{"meta":{"query_hash":"6e3b5b55b12b","filters":{"venue":"2022 International Joint Conference on Neural Networks (IJCNN)"},"cohort_total":20,"direct_labels_cover":0,"predictions_cover":20,"exported":20,"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/6e3b5b55b12b","api":"https://metacan.xera.ac/api/v1/cohort?venue=2022+International+Joint+Conference+on+Neural+Networks+%28IJCNN%29"},"results":[{"id":"W3209063479","doi":"10.1109/ijcnn55064.2022.9892343","title":"On the Effectiveness of Interpretable Feedforward Neural Network","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Computer science; Artificial intelligence; Feedforward neural network; Artificial neural network; Feed forward; Machine learning; Class (philosophy); Time delay neural network; Linear classifier; Support vector machine; Engineering","score_opus":0.019670531961297057,"score_gpt":0.2527584031892731,"score_spread":0.23308787122797606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209063479","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":[0.17060001,0.005092672,0.8070649,0.0031587393,0.00028137377,0.00006861008,0.00023773288,0.00086471986,0.0126312515],"genre_scores_gemma":[0.95509654,0.0016216977,0.040539507,0.000357882,0.00018934441,0.000051611216,0.00022241287,0.000094800904,0.0018261046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983157,0.0007834474,0.00009182978,0.0002943787,0.00040846624,0.000106230815],"domain_scores_gemma":[0.983937,0.012982395,0.0009645553,0.0012002544,0.000774514,0.00014133021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046689413,0.0015284256,0.00066637044,0.00082900684,0.0003671175,0.0010706359,0.0009496929,0.0017048913,0.0018086787],"category_scores_gemma":[0.025622254,0.00034872245,0.00056078105,0.00033267058,0.0023898385,0.0027382805,0.0013655208,0.0023576657,0.00028139347],"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":[0.00051425584,0.00010050388,0.0041023814,0.0002555934,0.00013243583,0.00034480917,0.00021941247,0.7942859,0.00878558,0.07244889,0.0021074496,0.11670274],"study_design_scores_gemma":[0.000014309016,0.00015095607,0.0009595467,0.000064988155,0.000027106444,0.000090734495,0.000027645341,0.9513721,0.003881986,0.04263116,0.0007598218,0.000019689594],"about_ca_topic_score_codex":0.0012027978,"about_ca_topic_score_gemma":0.00091304514,"teacher_disagreement_score":0.0046689413,"about_ca_system_score_codex":0.00092799845,"about_ca_system_score_gemma":0.00037508513,"threshold_uncertainty_score":0.024691999},"labels":[],"label_agreement":null},{"id":"W4312228327","doi":"10.1109/ijcnn55064.2022.9892459","title":"Learning to Stabilize Extreme Neural Machines with Metaplasticity","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Neural Networks and Reservoir Computing","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 Ottawa","funders":"","keywords":"Recurrent neural network; Computer science; Artificial intelligence; Ambiguity; Artificial neural network; Metaplasticity; Machine learning; Task (project management); Recall; Synaptic plasticity; Engineering; Psychology; Cognitive psychology","score_opus":0.037889341266372,"score_gpt":0.24646985906095353,"score_spread":0.20858051779458153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312228327","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":[0.0871347,0.0002923268,0.9099157,0.0002426587,0.00005343677,0.0000340572,0.000024899868,0.0007560905,0.0015461337],"genre_scores_gemma":[0.93320346,0.0001354311,0.06502863,0.00011636075,0.000029428798,0.00009297601,0.000045883284,0.00006920496,0.0012785897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997601,0.0000650967,0.000023022452,0.00006198942,0.00005318021,0.000036617945],"domain_scores_gemma":[0.9989225,0.00054142677,0.00019146768,0.00017456323,0.00012332221,0.000046556295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010187571,0.0006829587,0.0008105175,0.0003308519,0.00030609276,0.0006832061,0.0011829,0.0009784531,0.0011651381],"category_scores_gemma":[0.004158427,0.0005556457,0.00058231974,0.00025058948,0.0010802159,0.0014621343,0.0013206657,0.0013480464,0.00023527039],"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":[0.00005967218,0.00003496297,0.00067553634,0.0000597118,0.000060610484,0.00009576469,0.00007415127,0.94147044,0.010228212,0.016821468,0.0004062885,0.030013192],"study_design_scores_gemma":[0.0000033708486,0.000026085574,0.000035417965,0.0000024320445,0.000003466893,0.0000118888565,0.000002435675,0.99503195,0.00095467764,0.0038051056,0.000119379554,0.000003681191],"about_ca_topic_score_codex":0.0007604071,"about_ca_topic_score_gemma":0.0010866259,"teacher_disagreement_score":0.0011829,"about_ca_system_score_codex":0.00045817354,"about_ca_system_score_gemma":0.000431085,"threshold_uncertainty_score":0.005387783},"labels":[],"label_agreement":null},{"id":"W4312232211","doi":"10.1109/ijcnn55064.2022.9892702","title":"MRGAN: Multi-Criteria Relational GAN for Lyrics-Conditional Melody Generation","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Music and Audio Processing","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":"Concordia University","funders":"","keywords":"Computer science; Discriminator; Lyrics; Artificial intelligence; Generator (circuit theory); BLEU; Leverage (statistics); Speech recognition; Generative grammar; Key (lock); Metric (unit); Melody; Natural language processing; Machine learning; Musical; Machine translation; Power (physics)","score_opus":0.10709007266822583,"score_gpt":0.3022042917470935,"score_spread":0.19511421907886767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312232211","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":[0.029337816,0.0009931333,0.9610521,0.00027296142,0.00009346584,0.000100788864,0.00031712945,0.001911918,0.00592074],"genre_scores_gemma":[0.76045716,0.00053675025,0.22700645,0.0005849532,0.00008176584,0.00024403831,0.001848405,0.0003628927,0.008877503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965394,0.00012520626,0.000012529471,0.00009565828,0.00007638758,0.000036352725],"domain_scores_gemma":[0.9996277,0.00020678322,0.00002864609,0.000058239955,0.00005561473,0.000022952938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082243414,0.001055741,0.0006176582,0.0003475467,0.00018325454,0.00044290864,0.0011420056,0.00060956925,0.0026884058],"category_scores_gemma":[0.0015881365,0.00024148569,0.000519171,0.00034011743,0.00038449766,0.0006425657,0.0007651782,0.0010833623,0.0005795648],"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":[0.00019848521,0.000110518966,0.0014508053,0.00013094385,0.000116003495,0.00018133882,0.00006920316,0.7588423,0.01155624,0.013172491,0.008691199,0.20548052],"study_design_scores_gemma":[0.000008403066,0.000023345277,0.00012851224,0.000005031893,0.000008400741,0.0000321696,0.000004886697,0.99536693,0.0013114549,0.002383498,0.00072269596,0.000004712088],"about_ca_topic_score_codex":0.0020855118,"about_ca_topic_score_gemma":0.003772158,"teacher_disagreement_score":0.0026884058,"about_ca_system_score_codex":0.00055537064,"about_ca_system_score_gemma":0.0004884902,"threshold_uncertainty_score":0.008993626},"labels":[],"label_agreement":null},{"id":"W4312285549","doi":"10.1109/ijcnn55064.2022.9892667","title":"LockBoost: Detecting Malware Binaries by Locking False Alarms","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"False positive paradox; False positive rate; Computer science; Malware; Boosting (machine learning); True positive rate; Artificial intelligence; Classifier (UML); False positives and false negatives; Machine learning; Detector; Data mining; Computer security","score_opus":0.02287542947868864,"score_gpt":0.2541074936592573,"score_spread":0.23123206418056863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312285549","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":[0.26215127,0.0048919334,0.6718454,0.0013240715,0.0011374428,0.0008313333,0.0015025461,0.04831509,0.008000921],"genre_scores_gemma":[0.6962288,0.00046878896,0.29195833,0.0011395903,0.00027913827,0.00048180862,0.0022763337,0.0010334402,0.006133846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980661,0.0003049902,0.000110086476,0.00046226213,0.0007383203,0.0003183007],"domain_scores_gemma":[0.99641705,0.0016846854,0.0004493525,0.00046162252,0.0008140809,0.00017312133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029025574,0.002174129,0.0017506892,0.002429037,0.00083865324,0.0015343869,0.0030402278,0.0019505649,0.0021805076],"category_scores_gemma":[0.008086192,0.0005798728,0.00079101016,0.0013402821,0.00095504016,0.002155733,0.0012598855,0.0020901414,0.0017872348],"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":[0.0017465756,0.0014070505,0.026502779,0.000667704,0.00040746888,0.0002707206,0.00016035688,0.10020681,0.039298277,0.0029593278,0.04052712,0.78584594],"study_design_scores_gemma":[0.00014328561,0.00043624212,0.00404696,0.000067946225,0.00008595378,0.00035178993,0.00006097111,0.95577157,0.02855126,0.0039661834,0.0064476747,0.000070154834],"about_ca_topic_score_codex":0.0044282917,"about_ca_topic_score_gemma":0.0060868673,"teacher_disagreement_score":0.0044282917,"about_ca_system_score_codex":0.000967438,"about_ca_system_score_gemma":0.0019930482,"threshold_uncertainty_score":0.015350342},"labels":[],"label_agreement":null},{"id":"W4312326921","doi":"10.1109/ijcnn55064.2022.9892485","title":"Adversarial Fine-tune with Dynamically Regulated Adversary","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Adversarial system; Robustness (evolution); Computer science; Adversary; Artificial intelligence; Machine learning; Robot; Training set; Computer security","score_opus":0.015183575455262962,"score_gpt":0.2337178866282841,"score_spread":0.21853431117302113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312326921","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":[0.019827634,0.00021767954,0.9763713,0.0002768001,0.000057703597,0.0000540741,0.000042732343,0.00070223876,0.0024498857],"genre_scores_gemma":[0.9131884,0.00023302317,0.08180246,0.00035734355,0.00006975517,0.00012200711,0.00013559546,0.00016500588,0.003926355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990445,0.0003117653,0.000044695706,0.00023589995,0.00023484898,0.00012837688],"domain_scores_gemma":[0.9972276,0.0016880034,0.0002303965,0.0005643383,0.00019564746,0.000093995855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018365706,0.0013139145,0.0009745915,0.00037838603,0.00046568003,0.00071696413,0.0014758699,0.0011043467,0.0020499963],"category_scores_gemma":[0.006369205,0.00039375437,0.0007058669,0.00030254907,0.0018524976,0.0016076748,0.0027628215,0.0025968691,0.00058386446],"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":[0.00011883596,0.000049148042,0.00065267406,0.000052635005,0.000050920622,0.000101396276,0.000062285806,0.9396585,0.009384825,0.014590861,0.0013947574,0.033883043],"study_design_scores_gemma":[0.0000043223004,0.00003227126,0.000082196726,0.000005289246,0.000005413598,0.000030308931,0.0000050685517,0.99232763,0.0020047955,0.005109282,0.00038628408,0.0000070869864],"about_ca_topic_score_codex":0.0012399819,"about_ca_topic_score_gemma":0.0010273037,"teacher_disagreement_score":0.0020499963,"about_ca_system_score_codex":0.0006781307,"about_ca_system_score_gemma":0.00066198915,"threshold_uncertainty_score":0.009712815},"labels":[],"label_agreement":null},{"id":"W4312353038","doi":"10.1109/ijcnn55064.2022.9892285","title":"Named