{"meta":{"query_hash":"4c59733d4e4f","filters":{"venue":"ACM Transactions on Intelligent Systems and Technology"},"cohort_total":51,"direct_labels_cover":0,"predictions_cover":51,"exported":51,"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/4c59733d4e4f","api":"https://metacan.xera.ac/api/v1/cohort?venue=ACM+Transactions+on+Intelligent+Systems+and+Technology"},"results":[{"id":"W1988374254","doi":"10.1145/2700481","title":"Identifying Authorities in Online Communities","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Dependency (UML); Identification (biology); Set (abstract data type); Feature vector; Function (biology); Feature (linguistics); Artificial intelligence; Online community; Machine learning; Reading (process); Data mining; Information retrieval; World Wide Web","score_opus":0.09771654972236936,"score_gpt":0.31550471303863115,"score_spread":0.21778816331626177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988374254","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":[0.2673018,0.0033302382,0.7161132,0.0013983337,0.000108978435,0.00057738123,0.0010534782,0.0018075808,0.00830893],"genre_scores_gemma":[0.8704971,0.0006443748,0.12405388,0.00011648892,0.00016349627,0.00018006076,0.0010852086,0.000100212885,0.0031590832],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955113,0.0017009145,0.0002606398,0.0012217053,0.0009271908,0.00037834037],"domain_scores_gemma":[0.99065924,0.0040758587,0.0021172154,0.0011557412,0.0013686834,0.00062317774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038638648,0.0010683439,0.001557941,0.011600145,0.0025875953,0.002756689,0.0019293139,0.0033096108,0.0023255795],"category_scores_gemma":[0.016156072,0.00082518987,0.0015637043,0.0057698125,0.0027199103,0.007786306,0.0043504154,0.0015827157,0.0011688281],"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":[0.001096467,0.0008086803,0.16454425,0.0013797653,0.0005377489,0.0018396878,0.010558799,0.14677145,0.016533146,0.22422624,0.01634675,0.415357],"study_design_scores_gemma":[0.00006163858,0.00015208035,0.023120355,0.00015561172,0.00014623959,0.0011709398,0.003193076,0.79963225,0.0053600655,0.15044864,0.016386837,0.00017219258],"about_ca_topic_score_codex":0.0061675827,"about_ca_topic_score_gemma":0.005760965,"teacher_disagreement_score":0.011600145,"about_ca_system_score_codex":0.0016892191,"about_ca_system_score_gemma":0.0011838045,"threshold_uncertainty_score":0.02043432},"labels":[],"label_agreement":null},{"id":"W1993097961","doi":"10.1145/1961189.1961195","title":"A helpfulness modeling framework for electronic word-of-mouth on consumer opinion platforms","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Helpfulness; Computer science; Generative model; Benchmark (surveying); Probabilistic logic; Information retrieval; Artificial intelligence; Machine learning; Generative grammar","score_opus":0.07219975242516857,"score_gpt":0.32072468312518254,"score_spread":0.24852493070001397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993097961","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":[0.024852673,0.0002547,0.9718517,0.0005921535,0.000032563195,0.00013849199,0.00016329273,0.00032680904,0.0017877003],"genre_scores_gemma":[0.62331533,0.0004312889,0.36837655,0.00032904794,0.0002528202,0.0006356341,0.0005585899,0.00012975131,0.0059710993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975138,0.0012936668,0.000112800706,0.00048815907,0.00045199035,0.0001395538],"domain_scores_gemma":[0.9932776,0.0051158075,0.0004858146,0.00026365265,0.00071829057,0.00013887505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005703849,0.0010855742,0.001214207,0.002517854,0.00090345455,0.0017499211,0.002529234,0.002471707,0.0025511151],"category_scores_gemma":[0.016145524,0.00078007526,0.0014468651,0.0015296952,0.0013122857,0.003955302,0.0012010519,0.002179068,0.0008347812],"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":[0.00025540843,0.00027867017,0.006797764,0.0002564489,0.0001984316,0.00036693853,0.0011874656,0.6386351,0.0056946143,0.20561911,0.0053245625,0.13538548],"study_design_scores_gemma":[0.000006981083,0.000015899259,0.00028063194,0.0000059377435,0.000010427944,0.000026301168,0.000015204901,0.982276,0.00018913341,0.016784443,0.0003782802,0.000010691863],"about_ca_topic_score_codex":0.008240025,"about_ca_topic_score_gemma":0.00904854,"teacher_disagreement_score":0.008240025,"about_ca_system_score_codex":0.0018114022,"about_ca_system_score_gemma":0.0011569549,"threshold_uncertainty_score":0.030165195},"labels":[],"label_agreement":null},{"id":"W1993813055","doi":"10.1145/2735951","title":"An Approach to Ballet Dance Training through MS Kinect and Visualization in a CAVE Virtual Reality Environment","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Human Motion and Animation","field":"Engineering","cited_by":150,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Dance; Computer science; Context (archaeology); Visualization; Virtual reality; Ballet; Human–computer interaction; Motion capture; Artificial intelligence; Interface (matter); Motion (physics); Computer vision; Visual arts","score_opus":0.06213643073870243,"score_gpt":0.2789571152293263,"score_spread":0.2168206844906239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993813055","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":[0.012288628,0.00022295609,0.9822854,0.0001472585,0.00004146639,0.00014117833,0.0001151988,0.0015488269,0.0032091534],"genre_scores_gemma":[0.17193973,0.0004339507,0.82240963,0.00009760656,0.00002610833,0.0002803261,0.00022799765,0.00019656311,0.004388031],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995468,0.00007275262,0.000021939459,0.000100911115,0.00022024394,0.000037348902],"domain_scores_gemma":[0.99983525,0.000036250807,0.000017943825,0.00003256649,0.00004990413,0.000028001421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047258407,0.00044902557,0.000350396,0.00066976337,0.00028310358,0.0010070446,0.0012795732,0.00061917823,0.0027630697],"category_scores_gemma":[0.0008574916,0.00040066647,0.0005708903,0.00042385465,0.0004700298,0.00074097794,0.0013545273,0.00078707584,0.00042111462],"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":[0.00040903452,0.00035544482,0.0033651926,0.0005837765,0.0001372003,0.0006580742,0.0015237561,0.06597905,0.31470436,0.031060284,0.0056100264,0.57561374],"study_design_scores_gemma":[0.00009240795,0.0005860548,0.013875226,0.00022008117,0.00007981339,0.0024564092,0.0007531662,0.8055532,0.09475026,0.010201881,0.07114551,0.00028594735],"about_ca_topic_score_codex":0.004971174,"about_ca_topic_score_gemma":0.010068834,"teacher_disagreement_score":0.004971174,"about_ca_system_score_codex":0.00041438604,"about_ca_system_score_gemma":0.0010301307,"threshold_uncertainty_score":0.009884477},"labels":[],"label_agreement":null},{"id":"W2023786478","doi":"10.1145/2501603","title":"Perspectives in semantic adaptive social web","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Recommender Systems and Techniques","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 Saskatchewan","funders":"","keywords":"Social Semantic Web; Computer science; World Wide Web; Semantic Web Stack; Semantic Web; Personalization; Data Web; Social web; Web standards; Adaptation (eye); Web modeling; Web intelligence; Semantic analytics; Information retrieval; Web page; Social media","score_opus":0.030899543568427893,"score_gpt":0.2642734618029504,"score_spread":0.23337391823452253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023786478","genre_codex":"other","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016190656,0.10204391,0.18508658,0.3293629,0.009235161,0.00018671031,0.00043537864,0.0003874103,0.35707128],"genre_scores_gemma":[0.78216875,0.07767288,0.058273043,0.027665274,0.02215336,0.0006566347,0.00050280005,0.00031146733,0.030595839],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.992301,0.004465522,0.00028434352,0.0007417347,0.001795914,0.00041151873],"domain_scores_gemma":[0.9888954,0.008208759,0.0004187789,0.0008426614,0.0010653259,0.0005690889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008340956,0.000891794,0.0008354532,0.0054651937,0.0038390667,0.012034453,0.00191974,0.0078053316,0.005954534],"category_scores_gemma":[0.01027151,0.00038870497,0.000868304,0.0060158353,0.0146780545,0.022237085,0.004537167,0.006234481,0.0011805957],"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":[0.0000086310165,0.0000131708,0.00009724129,0.00010315784,0.000009183001,0.00006254202,0.0008262542,0.00033569144,0.000053636482,0.98782057,0.004095913,0.006573967],"study_design_scores_gemma":[0.0000064726028,0.0000103748025,0.00014060158,0.000115310664,0.000004709853,0.00007488803,0.001055661,0.0015609554,0.0000664852,0.92610306,0.07085196,0.000009559284],"about_ca_topic_score_codex":0.0016644892,"about_ca_topic_score_gemma":0.000977552,"teacher_disagreement_score":0.012034453,"about_ca_system_score_codex":0.0042745764,"about_ca_system_score_gemma":0.0017406921,"threshold_uncertainty_score":0.04411173},"labels":[],"label_agreement":null},{"id":"W2048647542","doi":"10.1145/2770879","title":"Where2Stand","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Photography; Set (abstract data type); Artificial intelligence; Construct (python library); Portrait; Computer vision; Visual arts; Art","score_opus":0.04655803020375763,"score_gpt":0.28755595955143853,"score_spread":0.2409979293476809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048647542","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008834847,0.00092286686,0.020536488,0.0050957752,0.0044404212,0.0006213225,0.022598255,0.008729682,0.9282204],"genre_scores_gemma":[0.072477676,0.0015965744,0.014296479,0.003736309,0.00079216895,0.00049561396,0.03379755,0.004296743,0.8685109],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991271,0.00009139694,0.000058417558,0.00027493175,0.00022980542,0.00021846489],"domain_scores_gemma":[0.9993382,0.000086693835,0.000045587694,0.00014657048,0.00027387828,0.000109019005],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007107354,0.00096721126,0.0005853441,0.001253486,0.0020608096,0.0042782,0.0012520791,0.0018081416,0.5516941],"category_scores_gemma":[0.0029993542,0.0003279577,0.0005958831,0.0009785976,0.0005501235,0.004632259,0.0026073647,0.0011238776,0.3526933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029541412,0.000095924945,0.002795313,0.0005038672,0.000023153325,0.0007014934,0.00084583025,0.0004261768,0.0029124788,0.05877561,0.7499318,0.18269293],"study_design_scores_gemma":[0.000020405174,0.000015564945,0.00080395525,0.00008364501,0.0000070929686,0.00021662124,0.00043936263,0.0003560226,0.0007320665,0.005972113,0.99133706,0.000016089241],"about_ca_topic_score_codex":0.009587386,"about_ca_topic_score_gemma":0.008951571,"teacher_disagreement_score":0.5516941,"about_ca_system_score_codex":0.0008781696,"about_ca_system_score_gemma":0.0010609967,"threshold_uncertainty_score":0.63945395},"labels":[],"label_agreement":null},{"id":"W2056767790","doi":"10.1145/2508037.2508038","title":"Mining