644,"about_ca_system_score_codex":0.00008659526,"about_ca_system_score_gemma":0.000101683996,"threshold_uncertainty_score":0.20297351},"labels":[],"label_agreement":null},{"id":"W4398268938","doi":"10.1155/2024/8014111","title":"An Intelligent COVID-19-Related Arabic Text Detection Framework Based on Transfer Learning Using Context Representation","year":2024,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"King Saud University","keywords":"Coronavirus disease 2019 (COVID-19); Transfer of learning; Context (archaeology); Arabic; Computer science; Representation (politics); Artificial intelligence; Natural language processing; Linguistics; Medicine; Geography","score_opus":0.06537820810809168,"score_gpt":0.3997826155834569,"score_spread":0.3344044074753652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398268938","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":[0.2639571,0.0014148182,0.71990246,0.00613617,0.007834845,0.0005209712,0.0000102238355,0.00016474309,0.00005868029],"genre_scores_gemma":[0.99457407,0.00022695035,0.00024948124,0.0035129965,0.0011857619,0.00001638007,0.000026206624,0.00007872233,0.00012941065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956495,0.00048250068,0.001559641,0.00048530783,0.0015505549,0.00027246465],"domain_scores_gemma":[0.9963918,0.001537454,0.0003718655,0.00029454078,0.00093706226,0.00046730094],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015224344,0.00032087017,0.00052203384,0.0015857392,0.0001243964,0.0004212795,0.00036820266,0.0002811572,0.00045588455],"category_scores_gemma":[0.0019735133,0.00027913204,0.0004883386,0.0005583239,0.00009364632,0.0003681379,0.000019529265,0.001188913,0.00010323873],"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":[0.0016072657,0.0006427183,0.003110918,0.00035196575,0.0010272142,0.0013567415,0.0037755969,0.9233905,0.01957338,0.0016078916,0.0004021912,0.043153636],"study_design_scores_gemma":[0.0011150541,0.0018087772,0.00021451068,0.0075088814,0.00041388595,0.0021100491,0.0030220635,0.8830581,0.04474219,0.00045159418,0.055144724,0.00041019236],"about_ca_topic_score_codex":0.00054146483,"about_ca_topic_score_gemma":0.000016720971,"teacher_disagreement_score":0.730617,"about_ca_system_score_codex":0.0026569061,"about_ca_system_score_gemma":0.0007432169,"threshold_uncertainty_score":0.9999661},"labels":[],"label_agreement":null},{"id":"W4400462316","doi":"10.1155/2024/2960447","title":"Deep Reinforcement Learning‐Based Multireconfigurable Intelligent Surface for MEC Offloading","year":2024,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Advanced Wireless Communication Technologies","field":"Engineering","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":"École de Technologie Supérieure","funders":"Natural Science Foundation of Zhejiang Province; Fundamental Research Funds for the Central Universities; Sichuan Province Science and Technology Support Program; Natural Science Foundation of Ningbo; King Saud University; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Server; Leverage (statistics); Optimization problem; Computation offloading; Mobile edge computing; Distributed computing; Wireless; Edge computing; Enhanced Data Rates for GSM Evolution; Mathematical optimization; Artificial intelligence; Computer network; Algorithm; Telecommunications","score_opus":0.026691265297336345,"score_gpt":0.2905613476179283,"score_spread":0.26387008232059195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400462316","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":[0.003789458,0.010526175,0.9788908,0.00025100532,0.0052270982,0.00030118783,0.0000045303727,0.00039356534,0.0006161893],"genre_scores_gemma":[0.99372554,0.0020172293,0.003409792,0.000014985114,0.00021830092,0.00003310191,0.000018638406,0.00005712091,0.000505273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805254,0.00003774212,0.0010573957,0.00014960238,0.00046952054,0.00023319392],"domain_scores_gemma":[0.998284,0.0005317052,0.00024876048,0.00021891299,0.00064182305,0.00007482777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054670457,0.00021761702,0.00030109516,0.00043726055,0.000053406744,0.00023040333,0.00088312157,0.00011060598,0.000079852405],"category_scores_gemma":[0.00023706582,0.00019835324,0.00024478172,0.00016193769,0.000042801195,0.00030457112,0.000046744077,0.00044606513,0.00006454986],"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":[0.000031254232,0.000018911194,0.000040113613,0.00015045091,0.00040139764,0.00001632061,0.00025361014,0.9743748,0.003928755,0.0032097402,0.0006919259,0.01688273],"study_design_scores_gemma":[0.00014664039,0.00008551523,0.000001809309,0.0007266693,0.000015706919,0.000053163858,0.0007772874,0.815044,0.091702126,0.00017672744,0.0911063,0.0001640849],"about_ca_topic_score_codex":0.0000145391605,"about_ca_topic_score_gemma":0.000003581025,"teacher_disagreement_score":0.9899361,"about_ca_system_score_codex":0.00070276955,"about_ca_system_score_gemma":0.00005696468,"threshold_uncertainty_score":0.808861},"labels":[],"label_agreement":null},{"id":"W4407667139","doi":"10.1155/int/7026120","title":"Neuron Segmentation via a Frequency and Spatial Domain–Integrated Encoder–Decoder Network","year":2025,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; British Columbia Innovation Council","keywords":"Computer science; Encoder; Segmentation; Domain (mathematical analysis); Frequency domain; Artificial intelligence; Computer vision; Pattern recognition (psychology); Speech