Entity Recognition for Audio De-Identification","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs","keywords":"Pipeline (software); Computer science; Task (project management); Named-entity recognition; Speech recognition; Identification (biology); Natural language processing; Audio mining; Artificial intelligence; Acoustic model; Speech processing; Programming language","score_opus":0.04035002746334673,"score_gpt":0.2873490868881336,"score_spread":0.24699905942478687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312353038","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":[0.015049714,0.002953917,0.93327844,0.0008051882,0.0005163353,0.00038266083,0.010386644,0.028950488,0.007676499],"genre_scores_gemma":[0.15797172,0.001833861,0.7848202,0.00039553485,0.00024527838,0.00047757864,0.043100607,0.00078707276,0.01036817],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99632865,0.0010479076,0.00034168424,0.0012973404,0.0007811256,0.00020328182],"domain_scores_gemma":[0.99482226,0.001654947,0.0004761971,0.0018253762,0.0011202246,0.00010101125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030179578,0.0013774879,0.00091223686,0.0033073416,0.0011608043,0.0015821952,0.0017070733,0.0013181217,0.00810804],"category_scores_gemma":[0.008510816,0.00038470703,0.0011688381,0.0027092823,0.00056693354,0.0042371685,0.0019614617,0.001483993,0.009284289],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039439075,0.00014137717,0.0026642052,0.0007968777,0.0001695832,0.00047930476,0.00039837105,0.008874414,0.053236637,0.010850634,0.04746371,0.87453043],"study_design_scores_gemma":[0.00007511585,0.00027863236,0.012390841,0.00033580486,0.0002920815,0.0022228444,0.0010535693,0.35880524,0.21314885,0.052829813,0.3582981,0.00026918025],"about_ca_topic_score_codex":0.0043763407,"about_ca_topic_score_gemma":0.0057663913,"teacher_disagreement_score":0.00810804,"about_ca_system_score_codex":0.0007884038,"about_ca_system_score_gemma":0.0011242519,"threshold_uncertainty_score":0.027124107},"labels":[],"label_agreement":null},{"id":"W4312380174","doi":"10.1109/ijcnn55064.2022.9891933","title":"Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Advanced Neural Network Applications","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":"Ericsson (Canada); École de Technologie Supérieure","funders":"","keywords":"Pascal (unit); Ground truth; Artificial intelligence; Computer science; Minimum bounding box; Annotation; Machine learning; Bounding overwatch; Object detection; Object (grammar); Source code; Pattern recognition (psychology); Natural language processing; Image (mathematics)","score_opus":0.03577843570185908,"score_gpt":0.267846178377684,"score_spread":0.23206774267582492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312380174","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":[0.06792201,0.00033702012,0.91716707,0.00016063672,0.00007092739,0.00021931218,0.00071675127,0.011722993,0.001683175],"genre_scores_gemma":[0.43794447,0.00016218233,0.5486577,0.0002657873,0.00007183988,0.00028859568,0.007582538,0.00090284174,0.004123921],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9952579,0.0015399989,0.00018803749,0.001574246,0.0010819242,0.0003578531],"domain_scores_gemma":[0.9882606,0.004876265,0.0007682789,0.0037251469,0.001943971,0.00042572178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044053067,0.0023838729,0.0020784452,0.0014245021,0.00068553706,0.0021591196,0.004341648,0.0028466864,0.0030790062],"category_scores_gemma":[0.013958742,0.0011250033,0.0014095543,0.0010351953,0.0017899266,0.004001492,0.003270381,0.0024146605,0.004344842],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002087795,0.00063660555,0.011278406,0.0004281114,0.00021327559,0.00036043653,0.00041537022,0.28508547,0.03850373,0.0066865026,0.020084156,0.6342202],"study_design_scores_gemma":[0.000021440663,0.000060149778,0.00069872616,0.000015915897,0.000012416053,0.00011552948,0.00003381696,0.9821801,0.012045334,0.0035449658,0.0012576057,0.000014019941],"about_ca_topic_score_codex":0.0042360304,"about_ca_topic_score_gemma":0.0062647588,"teacher_disagreement_score":0.0044053067,"about_ca_system_score_codex":0.0013075442,"about_ca_system_score_gemma":0.0013916735,"threshold_uncertainty_score":0.023297787},"labels":[],"label_agreement":null},{"id":"W4312427403","doi":"10.1109/ijcnn55064.2022.9892185","title":"RegTraffic: A Regression based Traffic Simulator for Spatiotemporal Traffic Modeling, Simulation and Visualization","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Traffic Prediction and Management Techniques","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":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic flow (computer networking); Visualization; Intersection (aeronautics); Traffic generation model; Floating car data; Traffic congestion; Traffic simulation; Simulation; Mean squared error; Real-time computing; Data mining; Transport engineering; Computer