search and browse logs for web search","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":60,"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":"Computer science; Web search query; Terabyte; Web search engine; Search engine; Search analytics; Information retrieval; World Wide Web; Web crawler; Ranking (information retrieval); Web log analysis software; Transaction log; Metasearch engine; Database; Web page; Static web page; Web navigation","score_opus":0.03611578898037965,"score_gpt":0.2776240375976158,"score_spread":0.24150824861723613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056767790","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":[0.48686495,0.010470663,0.44118428,0.002202977,0.00016151737,0.0012428174,0.036179483,0.01580373,0.0058896104],"genre_scores_gemma":[0.6979474,0.003472952,0.24835062,0.00023345073,0.0002232196,0.0007238422,0.04615836,0.0004018934,0.0024883277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978048,0.0005919825,0.00031347066,0.00033779978,0.00079618144,0.00015567966],"domain_scores_gemma":[0.9870617,0.0077763447,0.0013022635,0.001769589,0.0017963251,0.00029389292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023315127,0.0010311435,0.0012576447,0.011554946,0.0006983242,0.002165049,0.0012835286,0.000967674,0.00079088315],"category_scores_gemma":[0.023172092,0.0005779457,0.0012152508,0.008466845,0.0004119478,0.0043778927,0.0011185628,0.00132113,0.0012067],"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":[0.00073349976,0.0014588296,0.20035475,0.002215625,0.0005400669,0.00088820205,0.0017787578,0.03315032,0.013542418,0.009022283,0.03115232,0.7051629],"study_design_scores_gemma":[0.00007272461,0.00032383399,0.08191785,0.00028078226,0.00028111393,0.0012760949,0.0016628383,0.8479558,0.012889029,0.033130467,0.020085102,0.00012439548],"about_ca_topic_score_codex":0.005932498,"about_ca_topic_score_gemma":0.0129755465,"teacher_disagreement_score":0.011554946,"about_ca_system_score_codex":0.00067336374,"about_ca_system_score_gemma":0.0017446363,"threshold_uncertainty_score":0.012330353},"labels":[],"label_agreement":null},{"id":"W2076424778","doi":"10.1145/2168752.2168760","title":"A Generic Approach for Systematic Analysis of Sports Videos","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Probabilistic latent semantic analysis; Conditional random field; Artificial intelligence; Topic model; Bag-of-words model; Support vector machine; Pattern recognition (psychology); Categorization; Representation (politics); Field (mathematics); Probabilistic logic; Classifier (UML); Video content analysis; Histogram; Machine learning; Object (grammar); Video tracking; Image (mathematics)","score_opus":0.023500772465597664,"score_gpt":0.25227753013392207,"score_spread":0.2287767576683244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076424778","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":[0.002099005,0.00045015532,0.99190056,0.00009212223,0.000040274022,0.0003800865,0.00065979693,0.0024063918,0.0019715903],"genre_scores_gemma":[0.038292058,0.0007226525,0.9538846,0.00012588633,0.00007554473,0.0006556036,0.0028258741,0.00029753888,0.003120317],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965263,0.0005785928,0.00030863125,0.0013212981,0.001105947,0.00015917397],"domain_scores_gemma":[0.99821967,0.00024828853,0.00021514544,0.0005627258,0.00066524174,0.000088959256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002163215,0.0014862424,0.000880462,0.0068267705,0.00073020963,0.0026259492,0.0017108978,0.0012890329,0.004048966],"category_scores_gemma":[0.0043959557,0.00059185276,0.0020631577,0.0037529508,0.0010621278,0.0027345042,0.0028337718,0.0011615233,0.0031831795],"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":[0.00015830496,0.00020740047,0.004133647,0.0012161259,0.00024817,0.00047347837,0.0008973566,0.007259863,0.07272127,0.047005482,0.014612067,0.8510669],"study_design_scores_gemma":[0.00009448533,0.00066562695,0.024035715,0.0009363169,0.00041534967,0.004248395,0.002001725,0.4216637,0.10488332,0.12788185,0.3128096,0.0003639213],"about_ca_topic_score_codex":0.0023576084,"about_ca_topic_score_gemma":0.002781269,"teacher_disagreement_score":0.0068267705,"about_ca_system_score_codex":0.0008921484,"about_ca_system_score_gemma":0.0017751415,"threshold_uncertainty_score":0.0135451555},"labels":[],"label_agreement":null},{"id":"W2081989102","doi":"10.1145/2508037.2508049","title":"Validation of an ontological medical decision support system for patient treatment using a repository of patient data","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Probabilistic logic; Decision support system; Misinformation; Context (archaeology); Artificial intelligence; Data science","score_opus":0.03694217863045635,"score_gpt":0.3027034028506056,"score_spread":0.26576122422014925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081989102","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":[0.21499498,0.00045351742,0.72020775,0.0056195194,0.0005051658,0.0030971372,0.011022061,0.03471539,0.009384553],"genre_scores_gemma":[0.45173115,0.00021938817,0.5319209,0.00080873,0.000046121968,0.0006494264,0.012603,0.00039283425,0.0016285145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99161,0.0030119028,0.0015343677,0.0012389515,0.0023864568,0.00021839843],"domain_scores_gemma":[0.9716419,0.015371223,0.0011224841,0.0071613253,0.004155769,0.00054727687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018154599,0.0007060795,0.0009636323,0.0029686913,0.0014194443,0.0044640186,0.002124215,0.0015969352,0.0030276794],"category_scores_gemma":[0.05358837,0.000495102,0.0011856441,0.0017720179,0.00084413996,0.0029315993,0.0032849167,0.0014225602,0.0013061689],"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":[0.0040602307,0.002602729,0.08716838,0.0018282749,0.0009468057,0.0027750276,0.0043309513,0.11852309,0.022256736,0.046934955,0.029955758,0.6786172],"study_design_scores_gemma":[0.0006258573,0.0004764733,0.014596157,0.0005092646,0.00033139047,0.0007084869,0.0014188392,0.8722434,0.036580473,0.020479,0.05181942,0.00021135027],"about_ca_topic_score_codex":0.011045137,"about_ca_topic_score_gemma":0.010704473,"teacher_disagreement_score":0.018154599,"about_ca_system_score_codex":0.0023643484,"about_ca_system_score_gemma":0.0056782933,"threshold_uncertainty_score":0.09601188},"labels":[],"label_agreement":null},{"id":"W2083594659","doi":"10.1145/2337542.2337562","title":"Surface Sulfur Detection via Remote Sensing and Onboard Classification","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Aeronautics and Space Administration","keywords":"Computer science; Orbiter; Remote sensing; Sulfur; Feature (linguistics); Artificial intelligence; Geology; Materials science; Aerospace engineering","score_opus":0.01559452568680369,"score_gpt":0.22373512401016715,"score_spread":0.20814059832336346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083594659","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":[0.8399772,0.00023410685,0.14850864,0.00025381346,0.000083133506,0.00021114603,0.0004499337,0.0032702705,0.007011688],"genre_scores_gemma":[0.92772675,0.00009438912,0.069558814,0.0000724196,0.00003161724,0.00004881568,0.00051339815,0.00006837,0.0018853508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937576,0.0001561441,0.000032512653,0.00012909617,0.00021301374,0.00009347793],"domain_scores_gemma":[0.9989579,0.00038139956,0.00015693763,0.00013009297,0.00033126125,0.000042329037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008736602,0.000972619,0.0005954596,0.0015502039,0.00034347663,0.0013224984,0.0008206481,0.0007919295,0.0015688968],"category_scores_gemma":[0.002089969,0.00020572497,0.0005220434,0.00087299786,0.00031336123,0.0011614348,0.0006305114,0.0003671539,0.00056431023],"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":[0.00091976195,0.0005946768,0.05718052,0.000113078226,0.00015874437,0.00014910931,0.00015119335,0.13219975,0.060814183,0.0009937437,0.0023950879,0.74433017],"study_design_scores_gemma":[0.000024854598,0.00014206843,0.014662921,0.0000080162745,0.00003814909,0.00003813425,0.00006714794,0.9596666,0.023824386,0.00062691094,0.00088075653,0.000020021327],"about_ca_topic_score_codex":0.0152175855,"about_ca_topic_score_gemma":0.01813325,"teacher_disagreement_score":0.0152175855,"about_ca_system_score_codex":0.00089353253,"about_ca_system_score_gemma":0.00058596756,"threshold_uncertainty_score":0.030258},"labels":[],"label_agreement":null},{"id":"W2089982705","doi":"10.1145/2629673","title":"A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"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":"Artificial intelligence; Computer science; RGB color model; Visual odometry; Computer vision; Iterative closest point; Odometry; Feature (linguistics); Benchmark (surveying); Pose; Frame (networking); Matching (statistics); Simultaneous localization and mapping; Robot; Point cloud; Mathematics; Mobile