{"meta":{"query_hash":"0a3892842697","filters":{"venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04."},"cohort_total":31,"direct_labels_cover":0,"predictions_cover":31,"exported":31,"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/0a3892842697","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Annual+Meeting+of+the+Fuzzy+Information%2C+2004.+Processing+NAFIPS+%2704."},"results":[{"id":"W1597354914","doi":"10.1109/nafips.2004.1336239","title":"Reverse engineering software architecture using rough clusters","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Software Engineering Research","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 Victoria","funders":"","keywords":"Computer science; Reverse engineering; Architecture tradeoff analysis method; Architecture; Computer architecture; Software architecture; Reference architecture; Software; Software engineering; Operating system","score_opus":0.010444362581692734,"score_gpt":0.2355774498002982,"score_spread":0.22513308721860548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597354914","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006256884,0.00011192392,0.99254864,0.00010592055,0.000012438374,0.00005824489,0.000032534652,0.0001873115,0.000686129],"genre_scores_gemma":[0.11984502,0.00035353156,0.8779125,0.000048675374,0.000027084227,0.0001901254,0.00022239659,0.00008548889,0.0013150895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763024,0.00076725945,0.00016480389,0.0003993859,0.0008864339,0.0001517583],"domain_scores_gemma":[0.9951565,0.002367462,0.00056494947,0.00087200775,0.00091488526,0.00012427775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026893662,0.00096431095,0.0013574325,0.0048989947,0.0016337204,0.0033810039,0.0017766849,0.0013699426,0.0014081267],"category_scores_gemma":[0.011063549,0.0010626293,0.0030327302,0.0036278216,0.0015495613,0.00320834,0.0024680968,0.0017571487,0.0006071907],"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":[0.00007925699,0.0000530327,0.0016740641,0.00018472319,0.00016939225,0.00015872795,0.00071773713,0.7221665,0.0025445055,0.101938024,0.0020920653,0.16822195],"study_design_scores_gemma":[0.0000143764255,0.00003887062,0.00047128665,0.000033768963,0.000039695304,0.000069649555,0.00015435464,0.8981148,0.001564831,0.095996164,0.0034652725,0.000036920574],"about_ca_topic_score_codex":0.010834821,"about_ca_topic_score_gemma":0.009200764,"teacher_disagreement_score":0.010834821,"about_ca_system_score_codex":0.002390382,"about_ca_system_score_gemma":0.0021225854,"threshold_uncertainty_score":0.021543503},"labels":[],"label_agreement":null},{"id":"W2096786223","doi":"10.1109/nafips.2004.1337433","title":"On the direct scaling approach of eliciting aggregated fuzzy information: the psychophysical view","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Advanced Text Analysis 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":"Brock University","funders":"","keywords":"Scaling; Fuzzy logic; Operator (biology); Simple (philosophy); Computer science; Fuzzy set; Artificial intelligence; Mathematics","score_opus":0.010723362078098337,"score_gpt":0.2514054052676692,"score_spread":0.24068204318957084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096786223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09538493,0.0015892694,0.8244327,0.003637617,0.0004109974,0.0004869627,0.00015330662,0.00060278905,0.07330145],"genre_scores_gemma":[0.72905695,0.0010798221,0.26344255,0.0014126034,0.00024136218,0.0008201639,0.00007031553,0.00013333699,0.0037429873],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967765,0.0013779803,0.00013497911,0.0006186012,0.0010180954,0.00007383757],"domain_scores_gemma":[0.9893561,0.006969036,0.0007229235,0.0019489973,0.0008007782,0.00020208475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003670526,0.0009040931,0.00040276753,0.0009786782,0.00054827274,0.0020659785,0.0010889356,0.0012888365,0.006613118],"category_scores_gemma":[0.018706108,0.0003799664,0.0007024729,0.00043401797,0.003981192,0.0049910354,0.0019341451,0.0017226549,0.0007376767],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008883772,0.00052657875,0.0028274697,0.001596809,0.00015115336,0.0004043477,0.0032184434,0.009866813,0.20612644,0.53668493,0.003040099,0.23466845],"study_design_scores_gemma":[0.00023896428,0.0016059277,0.0101367645,0.00033234255,0.00013835957,0.0015329833,0.0011178204,0.079240106,0.08140557,0.80368567,0.020349458,0.00021603245],"about_ca_topic_score_codex":0.00034900432,"about_ca_topic_score_gemma":0.0002556218,"teacher_disagreement_score":0.006613118,"about_ca_system_score_codex":0.00059642486,"about_ca_system_score_gemma":0.00045487427,"threshold_uncertainty_score":0.022123039},"labels":[],"label_agreement":null},{"id":"W2097021024","doi":"10.1109/nafips.2004.1337440","title":"Fuzzy modeling and simulation of a single item inventory system with variable demand","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Variable (mathematics); Fuzzy logic; Fuzzy set; Industrial engineering; Artificial intelligence; Engineering; Mathematics","score_opus":0.014067613695828655,"score_gpt":0.2075796699162594,"score_spread":0.19351205622043074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097021024","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7500989,0.00047100426,0.22117779,0.00033948934,0.00009536622,0.0001762617,0.0007849764,0.0008040765,0.026052145],"genre_scores_gemma":[0.9887129,0.00011721127,0.008746287,0.000016506196,0.000006960288,0.000058853893,0.00015014228,0.00001258911,0.0021784762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979657,0.00005989572,0.000013591181,0.00002604287,0.000070055314,0.00003383534],"domain_scores_gemma":[0.999438,0.00032895769,0.000056228728,0.000039563092,0.00010218478,0.000035043035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043870337,0.00049444276,0.000564665,0.0004497624,0.00047115775,0.0010431603,0.0008397911,0.0009977448,0.0029537445],"category_scores_gemma":[0.0010282159,0.00028245224,0.0005689914,0.00037665438,0.00047502926,0.00061062136,0.00034393356,0.00064979756,0.00020684468],"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":[0.000035992638,0.000014114477,0.0001921011,0.000009843417,0.00000534815,0.000029200535,0.00001858875,0.9972066,0.00054405583,0.0011516322,0.000042123986,0.0007503986],"study_design_scores_gemma":[0.000005151514,0.0000135241735,0.00006468916,0.0000013118489,0.0000021799303,0.0000030934016,0.0000043723003,0.99943763,0.00021273052,0.00018788161,0.00006553596,0.0000019272634],"about_ca_topic_score_codex":0.020545542,"about_ca_topic_score_gemma":0.009421851,"teacher_disagreement_score":0.020545542,"about_ca_system_score_codex":0.00086969725,"about_ca_system_score_gemma":0.0008408597,"threshold_uncertainty_score":0.04085189},"labels":[],"label_agreement":null},{"id":"W2097870794","doi":"10.1109/nafips.2004.1337393","title":"Fuzzy logic in agent-based game design","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":37,"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":"Fuzzy logic; Computer science; Fuzzy electronics; Artificial intelligence; Game design; Human–computer interaction; Computer game; Quality (philosophy); Fuzzy control system; Neuro-fuzzy; Multimedia","score_opus":0.03220420032887639,"score_gpt":0.27464877432532087,"score_spread":0.24244457399644448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097870794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011364303,0.004951778,0.9182115,0.0033117472,0.00031193456,0.00023130768,0.00006070679,0.00018244745,0.06137423],"genre_scores_gemma":[0.5805868,0.0041426686,0.39792433,0.0006983424,0.00019612226,0.0006075778,0.000073655334,0.000049016722,0.015721437],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984106,0.0008407695,0.000088764384,0.00012654715,0.00042424895,0.000108990534],"domain_scores_gemma":[0.9990388,0.00063523866,0.00006594482,0.000045198336,0.00014996846,0.00006483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002881218,0.0007508082,0.00065787276,0.00085986644,0.00095626106,0.002454529,0.001359221,0.0019632464,0.003885174],"category_scores_gemma":[0.0037880929,0.00040770313,0.0006924503,0.00068161945,0.0028249545,0.002028056,0.00094894035,0.0017605863,0.0004691556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068901776,0.00006747733,0.0002646734,0.00017418565,0.00005176944,0.00018858312,0.0005320594,0.14087862,0.0012349804,0.8067652,0.002245951,0.047527585],"study_design_scores_gemma":[0.00008942859,0.00010275847,0.00012063061,0.0001250589,0.00003896606,0.00009566677,0.00016255767,0.25819072,0.0009884939,0.7184133,0.021638205,0.000034249897],"about_ca_topic_score_codex":0.0068122377,"about_ca_topic_score_gemma":0.0052713305,"teacher_disagreement_score":0.0068122377,"about_ca_system_score_codex":0.002210649,"about_ca_system_score_gemma":0.0014783435,"threshold_uncertainty_score":0.016039431},"labels":[],"label_agreement":null},{"id":"W2106116009","doi":"10.1109/nafips.2004.1337408","title":"Fuzzy