network; Engineering; Statistics","score_opus":0.04065527659608493,"score_gpt":0.2864802004722471,"score_spread":0.24582492387616217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312427403","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":[0.09461138,0.00027043984,0.8260561,0.0003101285,0.00015171716,0.00034759918,0.0067245835,0.055570435,0.015957579],"genre_scores_gemma":[0.7105913,0.0005464806,0.26710707,0.00015516594,0.000036614987,0.00075977895,0.009516205,0.0025902742,0.008697097],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997867,0.000047077323,0.000013842911,0.000032394146,0.0000938882,0.000026155913],"domain_scores_gemma":[0.99955803,0.00018969359,0.000032074695,0.000048592152,0.00013343443,0.000038102356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042275264,0.00068921375,0.00051303365,0.0005961353,0.00028996644,0.00052285736,0.0015884587,0.00058709877,0.0053394483],"category_scores_gemma":[0.0014718699,0.0002814533,0.0005007018,0.0006901885,0.00023701912,0.00064427726,0.00053124834,0.00088479184,0.0008003936],"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":[0.000093174756,0.000094612806,0.0025238122,0.000074961616,0.00004849528,0.00008759648,0.000075862605,0.9493278,0.005247007,0.0036430096,0.010487646,0.028295938],"study_design_scores_gemma":[0.000009855799,0.00001138501,0.00018535445,0.000002559802,0.000004063913,0.000013813436,0.0000045260535,0.99562293,0.0011787758,0.00037725625,0.0025814616,0.000007944593],"about_ca_topic_score_codex":0.04129022,"about_ca_topic_score_gemma":0.026655857,"teacher_disagreement_score":0.04129022,"about_ca_system_score_codex":0.0007344798,"about_ca_system_score_gemma":0.0014597144,"threshold_uncertainty_score":0.082099795},"labels":[],"label_agreement":null},{"id":"W4312442702","doi":"10.1109/ijcnn55064.2022.9891956","title":"Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Artificial intelligence; Initialization; Pattern recognition (psychology); Unsupervised learning; Random forest; Ensemble learning; Feature learning; Emotion recognition; Machine learning; Support vector machine; Feature (linguistics); Feature selection","score_opus":0.083390703633033,"score_gpt":0.31248590296305234,"score_spread":0.22909519933001934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312442702","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":[0.7088359,0.0026935386,0.27824688,0.00036118607,0.0003420512,0.00044516491,0.0006592584,0.00229993,0.006116056],"genre_scores_gemma":[0.91102153,0.00058978726,0.08393477,0.00010491723,0.00004866794,0.00017715465,0.0017710408,0.00011164728,0.0022405107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985593,0.0005237325,0.000087273635,0.0002410153,0.00047462436,0.00011405175],"domain_scores_gemma":[0.9979752,0.000877788,0.00012075371,0.00032105533,0.0006300969,0.00007521208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036391837,0.0008414756,0.00058664125,0.0008352196,0.0002418615,0.0005892116,0.0008050132,0.00075227354,0.0011309794],"category_scores_gemma":[0.006125843,0.00015798306,0.00055044633,0.00043915148,0.00041881582,0.0010215548,0.0007334383,0.00070585916,0.00038729238],"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":[0.0015166408,0.0012157849,0.0065356437,0.00041290783,0.0004782461,0.00011090565,0.00017625449,0.23304345,0.033221137,0.0022690403,0.004602629,0.7164173],"study_design_scores_gemma":[0.00004322101,0.0009395505,0.004420371,0.000028059,0.00006528276,0.00014423432,0.00006814252,0.97041816,0.02235309,0.00067571393,0.00082015275,0.00002401694],"about_ca_topic_score_codex":0.001463282,"about_ca_topic_score_gemma":0.0016534029,"teacher_disagreement_score":0.0036391837,"about_ca_system_score_codex":0.0005871458,"about_ca_system_score_gemma":0.00042049383,"threshold_uncertainty_score":0.019246042},"labels":[],"label_agreement":null},{"id":"W4312571812","doi":"10.1109/ijcnn55064.2022.9892638","title":"Audit of Computational Intelligence Techniques for EDI-aware Systems","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Audit; Computer science; Computational intelligence; Probabilistic logic; Equity (law); Artificial intelligence; Data science; Machine learning; Operations research; Accounting; Business; Engineering","score_opus":0.08784051540935311,"score_gpt":0.36801134346474745,"score_spread":0.28017082805539434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312571812","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":[0.024740916,0.005949652,0.8660203,0.034298893,0.00092226634,0.0007730754,0.0003544917,0.0038284448,0.06311193],"genre_scores_gemma":[0.45022494,0.0055143065,0.5316284,0.0018064211,0.00034970345,0.0004915752,0.0004705018,0.0005383612,0.008975742],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9473668,0.021007111,0.0032367522,0.0021729835,0.024762435,0.0014539593],"domain_scores_gemma":[0.8882153,0.039403714,0.005747045,0.04156768,0.023907334,0.0011590151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.036583506,0.00076488947,0.0008178334,0.0047465307,0.0020528876,0.010056066,0.0028400577,0.0018843908,0.005244329],"category_scores_gemma":[0.10941705,0.00083289854,0.00084567367,0.005155623,0.005099434,0.016192522,0.006399314,0.0064763166,0.0014336169],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016591806,0.0001345166,0.0024906145,0.00064038247,0.00006076475,0.00012566312,0.0009751415,0.01728606,0.0028039755,0.5479136,0.0099411225,0.41746232],"study_design_scores_gemma":[0.00006357953,0.00022881813,0.0017435468,0.0010793662,0.00007079374,0.00042630255,0.0010225974,0.19457403,0.022578897,0.5499004,0.22818689,0.00012480338],"about_ca_topic_score_codex":0.0023031346,"about_ca_topic_score_gemma":0.001991466,"teacher_disagreement_score":0.036583506,"about_ca_system_score_codex":0.00407845,"about_ca_system_score_gemma":0.009090624,"threshold_uncertainty_score":0.19347435},"labels":[],"label_agreement":null},{"id":"W4312757722","doi":"10.1109/ijcnn55064.2022.9892631","title":"Hand