recognition; Mathematics","score_opus":0.005818709018001526,"score_gpt":0.27443097600447747,"score_spread":0.26861226698647594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407667139","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":[0.156297,0.0022867592,0.83875155,0.00023107545,0.0009420268,0.00016590697,0.000003949769,0.000010177648,0.0013115499],"genre_scores_gemma":[0.99539894,0.00079735555,0.0023123017,0.00028003607,0.00060364173,0.000009163642,0.0000494068,0.000012940871,0.00053623307],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986103,0.0001266031,0.0006599328,0.000192569,0.00028064603,0.00012995249],"domain_scores_gemma":[0.9986573,0.000027499067,0.00040236255,0.00014553979,0.00071377703,0.000053531796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041357143,0.00014451158,0.00020110204,0.00019155131,0.000038984766,0.00010704698,0.00031699298,0.00009663982,0.000025587211],"category_scores_gemma":[0.00007703101,0.00012514988,0.00013456584,0.00009618308,0.000048821134,0.00001512408,0.000076167285,0.00013512665,0.0000032003543],"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":[0.0004144961,0.00021127777,0.027348218,0.00004260605,0.0017343821,0.00011266459,0.00016373809,0.0027563744,0.9107536,0.000619012,0.023313645,0.03252995],"study_design_scores_gemma":[0.0022124061,0.0013350556,0.0045103435,0.0010753568,0.00039628427,0.001429362,0.0012162478,0.012577899,0.81633115,0.006170784,0.15184674,0.0008983996],"about_ca_topic_score_codex":0.00031265273,"about_ca_topic_score_gemma":0.000113020746,"teacher_disagreement_score":0.8391019,"about_ca_system_score_codex":0.00008084968,"about_ca_system_score_gemma":0.00008705359,"threshold_uncertainty_score":0.5103464},"labels":[],"label_agreement":null},{"id":"W7092208968","doi":"10.1155/int/4962106","title":"Neural Incremental Dynamic Inversion Control of a Multirotor Robotic Airship","year":2025,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Aerospace Engineering and Energy Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Control theory (sociology); Multirotor; Inversion (geology); Artificial neural network; Robustness (evolution); Nonlinear system; Inverse","score_opus":0.007307084416572383,"score_gpt":0.22976384559399113,"score_spread":0.22245676117741875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7092208968","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":[0.5256573,0.0035301317,0.45222366,0.00016234237,0.017518569,0.00024285715,0.000015898875,0.00008235801,0.0005668896],"genre_scores_gemma":[0.9991991,0.000084254665,0.00011878055,0.00001669647,0.00019226769,0.0000065346176,0.000003802511,0.000018948549,0.000359628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849504,0.00004771753,0.00080116227,0.00008400095,0.00042519678,0.00014686129],"domain_scores_gemma":[0.9991798,0.00010360914,0.00022131954,0.000113888396,0.0003136706,0.00006772497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030208123,0.00015666914,0.00034037698,0.00038980469,0.000016543247,0.00003881162,0.00041528427,0.000080745864,0.000011634631],"category_scores_gemma":[0.000067160094,0.00013973063,0.00017712453,0.00011949181,0.000026927635,0.00013410301,0.000024589423,0.00019436677,0.00000831899],"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":[0.00006129411,0.0000357412,0.0027571458,0.00013505931,0.00057410233,0.000023889274,0.00016836046,0.9748905,0.019523786,0.00044190054,0.00062045036,0.00076778297],"study_design_scores_gemma":[0.0007910698,0.000075144606,0.000640976,0.0009936057,0.000041598323,0.000087211985,0.00067718484,0.98898053,0.0060199657,0.00001054217,0.0015482237,0.00013396036],"about_ca_topic_score_codex":0.00010162094,"about_ca_topic_score_gemma":0.000009341937,"teacher_disagreement_score":0.47354177,"about_ca_system_score_codex":0.00037772776,"about_ca_system_score_gemma":0.000028397779,"threshold_uncertainty_score":0.56980497},"labels":[],"label_agreement":null},{"id":"W79425221","doi":"","title":"A fuzzy-based multimodel system for reasoning about the number of software defects: Research Articles","year":2005,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fuzzy logic; Software; Artificial intelligence; Function (biology); Machine learning; Data mining; Software engineering","score_opus":0.0621005207150327,"score_gpt":0.3703374831358635,"score_spread":0.3082369624208308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W79425221","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":[0.12670109,0.0009072906,0.86992824,0.00045903013,0.0015186786,0.0003341835,0.000011290898,0.000054627977,0.00008559601],"genre_scores_gemma":[0.9583999,0.000016408243,0.040756993,0.000021706117,0.0006547684,0.000046650064,0.0000011908254,0.000020962381,0.00008145567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963335,0.000253112,0.00092081283,0.00021072704,0.0019305239,0.00035135972],"domain_scores_gemma":[0.99128026,0.0038788165,0.00043137083,0.000402913,0.003880229,0.00012640218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044219224,0.00013869714,0.00026399508,0.000428224,0.00010235753,0.00030705103,0.0024493502,0.0000736137,0.0000055709625],"category_scores_gemma":[0.0030077093,0.00009713963,0.00024055592,0.0003578009,0.0000822655,0.00035069918,0.00016832944,0.00037452095,0.000040387316],"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":[0.00050816167,0.0005893323,0.055891555,0.0007599905,0.0010038605,0.00013151491,0.0043164194,0.7807028,0.0031599011,0.09274707,0.005686777,0.05450259],"study_design_scores_gemma":[0.001523016,0.00028041346,0.0021188473,0.0041237785,0.000026088735,0.00094173366,0.0012968783,0.9339444,0.046378136,0.0006237438,0.008404278,0.0003386778],"about_ca_topic_score_codex":0.00008998404,"about_ca_topic_score_gemma":0.000004466652,"teacher_disagreement_score":0.8316988,"about_ca_system_score_codex":0.0005740208,"about_ca_system_score_gemma":0.0003039795,"threshold_uncertainty_score":0.45515427},"labels":[],"label_agreement":null}]}