situation based navigation of autonomous mobile robot using reinforcement learning","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mobile robot; Computer science; Artificial intelligence; Fuzzy control system; Fuzzy logic; Reinforcement learning; Robot; Fuzzy rule; Neuro-fuzzy; Ambiguity; Robot learning; Control engineering; Engineering","score_opus":0.011886904105313225,"score_gpt":0.23672731055405247,"score_spread":0.22484040644873923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106116009","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08041485,0.0002196416,0.9134344,0.00021021703,0.000054551918,0.000080846025,0.000016766924,0.00057834364,0.00499046],"genre_scores_gemma":[0.94122714,0.00008287641,0.057222903,0.0000372454,0.000012739883,0.00006564916,0.00002141185,0.000011313573,0.001318634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998221,0.000047289446,0.000012693223,0.00003877065,0.000056889192,0.00002243214],"domain_scores_gemma":[0.9997341,0.00007805786,0.000049005674,0.000026939748,0.000082809194,0.000029074388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000475135,0.0003476584,0.00045488382,0.0002481752,0.00033204633,0.0004321107,0.0006383272,0.00046308822,0.00080669986],"category_scores_gemma":[0.0010027182,0.00018573651,0.00035320787,0.00015026735,0.000637269,0.00043047787,0.0005082943,0.00039866433,0.00016163626],"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":[0.00012907923,0.000088902794,0.00116502,0.000058071368,0.000052178944,0.00020215695,0.00022853573,0.91339236,0.010828203,0.012550368,0.00061633694,0.06068879],"study_design_scores_gemma":[0.000015879688,0.00004852145,0.00013935498,0.000004576207,0.0000071925497,0.000022123724,0.000008113947,0.99550277,0.00096207176,0.0029163922,0.00036700306,0.0000059540707],"about_ca_topic_score_codex":0.0047270386,"about_ca_topic_score_gemma":0.0033750948,"teacher_disagreement_score":0.0047270386,"about_ca_system_score_codex":0.00053370657,"about_ca_system_score_gemma":0.0006122887,"threshold_uncertainty_score":0.009399056},"labels":[],"label_agreement":null},{"id":"W2111526282","doi":"10.1109/nafips.2004.1336245","title":"Fuzzy modeling estimation of mercury removal by wetland components","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Constructed Wetlands for Wastewater Treatment","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wetland; Fuzzy logic; Mercury (programming language); Environmental science; Wastewater; Computer science; Environmental engineering; Biochemical engineering; Engineering; Ecology; Artificial intelligence","score_opus":0.008590533406011248,"score_gpt":0.21831050973207247,"score_spread":0.20971997632606124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111526282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4065823,0.00017920433,0.5891852,0.000105153566,0.000021764119,0.000028488193,0.00012377021,0.00023712477,0.0035369904],"genre_scores_gemma":[0.98431236,0.0000663416,0.014132719,0.0000043048576,0.0000036242377,0.00001809287,0.00004095764,0.0000063006396,0.0014153564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993,0.000013252369,0.0000035214616,0.000013720565,0.00002917848,0.000010186102],"domain_scores_gemma":[0.9998703,0.000063628446,0.00001694825,0.000006423028,0.000037071513,0.0000056130516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018303434,0.00032244725,0.00028016182,0.0003935362,0.00021474066,0.00049413834,0.0003643816,0.00042877826,0.00057994406],"category_scores_gemma":[0.0005548473,0.00019822162,0.00041721135,0.00025301112,0.0001662365,0.00026885734,0.00020524941,0.00022858949,0.00010935707],"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":[0.000051012357,0.000012419681,0.0006544132,0.000015909089,0.0000117121035,0.00002245866,0.000017644586,0.9839167,0.0055534802,0.00079261255,0.000058488135,0.008893083],"study_design_scores_gemma":[9.055122e-7,0.000007924142,0.00017259315,6.7800977e-7,0.000002633531,0.0000021028504,0.000002576191,0.99879277,0.00083061354,0.00014485011,0.000040457984,0.0000018069125],"about_ca_topic_score_codex":0.022517035,"about_ca_topic_score_gemma":0.013880249,"teacher_disagreement_score":0.022517035,"about_ca_system_score_codex":0.00069518574,"about_ca_system_score_gemma":0.00042478502,"threshold_uncertainty_score":0.04477191},"labels":[],"label_agreement":null},{"id":"W2116373577","doi":"10.1109/nafips.2004.1337356","title":"A fuzzy set approach to extracting keywords from abstracts","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Advanced Text Analysis Techniques","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 Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Fuzzy set; Fuzzy logic; Relevance (law); Set (abstract data type); Vocabulary; Fuzzy clustering; Natural language processing; Data mining; Fuzzy classification; Natural language; Information retrieval; Cluster analysis; Linguistics","score_opus":0.01571152251035344,"score_gpt":0.2666254911786536,"score_spread":0.2509139686683001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116373577","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039918222,0.0010325866,0.99030626,0.00022000924,0.00012150817,0.0002850106,0.00031711286,0.00067063177,0.0030551276],"genre_scores_gemma":[0.026320847,0.0006905467,0.9689941,0.00007073204,0.00008686692,0.00040262102,0.0004640103,0.000049607137,0.0029207512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794966,0.00038822187,0.00032648782,0.0004357156,0.00082613743,0.00007389444],"domain_scores_gemma":[0.9983998,0.0005621753,0.00013292654,0.00011940871,0.0007273913,0.000058181395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015083482,0.0010095994,0.0012973264,0.005998476,0.0015294118,0.002321532,0.0018268296,0.0010212215,0.0024566874],"category_scores_gemma":[0.0039930935,0.00049738697,0.001669088,0.003965661,0.00088328356,0.0021010602,0.0008701949,0.0010992482,0.0012735467],"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":[0.00019948758,0.00017190265,0.0011496086,0.0013532599,0.00027810846,0.0005340156,0.0010802195,0.023278039,0.04624304,0.051971532,0.007516254,0.86622447],"study_design_scores_gemma":[0.00014994958,0.000609374,0.003930561,0.00058172416,0.0006152942,0.002414517,0.0014104702,0.57903814,0.08587299,0.18280981,0.14213896,0.00042817672],"about_ca_topic_score_codex":0.0042336434,"about_ca_topic_score_gemma":0.00534093,"teacher_disagreement_score":0.005998476,"about_ca_system_score_codex":0.0014992865,"about_ca_system_score_gemma":0.002748538,"threshold_uncertainty_score":0.010878086},"labels":[],"label_agreement":null},{"id":"W2117357501","doi":"10.1109/nafips.2004.1337368","title":"Blending methodologies for optimizing fuzzy inference engine designs","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Fuzzy Logic and Control Systems","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; Software; Task (project management); Computation; Fuzzy logic; Inference engine; Set (abstract data type); Fuzzy control system; Distributed computing; Inference; Programming language; Artificial intelligence","score_opus":0.04822446919953906,"score_gpt":0.2944309640182491,"score_spread":0.24620649481871004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117357501","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008343518,0.00024032127,0.9875031,0.00004891049,0.000021090236,0.00006529691,0.000017548333,0.00031779337,0.0034424847],"genre_scores_gemma":[0.25170746,0.00032665636,0.7448675,0.000060350543,0.000029701709,0.00021576876,0.00007547538,0.000115341776,0.0026017935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993463,0.00014874914,0.00005049841,0.00008422853,0.00030544226,0.00006472009],"domain_scores_gemma":[0.9992466,0.0003939569,0.00007995076,0.00009203303,0.00016934618,0.000018151593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016939621,0.00089238386,0.0006952649,0.001099782,0.0003382157,0.0012637845,0.0015668985,0.0009528392,0.004319234],"category_scores_gemma":[0.0035945007,0.0006001124,0.0006343485,0.0009512238,0.00036659307,0.0012382022,0.0006743762,0.000801065,0.00070115837],"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":[0.000091694434,0.00011082232,0.00061603397,0.00021938636,0.00008607006,0.0000970637,0.00012502988,0.59455305,0.010967393,0.03718711,0.0009461078,0.3550002],"study_design_scores_gemma":[0.000021422968,0.000057628597,0.00007908743,0.000018412109,0.000028287272,0.000031105614,0.00001991382,0.9794341,0.003294441,0.014425835,0.0025833552,0.000006340134],"about_ca_topic_score_codex":0.001417838,"about_ca_topic_score_gemma":0.0028756876,"teacher_disagreement_score":0.004319234,"about_ca_system_score_codex":0.00072905887,"about_ca_system_score_gemma":0.0007965652,"threshold_uncertainty_score":0.014449239},"labels":[],"label_agreement":null},{"id":"W2118597821","doi":"10.1109/nafips.2004.1337367","title":"Hardware