robot; Geography","score_opus":0.031346664588934904,"score_gpt":0.22293637405823682,"score_spread":0.19158970946930193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089982705","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":[0.0019448174,0.000081292885,0.99640346,0.000028927714,0.000022815066,0.000016220849,0.000077144985,0.0011442361,0.00028113517],"genre_scores_gemma":[0.060253795,0.00021057419,0.9377339,0.000059276852,0.000032889275,0.00007523585,0.0005945046,0.0002913591,0.00074860716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980344,0.00021550653,0.00008375422,0.00050518184,0.0010468804,0.00011426151],"domain_scores_gemma":[0.99907756,0.000121927515,0.0001091545,0.0004176374,0.00021091329,0.00006278583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086799136,0.0017677939,0.0017052015,0.0023072076,0.00055092527,0.0022189443,0.0031639119,0.0013839278,0.0021551494],"category_scores_gemma":[0.0029167912,0.0014951684,0.0017353103,0.00301151,0.0010713653,0.0028463318,0.00497852,0.0022304645,0.001906473],"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":[0.00023016568,0.00020044188,0.0023821106,0.00039628736,0.00030102115,0.0002579022,0.00043486748,0.28455415,0.055549875,0.023645682,0.00698349,0.625064],"study_design_scores_gemma":[0.000026133948,0.000057914604,0.0008961802,0.00003723171,0.000033044347,0.00025404172,0.0001059744,0.9627618,0.017196136,0.0098393485,0.008729459,0.000062727006],"about_ca_topic_score_codex":0.0049800435,"about_ca_topic_score_gemma":0.0064346353,"teacher_disagreement_score":0.0049800435,"about_ca_system_score_codex":0.00053916627,"about_ca_system_score_gemma":0.0016899864,"threshold_uncertainty_score":0.00990212},"labels":[],"label_agreement":null},{"id":"W2093447054","doi":"10.1145/2630075","title":"Identifying Controversial Wikipedia Articles Using Editor Collaboration Networks","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Pairwise comparison; Rendering (computer graphics); Process (computing); Social media; Data science; Information retrieval; World Wide Web; Artificial intelligence","score_opus":0.05064147455509979,"score_gpt":0.3433290702860089,"score_spread":0.2926875957309091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093447054","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":[0.73057985,0.007871183,0.22263828,0.002275865,0.00054594805,0.00084804796,0.004524618,0.0037834337,0.026932726],"genre_scores_gemma":[0.92597425,0.0012231715,0.06038311,0.00018739239,0.0008934383,0.00023157445,0.005539674,0.00016488123,0.005402514],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99592173,0.0010677655,0.0002847941,0.0013539935,0.0011587424,0.00021292348],"domain_scores_gemma":[0.9811428,0.010900176,0.0038171008,0.0009561108,0.0023159229,0.0008678307],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0035837183,0.0015107263,0.00097278773,0.009446573,0.0017284293,0.0042863577,0.0016950624,0.0020185325,0.0013925651],"category_scores_gemma":[0.020467417,0.00049226306,0.0007827355,0.0036587983,0.0005860246,0.005872533,0.0026841725,0.0013815686,0.0011772723],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016469782,0.0010399346,0.24843672,0.0015233179,0.0011529913,0.0030724977,0.00615217,0.037523657,0.021115735,0.013469169,0.02539178,0.63947505],"study_design_scores_gemma":[0.000104513514,0.00041571073,0.06416792,0.00030592983,0.00081754156,0.0018030076,0.0033452825,0.8331314,0.021791708,0.024317415,0.049630735,0.00016889832],"about_ca_topic_score_codex":0.0025114194,"about_ca_topic_score_gemma":0.0053320057,"teacher_disagreement_score":0.9982716,"about_ca_system_score_codex":0.0008368682,"about_ca_system_score_gemma":0.001255513,"threshold_uncertainty_score":0.018952727},"labels":[],"label_agreement":null},{"id":"W2094190061","doi":"10.1145/2534398","title":"Location- and Query-Aware Modeling of Browsing and Click Behavior in Sponsored Search","year":2014,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Web Data Mining and Analysis","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 Waterloo","funders":"","keywords":"Perplexity; Computer science; Information retrieval; Search engine; Selection (genetic algorithm); Quality (philosophy); Click-through rate; Web search query; Language model; World Wide Web; Machine learning; Artificial intelligence","score_opus":0.028719234723659545,"score_gpt":0.2720875666664144,"score_spread":0.24336833194275487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094190061","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":[0.80589926,0.001581012,0.18789856,0.0008004737,0.000039178445,0.000073301155,0.00086016656,0.0005001439,0.0023479017],"genre_scores_gemma":[0.98875743,0.00046110476,0.008114911,0.0000470247,0.000033912314,0.000045634355,0.0003950991,0.000027637145,0.00211716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924904,0.0002983092,0.000043566273,0.00016286726,0.000119911005,0.00012616145],"domain_scores_gemma":[0.9959758,0.0029000733,0.0004744574,0.0002558405,0.00025290015,0.0001409533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024051012,0.0007149949,0.001040375,0.0010300885,0.00032134986,0.0012026494,0.001600414,0.0015040761,0.0008690063],"category_scores_gemma":[0.008466736,0.00078182336,0.000766349,0.0015803522,0.00081708253,0.0019070719,0.0007227003,0.0012800912,0.00045189477],"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":[0.0002746146,0.0001483708,0.024312561,0.0000637388,0.000060439066,0.00017219703,0.0002654029,0.9451481,0.001941417,0.009134542,0.0007798393,0.017698914],"study_design_scores_gemma":[0.000006661104,0.000020898071,0.0017706016,0.000004189866,0.000008582237,0.000029620538,0.0000093165345,0.99605906,0.000119408345,0.0018610099,0.000103710925,0.000006877992],"about_ca_topic_score_codex":0.026590547,"about_ca_topic_score_gemma":0.02399438,"teacher_disagreement_score":0.026590547,"about_ca_system_score_codex":0.0013207601,"about_ca_system_score_gemma":0.0009924216,"threshold_uncertainty_score":0.052871585},"labels":[],"label_agreement":null},{"id":"W2133447875","doi":"10.1145/1869397.1869402","title":"CORALS","year":2010,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"AI-based Problem Solving and Planning","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":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Planner; Plan (archaeology); Cruise missile; Process (computing); Set (abstract data type); Resource (disambiguation); Operations research; Conflict resolution; Resource allocation; Variety (cybernetics); Computer security; Artificial intelligence; Missile; Computer network; Law","score_opus":0.01746672045011682,"score_gpt":0.25081617406568346,"score_spread":0.23334945361556664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133447875","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008206327,0.004563843,0.055100366,0.006292712,0.0034250375,0.0007216119,0.018862525,0.03605328,0.8667743],"genre_scores_gemma":[0.08465374,0.0071931286,0.09192005,0.005501998,0.00090138277,0.0010784594,0.046389457,0.012219471,0.75014234],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862707,0.00018722343,0.00009003671,0.00027800284,0.0006405017,0.00017731167],"domain_scores_gemma":[0.99804235,0.0002829454,0.00017171577,0.00043451734,0.00065289496,0.0004154965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00122024,0.0014283975,0.00069599587,0.0022543815,0.001733939,0.0039760126,0.0021845927,0.0020285307,0.23365156],"category_scores_gemma":[0.003978871,0.0006265491,0.0009694997,0.002052834,0.0008573621,0.0028266017,0.0045649325,0.0019604715,0.1596304],"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":[0.0005432585,0.0001261724,0.0017523267,0.000952574,0.000047569643,0.0005765049,0.0004937119,0.0024169784,0.0072949966,0.051239952,0.58861023,0.3459458],"study_design_scores_gemma":[0.000022039088,0.000032497413,0.00035476513,0.00007056612,0.0000103236525,0.00018353503,0.000059287402,0.0006642955,0.00084445195,0.0033339693,0.9944106,0.000013629822],"about_ca_topic_score_codex":0.004368666,"about_ca_topic_score_gemma":0.0064611756,"teacher_disagreement_score":0.23365156,"about_ca_system_score_codex":0.001228686,"about_ca_system_score_gemma":0.0027792978,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2139420544","doi":"10.1145/2036264.2036266","title":"Efficient Tag Recommendation for Real-Life Data","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Scalability; Recommender system; Adaptation (eye); Content adaptation; Process (computing); Task (project management); Tag system; Set (abstract data type); Resource (disambiguation); Information retrieval; Data mining; Database; Human–computer interaction; Ubiquitous computing","score_opus":0.12283930114811219,"score_gpt":0.30944536508047465,"score_spread":0.18660606393236245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139420544","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":[0.061438065,0.0015601051,0.9172812,0.0006666222,0.00012974457,0.00028678388,0.0026046103,0.01443045,0.001602418],"genre_scores_gemma":[0.22034103,0.0005098536,0.7716801,0.00018426338,0.00006376346,0.00017404144,0.004807552,0.00023668238,0.0020027428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697053,0.000902473,0.00026981346,0.0009559047,0.00073190307,0.00016945844],"domain_scores_gemma":[0.9894031,0.0046840943,0.00047143942,0.003749948,0.0014843697,0.00020702978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004895907,0.0011247649,0.0018927185,0.003542719,0.001556393,0.0022688652,0.0026941411,0.0021989273,0.0017651258],"category_scores_gemma":[0.013827177,0.00083371525,0.001072455,0.0055235103,0.0005677655,0.00369419,0.0011984463,0.0015060173,0.0025485756],"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":[0.0010313763,0.00059315475,0.015845114,0.0008034347,0.00055382325,0.0006577846,0.00083058403,0.13778558,0.040988505,0.0052758367,0.027134517,0.7685004],"study_design_scores_gemma":[0.00007777551,0.000106405714,0.0037655267,0.00004072921,0.0000852179,0.000396273,0.00029559314,0.96237445,0.014008073,0.008814751,0.009965078,0.000070045666],"about_ca_topic_score_codex":0.01573521,"about_ca_topic_score_gemma":0.036325894,"teacher_disagreement_score":0.01573521,"about_ca_system_score_codex":0.0012389123,"about_ca_system_score_gemma":0.0012380518,"threshold_uncertainty_score":0.031287253},"labels":[],"label_agreement":null},{"id":"W2141296717","doi":"10.1145/1989734.1989741","title":"MoveMine","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Army Research Laboratory; Air Force Office of Scientific Research; Division of Computing and Communication Foundations; National Science Foundation","keywords":"Computer science; Data mining; Object (grammar); Swarm behaviour; Focus (optics); Trajectory; Cluster analysis; Data stream mining; Movement (music); Artificial