Gesture Classification on Praxis Dataset: Trading Accuracy for Expense","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Gesture; Computer science; Gesture recognition; Artificial intelligence; Recurrent neural network; Deep learning; Joint (building); Praxis; Pattern recognition (psychology); Speech recognition; Machine learning; Artificial neural network; Engineering","score_opus":0.09535500339808643,"score_gpt":0.31137331760407716,"score_spread":0.2160183142059907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312757722","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":[0.6759672,0.020977443,0.1284765,0.0033833135,0.0026933874,0.0022609953,0.08107955,0.060964704,0.024196915],"genre_scores_gemma":[0.67434025,0.002711034,0.18391427,0.00092553423,0.00049788656,0.0014991588,0.12092434,0.001353525,0.013833936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99669987,0.0005157549,0.00041225232,0.0009637988,0.0011137332,0.0002945226],"domain_scores_gemma":[0.9968971,0.0012896555,0.00019303393,0.00084549567,0.00064082607,0.00013402553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027095103,0.0033743505,0.0022014596,0.0030819525,0.00066758605,0.001599577,0.0020309542,0.0020176962,0.0047906865],"category_scores_gemma":[0.008463883,0.00036909617,0.001126745,0.0020886748,0.00048824883,0.0024088398,0.001890502,0.0015626798,0.0059748366],"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":[0.0023271167,0.0007602075,0.019376205,0.0007965877,0.00055660255,0.0007299516,0.000099015844,0.033282727,0.021493407,0.00060268264,0.060843863,0.8591317],"study_design_scores_gemma":[0.00042310279,0.001239227,0.042377982,0.00026401778,0.00025691622,0.0016579368,0.00038326322,0.8728141,0.04837771,0.0029339003,0.029105606,0.00016620933],"about_ca_topic_score_codex":0.008007398,"about_ca_topic_score_gemma":0.01572537,"teacher_disagreement_score":0.008007398,"about_ca_system_score_codex":0.0008413065,"about_ca_system_score_gemma":0.0007834769,"threshold_uncertainty_score":0.016026437},"labels":[],"label_agreement":null},{"id":"W4312790299","doi":"10.1109/ijcnn55064.2022.9892629","title":"Imposing Connectome-Derived Topology on an Echo State Network","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Echo (communications protocol); Connectome; Computer science; Topology (electrical circuits); Network topology; State (computer science); Computer network; Distributed computing; Functional connectivity; Neuroscience; Algorithm; Psychology; Engineering; Electrical engineering","score_opus":0.03079725123440288,"score_gpt":0.2688755193182981,"score_spread":0.23807826808389523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312790299","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":[0.81322914,0.00025244572,0.18088783,0.0010308968,0.000065712295,0.000036786318,0.00024585178,0.00026893322,0.00398248],"genre_scores_gemma":[0.9925391,0.00007054107,0.0067245,0.000054874083,0.000008559072,0.000022351163,0.00010530204,0.000019345016,0.00045551843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966824,0.00013831607,0.000017367716,0.00007569747,0.00005727486,0.0000429974],"domain_scores_gemma":[0.99587494,0.0028437767,0.00033737626,0.000497098,0.0003083392,0.00013846249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013468477,0.0004891714,0.00044817844,0.00027933076,0.00030716183,0.0007971264,0.00071166735,0.00084283424,0.0017995678],"category_scores_gemma":[0.013622194,0.00035415785,0.0003599912,0.00024076633,0.0010300026,0.002692964,0.0011083431,0.0011859654,0.00016656287],"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":[0.00011725923,0.000021637163,0.0027988718,0.00004509817,0.000035282526,0.00009135172,0.000041084564,0.9758158,0.0037665037,0.0074219364,0.00031005088,0.009535214],"study_design_scores_gemma":[0.000008042836,0.00004340153,0.00075025606,0.000006043151,0.0000075880407,0.0000238615,0.000011326459,0.9928328,0.0011789544,0.0050052307,0.0001239282,0.000008653122],"about_ca_topic_score_codex":0.0029935618,"about_ca_topic_score_gemma":0.003800979,"teacher_disagreement_score":0.0029935618,"about_ca_system_score_codex":0.00052354956,"about_ca_system_score_gemma":0.00059442356,"threshold_uncertainty_score":0.0071228743},"labels":[],"label_agreement":null},{"id":"W4312808294","doi":"10.1109/ijcnn55064.2022.9892192","title":"Transfer Learning Framework for Forecasting Fresh Produce Yield and Price","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Computer science; Artificial intelligence; Transfer of learning; Similarity (geometry); Artificial neural network; Bootstrapping (finance); Deep learning; Feed forward; Machine learning; Generalization; Feedforward neural network; Measure (data warehouse); Data mining; Pattern recognition (psychology); Econometrics; Mathematics; Image (mathematics); Engineering","score_opus":0.20726932340726598,"score_gpt":0.37437057708118643,"score_spread":0.16710125367392045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312808294","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":[0.102526955,0.0015576978,0.8865721,0.00076432235,0.00020585753,0.000065103064,0.00048982335,0.0015226175,0.006295556],"genre_scores_gemma":[0.9546108,0.00054851756,0.037558623,0.00013947868,0.00011708455,0.00011299465,0.000646548,0.000048721555,0.0062171337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998005,0.000037166417,0.000012112425,0.000065049186,0.000044419638,0.000040672283],"domain_scores_gemma":[0.99979454,0.00007506446,0.000024488678,0.000014132955,0.000079473044,0.000012357291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006339451,0.00085086765,0.00069474196,0.000519923,0.00024628168,0.000822185,0.0013781558,0.0010469835,0.0019031996],"category_scores_gemma":[0.0011703472,0.0002825422,0.00070915354,0.0007107584,0.00032840137,0.001056995,0.0005418982,0.0012241843,0.00044193573],"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":[0.00005776791,0.00006534884,0.0011539713,0.000028092572,0.00004122262,0.0000690449,0.000024343693,0.9507694,0.00083818677,0.0032982973,0.0012685837,0.042385638],"study_design_scores_gemma":[0.0000010839132,0.0000045762968,0.00008322722,8.4853997e-7,0.0000021112073,0.0000017515932,0.000001585239,0.9991241,0.00006493034,0.0006245948,0.00008976106,0.0000014393064],"about_ca_topic_score_codex":0.02492119,"about_ca_topic_score_gemma":0.01306929,"teacher_disagreement_score":0.02492119,"about_ca_system_score_codex":0.0010225917,"about_ca_system_score_gemma":0.001166768,"threshold_uncertainty_score":0.04955226},"labels":[],"label_agreement":null},{"id":"W4312873207","doi":"10.1109/ijcnn55064.2022.9892584","title":"Deep Reinforcement Learning for Penetration Testing of Cyber-Physical Attacks in the Smart Grid","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":16,"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; Artificial Intelligence in Medicine (Canada); Ericsson (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cyber-physical system; Reinforcement learning; Smart grid; Sandbox (software development); Distributed computing; Emulation; Cyber-attack; Computer security; Interconnectivity; Grid; Markov decision process; Artificial intelligence; Markov process; Engineering; Software engineering; Operating system","score_opus":0.032315920612443975,"score_gpt":0.25456673481043823,"score_spread":0.22225081419799425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312873207","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":[0.32030708,0.0008591355,0.67149687,0.0008443104,0.000083377585,0.000104415674,0.00012622701,0.0024482876,0.003730269],"genre_scores_gemma":[0.9813814,0.00007021426,0.017580034,0.000089527224,0.000008478034,0.000045056287,0.00007165505,0.00002664979,0.00072705926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955946,0.00016686629,0.000021889138,0.00009263377,0.00006822086,0.00009082457],"domain_scores_gemma":[0.99822503,0.0012027273,0.00016773061,0.00008346337,0.00021044452,0.00011056741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013048763,0.0009228187,0.0009236337,0.00039840807,0.0002650862,0.0005186309,0.0010021768,0.0008443628,0.0012349437],"category_scores_gemma":[0.0042504882,0.00046213198,0.0004785125,0.00022658489,0.000802225,0.00078829966,0.00092732447,0.0016417837,0.00016010462],"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":[0.000058234287,0.000059305774,0.0012925776,0.000024892995,0.00002227762,0.000042083135,0.000021228603,0.9823951,0.00048285935,0.0010166822,0.00023925769,0.014345526],"study_design_scores_gemma":[0.0000023779965,0.000010498138,0.000050828872,0.0000010346458,0.0000013781518,0.0000013775815,0.0000012522523,0.9994899,0.00009608699,0.00032353835,0.000020869362,8.9369235e-7],"about_ca_topic_score_codex":0.011407267,"about_ca_topic_score_gemma":0.0077275704,"teacher_disagreement_score":0.011407267,"about_ca_system_score_codex":0.0010813356,"about_ca_system_score_gemma":0.0012575495,"threshold_uncertainty_score":0.022681713},"labels":[],"label_agreement":null},{"id":"W4312896061","doi":"10.1109/ijcnn55064.2022.9892722","title":"Meta-free few-shot learning via representation learning with weight averaging","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Machine learning; Computer science; Probabilistic logic; Meta learning (computer science); Benchmark (surveying); Representation (politics); Transfer of learning; Feature learning; Range (aeronautics); Task (project management)","score_opus":0.07457992658008096,"score_gpt":0.26766154343015214,"score_spread":0.1930816168500712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312896061","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":[0.015002108,0.00058639,0.9823497,0.00017677722,0.000058751146,0.000055650937,0.0000862344,0.0009917964,0.0006926661],"genre_scores_gemma":[0.64181685,0.0006654258,0.34984392,0.00062425504,0.00026475536,0.0003371077,0.0013348631,0.0004042685,0.004708531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986143,0.0003856201,0.00008459503,0.0005253657,0.0002588114,0.00013125333],"domain_scores_gemma":[0.99688834,0.0015366167,0.0002844654,0.0007272074,0.00040236057,0.00016100817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024359098,0.0019036534,0.0026883485,0.0014799034,0.00081191544,0.0016896512,0.004404592,0.0022296591,0.0021163016],"category_scores_gemma":[0.008747468,0.00085632567,0.0016721141,0.0015377356,0.0014224531,0.005005335,0.0027580408,0.0035852424,0.00093045033],"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":[0.00027826714,0.00035176327,0.0022013204,0.00031145228,0.0004079735,0.00016826052,0.000327515,0.48127836,0.008056238,0.017321773,0.0052337823,0.48406336],"study_design_scores_gemma":[0.0000083164905,0.00004832665,0.00014750558,0.000013017216,0.000020827732,0.00003106711,0.000015898608,0.9839983,0.001618037,0.01370553,0.000378696,0.000014451473],"about_ca_topic_score_codex":0.0036276581,"about_ca_topic_score_gemma":0.00426031,"teacher_disagreement_score":0.004404592,"about_ca_system_score_codex":0.0012545318,"about_ca_system_score_gemma":0.001196795,"threshold_uncertainty_score":0.012882471},"labels":[],"label_agreement":null},{"id":"W4313006142","doi":"10.1109/ijcnn55064.2022.9892861","title":"Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free