design issues of fuzzy neural networks","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Neural Networks and Applications","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":"Exploit; Interpretability; Computer science; Realization (probability); Fuzzy logic; Transparency (behavior); Artificial neural network; Neuro-fuzzy; Fuzzy control system; Computer architecture; Artificial intelligence; Embedded system; Distributed computing; Computer security","score_opus":0.016558344202922688,"score_gpt":0.2518491931598035,"score_spread":0.2352908489568808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118597821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02591294,0.0043075257,0.935301,0.002439087,0.00039325506,0.00013383135,0.000057321362,0.0007256936,0.030729337],"genre_scores_gemma":[0.6206293,0.0030707086,0.36369804,0.0005769077,0.00024572006,0.00022777118,0.00008384941,0.00007581704,0.011391894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961203,0.00009669103,0.00003200123,0.000045162382,0.00018002419,0.000033994675],"domain_scores_gemma":[0.9991954,0.0003790982,0.000050721424,0.00009376246,0.00026408746,0.000016964193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084795954,0.00035039283,0.00026772826,0.0003267403,0.0003826773,0.0013609728,0.0012971997,0.0008377878,0.004733158],"category_scores_gemma":[0.0030554438,0.0003509946,0.00018056833,0.0003105341,0.00045991162,0.0013912527,0.00034678564,0.00078385917,0.0009447414],"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":[0.00029711885,0.000093785144,0.0010654065,0.001063536,0.000069014866,0.0003283494,0.00027874438,0.15822145,0.041973222,0.31458807,0.0055457177,0.47647548],"study_design_scores_gemma":[0.00012505641,0.0004300703,0.00082400936,0.0002778464,0.00008339193,0.00069817354,0.00016402072,0.6931723,0.06741439,0.14519294,0.091571644,0.000046146342],"about_ca_topic_score_codex":0.0012666096,"about_ca_topic_score_gemma":0.0021829458,"teacher_disagreement_score":0.004733158,"about_ca_system_score_codex":0.00084977905,"about_ca_system_score_gemma":0.00063971616,"threshold_uncertainty_score":0.015833974},"labels":[],"label_agreement":null},{"id":"W2120854520","doi":"10.1109/nafips.2004.1336330","title":"A fuzzy enhancement schema for density-typed mammograms","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"AI in cancer detection","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 Guelph","funders":"","keywords":"Mammography; Fuzzy logic; Artificial intelligence; Computer science; Breast cancer; Contrast enhancement; Contrast (vision); Pattern recognition (psychology); Breast tissue; Computer vision; Cancer; Radiology; Medicine","score_opus":0.012217601259420558,"score_gpt":0.24889712809366749,"score_spread":0.23667952683424692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120854520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013509534,0.0003580179,0.96824783,0.00030240012,0.000083203355,0.00029820713,0.00037653995,0.0012677744,0.015556438],"genre_scores_gemma":[0.11493074,0.000570339,0.8730433,0.00018523438,0.00007887362,0.0002547738,0.0006803583,0.00017017218,0.010086168],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993487,0.00010788935,0.00010485725,0.00014836837,0.00024534634,0.000044957775],"domain_scores_gemma":[0.9993864,0.00016004345,0.000053184165,0.00013663429,0.00022852584,0.000035261877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015113716,0.00029632583,0.00027327318,0.0011376066,0.0005568309,0.0018241822,0.00089968165,0.0006688276,0.0050471425],"category_scores_gemma":[0.0017998094,0.000324434,0.0008140043,0.0007300632,0.00068264664,0.0020995531,0.00094546995,0.00079985755,0.0015404843],"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":[0.0003508095,0.00014903433,0.0020637154,0.00047528572,0.00009022422,0.0016434767,0.0026295865,0.02288759,0.06677689,0.53441566,0.0088039255,0.35971388],"study_design_scores_gemma":[0.00011072809,0.0003334111,0.00384115,0.0004585382,0.00026947728,0.004467852,0.0007848724,0.2816476,0.097786136,0.19270223,0.41742525,0.00017268123],"about_ca_topic_score_codex":0.0010222236,"about_ca_topic_score_gemma":0.0011505865,"teacher_disagreement_score":0.0050471425,"about_ca_system_score_codex":0.00048347493,"about_ca_system_score_gemma":0.00040876144,"threshold_uncertainty_score":0.016884387},"labels":[],"label_agreement":null},{"id":"W2122661534","doi":"10.1109/nafips.2004.1337412","title":"Experimental results of an adaptive fuzzy network Kalman filtering integration for low cost navigation applications","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":10,"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 Calgary","funders":"Canada Research Chairs","keywords":"Kalman filter; Fast Kalman filter; Computer science; Global Positioning System; Inertial measurement unit; Fuzzy logic; Control theory (sociology); Control engineering; Extended Kalman filter; SIGNAL (programming language); Engineering; Artificial intelligence; Telecommunications","score_opus":0.010884650255869665,"score_gpt":0.2474366266065348,"score_spread":0.23655197635066513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122661534","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8507479,0.00048410762,0.13938646,0.0001716073,0.00018152123,0.00014800573,0.0003736353,0.0015562215,0.0069505335],"genre_scores_gemma":[0.96901304,0.000124659,0.027453583,0.000024579678,0.0000075096013,0.000053285316,0.00016519689,0.0000341946,0.0031239826],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964654,0.000037866022,0.000019234418,0.00006885444,0.00019541687,0.00003209672],"domain_scores_gemma":[0.99939907,0.00013588284,0.000034743884,0.00006869194,0.00033136553,0.000030209712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062592985,0.00029652566,0.00031501948,0.0003412809,0.00040595944,0.00040138979,0.0005271821,0.0006207246,0.0023094686],"category_scores_gemma":[0.0010735987,0.00015385552,0.00020189521,0.00033706735,0.00021027328,0.0005181904,0.000223702,0.0002871381,0.00045418105],"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":[0.0031222748,0.0012163119,0.0053021978,0.000593632,0.0001380975,0.000391992,0.00054854515,0.0810846,0.6563502,0.0019752474,0.0025148273,0.24676207],"study_design_scores_gemma":[0.00017285968,0.002852513,0.015996037,0.000037213253,0.000163158,0.00020353518,0.00014081836,0.53677934,0.4380215,0.00060909777,0.004956674,0.00006723519],"about_ca_topic_score_codex":0.0053161727,"about_ca_topic_score_gemma":0.004546429,"teacher_disagreement_score":0.0053161727,"about_ca_system_score_codex":0.00028053203,"about_ca_system_score_gemma":0.00032832657,"threshold_uncertainty_score":0.0105704665},"labels":[],"label_agreement":null},{"id":"W2123524465","doi":"10.1109/nafips.2004.1336248","title":"Evolutionary algorithms for multi-objective optimization in HVAC system control strategy","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":58,"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":"","keywords":"HVAC; Evolutionary algorithm; Multi-objective optimization; Mathematical optimization; Sorting; Computer science; Genetic algorithm; Optimization problem; Simulated annealing; Energy consumption; Energy (signal processing); Pareto principle; Engineering; Algorithm; Mathematics; Air conditioning; Mechanical engineering","score_opus":0.010328325967602067,"score_gpt":0.2253361381911341,"score_spread":0.21500781222353205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123524465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013286226,0.0009942775,0.9818973,0.00018823077,0.00003786204,0.00006193054,0.000017512915,0.000102996186,0.0034136632],"genre_scores_gemma":[0.44059074,0.0015259892,0.5497372,0.00016113262,0.00007360934,0.00071434246,0.000083276616,0.00008313156,0.007030628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995522,0.00023686836,0.000022994542,0.00003528677,0.00012187509,0.00003069688],"domain_scores_gemma":[0.9993806,0.00045152116,0.000039932864,0.000018998911,0.0000961168,0.000012821697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001572332,0.0009055876,0.0008697726,0.00064324256,0.00038618987,0.0008356463,0.00070189545,0.0010564044,0.0017283272],"category_scores_gemma":[0.0024444168,0.00046261481,0.0005686366,0.00082668255,0.0006104157,0.00057453394,0.00057379244,0.0011651381,0.00024072961],"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":[0.000013129402,0.000019103112,0.00011904631,0.000033412194,0.000027165863,0.000020982605,0.000025959303,0.9695878,0.0004094753,0.00782728,0.00022911115,0.021687532],"study_design_scores_gemma":[0.000009889956,0.000016752521,0.000054006214,0.000008451257,0.000004761945,0.000005387979,0.0000045328125,0.99679786,0.00011863145,0.0025731567,0.00040345537,0.0000030641893],"about_ca_topic_score_codex":0.004736385,"about_ca_topic_score_gemma":0.0030309875,"teacher_disagreement_score":0.004736385,"about_ca_system_score_codex":0.0008202149,"about_ca_system_score_gemma":0.0006968198,"threshold_uncertainty_score":0.009417653},"labels":[],"label_agreement":null},{"id":"W2125618387","doi":"10.1109/nafips.2004.1336295","title":"Cluster