intelligence","score_opus":0.03626296199891101,"score_gpt":0.23551538622712453,"score_spread":0.19925242422821351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141296717","genre_codex":"software","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":[0.008668625,0.002665977,0.14590017,0.002634298,0.0015577022,0.0008609844,0.11365734,0.5144928,0.20956208],"genre_scores_gemma":[0.06268043,0.0028840748,0.2042394,0.005091137,0.00041423025,0.0025073115,0.39135522,0.08381043,0.24701785],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843484,0.00015160328,0.0001157001,0.0005453502,0.0005744055,0.00017809142],"domain_scores_gemma":[0.9985227,0.00027674143,0.00011685495,0.00046850537,0.0004716113,0.00014357608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014285825,0.0016639893,0.0014722099,0.0022373344,0.0011889961,0.0037357162,0.0053944057,0.0024659694,0.15079369],"category_scores_gemma":[0.004899097,0.0012517519,0.0017221819,0.0021570616,0.0006767252,0.0051332805,0.0044148476,0.00204875,0.16011532],"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":[0.00083250867,0.00015637137,0.00307772,0.0012959273,0.00017091083,0.00037243412,0.00036424724,0.0023255113,0.0063390047,0.015585752,0.8117526,0.15772703],"study_design_scores_gemma":[0.00016417289,0.00012180123,0.0015925094,0.00013019958,0.00006167119,0.00044292875,0.0001026549,0.01003466,0.0048544183,0.009231811,0.9731712,0.000092025744],"about_ca_topic_score_codex":0.0036452315,"about_ca_topic_score_gemma":0.006707834,"teacher_disagreement_score":0.15079369,"about_ca_system_score_codex":0.00097189145,"about_ca_system_score_gemma":0.0018562364,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2156354968","doi":"10.1145/2438653.2438659","title":"A framework for trust modeling in multiagent electronic marketplaces with buying advisors to consider varying seller behavior and the limiting of seller bids","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Auction Theory and Applications","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Limiting; Computer science; Honesty; Trustworthiness; Profit (economics); Weighting; Value (mathematics); Internet privacy; Microeconomics; Machine learning","score_opus":0.07490294711862497,"score_gpt":0.34542308948397815,"score_spread":0.27052014236535316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156354968","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":[0.008726787,0.00013698789,0.9869156,0.0004694818,0.00004764698,0.00007792653,0.00005896525,0.0001167128,0.0034499285],"genre_scores_gemma":[0.6310277,0.0004463185,0.36039075,0.00021696198,0.00017848457,0.0004936113,0.00012605486,0.00007648399,0.007043718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972012,0.0015483864,0.00016606177,0.0004383034,0.00043220806,0.00021383724],"domain_scores_gemma":[0.99454004,0.0031353629,0.0007593352,0.0005414276,0.0006510007,0.0003727944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053248205,0.0010187582,0.0010880133,0.0010199161,0.0011033152,0.0025565845,0.0031019212,0.0023432083,0.0035465218],"category_scores_gemma":[0.0134674655,0.00092497014,0.0016553301,0.0010748012,0.0017136412,0.004330137,0.001833833,0.002838501,0.0007416077],"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":[0.00008131165,0.00017482984,0.0019388217,0.000097579745,0.00011989517,0.00031498467,0.00059750536,0.7223338,0.0011603136,0.2536203,0.0013346686,0.018226026],"study_design_scores_gemma":[0.000016717966,0.000034640594,0.00014331465,0.000010741333,0.000019663723,0.000036448662,0.00004478566,0.9658604,0.00010380149,0.032468732,0.0012483209,0.000012356928],"about_ca_topic_score_codex":0.009530692,"about_ca_topic_score_gemma":0.0096797105,"teacher_disagreement_score":0.009530692,"about_ca_system_score_codex":0.0018586258,"about_ca_system_score_gemma":0.0018523141,"threshold_uncertainty_score":0.028160691},"labels":[],"label_agreement":null},{"id":"W2171045163","doi":"10.1145/2036264.2036281","title":"Mining Concept Sequences from Large-Scale Search Logs for Context-Aware Query Suggestion","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Web search query; Information retrieval; Query expansion; Web query classification; Query language; Context (archaeology); Query optimization; Sargable; Session (web analytics); Search engine; Data mining; World Wide Web","score_opus":0.048680879206956164,"score_gpt":0.26151993178890337,"score_spread":0.2128390525819472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171045163","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":[0.22857964,0.0046251444,0.7473,0.00088899705,0.00013077591,0.00070718257,0.0038426784,0.012284695,0.0016408674],"genre_scores_gemma":[0.57698655,0.0008086954,0.41427484,0.00019641621,0.00015938112,0.0003868945,0.006061923,0.00020445239,0.0009209113],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984255,0.00040148952,0.00015865662,0.00037984777,0.0005371663,0.00009740401],"domain_scores_gemma":[0.9931938,0.004227514,0.0006485558,0.000672414,0.0010270784,0.00023059036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014675949,0.0015314134,0.0015852201,0.0060125086,0.00078671216,0.0010245449,0.0014827782,0.001303921,0.0010869132],"category_scores_gemma":[0.011944457,0.0004993415,0.0010173479,0.004496618,0.00036353132,0.0031175308,0.00069181964,0.0013080849,0.001098651],"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":[0.0014858237,0.0017470329,0.05848413,0.001734427,0.0005053671,0.0009192061,0.0017345693,0.074746825,0.06119771,0.0048697796,0.021609299,0.77096575],"study_design_scores_gemma":[0.00008855945,0.00030121914,0.008726636,0.00006234983,0.00016843819,0.0006283025,0.0004998153,0.96163493,0.012715613,0.008410099,0.0066767014,0.00008736167],"about_ca_topic_score_codex":0.005878945,"about_ca_topic_score_gemma":0.012607669,"teacher_disagreement_score":0.0060125086,"about_ca_system_score_codex":0.00061135547,"about_ca_system_score_gemma":0.0020898818,"threshold_uncertainty_score":0.011689484},"labels":[],"label_agreement":null},{"id":"W2250547980","doi":"10.1145/2700480","title":"Empowering Patients and Caregivers to Manage Healthcare Via Streamlined Presentation of Web Objects Selected by Modeling Learning Benefits Obtained by Similar Peers","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Recommender Systems and Techniques","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":"University of Waterloo","funders":"","keywords":"Computer science; Presentation (obstetrics); Process (computing); Selection (genetic algorithm); World Wide Web; Health care; Order (exchange); Value (mathematics); Multimedia; Artificial intelligence; Medicine","score_opus":0.018775840716041995,"score_gpt":0.2649832120633505,"score_spread":0.24620737134730852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250547980","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":[0.24781136,0.0005651673,0.7357102,0.0017041804,0.00008216061,0.00079651823,0.00018399462,0.0013123513,0.011834091],"genre_scores_gemma":[0.8082212,0.0004322011,0.18558764,0.00010901597,0.00006135279,0.00037288884,0.00017571673,0.00006928869,0.0049706185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986229,0.0008854249,0.000060651146,0.00016182987,0.00019215923,0.00007714992],"domain_scores_gemma":[0.9974252,0.0014746687,0.0002769666,0.00023561556,0.00032914415,0.00025851087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019541383,0.0007280646,0.00042204678,0.0008047543,0.00050590193,0.001997413,0.00119811,0.0012404243,0.0026700457],"category_scores_gemma":[0.007019941,0.0002737535,0.00048415796,0.00036306723,0.00061432,0.0022559843,0.0017849836,0.0008586265,0.0007099356],"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":[0.0013035777,0.002557993,0.031504463,0.0011541605,0.00026079413,0.002314706,0.01198865,0.39776438,0.0629603,0.055869672,0.006448951,0.42587245],"study_design_scores_gemma":[0.00013756724,0.0012286752,0.004139807,0.000101830505,0.00015350994,0.0005625768,0.0021691937,0.9361763,0.010270711,0.028515683,0.016443782,0.000100404664],"about_ca_topic_score_codex":0.0014832154,"about_ca_topic_score_gemma":0.0021555657,"teacher_disagreement_score":0.0026700457,"about_ca_system_score_codex":0.0005546337,"about_ca_system_score_gemma":0.0008335166,"threshold_uncertainty_score":0.010334611},"labels":[],"label_agreement":null},{"id":"W2265696903","doi":"10.1145/2799648","title":"Gestures à Go Go","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":53,"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":"Gesture; Computer science; Bootstrapping (finance); Human–computer interaction; Gesture recognition; Quality (philosophy); Artificial intelligence","score_opus":0.04107652253546483,"score_gpt":0.2715265347485359,"score_spread":0.23045001221307104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2265696903","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":[0.1756312,0.00075089076,0.613137,0.0012823527,0.0008723991,0.0014972861,0.007319026,0.14029513,0.059214637],"genre_scores_gemma":[0.5351655,0.0006132449,0.38014185,0.0015625362,0.00010405099,0.0014625473,0.011952941,0.008319393,0.060677953],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995646,0.0000714858,0.000025510206,0.00010837996,0.00018019101,0.000049880055],"domain_scores_gemma":[0.99922025,0.00021914634,0.000032162316,0.00029066496,0.00012781804,0.00010998509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004530615,0.0013100293,0.00059022644,0.00061994384,0.0004869363,0.0009619436,0.0009276263,0.0012796011,0.029265149],"category_scores_gemma":[0.0028115192,0.0004395971,0.0008708477,0.00039561477,0.0006666384,0.0011886786,0.0018568397,0.0008710274,0.01350349],"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":[0.002505293,0.00041834803,0.0084156655,0.001071233,0.00016638356,0.00256606,0.0017052907,0.018807696,0.2553137,0.015043398,0.08726716,0.60671985],"study_design_scores_gemma":[0.00035224564,0.0021588518,0.028446076,0.0003547924,0.00019362825,0.0065840883,0.0015653294,0.3193706,0.16191201,0.023609344,0.45510158,0.00035144668],"about_ca_topic_score_codex":0.0020190428,"about_ca_topic_score_gemma":0.004075712,"teacher_disagreement_score":0.029265149,"about_ca_system_score_codex":0.00028077525,"about_ca_system_score_gemma":0.0005652614,"threshold_uncertainty_score":0.09790164},"labels":[],"label_agreement":null},{"id":"W2283763484","doi":"10.1145/2651444","title":"Recognition of Patient-Related Named Entities in Noisy Tele-Health Texts","year":2015,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Relationship extraction; Noise (video); Information extraction; Information retrieval; Sentence; Named-entity recognition; Phone; Filter (signal processing); Machine learning; Task (project management); Speech