Evaluation","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de Recherche du Québec-Société et Culture; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Firefly algorithm; Computer science; Metric (unit); Artificial neural network; Artificial intelligence; Machine learning; Algorithm; Baseline (sea); Architecture; Data mining; Engineering","score_opus":0.04243884968605028,"score_gpt":0.2803751729514579,"score_spread":0.23793632326540765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313006142","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":[0.0705789,0.0007851806,0.92098886,0.00036281513,0.00023084573,0.0002539165,0.00009202547,0.0031941317,0.0035133236],"genre_scores_gemma":[0.44070157,0.00028047082,0.55429506,0.00040339338,0.000115909024,0.00038403698,0.0003308761,0.0005170415,0.0029717328],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983846,0.00049334276,0.00011824491,0.00033618422,0.00047765582,0.00018991665],"domain_scores_gemma":[0.9974474,0.0011119462,0.00025082778,0.00038934272,0.00064261345,0.00015798915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043151593,0.002682048,0.002017576,0.002006606,0.0007594393,0.0011808291,0.0027801674,0.0022778877,0.0024603175],"category_scores_gemma":[0.008144253,0.0006512282,0.0012051637,0.0011605589,0.0010456747,0.0022738457,0.0015998055,0.0017886518,0.0007362093],"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":[0.00024683235,0.0003466482,0.0028321377,0.00014241175,0.00018171087,0.00008247255,0.00009701362,0.76681924,0.011112717,0.0044822018,0.003350857,0.21030591],"study_design_scores_gemma":[0.000033922388,0.00009651202,0.00017099685,0.000008996256,0.000017593098,0.00001867484,0.000008660506,0.99665534,0.0016994525,0.00087014993,0.00041162482,0.000008082501],"about_ca_topic_score_codex":0.006737082,"about_ca_topic_score_gemma":0.0074265166,"teacher_disagreement_score":0.006737082,"about_ca_system_score_codex":0.001841666,"about_ca_system_score_gemma":0.0023268822,"threshold_uncertainty_score":0.02282101},"labels":[],"label_agreement":null},{"id":"W4313016919","doi":"10.1109/ijcnn55064.2022.9892810","title":"E-LSTM: An extension to the LSTM architecture for incorporating long lag dependencies","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Compute Canada","keywords":"Computer science; Extension (predicate logic); Lag; Process (computing); Recurrent neural network; Long short term memory; Architecture; Artificial intelligence; Transmission (telecommunications); Artificial neural network; Pattern recognition (psychology); Machine learning","score_opus":0.04506472041204799,"score_gpt":0.27954777963322164,"score_spread":0.23448305922117366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313016919","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":[0.01598133,0.0006975806,0.97440004,0.0003076332,0.00023370357,0.000071884475,0.00049712637,0.0033616265,0.0044490383],"genre_scores_gemma":[0.40938252,0.001179333,0.5720125,0.00052400026,0.00019954953,0.00022099727,0.0015029957,0.00042162606,0.014556403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998342,0.00003246481,0.000015571974,0.000048465332,0.000044397624,0.000024926456],"domain_scores_gemma":[0.99976844,0.00008398516,0.000022206044,0.00004009936,0.0000717495,0.000013595479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043282044,0.0007101101,0.00036282244,0.00030068058,0.0002470194,0.00053524994,0.0012328124,0.00095672446,0.005826331],"category_scores_gemma":[0.0014502524,0.00029624053,0.00046034958,0.0004982983,0.00023819171,0.0015950805,0.0007280348,0.0011765726,0.0014850158],"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":[0.00031288664,0.00019564379,0.0015792706,0.00037688782,0.00022855544,0.00059449393,0.00017784662,0.20352003,0.061764542,0.017033268,0.016824605,0.697392],"study_design_scores_gemma":[0.00001577153,0.00006643469,0.00055642775,0.000029365197,0.000035934892,0.00017243421,0.00001774164,0.969815,0.011920092,0.008026388,0.009324448,0.000019935407],"about_ca_topic_score_codex":0.0030644974,"about_ca_topic_score_gemma":0.007059559,"teacher_disagreement_score":0.005826331,"about_ca_system_score_codex":0.00031509277,"about_ca_system_score_gemma":0.00074595545,"threshold_uncertainty_score":0.019491017},"labels":[],"label_agreement":null},{"id":"W4313025015","doi":"10.1109/ijcnn55064.2022.9892052","title":"Exploring the Effectiveness of Appearance Descriptor in DeepSORT","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Climate Forum","keywords":"Artificial intelligence; sort; Computer science; Intuition; Tracking (education); Computer vision; Active appearance model; Video tracking; Pattern recognition (psychology); Object detection; Component (thermodynamics); Machine learning; Object (grammar); Image (mathematics)","score_opus":0.11738076830328271,"score_gpt":0.2886747889039362,"score_spread":0.17129402060065352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313025015","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":[0.4059015,0.0013849415,0.5856335,0.000594952,0.0001552463,0.0000949648,0.00018518016,0.0023452437,0.0037044953],"genre_scores_gemma":[0.9089385,0.00024815896,0.08808074,0.0002127823,0.000032628264,0.00005055343,0.0003495668,0.000113079506,0.0019740365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995161,0.0000895236,0.000024988422,0.00014777946,0.00013101977,0.000090539834],"domain_scores_gemma":[0.9988913,0.00051601423,0.00010376982,0.00014977953,0.00024318665,0.00009610897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014929887,0.0010392223,0.001071421,0.0006570018,0.00029654996,0.0009209131,0.0017456989,0.0013127495,0.001415864],"category_scores_gemma":[0.003709499,0.0003557985,0.00051616895,0.0006242858,0.0006356282,0.002243975,0.0013482126,0.0012650159,0.00032503213],"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":[0.00038834504,0.00042678966,0.0040989253,0.00015446164,0.00011031382,0.0001296791,0.00008859266,0.65962934,0.014636933,0.0065316097,0.0020540473,0.31175095],"study_design_scores_gemma":[0.000010095542,0.00007458799,0.00020661743,0.0000031712123,0.000009063663,0.000016038255,0.000007741797,0.9968478,0.0015888948,0.0010836681,0.00014848397,0.0000038141384],"about_ca_topic_score_codex":0.005810504,"about_ca_topic_score_gemma":0.005332218,"teacher_disagreement_score":0.005810504,"about_ca_system_score_codex":0.0008820195,"about_ca_system_score_gemma":0.0011495338,"threshold_uncertainty_score":0.011553347},"labels":[],"label_agreement":null},{"id":"W4313042211","doi":"10.1109/ijcnn55064.2022.9892054","title":"Fine-grained Early Frequency Attention for Deep Speaker Recognition","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Focus (optics); Deep neural networks; Deep learning; Artificial intelligence; Key (lock); Artificial neural network; Speech recognition; Pattern recognition (psychology)","score_opus":0.054296543505739286,"score_gpt":0.2565625299664059,"score_spread":0.2022659864606666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313042211","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":[0.10760337,0.0040042456,0.85946167,0.0008937825,0.0004642307,0.00010592377,0.0012979616,0.019858724,0.0063100643],"genre_scores_gemma":[0.7852023,0.0010098203,0.19876003,0.00066029566,0.0001732167,0.00012732977,0.003462397,0.0004115044,0.010193047],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996562,0.000067014465,0.00001282209,0.00011606657,0.00007076295,0.00007701579],"domain_scores_gemma":[0.99962735,0.00013685675,0.000027427634,0.00008497288,0.00008794555,0.000035467434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085607974,0.0013112164,0.00058808614,0.00058450474,0.00036156524,0.00060190563,0.001340536,0.00091622904,0.0052200863],"category_scores_gemma":[0.0015497649,0.0003481119,0.00057694403,0.00047455973,0.00036471226,0.0016995877,0.0015373132,0.0020746358,0.0019194849],"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":[0.0006445996,0.00034200915,0.0027288327,0.0002008845,0.00017384821,0.00016672678,0.0001624104,0.12286444,0.08692648,0.0062064664,0.021249495,0.75833386],"study_design_scores_gemma":[0.000034770288,0.0001350575,0.0013468856,0.00002244245,0.000046281068,0.00008639675,0.00003507208,0.94748664,0.036445584,0.009200965,0.005136097,0.000023799652],"about_ca_topic_score_codex":0.008406718,"about_ca_topic_score_gemma":0.01472137,"teacher_disagreement_score":0.008406718,"about_ca_system_score_codex":0.00078956084,"about_ca_system_score_gemma":0.0007592302,"threshold_uncertainty_score":0.01746291},"labels":[],"label_agreement":null},{"id":"W4313056376","doi":"10.1109/ijcnn55064.2022.9892600","title":"Analysis of Augmentations for Contrastive ECG Representation Learning","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Encoder; Range (aeronautics); Noise (video); Representation (politics); Supervised learning; Natural language processing; Machine learning; Pattern recognition (psychology); Speech recognition; Image (mathematics); Artificial neural network; Engineering","score_opus":0.057647196853598925,"score_gpt":0.3430698317087837,"score_spread":0.28542263485518476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313056376","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":[0.14044167,0.0035710654,0.8470009,0.0007317448,0.00015848038,0.0002624185,0.00021959223,0.0024924276,0.0051216898],"genre_scores_gemma":[0.81159234,0.0008737662,0.18367384,0.00030122203,0.00013176104,0.00032400436,0.0006090672,0.00026882841,0.0022251445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851733,0.0006126075,0.00010514457,0.00031027,0.00033531903,0.00011932145],"domain_scores_gemma":[0.98784536,0.008831668,0.0006424884,0.0013972742,0.0010777124,0.00020547386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043099523,0.001289862,0.00087560294,0.00072482025,0.00042323628,0.0010451997,0.0015702934,0.0012902528,0.0021932789],"category_scores_gemma":[0.022472823,0.00048767927,0.0009820042,0.00046370554,0.001052176,0.0025888975,0.0017272226,0.0020807835,0.0005526166],"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":[0.00091471674,0.0005868159,0.006345827,0.00057798624,0.00025112444,0.00025860706,0.0003001364,0.5255105,0.025873926,0.017070962,0.0031560573,0.41915342],"study_design_scores_gemma":[0.00001638154,0.00020645375,0.0007511849,0.000038385086,0.000038680173,0.00010192899,0.000017180533,0.9871547,0.005889669,0.004861098,0.00091202016,0.000012290228],"about_ca_topic_score_codex":0.0010707725,"about_ca_topic_score_gemma":0.0012996176,"teacher_disagreement_score":0.0043099523,"about_ca_system_score_codex":0.0008496488,"about_ca_system_score_gemma":0.0008256729,"threshold_uncertainty_score":0.022793472},"labels":[],"label_agreement":null}]}