validation indices for fMRI data: Fuzzy C-Means with feature partitions versus cluster merging strategies","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Pattern recognition (psychology); Computer science; Centroid; Artificial intelligence; Fuzzy logic; False positive paradox; Cluster analysis; Data mining; Feature (linguistics); Fuzzy set; Cluster (spacecraft); Fuzzy clustering","score_opus":0.022099809126403996,"score_gpt":0.272613496844768,"score_spread":0.250513687718364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125618387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0418605,0.000772679,0.9549921,0.00022771618,0.00004086115,0.0003475529,0.0001602694,0.0007455679,0.0008527244],"genre_scores_gemma":[0.16146888,0.00021901858,0.83674526,0.000083310944,0.000037437796,0.00046673455,0.0003994435,0.00022508933,0.00035472904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941736,0.0024749306,0.0005494205,0.0008092395,0.0017887878,0.00020405184],"domain_scores_gemma":[0.98262066,0.011283692,0.0011725442,0.0013616305,0.0033202637,0.00024116771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01762937,0.0013070169,0.0019603923,0.004673014,0.0018138369,0.0022519939,0.0024988805,0.001923516,0.0009636704],"category_scores_gemma":[0.046245065,0.0005181373,0.0012564438,0.0037130322,0.001375545,0.0025042861,0.0018324441,0.0019700432,0.000356246],"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":[0.0011628357,0.00023035349,0.006913924,0.0004150988,0.00051387877,0.00007916295,0.000978459,0.26089045,0.0062858826,0.017334854,0.0033991467,0.70179594],"study_design_scores_gemma":[0.000055151806,0.00017600138,0.0033671386,0.00006911084,0.000079060555,0.00007240006,0.00014006824,0.9793936,0.006077186,0.009463068,0.0010420136,0.00006512455],"about_ca_topic_score_codex":0.0067672413,"about_ca_topic_score_gemma":0.007024892,"teacher_disagreement_score":0.01762937,"about_ca_system_score_codex":0.0020377957,"about_ca_system_score_gemma":0.0023347693,"threshold_uncertainty_score":0.09323412},"labels":[],"label_agreement":null},{"id":"W2126299630","doi":"10.1109/nafips.2004.1336288","title":"Dynamic neural network based training for support vector machines","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Neural Networks and Applications","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 Saskatchewan","funders":"","keywords":"Support vector machine; Computer science; Artificial neural network; Artificial intelligence; Kernel (algebra); Structured support vector machine; Dual (grammatical number); Relevance vector machine; Machine learning; Kernel method; Least squares support vector machine; Mathematics","score_opus":0.01486319486230855,"score_gpt":0.258010899036116,"score_spread":0.24314770417380746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126299630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006527505,0.0003854935,0.9913078,0.00014918207,0.00006230718,0.000038141192,0.000026965698,0.00028075758,0.0012218616],"genre_scores_gemma":[0.47670916,0.00083580863,0.51743346,0.00021263763,0.00014045175,0.00040221596,0.000302169,0.00010537495,0.0038586925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907565,0.00031011208,0.00007542482,0.00017676175,0.0002925765,0.00006938157],"domain_scores_gemma":[0.9982767,0.0010488787,0.00010130759,0.00012992183,0.00040908437,0.00003409849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013488341,0.0007078899,0.00086078054,0.0007768473,0.00043481574,0.0008088432,0.0012219364,0.0011836704,0.002690817],"category_scores_gemma":[0.0074174814,0.00041033712,0.0004450171,0.0010426755,0.00058061833,0.0015847137,0.0007951141,0.0018904255,0.00070484483],"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":[0.00017389847,0.00009793985,0.0009048454,0.00022690179,0.000068719884,0.000103781655,0.00007204668,0.51745427,0.006574665,0.026197119,0.0022458832,0.4458799],"study_design_scores_gemma":[0.000004098572,0.00001708499,0.00007229361,0.000009142059,0.0000031323618,0.000014468611,0.0000031591126,0.9952193,0.0011588458,0.0028015925,0.0006924759,0.0000043749233],"about_ca_topic_score_codex":0.0018029149,"about_ca_topic_score_gemma":0.0013935321,"teacher_disagreement_score":0.002690817,"about_ca_system_score_codex":0.0006947277,"about_ca_system_score_gemma":0.0005849785,"threshold_uncertainty_score":0.009001672},"labels":[],"label_agreement":null},{"id":"W2130899543","doi":"10.1109/nafips.2004.1336284","title":"Fuzzy sliding mode control for a singularly perturbed systems","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Control theory (sociology); Fuzzy logic; Sliding mode control; Singular perturbation; Nonlinear system; Controller (irrigation); Dimension (graph theory); Fuzzy control system; Computer science; Perturbation (astronomy); Mathematics; Control (management); Artificial intelligence; Physics; Mathematical analysis","score_opus":0.010221553042712222,"score_gpt":0.22879168344958914,"score_spread":0.21857013040687692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130899543","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033182893,0.0006662168,0.96042746,0.00024197441,0.00014665254,0.00004383599,0.000026300802,0.00025859894,0.0050060367],"genre_scores_gemma":[0.9424062,0.00055443554,0.052481115,0.00007268624,0.00008120345,0.0000914601,0.000039271243,0.000015794158,0.0042577726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998584,0.000033638167,0.000008796541,0.000030319847,0.000060110353,0.0000086944565],"domain_scores_gemma":[0.99988484,0.000037822712,0.000018116289,0.000011406714,0.00003967905,0.000008161844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034969402,0.00044482155,0.00037664507,0.00023880773,0.000301048,0.00065179204,0.00030726843,0.0004737231,0.0010599807],"category_scores_gemma":[0.00065543765,0.00009290163,0.0002751865,0.0001923806,0.0005588018,0.0003469872,0.00032826615,0.00055181346,0.00016224138],"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":[0.00028059448,0.000073333824,0.0003124063,0.00024106541,0.0000684999,0.00038120776,0.00038491792,0.71577567,0.07631536,0.08766534,0.001777187,0.116724506],"study_design_scores_gemma":[0.000011736519,0.000094183844,0.00011506206,0.000006335799,0.0000058240557,0.000019226434,0.000010673124,0.9908572,0.001721789,0.006069553,0.0010827048,0.000005722225],"about_ca_topic_score_codex":0.0023196319,"about_ca_topic_score_gemma":0.0011467034,"teacher_disagreement_score":0.0023196319,"about_ca_system_score_codex":0.00036846966,"about_ca_system_score_gemma":0.00032716888,"threshold_uncertainty_score":0.004612267},"labels":[],"label_agreement":null},{"id":"W2137347599","doi":"10.1109/nafips.2004.1337360","title":"Intelligent medical diagnosis system using the fuzzy and neural networks","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Asynergy; Computer science; Ventricle; Artificial intelligence; Fuzzy inference system; Phonocardiogram; Backpropagation; Fuzzy logic; Pattern recognition (psychology); Adaptive neuro fuzzy inference system; Data mining; Computer vision; Fuzzy control system; Cardiology; Medicine; Heart