recognition","score_opus":0.03429504103428756,"score_gpt":0.2580997059002944,"score_spread":0.22380466486600686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283763484","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":[0.32677513,0.0016568666,0.652484,0.0025331548,0.00024019377,0.0004659769,0.009139421,0.0030223855,0.003682897],"genre_scores_gemma":[0.52741754,0.0008241617,0.45851427,0.0003631127,0.0002618702,0.00033114012,0.0100140525,0.00027840445,0.001995434],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967347,0.0011910094,0.00047014945,0.00081382017,0.0006693202,0.000120956654],"domain_scores_gemma":[0.9774418,0.015782045,0.0037429077,0.0013073559,0.0015276582,0.00019822727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026505992,0.00076540135,0.0005560298,0.0032365662,0.00053045165,0.0016177779,0.0010209448,0.0015253404,0.0013035845],"category_scores_gemma":[0.016065415,0.0004215353,0.0004612916,0.0027227083,0.00087069423,0.0029928132,0.0010937565,0.00090800115,0.0010103538],"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":[0.0015036484,0.0005416124,0.070052415,0.0038945808,0.00026445568,0.009370404,0.018980779,0.049423583,0.17723043,0.026497673,0.015931627,0.6263088],"study_design_scores_gemma":[0.00012092923,0.0003979985,0.075536065,0.00057599065,0.00029000922,0.0050613876,0.0068992306,0.62402993,0.13774385,0.058253575,0.09082671,0.0002643023],"about_ca_topic_score_codex":0.0014354317,"about_ca_topic_score_gemma":0.0022331232,"teacher_disagreement_score":0.0032365662,"about_ca_system_score_codex":0.0006943485,"about_ca_system_score_gemma":0.0007549339,"threshold_uncertainty_score":0.01401788},"labels":[],"label_agreement":null},{"id":"W2475215202","doi":"10.1145/2875441","title":"Multiagent Resource Allocation for Dynamic Task Arrivals with Preemption","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Optimization and Search Problems","field":"Computer Science","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":"Preemption; Computer science; Leverage (statistics); Task (project management); Distributed computing; Proxy (statistics); Resource (disambiguation); Resource allocation; Multi-agent system; Computer network; Artificial intelligence; Machine learning","score_opus":0.018738801178144377,"score_gpt":0.2621758147648627,"score_spread":0.2434370135867183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2475215202","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":[0.0124827465,0.00016086607,0.9853731,0.00015130283,0.00004190583,0.0000564525,0.000011838662,0.00025374896,0.0014680502],"genre_scores_gemma":[0.6685856,0.00021361868,0.3257443,0.00015056449,0.000079337755,0.00025016497,0.00005589166,0.00011958105,0.004800999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866533,0.00051890843,0.000079259684,0.00028782256,0.00027212966,0.00017649766],"domain_scores_gemma":[0.9980059,0.0010519626,0.0002485322,0.00026874963,0.00024657123,0.00017820379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021145772,0.0008582163,0.0010504093,0.0005157133,0.001044677,0.0013345852,0.0019933633,0.0009953191,0.00209973],"category_scores_gemma":[0.005498401,0.00046105945,0.00049518864,0.00056462194,0.00084188464,0.0018694566,0.0018161591,0.0014568113,0.00050428114],"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":[0.00013634171,0.00010149201,0.000536837,0.000075103635,0.00004086504,0.00014128596,0.00015488826,0.9195549,0.0030851823,0.026390174,0.0014772122,0.04830577],"study_design_scores_gemma":[0.000013088412,0.00002123701,0.000042249143,0.0000031942172,0.0000050454405,0.00002250996,0.000013432557,0.9922896,0.00040051856,0.006285051,0.0008999215,0.0000041822586],"about_ca_topic_score_codex":0.0029113202,"about_ca_topic_score_gemma":0.0028159465,"teacher_disagreement_score":0.0029113202,"about_ca_system_score_codex":0.0011236095,"about_ca_system_score_gemma":0.0016097985,"threshold_uncertainty_score":0.011183083},"labels":[],"label_agreement":null},{"id":"W2749789150","doi":"10.1145/3070658","title":"Detecting Communities of Authority and Analyzing Their Influence in Dynamic Social Networks","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","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":"Université de Sherbrooke; Computer Research Institute of Montréal","funders":"","keywords":"Betweenness centrality; Computer science; Identification (biology); Variety (cybernetics); Centrality; Data science; Social network (sociolinguistics); Causality (physics); Social network analysis; Recommender system; Community structure; Data mining; World Wide Web; Artificial intelligence; Social media","score_opus":0.019880059226222862,"score_gpt":0.29349434362826354,"score_spread":0.27361428440204066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749789150","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":[0.5693777,0.0012603417,0.42427963,0.00042662618,0.000030552364,0.00014854723,0.00028986862,0.00049133366,0.003695409],"genre_scores_gemma":[0.9505931,0.00027795488,0.048165865,0.000026049724,0.00004408202,0.000065293614,0.00023937019,0.000034901124,0.0005533674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975249,0.0009941024,0.000109686494,0.00045476755,0.00072573405,0.00019080234],"domain_scores_gemma":[0.9835629,0.011136609,0.0025186697,0.0007484021,0.0015288856,0.00050457823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026695493,0.0005445913,0.000735041,0.0068805837,0.0011636867,0.0016858083,0.00093646394,0.0011401931,0.00047810376],"category_scores_gemma":[0.021241037,0.00041246595,0.0006651376,0.0037102108,0.0011909968,0.003550457,0.0016025732,0.0008451915,0.00021417161],"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":[0.0007117992,0.0005051939,0.2604292,0.00060951785,0.0005531758,0.0014953733,0.007949726,0.2829124,0.026225548,0.08586801,0.0039977184,0.3287424],"study_design_scores_gemma":[0.000031305044,0.00010094719,0.037817903,0.000052819072,0.00009753421,0.0006326449,0.0015323017,0.9011895,0.0047913417,0.04832607,0.0053528734,0.00007471649],"about_ca_topic_score_codex":0.006489253,"about_ca_topic_score_gemma":0.0063966224,"teacher_disagreement_score":0.0068805837,"about_ca_system_score_codex":0.0009332471,"about_ca_system_score_gemma":0.00054235343,"threshold_uncertainty_score":0.014118075},"labels":[],"label_agreement":null},{"id":"W2754052029","doi":"10.1145/3035968","title":"i <sup>2</sup> tag","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Multipath propagation; Multipath interference; Radio-frequency identification; Identification (biology); Dynamic time warping; Fingerprint (computing); Real-time computing; Interference (communication); Artificial intelligence; Telecommunications; Computer security","score_opus":0.017192009452037325,"score_gpt":0.23989495372512115,"score_spread":0.22270294427308382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754052029","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038771346,0.003557879,0.63458735,0.0033851287,0.007823565,0.0006437508,0.006938964,0.04107046,0.26322156],"genre_scores_gemma":[0.40472603,0.0016036301,0.1608068,0.0075467327,0.0013099212,0.00040096376,0.011561616,0.003137352,0.40890703],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993424,0.00006770327,0.00005796007,0.00012473397,0.00032565018,0.000081409984],"domain_scores_gemma":[0.9987471,0.00015429688,0.00013366164,0.00034472972,0.00056134554,0.00005879208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005057202,0.00084322464,0.00053114706,0.00060673367,0.00047488572,0.0018002437,0.0014931131,0.0013959536,0.046469547],"category_scores_gemma":[0.0014677576,0.00030109767,0.00033784477,0.0008379723,0.00047327153,0.0016729819,0.00097107404,0.00060166675,0.040990178],"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":[0.0020050404,0.00011324892,0.007821329,0.00074667414,0.000117197706,0.0017449431,0.00025103358,0.0038455403,0.1646905,0.016181191,0.30561084,0.49687245],"study_design_scores_gemma":[0.00008238592,0.00058770884,0.0033656377,0.00009633452,0.00013746848,0.0035859137,0.00021751014,0.041875873,0.256062,0.003972799,0.68990344,0.00011295799],"about_ca_topic_score_codex":0.00044812547,"about_ca_topic_score_gemma":0.00088196114,"teacher_disagreement_score":0.046469547,"about_ca_system_score_codex":0.0004639339,"about_ca_system_score_gemma":0.00030065648,"threshold_uncertainty_score":0.15545613},"labels":[],"label_agreement":null},{"id":"W2787850647","doi":"10.1145/3161607","title":"Modeling Queries with Contextual Snippets for Information Retrieval","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Information Retrieval and Search Behavior","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Information retrieval; Query expansion; Context (archaeology); Relevance (law); Boosting (machine learning); Focus (optics); Topic model; Artificial intelligence","score_opus":0.02722558212257443,"score_gpt":0.26832832869885065,"score_spread":0.24110274657627623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787850647","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":[0.035723835,0.0034346948,0.9508979,0.00065131433,0.00010635522,0.00050359266,0.0021519011,0.0043184967,0.0022119735],"genre_scores_gemma":[0.44519302,0.003387493,0.5404049,0.0004136327,0.0003413783,0.00118245,0.004976442,0.00042087352,0.0036798622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830616,0.00061643805,0.00013140141,0.00039821933,0.00045524442,0.00009262029],"domain_scores_gemma":[0.9966953,0.0020762512,0.0002901064,0.00044351557,0.00043280504,0.00006202422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015421,0.0012953109,0.0010312516,0.003077913,0.00051329605,0.0010574164,0.0012799461,0.0015071457,0.0023671372],"category_scores_gemma":[0.010692875,0.00045670537,0.0013721825,0.003548924,0.0008504551,0.0041243406,0.0010048705,0.0011798644,0.0014055617],"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":[0.0013137617,0.0005643092,0.00735606,0.0019396169,0.0003149773,0.0017519052,0.0013102388,0.31335893,0.08301545,0.060893297,0.022468077,0.50571334],"study_design_scores_gemma":[0.00004271153,0.00021432583,0.0016928549,0.000042404252,0.00008037288,0.00048449278,0.00012617688,0.9524477,0.008939826,0.025887273,0.009984374,0.00005753574],"about_ca_topic_score_codex":0.00600361,"about_ca_topic_score_gemma":0.008320714,"teacher_disagreement_score":0.00600361,"about_ca_system_score_codex":0.00084150094,"about_ca_system_score_gemma":0.001094607,"threshold_uncertainty_score":0.01193732},"labels":[],"label_agreement":null},{"id":"W2789183464","doi":"10.1145/3102302","title":"Simulating