failure","score_opus":0.010243314337173433,"score_gpt":0.23458210845871763,"score_spread":0.2243387941215442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137347599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04159843,0.001568972,0.94223446,0.00064956653,0.00025775802,0.00021014136,0.0001730494,0.0018727124,0.011434807],"genre_scores_gemma":[0.7003331,0.0010430429,0.2889908,0.00033726348,0.0001868816,0.00033824367,0.00027308133,0.00003133723,0.008466352],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996088,0.00006490808,0.000036209087,0.00008846064,0.00016291271,0.000038705082],"domain_scores_gemma":[0.9997055,0.00008021573,0.000028076185,0.000018490358,0.00015249633,0.000015139771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064108946,0.00047640188,0.0005643823,0.0007342565,0.0006360754,0.0008306021,0.0006106744,0.00084707764,0.001976405],"category_scores_gemma":[0.0013198659,0.00023171956,0.0004860239,0.00037842587,0.0002973957,0.000842125,0.00045637917,0.0004812055,0.00044464925],"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":[0.00046850773,0.00025634564,0.003385859,0.00037103053,0.00019869392,0.0004967928,0.00022162547,0.22457477,0.031023137,0.015872745,0.007728509,0.7154019],"study_design_scores_gemma":[0.000047041816,0.000113670176,0.00112411,0.00003273292,0.00006453586,0.00015942752,0.000024330377,0.9825607,0.005460945,0.0057550175,0.0046220357,0.000035487465],"about_ca_topic_score_codex":0.006895598,"about_ca_topic_score_gemma":0.0057008723,"teacher_disagreement_score":0.006895598,"about_ca_system_score_codex":0.0007182262,"about_ca_system_score_gemma":0.0007303384,"threshold_uncertainty_score":0.013710916},"labels":[],"label_agreement":null},{"id":"W2140848254","doi":"10.1109/nafips.2004.1336250","title":"On the implication problem in granular knowledge systems","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Bayesian Modeling and Causal Inference","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 Regina","funders":"","keywords":"Probabilistic logic; Computer science; Representation (politics); Bayesian network; Set (abstract data type); Theoretical computer science; Knowledge representation and reasoning; Markov chain; Markov process; Logical conjunction; Bayesian probability; Artificial intelligence; Mathematics; Machine learning; Programming language","score_opus":0.015188263274797477,"score_gpt":0.24801307254569552,"score_spread":0.23282480927089805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140848254","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059678197,0.0024694616,0.90086055,0.00877442,0.00021930388,0.00008232905,0.00023587905,0.00019822926,0.027481668],"genre_scores_gemma":[0.80534947,0.0021615655,0.18613821,0.0010095945,0.00056567485,0.00015548791,0.00034199335,0.00006968461,0.0042083333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950046,0.002394769,0.0004098169,0.00062316755,0.001236794,0.00033084425],"domain_scores_gemma":[0.9763984,0.021025673,0.0007563078,0.0007416724,0.00078778906,0.00029009485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007922913,0.00049707084,0.0010326781,0.0013837432,0.0019802086,0.003695129,0.0015199155,0.002693398,0.00444172],"category_scores_gemma":[0.02851455,0.00053992664,0.0010998186,0.0023636995,0.0044681085,0.009826812,0.0035180317,0.0040302034,0.0003463426],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087529035,0.00002811716,0.0005056769,0.00014119434,0.000028317207,0.0004642029,0.0003751807,0.045568157,0.00032272612,0.92495936,0.0015585871,0.02596097],"study_design_scores_gemma":[0.000009259871,0.000005330434,0.00006595684,0.000017725615,0.0000062743684,0.000046746318,0.000042074353,0.04908754,0.000095471405,0.94981116,0.00080659206,0.000005885315],"about_ca_topic_score_codex":0.002926416,"about_ca_topic_score_gemma":0.001742807,"teacher_disagreement_score":0.007922913,"about_ca_system_score_codex":0.0021145681,"about_ca_system_score_gemma":0.001051892,"threshold_uncertainty_score":0.041900873},"labels":[],"label_agreement":null},{"id":"W2143895206","doi":"10.1109/nafips.2004.1336311","title":"A fuzzy expert system for deterioration modeling of buried metallic pipes","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Water Systems and Optimization","field":"Engineering","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":"National Research Council Canada","funders":"","keywords":"Expert system; Knowledge base; Data mining; Field (mathematics); Fuzzy set; Fuzzy logic; Computer science; Subject-matter expert; Process (computing); Legal expert system; Data modeling; Knowledge-based systems; Set (abstract data type); Engineering; Machine learning; Artificial intelligence; Database; Mathematics","score_opus":0.012127495567547542,"score_gpt":0.21605941412771135,"score_spread":0.2039319185601638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143895206","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011851298,0.00009709595,0.98527527,0.00007527541,0.000017905619,0.000058907233,0.00009820105,0.00079021265,0.0017358166],"genre_scores_gemma":[0.50417405,0.00024650467,0.4909868,0.000086935506,0.000036156776,0.00029219655,0.00037493778,0.00004870194,0.0037536984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999548,0.00013027115,0.000040275456,0.000115103656,0.00013001064,0.000036280366],"domain_scores_gemma":[0.9995289,0.00020554071,0.00003572886,0.000039733524,0.00016818545,0.0000218413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091258076,0.0005366083,0.00076249003,0.0005306013,0.0005380379,0.0009998702,0.00097416685,0.0012824789,0.002978599],"category_scores_gemma":[0.0023982797,0.00030501143,0.0005266864,0.00035844636,0.00028810857,0.00078009313,0.000492946,0.0007040516,0.000726991],"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":[0.00025046364,0.00011272186,0.0012492526,0.00019817839,0.00007198241,0.0004127827,0.0003232276,0.80779415,0.014849138,0.012948571,0.0023190216,0.1594705],"study_design_scores_gemma":[0.000015364329,0.000023229906,0.0001284303,0.0000112059215,0.000012849368,0.00003321928,0.000013147963,0.9950995,0.0012343603,0.001867011,0.0015536518,0.000007993815],"about_ca_topic_score_codex":0.0071227606,"about_ca_topic_score_gemma":0.0059092315,"teacher_disagreement_score":0.0071227606,"about_ca_system_score_codex":0.00057590543,"about_ca_system_score_gemma":0.001005377,"threshold_uncertainty_score":0.0141626},"labels":[],"label_agreement":null},{"id":"W2143985241","doi":"10.1109/nafips.2004.1336252","title":"Rough set approximations in formal concept analysis","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":80,"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 Regina","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Rough set; Formal concept analysis; Set (abstract data type); Approximations of π; Computer science; Universal set; Mathematics; Set theory; Dominance-based rough set approach; Approximation theory; Lattice (music); Algebra over a field; Discrete mathematics; Theoretical computer science; Algorithm; Artificial intelligence; Applied mathematics; Pure mathematics","score_opus":0.013387805211441556,"score_gpt":0.24749607275709765,"score_spread":0.23410826754565608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143985241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005816374,0.007365257,0.9773796,0.0012848187,0.00027214858,0.00006031932,0.00006840212,0.00012921996,0.007623808],"genre_scores_gemma":[0.31939238,0.008624284,0.6652621,0.00047419197,0.0009790097,0.00040538423,0.00026223305,0.00010458258,0.0044958084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9908765,0.0044276756,0.00051182264,0.00080782664,0.003056884,0.00031932874],"domain_scores_gemma":[0.9905661,0.0067469315,0.00061196095,0.0010618736,0.0007927373,0.00022053381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011793981,0.00090139656,0.0018331978,0.0033623644,0.0012019502,0.0049569434,0.0015842582,0.0012943773,0.0021002505],"category_scores_gemma":[0.021394935,0.00068045384,0.0020219455,0.0035755849,0.0074346685,0.008571519,0.0032023236,0.0049229404,0.00059379445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011390699,0.000007544691,0.0000753271,0.00007267879,0.000023047576,0.000042858286,0.00025212596,0.012423279,0.00017783741,0.9767046,0.00052489195,0.009684445],"study_design_scores_gemma":[0.000006178753,0.0000104652545,0.000041829528,0.000034870776,0.0000072262364,0.000029906223,0.000055378037,0.035634737,0.00013910928,0.96022916,0.0038010657,0.000010099508],"about_ca_topic_score_codex":0.0022815936,"about_ca_topic_score_gemma":0.0011498897,"teacher_disagreement_score":0.011793981,"about_ca_system_score_codex":0.0032034984,"about_ca_system_score_gemma":0.0016645803,"threshold_uncertainty_score":0.06237328},"labels":[],"label_agreement":null},{"id":"W2147548356","doi":"10.1109/nafips.2004.1336318","title":"Approximate reasoning and Semantic Web Services","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Semantic Web and Ontologies","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Social Semantic Web; Semantic Web; World Wide Web; Web service; Ontology; OWL-S; Semantic Web Stack; Interoperability; Web modeling","score_opus":0.007841253773303788,"score_gpt":0.22649826287591274,"score_spread":0.21865700910260893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147548356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011980255,0.003232407,0.9712336,0.002011645,0.000117331336,0.0000660347,0.0001380933,0.00040230007,0.01081829],"genre_scores_gemma":[0.6060537,0.0043684575,0.38316146,0.0005581199,0.0002923681,0.00017823355,0.00051353517,0.000068985944,0.00480518],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99657154,0.0011490595,0.00026630238,0.00034471785,0.0014791494,0.00018915316],"domain_scores_gemma":[0.9972088,0.0017054111,0.00027260746,0.00037568653,0.0003721372,0.000065447326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028303568,0.0006820763,0.0008396307,0.0023154325,0.0008703098,0.003617288,0.0014754273,0.0018135391,0.0020824377],"category_scores_gemma":[0.0108272955,0.0004370027,0.0013005851,0.0026478285,0.0029373849,0.0053910017,0.0017180174,0.0017665582,0.00040967308],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009338547,0.000045807676,0.0006978238,0.00024313394,0.000110993424,0.00031495135,0.00034673672,0.119012155,0.0008917241,0.81118315,0.0020881437,0.06497194],"study_design_scores_gemma":[0.000014855175,0.00001294165,0.00013440072,0.000043201046,0.00002334392,0.00009641998,0.00010230217,0.26692885,0.000626724,0.72529423,0.006709253,0.000013518003],"about_ca_topic_score_codex":0.008859464,"about_ca_topic_score_gemma":0.0049285004,"teacher_disagreement_score":0.008859464,"about_ca_system_score_codex":0.00268858,"about_ca_system_score_gemma":0.0011395464,"threshold_uncertainty_score":0.01950717},"labels":[],"label_agreement":null},{"id":"W2151039721","doi":"10.1109/nafips.2004.1336247","title":"Preliminary hazard analysis for the design alternatives based on fuzzy methodology","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":7,"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":"Polytechnique Montréal","keywords":"Fuzzy logic; Maintainability; Operability; Computer science; Hazard; Ranking (information retrieval); Reliability engineering; Risk analysis (engineering); Data mining; Artificial intelligence; Machine learning; Engineering","score_opus":0.17537804519322708,"score_gpt":0.4083853243582305,"score_spread":0.2330072791650034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151039721","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013038373,0.0001569098,0.98063827,0.00006725386,0.000016127447,0.00026179044,0.000060838815,0.00007553281,0.005685046],"genre_scores_gemma":[0.3155816,0.00035867005,0.67852414,0.00004575894,0.000031009207,0.00084015646,0.00017580483,0.000039643608,0.004403254],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975684,0.00097079895,0.000096468786,0.0001429204,0.001085775,0.00013562659],"domain_scores_gemma":[0.99638593,0.0026504486,0.00016652903,0.00010736338,0.0006435755,0.000046181307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004568956,0.00090581324,0.0007519274,0.0038238044,0.0007821334,0.0021913408,0.0009237244,0.0007107926,0.009458699],"category_scores_gemma":[0.0097098695,0.00037813903,0.0016230188,0.001812457,0.00080907333,0.0015841126,0.0006668227,0.0009005179,0.00054370874],"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":[0.00023060058,0.00014976616,0.001313769,0.00069433823,0.00010541788,0.00025690222,0.0007199348,0.4794207,0.00841742,0.2559232,0.0018344187,0.25093353],"study_design_scores_gemma":[0.000041321964,0.00044519667,0.0009921207,0.00013038352,0.00007216321,0.0001335967,0.00025828223,0.87113476,0.0041672755,0.11345455,0.0091143325,0.000056013854],"about_ca_topic_score_codex":0.0024900981,"about_ca_topic_score_gemma":0.002441448,"teacher_disagreement_score":0.009458699,"about_ca_system_score_codex":0.0019778493,"about_ca_system_score_gemma":0.0020090232,"threshold_uncertainty_score":0.031642497},"labels":[],"label_agreement":null},{"id":"W2152667044","doi":"10.1109/nafips.2004.1337410","title":"Estimating outlier impact on FastICA using fuzzy inference","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Blind Source Separation Techniques","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 Manitoba","funders":"","keywords":"FastICA; Outlier; Independent component analysis; Robustness (evolution); Computer science; Pattern recognition (psychology); Artificial intelligence; Estimator; Anomaly detection; Fuzzy logic; Mathematics; Algorithm; Blind signal separation; Statistics","score_opus":0.018901864679363927,"score_gpt":0.30212576289445076,"score_spread":0.2832238982150868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152667044","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13478115,0.00018227768,0.8636762,0.00009001792,0.000017368533,0.00003698089,0.000025489684,0.0002802594,0.0009101872],"genre_scores_gemma":[0.8379408,0.00016838394,0.16127315,0.000035832993,0.000015655158,0.000039838196,0.00005037288,0.000030274314,0.00044575494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892956,0.00024162694,0.00009061641,0.00016851413,0.00048420305,0.000085532716],"domain_scores_gemma":[0.9928256,0.005408044,0.00045421557,0.00027629975,0.00097033137,0.00006543022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028047161,0.000719218,0.0006446658,0.0012336723,0.0005922556,0.0012428724,0.0005075952,0.0008664436,0.0004933068],"category_scores_gemma":[0.015112087,0.00032829086,0.00068398914,0.00067374215,0.0007552476,0.001222765,0.00063262356,0.00093535456,0.00012735864],"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":[0.0005246504,0.0000772721,0.003850953,0.00013263474,0.00011273377,0.00020845172,0.00027653895,0.7773741,0.026748385,0.0049523995,0.00018262862,0.18555926],"study_design_scores_gemma":[0.000009224237,0.000068670975,0.0019735375,0.000009986075,0.000019306613,0.00005873835,0.000021808475,0.9849807,0.010251828,0.0024465586,0.00013998729,0.000019545112],"about_ca_topic_score_codex":0.005961083,"about_ca_topic_score_gemma":0.0038170153,"teacher_disagreement_score":0.005961083,"about_ca_system_score_codex":0.0009777918,"about_ca_system_score_gemma":0.00097012625,"threshold_uncertainty_score":0.014832914},"labels":[],"label_agreement":null},{"id":"W2156769952","doi":"10.1109/nafips.2004.1337411","title":"Signal separation by independent component analysis and fuzzy estimators","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Blind Source Separation Techniques","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 Manitoba","funders":"","keywords":"Independent component analysis; Estimator; Principal component analysis; Computer science; Signal processing; Statistical signal processing; Fuzzy logic; Artificial intelligence; Pattern recognition (psychology); Blind signal separation; Component (thermodynamics); Noise (video); Gaussian; SIGNAL (programming language); Artificial neural network; Mathematics; Statistics; Digital signal processing","score_opus":0.007886143321598904,"score_gpt":0.25518320351125007,"score_spread":0.24729706018965117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156769952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009365353,0.0016838314,0.99540967,0.00017043202,0.000057433568,0.000019434277,0.000011932712,0.000056195007,0.0016544868],"genre_scores_gemma":[0.061668932,0.004976357,0.9297605,0.00016537066,0.00040903135,0.00014217866,0.000066062494,0.000036455764,0.0027751417],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894255,0.00034902245,0.00006930071,0.00017155803,0.0004272833,0.000040253286],"domain_scores_gemma":[0.99934214,0.00036335355,0.00007907512,0.000053407934,0.00014812112,0.000013872629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016712849,0.0007425538,0.00087296707,0.0015768133,0.00050152483,0.0012496505,0.00075982476,0.0015335724,0.0011271568],"category_scores_gemma":[0.003705823,0.00033063215,0.00066980516,0.0015875159,0.0016249524,0.0017275113,0.0009977263,0.001433683,0.0007834238],"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":[0.00010000335,0.000032213742,0.00030872357,0.00037602143,0.00009588601,0.00016158844,0.00019398547,0.0981457,0.0118953865,0.54911685,0.0033980298,0.33617556],"study_design_scores_gemma":[0.000038451406,0.000068661735,0.00038847825,0.00012392868,0.00004339814,0.00024602647,0.000055575816,0.6210619,0.007522675,0.34901717,0.021365993,0.0000677911],"about_ca_topic_score_codex":0.0013316656,"about_ca_topic_score_gemma":0.0010934591,"teacher_disagreement_score":0.0016712849,"about_ca_system_score_codex":0.00063469505,"about_ca_system_score_gemma":0.0007690375,"threshold_uncertainty_score":0.008838713},"labels":[],"label_agreement":null},{"id":"W2158787990","doi":"10.1109/nafips.2004.1337439","title":"Fuzzy modeling and prediction of cylindricity error in BTA deep hole boring process","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Process (computing); Computer science; Set (abstract data type); Operator (biology); Fuzzy