Urban Pedestrian Crowds of Different Cultures","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Israel Science Foundation","keywords":"Crowds; Pedestrian; Computer science; Crowd simulation; Crowd psychology; Macro; Dynamics (music); Artificial intelligence; Data science; Human–computer interaction; Computer security; Transport engineering; Sociology","score_opus":0.014407800094227699,"score_gpt":0.25752608964507634,"score_spread":0.24311828955084863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789183464","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":[0.96429294,0.000085464926,0.027917419,0.00021585634,0.000029671713,0.00007501695,0.0002541261,0.00010437693,0.0070251343],"genre_scores_gemma":[0.9921887,0.00007360475,0.006486408,0.000030790023,0.000005541878,0.00005111266,0.00009080259,0.000009659393,0.0010633823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979824,0.00009112402,0.00000892797,0.00002975729,0.000031016454,0.000040982628],"domain_scores_gemma":[0.999361,0.00030492232,0.00008658171,0.0000629472,0.000093664305,0.00009087533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004015354,0.00054265856,0.00039216963,0.00044685623,0.00072554,0.0008074712,0.0007976532,0.0008658923,0.0011659686],"category_scores_gemma":[0.0016197421,0.0003215941,0.00048183178,0.0005133127,0.0008046432,0.000750132,0.0012339742,0.00046834574,0.00014225201],"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":[0.000055689048,0.00006291407,0.0046015955,0.000025070683,0.000026187949,0.0001676661,0.00031201317,0.9872699,0.00088804675,0.0046813404,0.00026609193,0.00164358],"study_design_scores_gemma":[0.00001734632,0.00003360059,0.0008415062,0.0000062615754,0.000008033262,0.000019828793,0.00023543132,0.9963032,0.00048599363,0.0016273253,0.0004111618,0.000010201996],"about_ca_topic_score_codex":0.03139998,"about_ca_topic_score_gemma":0.020886892,"teacher_disagreement_score":0.03139998,"about_ca_system_score_codex":0.0011207756,"about_ca_system_score_gemma":0.0006737037,"threshold_uncertainty_score":0.062434375},"labels":[],"label_agreement":null},{"id":"W2963242516","doi":"10.1145/3152875","title":"A Novel Image-Centric Approach Toward Direct Volume Rendering","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Computer Graphics and Visualization Techniques","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Voxel; Volume rendering; Artificial intelligence; Rendering (computer graphics); Classifier (UML); Nonparametric statistics; Pattern recognition (psychology); Volume (thermodynamics); Computer vision; Machine learning","score_opus":0.03349709352924716,"score_gpt":0.27689441697031947,"score_spread":0.24339732344107232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963242516","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":[0.00094658474,0.00004582882,0.9976628,0.000041729363,0.000017372577,0.000020580845,0.00003108065,0.0007335817,0.00050058315],"genre_scores_gemma":[0.06723882,0.00019747808,0.92864084,0.00015839656,0.000095470496,0.00013745022,0.00033954336,0.00059996237,0.0025920477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883026,0.00017942749,0.000041676045,0.00018178436,0.000697652,0.00006923538],"domain_scores_gemma":[0.9990293,0.00027298564,0.00006613048,0.00027237818,0.00030327355,0.000055989916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007012716,0.00092621276,0.0010206656,0.0013417074,0.00039027483,0.0018175676,0.0023268356,0.00094582135,0.0040333457],"category_scores_gemma":[0.0032364493,0.0005450797,0.0012847688,0.0011420574,0.0006444305,0.0016581559,0.002167793,0.0021627727,0.0020685517],"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":[0.00014847178,0.00014830352,0.00062989094,0.00022712466,0.000066755994,0.00021641384,0.0002851497,0.12750892,0.07821187,0.033494245,0.012689773,0.74637306],"study_design_scores_gemma":[0.000013264903,0.000040937957,0.00013905563,0.000009501422,0.000009873389,0.00025785202,0.000022617316,0.9656957,0.012812196,0.012942475,0.008037492,0.000019072226],"about_ca_topic_score_codex":0.0011798459,"about_ca_topic_score_gemma":0.0016675625,"teacher_disagreement_score":0.0040333457,"about_ca_system_score_codex":0.0004731475,"about_ca_system_score_gemma":0.0006409025,"threshold_uncertainty_score":0.013492942},"labels":[],"label_agreement":null},{"id":"W2981449178","doi":"10.1145/3341104","title":"Efficient and Privacy-preserving Fog-assisted Health Data Sharing Scheme","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":48,"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":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Computer science; Encryption; Access control; Collusion; Computer security; Information privacy; Node (physics); Security analysis; Secret sharing; Data sharing; Data access; Energy consumption; Computer network; Confidentiality; Cloud computing; Cryptography; Database; Business","score_opus":0.06249545469440656,"score_gpt":0.3027314696988355,"score_spread":0.24023601500442895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981449178","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":[0.0940948,0.00081117335,0.89636046,0.0007896608,0.00024533668,0.00028869513,0.00043763066,0.000640627,0.0063315295],"genre_scores_gemma":[0.9449921,0.00025820074,0.051568866,0.00024354387,0.000057218076,0.00008984489,0.00022294218,0.000018948387,0.0025482674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985435,0.00029691434,0.00010592563,0.0002937671,0.00041280183,0.00034709723],"domain_scores_gemma":[0.9988374,0.0002167811,0.00011668862,0.0005219164,0.00020604712,0.00010125512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009826298,0.00052703667,0.0009751493,0.00050662074,0.001378354,0.0010292688,0.0016362423,0.00094367366,0.0011731795],"category_scores_gemma":[0.0017701203,0.0002410753,0.0010276453,0.00097396824,0.00078006485,0.002482487,0.0031208477,0.0009271415,0.0003193558],"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":[0.002772429,0.0006698954,0.006671841,0.00048151685,0.0005998592,0.0024803095,0.0023989326,0.21166703,0.121835515,0.2396268,0.024772419,0.38602355],"study_design_scores_gemma":[0.00018202241,0.0003505761,0.0021509936,0.000038316597,0.00016009313,0.0019589802,0.00040175774,0.8620842,0.030648679,0.08629451,0.015591075,0.00013876513],"about_ca_topic_score_codex":0.0015253527,"about_ca_topic_score_gemma":0.0014268135,"teacher_disagreement_score":0.0016362423,"about_ca_system_score_codex":0.00088503,"about_ca_system_score_gemma":0.0014606793,"threshold_uncertainty_score":0.0064213276},"labels":[],"label_agreement":null},{"id":"W3021295796","doi":"10.1145/3377552","title":"Learning Three-dimensional Skeleton Data from Sign Language Video","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Sign language; Motion capture; Artificial intelligence; Computer vision; Avatar; Animation; Gesture; Classifier (UML); Character animation; Motion (physics); Computer animation; Speech recognition; Computer graphics (images); Human–computer interaction","score_opus":0.04467870537470501,"score_gpt":0.2670606905505209,"score_spread":0.2223819851758159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021295796","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":[0.112219006,0.00032686445,0.8721962,0.00016890024,0.00016294084,0.00027804164,0.002493733,0.009269753,0.0028845053],"genre_scores_gemma":[0.51236355,0.0005632922,0.46840462,0.000089103836,0.00007512367,0.00034351659,0.012318643,0.0005118011,0.005330347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999666,0.000042602034,0.000019788144,0.000117951844,0.000116414456,0.00003730124],"domain_scores_gemma":[0.9994055,0.00012521121,0.00006382678,0.00013866981,0.00022688632,0.00003990979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047904853,0.0009017706,0.00060442847,0.0014531243,0.00019325978,0.00052286586,0.00064032426,0.0006475121,0.004463114],"category_scores_gemma":[0.0024586292,0.000325034,0.0006520101,0.00087613036,0.000342039,0.00076140853,0.0008431588,0.0006872467,0.00262224],"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":[0.0003441533,0.00018518956,0.0035259398,0.00022097216,0.000060189894,0.00028660492,0.000097594,0.061383616,0.16210416,0.0020123515,0.008204801,0.7615744],"study_design_scores_gemma":[0.00002783653,0.00033435595,0.006007025,0.000046486795,0.000023258912,0.0003141295,0.0001073312,0.9162342,0.065438576,0.004017948,0.0074090464,0.00003981091],"about_ca_topic_score_codex":0.0021435563,"about_ca_topic_score_gemma":0.0038643437,"teacher_disagreement_score":0.004463114,"about_ca_system_score_codex":0.00034810562,"about_ca_system_score_gemma":0.0006996148,"threshold_uncertainty_score":0.014930606},"labels":[],"label_agreement":null},{"id":"W3110535306","doi":"10.1145/3423067","title":"BiNeTClus","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","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":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Bipartite graph; Modularity (biology); Theoretical computer science; Transformation (genetics); Data mining; Graph","score_opus":0.02302654267139166,"score_gpt":0.2591309411811335,"score_spread":0.23610439850974185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110535306","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014986953,0.002068875,0.8393237,0.0027794498,0.0012548631,0.0009985123,0.028183738,0.08282634,0.02757758],"genre_scores_gemma":[0.088015035,0.0013276483,0.81873584,0.0013566574,0.000373258,0.0015453912,0.05855758,0.011809413,0.018279094],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970433,0.0007004041,0.00013668794,0.0008896317,0.0009230639,0.0003068913],"domain_scores_gemma":[0.9960716,0.0017293714,0.00020730452,0.0010666782,0.00059864164,0.00032632807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022093463,0.00244526,0.0024241772,0.0040883357,0.0031836892,0.005678051,0.008093423,0.0034782705,0.033488575],"category_scores_gemma":[0.011025528,0.0013049907,0.002497596,0.0073986286,0.0013237831,0.005243369,0.005780071,0.0032061338,0.016977485],"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":[0.001063149,0.0005226085,0.0048866994,0.0023601393,0.00075009034,0.0006354806,0.0006228896,0.099800944,0.0058391946,0.13840291,0.44501296,0.30010298],"study_design_scores_gemma":[0.00021296539,0.00011181945,0.0008386107,0.00012890472,0.000066166285,0.00068032497,0.00023899217,0.6501946,0.0065771104,0.15725328,0.18361424,0.00008303623],"about_ca_topic_score_codex":0.0053893477,"about_ca_topic_score_gemma":0.010676166,"teacher_disagreement_score":0.033488575,"about_ca_system_score_codex":0.0016752493,"about_ca_system_score_gemma":0.0030849883,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3115729019","doi":"10.1145/3426239","title":"Deep