logic; Drilling; Mode (computer interface); Artificial intelligence; Algorithm; Mechanical engineering; Engineering","score_opus":0.009473853066686338,"score_gpt":0.23329195204887365,"score_spread":0.22381809898218732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158787990","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54544544,0.00033812434,0.44988522,0.00019559544,0.000041664178,0.000023956432,0.000054588036,0.0002525247,0.0037629772],"genre_scores_gemma":[0.9947455,0.00006046499,0.0044668335,0.000005207318,0.0000029598166,0.0000050033036,0.000012694873,0.000004629435,0.0006968324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983776,0.00003850517,0.000007639822,0.000028558727,0.00006561849,0.000021814136],"domain_scores_gemma":[0.9996203,0.0001829273,0.00007061501,0.000028830435,0.00008009883,0.000017133381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041836035,0.00038053025,0.00042874945,0.0003253502,0.00032345467,0.0007451906,0.00036394762,0.00064588716,0.00046110695],"category_scores_gemma":[0.001020076,0.00024661957,0.00031280244,0.00020866591,0.00036670108,0.00050858315,0.00021499513,0.0004092209,0.00010663743],"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":[0.00007737947,0.000018293898,0.0009674754,0.000017051203,0.000008264159,0.000065670974,0.000050192513,0.9833294,0.006716142,0.0014470113,0.00007173692,0.007231473],"study_design_scores_gemma":[0.0000015236028,0.000013498237,0.00026440708,9.250243e-7,0.0000013985725,0.000004273029,0.0000031725276,0.99889463,0.0005000954,0.00028079096,0.000032767843,0.000002562274],"about_ca_topic_score_codex":0.011906857,"about_ca_topic_score_gemma":0.010116254,"teacher_disagreement_score":0.011906857,"about_ca_system_score_codex":0.00047958604,"about_ca_system_score_gemma":0.00041785775,"threshold_uncertainty_score":0.023675084},"labels":[],"label_agreement":null},{"id":"W2159890346","doi":"10.1109/nafips.2004.1337374","title":"Inference systems by using ordinal sums and genetic algorithms","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Fuzzy Logic and Control Systems","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":"","keywords":"Adaptive neuro fuzzy inference system; Benchmark (surveying); Relation (database); Computer science; Artificial neural network; Inference; Genetic algorithm; Algorithm; Parametric statistics; Artificial intelligence; Neuro-fuzzy; Fuzzy logic; Function (biology); Data mining; Fuzzy control system; Machine learning; Mathematics; Statistics","score_opus":0.012680675292532645,"score_gpt":0.23545337161069796,"score_spread":0.22277269631816532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159890346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020816324,0.00025313962,0.9966536,0.000067315115,0.0000361711,0.000023355424,0.000010919733,0.000091172595,0.00078272924],"genre_scores_gemma":[0.1507374,0.00054442114,0.8459148,0.00012021896,0.000118024094,0.00024751195,0.00008867603,0.0000638676,0.0021650416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965823,0.0017578076,0.00028789387,0.00043033034,0.0008352903,0.00010643716],"domain_scores_gemma":[0.9968851,0.0019792705,0.00028949472,0.00024389735,0.0005400458,0.00006217949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005189866,0.0008980463,0.0012300588,0.0020384716,0.00063607114,0.0021447581,0.0015020395,0.0009090183,0.0016710534],"category_scores_gemma":[0.010832521,0.0004090374,0.0012164812,0.001597885,0.0013954832,0.002407142,0.0016348198,0.001594423,0.0005114292],"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":[0.00009846472,0.000062666346,0.0006830818,0.00024330588,0.00031616076,0.0001148797,0.0002755873,0.5601221,0.0024297652,0.17896369,0.0009876892,0.2557026],"study_design_scores_gemma":[0.00001451552,0.000044403554,0.00010415782,0.000026094145,0.000033485678,0.00004198619,0.000029315168,0.907055,0.001177409,0.08932913,0.0021217242,0.000022788647],"about_ca_topic_score_codex":0.0026078247,"about_ca_topic_score_gemma":0.0020185441,"teacher_disagreement_score":0.005189866,"about_ca_system_score_codex":0.001009048,"about_ca_system_score_gemma":0.00083426625,"threshold_uncertainty_score":0.027446985},"labels":[],"label_agreement":null},{"id":"W2161383373","doi":"10.1109/nafips.2004.1336316","title":"Scheduling exploration/exploitation levels in genetically-generated fuzzy knowledge bases","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","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":"Polytechnique Montréal","funders":"","keywords":"Crossover; Computer science; Scheduling (production processes); Fuzzy set; Fuzzy logic; Genetic algorithm; Artificial intelligence; Mathematical optimization; Machine learning; Mathematics","score_opus":0.027515157255777075,"score_gpt":0.2713248296591385,"score_spread":0.24380967240336143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161383373","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79045695,0.00032077375,0.20637283,0.00013395362,0.000019540563,0.000088403955,0.000018666624,0.0002013588,0.0023874794],"genre_scores_gemma":[0.95668113,0.00008493938,0.0426917,0.000027716225,0.000006225416,0.000061719686,0.000026051539,0.000041637944,0.0003788935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923956,0.0002728708,0.00005268736,0.00009209727,0.00021893436,0.00012389544],"domain_scores_gemma":[0.99337304,0.0051137186,0.0005809606,0.00027733992,0.00049994746,0.00015503567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022279923,0.00045840838,0.00043101044,0.00065397634,0.0004235483,0.0009857833,0.00052724546,0.0006052344,0.0006739801],"category_scores_gemma":[0.013816931,0.00028616135,0.00017793116,0.0004008504,0.0005549737,0.0012661495,0.00054233155,0.00056701945,0.00013656764],"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":[0.000823365,0.0003133814,0.004215884,0.00012841023,0.0000679021,0.00017614379,0.00046369992,0.7952428,0.051758952,0.007044636,0.00017253419,0.13959233],"study_design_scores_gemma":[0.00010200679,0.00070389116,0.0023467124,0.000033254044,0.00007271883,0.0001009861,0.00015582329,0.93974066,0.048902232,0.0070203184,0.000789538,0.00003180755],"about_ca_topic_score_codex":0.0011408883,"about_ca_topic_score_gemma":0.0010678107,"teacher_disagreement_score":0.0022279923,"about_ca_system_score_codex":0.00074651325,"about_ca_system_score_gemma":0.00067852665,"threshold_uncertainty_score":0.011782885},"labels":[],"label_agreement":null},{"id":"W2162711978","doi":"10.1109/nafips.2004.1336322","title":"A rough sets based approach to feature selection","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":68,"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 Regina","funders":"","keywords":"Rough set; Feature selection; Parameterized complexity; Heuristic; Computer science; Feature (linguistics); Artificial intelligence; Set (abstract data type); Pattern recognition (psychology); Selection (genetic algorithm); Data mining; Machine learning; Algorithm","score_opus":0.01177667978037862,"score_gpt":0.23319517097914638,"score_spread":0.22141849119876778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162711978","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012023505,0.001506181,0.99324244,0.00042184966,0.00010574864,0.00025686543,0.0002279842,0.00021707527,0.0028196168],"genre_scores_gemma":[0.06931592,0.0031361347,0.9225951,0.00032209774,0.00035033468,0.00074088725,0.0006398514,0.00006355104,0.0028361015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99274033,0.0025733479,0.00060460134,0.0007142608,0.0031834936,0.00018406285],"domain_scores_gemma":[0.9968311,0.0018898397,0.00025099935,0.00030706252,0.0006635151,0.000057468544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00519661,0.0015668164,0.0031480242,0.006271367,0.00092803553,0.0041733705,0.0024744244,0.0015470551,0.0036684948],"category_scores_gemma":[0.01168391,0.0007074795,0.003122449,0.00522484,0.001216815,0.0021721697,0.0018005961,0.0023600466,0.00159705],"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":[0.00013170065,0.00014177347,0.0012370001,0.0012280165,0.0007370467,0.00056855974,0.00040114805,0.18698342,0.0052356655,0.26068392,0.013873742,0.528778],"study_design_scores_gemma":[0.00012789559,0.0003812679,0.0013713881,0.0004576013,0.0003362767,0.00078594673,0.00026038848,0.3930489,0.0044080173,0.5317062,0.06691895,0.00019705172],"about_ca_topic_score_codex":0.0016931946,"about_ca_topic_score_gemma":0.00153245,"teacher_disagreement_score":0.006271367,"about_ca_system_score_codex":0.0014919449,"about_ca_system_score_gemma":0.0015756328,"threshold_uncertainty_score":0.027482688},"labels":[],"label_agreement":null},{"id":"W2165243821","doi":"10.1109/nafips.2004.1336243","title":"Parallel fuzzy