Learning Thermal Image Translation for Night Vision Perception","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Image translation; Computer science; Artificial intelligence; Convolutional neural network; Translation (biology); Deep learning; Computer vision; Perception; Grayscale; Image (mathematics); Pattern recognition (psychology)","score_opus":0.021768421573466524,"score_gpt":0.27530560188629055,"score_spread":0.253537180312824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115729019","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":[0.05747888,0.0010383769,0.9341554,0.00028510223,0.00019567992,0.000046742407,0.00012561264,0.0018799413,0.004794328],"genre_scores_gemma":[0.7892332,0.001038904,0.2003613,0.00042709662,0.00015125921,0.00005868769,0.00049075705,0.0003187648,0.0079199495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998424,0.000025049962,0.000004194253,0.00005207633,0.000046225687,0.000030045394],"domain_scores_gemma":[0.99987614,0.000027848344,0.000018084245,0.000031830903,0.000033952394,0.000012087226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025335702,0.0006895488,0.00040783777,0.00024979943,0.00020514279,0.00052915205,0.00076194055,0.0004940293,0.0018549486],"category_scores_gemma":[0.0007222184,0.00019450115,0.0005967399,0.000300382,0.00036472338,0.0009657383,0.00076809525,0.0013014227,0.00046636857],"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":[0.0003227711,0.00016697902,0.001324314,0.0002033322,0.00011402835,0.0002072522,0.00014130367,0.25863457,0.13366584,0.011590611,0.0060507436,0.58757824],"study_design_scores_gemma":[0.000008537116,0.00006705695,0.00075696385,0.000010949461,0.000028233308,0.000075086966,0.000022064964,0.9698379,0.021390336,0.0047841934,0.0030058632,0.000012822427],"about_ca_topic_score_codex":0.002420663,"about_ca_topic_score_gemma":0.0031538757,"teacher_disagreement_score":0.002420663,"about_ca_system_score_codex":0.00047471828,"about_ca_system_score_gemma":0.00048431096,"threshold_uncertainty_score":0.0062054396},"labels":[],"label_agreement":null},{"id":"W3119260152","doi":"10.1145/3430767","title":"RHUPS","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Research Foundation of Korea","keywords":"Computer science; Scalability; Data mining; Database; Sliding window protocol; Data stream; Data stream mining; Window (computing)","score_opus":0.02132961777939066,"score_gpt":0.2588025962539239,"score_spread":0.23747297847453322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119260152","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005295831,0.00334356,0.035356738,0.0033745805,0.004494719,0.0008676498,0.036295786,0.027045364,0.88392574],"genre_scores_gemma":[0.040348265,0.0034221068,0.032432683,0.0028209935,0.0011585879,0.00061775674,0.065269046,0.0073998403,0.84653074],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980703,0.00021227018,0.00017703375,0.00049866445,0.00078104995,0.00026059378],"domain_scores_gemma":[0.99755436,0.00025690027,0.000115681,0.0008813016,0.000870501,0.0003213257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012837773,0.0017167708,0.0011890212,0.0034600925,0.0021668803,0.006512354,0.002892432,0.0021465651,0.5993523],"category_scores_gemma":[0.0049475795,0.00076883304,0.0010578859,0.003178411,0.00071975385,0.0053543826,0.004761909,0.0016921463,0.5273242],"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":[0.00041381636,0.00014194447,0.0017146734,0.0007822307,0.000044376517,0.00050749833,0.0003074004,0.0006505026,0.0043675075,0.024976509,0.5994761,0.36661744],"study_design_scores_gemma":[0.000024450625,0.000031683794,0.0005327868,0.00009555267,0.00001354579,0.0003081103,0.000105730054,0.0005329906,0.0015824104,0.0035887589,0.9931645,0.000019489018],"about_ca_topic_score_codex":0.0030555164,"about_ca_topic_score_gemma":0.0030552999,"teacher_disagreement_score":0.5993523,"about_ca_system_score_codex":0.0012500784,"about_ca_system_score_gemma":0.0022046356,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W3166240918","doi":"10.1145/3452009","title":"A Scale and Rotational Invariant Key-point Detector based on Sparse Coding","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Scale-invariant feature transform; Detector; Artificial intelligence; Computer vision; Scale invariance; Robustness (evolution); Pixel; Invariant (physics); Affine transformation; Coding (social sciences); Rotation (mathematics); Offset (computer science); Algorithm; Pattern recognition (psychology); Mathematics; Feature extraction; Geometry","score_opus":0.023689168000405075,"score_gpt":0.26583683251751766,"score_spread":0.2421476645171126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166240918","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":[0.0039578145,0.00016308749,0.99490345,0.00006325027,0.00006493541,0.000053944925,0.000056798268,0.00035405473,0.00038272314],"genre_scores_gemma":[0.12637831,0.00046403985,0.86949223,0.00021037586,0.00009149206,0.00017110564,0.00061030604,0.00007527404,0.0025068335],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990337,0.00012041522,0.00004592791,0.00018749571,0.00053411647,0.00007826412],"domain_scores_gemma":[0.9988256,0.00025364629,0.0001060702,0.00019596994,0.0005494331,0.000069282614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008974727,0.00071027427,0.0015023256,0.0016366597,0.00035229584,0.00069084397,0.0012964995,0.0010273424,0.0012460486],"category_scores_gemma":[0.0025730578,0.00049495883,0.000770277,0.0017582212,0.000583796,0.0014902757,0.0011546479,0.001365316,0.0014127567],"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":[0.0002668697,0.00015807782,0.0012973826,0.00022837635,0.00009794825,0.00017771353,0.000058485788,0.043219548,0.13331029,0.012967455,0.0064258673,0.801792],"study_design_scores_gemma":[0.000038737187,0.00022344667,0.0010403272,0.000015244096,0.000043949356,0.00060164154,0.000019522988,0.9419609,0.046752237,0.003380155,0.005864985,0.000058921658],"about_ca_topic_score_codex":0.0018321082,"about_ca_topic_score_gemma":0.0022903385,"teacher_disagreement_score":0.0018321082,"about_ca_system_score_codex":0.00052825426,"about_ca_system_score_gemma":0.0011870407,"threshold_uncertainty_score":0.004746318},"labels":[],"label_agreement":null},{"id":"W3173340069","doi":"10.1145/3447686","title":"Improving Action Recognition via Temporal and Complementary Learning","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Human Pose and Action 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":"Toronto Metropolitan University; Vector Institute","funders":"Nvidia","keywords":"Computer science; Pooling; Representation (politics); Fuse (electrical); Artificial intelligence; Feature learning; Machine learning; Action recognition; Deep learning; Pattern recognition (psychology); Temporal database; Action (physics); Data mining; Class (philosophy)","score_opus":0.04123009817537312,"score_gpt":0.2678034478186185,"score_spread":0.22657334964324535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173340069","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":[0.054428082,0.0017776035,0.9336687,0.0003692322,0.00027273007,0.00007239188,0.0005861576,0.004258541,0.004566655],"genre_scores_gemma":[0.6694498,0.0014784161,0.3165133,0.00065362983,0.00025417164,0.00009851334,0.0023512773,0.00027252856,0.008928388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992021,0.000078487064,0.00003289015,0.0003625187,0.00021134794,0.00011278234],"domain_scores_gemma":[0.99941754,0.00016549877,0.00008474919,0.00015113968,0.00012590158,0.00005514035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007702509,0.0015102855,0.0011776233,0.0011154239,0.00031599554,0.00082473323,0.0015091584,0.0010702957,0.0029927946],"category_scores_gemma":[0.0020381089,0.00034337185,0.0010136174,0.0012779793,0.0006388038,0.0024760433,0.0012655322,0.0013837789,0.0017598104],"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":[0.00028567112,0.00027398966,0.0019206811,0.00013796763,0.00011910771,0.00015625,0.000062441344,0.06456761,0.04275106,0.004346781,0.0071564717,0.8782219],"study_design_scores_gemma":[0.000011076487,0.00012790028,0.0015603452,0.000016640006,0.000056840665,0.00016810138,0.00003169364,0.9716465,0.018174054,0.005703702,0.0024836538,0.000019551486],"about_ca_topic_score_codex":0.0068458035,"about_ca_topic_score_gemma":0.010108149,"teacher_disagreement_score":0.0068458035,"about_ca_system_score_codex":0.000723669,"about_ca_system_score_gemma":0.00095181674,"threshold_uncertainty_score":0.013611913},"labels":[],"label_agreement":null},{"id":"W3191813552","doi":"10.1145/3466684","title":"Mining Customers’ Changeable Electricity Consumption for Effective Load Forecasting","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electricity; Computer science; Consumption (sociology); Exploit; Task (project management); Artificial neural network; Electricity market; Energy consumption; Artificial intelligence; Machine learning; Computer