cognitive maps as a tool for modeling software development projects","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":35,"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":"Fuzzy cognitive map; Computer science; Simple (philosophy); Process (computing); Realization (probability); Software; Software engineering; Simplicity; Fuzzy logic; Software development; Artificial intelligence; Systems engineering; Machine learning; Fuzzy set; Engineering; Programming language; Fuzzy classification","score_opus":0.02376407694336632,"score_gpt":0.26349608265084,"score_spread":0.2397320057074737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165243821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015784258,0.00014281656,0.97810566,0.00009926863,0.000032664968,0.00010607356,0.00024916584,0.00095564453,0.0045244507],"genre_scores_gemma":[0.4021876,0.00043387245,0.59159553,0.000050493272,0.00004310664,0.00068248797,0.00043709335,0.00011398657,0.00445581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953365,0.00013743495,0.000034639314,0.00008558207,0.00015021325,0.00005848197],"domain_scores_gemma":[0.9993549,0.00032278214,0.00008296303,0.00006600495,0.00012813174,0.000045190824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008198746,0.0009180165,0.0005018765,0.0021126051,0.0009031198,0.0015916495,0.0015669649,0.0010297445,0.003090332],"category_scores_gemma":[0.002206135,0.00043161277,0.0013477358,0.0015772795,0.0007108675,0.0012720138,0.001030188,0.0010066661,0.00041687462],"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":[0.000057695535,0.000048415546,0.0009919532,0.000059726124,0.000055248067,0.00015982884,0.0002239342,0.91285044,0.0010221562,0.056070358,0.00082731503,0.027632942],"study_design_scores_gemma":[0.00000629483,0.000010026145,0.000093417184,0.000005924792,0.000010857236,0.000015134218,0.000018586315,0.9864086,0.0002735076,0.011100038,0.0020509928,0.00000671258],"about_ca_topic_score_codex":0.03524002,"about_ca_topic_score_gemma":0.018041354,"teacher_disagreement_score":0.03524002,"about_ca_system_score_codex":0.0018262324,"about_ca_system_score_gemma":0.0020136884,"threshold_uncertainty_score":0.07006979},"labels":[],"label_agreement":null},{"id":"W2166134037","doi":"10.1109/nafips.2004.1336329","title":"A fuzzy approach to segmenting the breast region in mammograms","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"AI in cancer detection","field":"Computer Science","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 Guelph","funders":"","keywords":"Thresholding; Artificial intelligence; Segmentation; Computer science; Pixel; Image segmentation; Pattern recognition (psychology); Computer vision; Fuzzy logic; Image (mathematics)","score_opus":0.011792257961767187,"score_gpt":0.22704875014040865,"score_spread":0.21525649217864146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166134037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006217974,0.0006772003,0.98961836,0.0002218546,0.00006155218,0.00007644063,0.00006553514,0.00012504554,0.0029359262],"genre_scores_gemma":[0.10671617,0.0010365875,0.8878738,0.00014439374,0.00016069834,0.000093932635,0.0000988021,0.000043869848,0.0038316965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994567,0.00009050597,0.00004717768,0.00011158796,0.00025558283,0.00003844629],"domain_scores_gemma":[0.9995436,0.00021506971,0.000040836436,0.000039714825,0.00013875711,0.00002185888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008527389,0.00038023578,0.0004892549,0.0018578493,0.0008384091,0.0014145559,0.001081076,0.0012548652,0.001973531],"category_scores_gemma":[0.0018160708,0.0003902875,0.000857405,0.0012053049,0.00114304,0.00091329793,0.0005735201,0.00090896845,0.0005047608],"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":[0.00024815692,0.00011187218,0.0016043974,0.00057284156,0.000093462724,0.000773926,0.0010333396,0.25745094,0.07983735,0.13229908,0.0038023198,0.5221723],"study_design_scores_gemma":[0.000019321156,0.00012056059,0.001646868,0.00013397544,0.00006590123,0.0008012114,0.00021812909,0.90228415,0.013496984,0.065002576,0.016142262,0.00006800295],"about_ca_topic_score_codex":0.0069240425,"about_ca_topic_score_gemma":0.008254853,"teacher_disagreement_score":0.0069240425,"about_ca_system_score_codex":0.0009623382,"about_ca_system_score_gemma":0.0007901189,"threshold_uncertainty_score":0.013767481},"labels":[],"label_agreement":null},{"id":"W2169846713","doi":"10.1109/nafips.2004.1337420","title":"Similarity confidence level for fuzzy rulebases","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Fuzzy logic; Fuzzy classification; Fuzzy set; Defuzzification; Fuzzy number; Thresholding; Fuzzy set operations; Euclidean distance; Signed distance function; Artificial intelligence; Algorithm; Computer science; Image (mathematics)","score_opus":0.02535880933477681,"score_gpt":0.25207630214991394,"score_spread":0.22671749281513714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169846713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11865241,0.0007924368,0.8741648,0.00024459127,0.000077382465,0.00018963181,0.000366331,0.0011095721,0.004402819],"genre_scores_gemma":[0.74352396,0.00016881878,0.25391784,0.00006956069,0.00004731153,0.00016725058,0.0008468806,0.00011089465,0.0011474675],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9855409,0.0019926429,0.0015921043,0.002685955,0.007557787,0.0006307217],"domain_scores_gemma":[0.96290094,0.022144405,0.0028372072,0.0029476748,0.008331555,0.0008382026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011068751,0.0005899095,0.0014675494,0.0060780807,0.00095309166,0.004731108,0.002135769,0.0020320504,0.0035486394],"category_scores_gemma":[0.08813826,0.0005247774,0.0014978816,0.0024161222,0.0014079564,0.0044409693,0.0022451067,0.0013345502,0.0009920908],"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":[0.0024748247,0.00038506606,0.030869957,0.0009716159,0.00049375376,0.0006113108,0.0015415348,0.25667867,0.017792087,0.09791856,0.0045366487,0.58572596],"study_design_scores_gemma":[0.000060603586,0.0003669232,0.0072079753,0.00011549351,0.00009330144,0.00035233577,0.00031253212,0.91938907,0.012891853,0.05608171,0.0030395628,0.000088704255],"about_ca_topic_score_codex":0.0024533558,"about_ca_topic_score_gemma":0.0011991468,"teacher_disagreement_score":0.011068751,"about_ca_system_score_codex":0.0020133895,"about_ca_system_score_gemma":0.00088001654,"threshold_uncertainty_score":0.05853784},"labels":[],"label_agreement":null},{"id":"W2172183322","doi":"10.1109/nafips.2004.1337401","title":"Interval clustering using fuzzy and rough set theory","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Fuzzy set; Cluster analysis; Extension (predicate logic); Rough set; Fuzzy clustering; Mathematics; Upper and lower bounds; Interval (graph theory); Cluster (spacecraft); Pattern recognition (psychology); Data mining; Ambiguity; Fuzzy logic; Computer science; Artificial intelligence; Representation (politics); Algorithm; Combinatorics","score_opus":0.01914241645866393,"score_gpt":0.25458522510684717,"score_spread":0.23544280864818323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172183322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029721956,0.00096879405,0.99247205,0.000118681644,0.00007740134,0.00007362515,0.00006356837,0.00020755533,0.0030461836],"genre_scores_gemma":[0.081769,0.0012866762,0.91487473,0.00007714927,0.00018349258,0.00015794294,0.00023110407,0.00007374648,0.001346213],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962261,0.0011617292,0.00028017492,0.0005980531,0.0016219495,0.00011207344],"domain_scores_gemma":[0.99743205,0.0012798323,0.000251485,0.00042795853,0.0005545705,0.000054063774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033871601,0.0007923503,0.0016945589,0.004145484,0.0011222262,0.002960255,0.0013247473,0.0011472469,0.0018763043],"category_scores_gemma":[0.010029485,0.00048572847,0.001662845,0.004832122,0.0013736626,0.0032196657,0.0015664338,0.0015148268,0.00092796],"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":[0.00017590671,0.00006472036,0.00090801826,0.0005691627,0.00021695286,0.0002500885,0.00082567154,0.23841815,0.005567376,0.31864718,0.0046291775,0.4297276],"study_design_scores_gemma":[0.000035561814,0.00011287561,0.0010609464,0.00014438608,0.00008021227,0.00038240245,0.0002523482,0.60330343,0.006061711,0.35844612,0.02997221,0.00014777613],"about_ca_topic_score_codex":0.0020547376,"about_ca_topic_score_gemma":0.0015927887,"teacher_disagreement_score":0.004145484,"about_ca_system_score_codex":0.0011498453,"about_ca_system_score_gemma":0.0010517576,"threshold_uncertainty_score":0.017913163},"labels":[],"label_agreement":null}]}