security; Economics","score_opus":0.02931521277631001,"score_gpt":0.24188804453331433,"score_spread":0.2125728317570043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191813552","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":[0.62927854,0.0011119507,0.3507953,0.0010729795,0.000115901625,0.0001714443,0.007257834,0.0029096333,0.0072864564],"genre_scores_gemma":[0.9551132,0.0003543474,0.03764791,0.000047129266,0.000053361386,0.000051863855,0.005121543,0.000060799324,0.0015498631],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978524,0.000034907425,0.000014966849,0.0000683404,0.00006479014,0.00003168334],"domain_scores_gemma":[0.999496,0.00018674142,0.0000925696,0.00007767329,0.00012270536,0.00002422843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003135689,0.00071097957,0.0005149299,0.002023396,0.00026655284,0.00054141576,0.0007926388,0.0005827522,0.0013244156],"category_scores_gemma":[0.001551279,0.00025493398,0.00041770362,0.002850495,0.00013027508,0.0010552976,0.00035857284,0.0006355585,0.00079598435],"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":[0.00031004223,0.0005880068,0.14160554,0.00025760237,0.0002524985,0.000556145,0.00040203225,0.3965557,0.010835277,0.0054189693,0.019491747,0.42372644],"study_design_scores_gemma":[0.000003457145,0.000016353222,0.011705811,0.000009602063,0.000013490386,0.0000380828,0.00007392167,0.98266935,0.0012172366,0.002623703,0.0016193582,0.000009553316],"about_ca_topic_score_codex":0.013404782,"about_ca_topic_score_gemma":0.025444198,"teacher_disagreement_score":0.013404782,"about_ca_system_score_codex":0.00053032726,"about_ca_system_score_gemma":0.00044769337,"threshold_uncertainty_score":0.026653528},"labels":[],"label_agreement":null},{"id":"W3214905160","doi":"10.1145/3462675","title":"Origin-Aware Location Prediction Based on Historical Vehicle Trajectories","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Trajectory; Baseline (sea); Exploit; Markov chain; Data mining; Machine learning; Predictive modelling; Temporal difference learning; Travel time; Artificial intelligence","score_opus":0.02315088312893285,"score_gpt":0.2409021286629922,"score_spread":0.21775124553405936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214905160","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":[0.39139497,0.0014324296,0.59006,0.0006937269,0.00020227184,0.00009946238,0.009252069,0.0034670737,0.0033980887],"genre_scores_gemma":[0.9404292,0.00058568607,0.048498824,0.000037141996,0.00006023374,0.000034391116,0.008818276,0.00006925988,0.001467029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970454,0.00003289138,0.000020111966,0.00013095516,0.0000683029,0.00004310164],"domain_scores_gemma":[0.99899405,0.00024975312,0.00016023392,0.00020172996,0.00032260804,0.00007170424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035479607,0.00078464014,0.00061407936,0.001373314,0.00035239558,0.00061195326,0.0013923667,0.0005016051,0.0010370421],"category_scores_gemma":[0.002196263,0.00031326863,0.00047846208,0.0020614895,0.00023282954,0.0020245807,0.0007625271,0.0010350414,0.0010733856],"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":[0.0003882329,0.00017225585,0.10251975,0.00020404025,0.0001396171,0.00040542273,0.00019254135,0.71112233,0.004262479,0.0036692815,0.0089945635,0.16792956],"study_design_scores_gemma":[0.0000046670893,0.00002040296,0.0035229481,0.000010235486,0.000017174936,0.00005956563,0.0000498256,0.9927527,0.0011507731,0.001504811,0.00089840643,0.0000086369],"about_ca_topic_score_codex":0.022788435,"about_ca_topic_score_gemma":0.039864328,"teacher_disagreement_score":0.022788435,"about_ca_system_score_codex":0.0005236472,"about_ca_system_score_gemma":0.0007760585,"threshold_uncertainty_score":0.04531157},"labels":[],"label_agreement":null},{"id":"W4315977512","doi":"10.1145/3579839","title":"Ontology-Based Driving Simulation for Traffic Lights Optimization","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Traffic control and management","field":"Engineering","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 British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Computer science; Traffic flow (computer networking); Principal (computer security); Traffic simulation; Traffic signal; Ontology; Real-time computing; Traffic congestion; Simulation; Transport engineering; Computer network; Computer security; Microsimulation; Engineering","score_opus":0.015541243578063876,"score_gpt":0.2379926969042367,"score_spread":0.2224514533261728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315977512","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":[0.042654347,0.00010213305,0.9474292,0.00014206272,0.000049227867,0.00010966685,0.00032789845,0.00075475033,0.008430717],"genre_scores_gemma":[0.6491465,0.00030389978,0.34600005,0.00006617988,0.000020482672,0.00047314627,0.0008096829,0.00015424527,0.0030258137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974793,0.000080643775,0.000020192714,0.000041462008,0.00007885159,0.000030939715],"domain_scores_gemma":[0.99971277,0.00014836172,0.00002321602,0.0000329964,0.000063868334,0.000018784294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045753957,0.0006064039,0.00049688894,0.00065390405,0.0005985341,0.0009236271,0.00085164513,0.00069158705,0.0023476812],"category_scores_gemma":[0.0010614955,0.00031261254,0.0013239383,0.00060707645,0.00044716196,0.00073654123,0.0008811367,0.00087094936,0.00026853173],"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":[0.000014320059,0.00003846779,0.00052902324,0.000037998496,0.000022611115,0.000038812115,0.00006248746,0.9774131,0.0012262932,0.011239922,0.00025765647,0.009119332],"study_design_scores_gemma":[0.000002962508,0.0000046476835,0.00006672331,0.0000027305962,0.000005241512,0.000004359899,0.000008899402,0.99685764,0.00029410128,0.001987164,0.00076266716,0.0000028466125],"about_ca_topic_score_codex":0.019436104,"about_ca_topic_score_gemma":0.017056482,"teacher_disagreement_score":0.019436104,"about_ca_system_score_codex":0.001062319,"about_ca_system_score_gemma":0.0014688729,"threshold_uncertainty_score":0.038645923},"labels":[],"label_agreement":null},{"id":"W4323864040","doi":"10.1145/3587253","title":"A Discriminant Information Theoretic Learning Framework for Multi-modal Feature Representation","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Advanced Image and Video Retrieval 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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Representation (politics); Generality; Modal; Feature learning; Pattern recognition (psychology); Machine learning; Discriminant; Cognitive neuroscience of visual object recognition; Facial recognition system; Feature extraction","score_opus":0.041927764130490805,"score_gpt":0.34288184445365805,"score_spread":0.30095408032316723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323864040","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":[0.0012917641,0.00022168989,0.99788445,0.000084797226,0.000013944247,0.000014860316,0.000033363754,0.00008388328,0.00037125644],"genre_scores_gemma":[0.28510326,0.0010907076,0.7092652,0.00031859343,0.00021969795,0.0002998828,0.00060514593,0.0001054782,0.002992172],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99900925,0.00038805374,0.000050887156,0.00018441543,0.00030600125,0.00006143281],"domain_scores_gemma":[0.999226,0.00032410782,0.0000869857,0.00012359566,0.00019732946,0.000041951083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018798931,0.0008663359,0.0009288184,0.0014075079,0.00038538093,0.0011295285,0.0015023636,0.00083046476,0.0018925519],"category_scores_gemma":[0.003149608,0.0003058072,0.0009952115,0.0015561412,0.001122238,0.0015718305,0.0014018974,0.0017796076,0.0009039949],"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":[0.0001956264,0.0001716423,0.0010052215,0.0003249662,0.00014702375,0.00016557479,0.00015588822,0.3569541,0.018724106,0.12732683,0.005736451,0.48909247],"study_design_scores_gemma":[0.000007786278,0.00006376353,0.00019327166,0.000010403195,0.000014814603,0.00005791341,0.000012521406,0.9675725,0.001647021,0.028679056,0.0017207586,0.000020191716],"about_ca_topic_score_codex":0.0016501726,"about_ca_topic_score_gemma":0.0012976879,"teacher_disagreement_score":0.0018925519,"about_ca_system_score_codex":0.00084104115,"about_ca_system_score_gemma":0.0008087719,"threshold_uncertainty_score":0.009941876},"labels":[],"label_agreement":null},{"id":"W4386928368","doi":"10.1145/3625238","title":"Exploring Structure Incentive Domain Adversarial Learning for Generalizable Sleep Stage Classification","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":9,"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":"Beijing Municipal Science and Technology Commission; National Natural Science Foundation of China; York University; Case Western Reserve University; Johns Hopkins University; National Heart, Lung, and Blood Institute; University of California, Davis; University of Minnesota; University of Washington; New York University","keywords":"Computer science; Artificial intelligence; Machine learning; Classifier (UML); Sleep (system call); Sleep Stages; Domain (mathematical analysis); Adversarial system; Benchmark (surveying); Psychology; Polysomnography; Mathematics; Psychiatry","score_opus":0.10421233610170479,"score_gpt":0.32424640297613216,"score_spread":0.2200340668744274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386928368","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":[0.06706851,0.000991153,0.92827433,0.00072929653,0.00011038214,0.000080894504,0.00022847185,0.00063865003,0.0018782024],"genre_scores_gemma":[0.90573335,0.00046634025,0.08725385,0.00067867246,0.00015084134,0.00016467129,0.0007333158,0.00009754032,0.0047214064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993716,0.00027721163,0.000023873683,0.00016837243,0.000088937995,0.00007004103],"domain_scores_gemma":[0.9979685,0.0014456641,0.00013512274,0.00019028956,0.00017208008,0.00008830706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021706272,0.0009962427,0.0008621776,0.0004490202,0.00027416516,0.0005849528,0.0012038144,0.0010349571,0.0013388047],"category_scores_gemma":[0.004058765,0.00035281258,0.00082272734,0.0003928862,0.0009346923,0.0008926491,0.0013225065,0.0020894075,0.00033687652],"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":[0.00021872038,0.00017905948,0.004277376,0.000091410606,0.000119027056,0.00012328532,0.00012544266,0.8728262,0.0040857694,0.009335132,0.003989724,0.10462887],"study_design_scores_gemma":[0.000004291668,0.00002289633,0.00024796333,0.000004575477,0.0000053899857,0.00001038936,0.0000051616207,0.99579775,0.000299824,0.0034129266,0.00018465596,0.0000042084753],"about_ca_topic_score_codex":0.002761001,"about_ca_topic_score_gemma":0.0028993879,"teacher_disagreement_score":0.002761001,"about_ca_system_score_codex":0.0007330578,"about_ca_system_score_gemma":0.00081229775,"threshold_uncertainty_score":0.011479497},"labels":[],"label_agreement":null},{"id":"W4387055551","doi":"10.1145/3625224","title":"Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Machine Learning in Healthcare","field":"Computer Science","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":"McGill University","funders":"","keywords":"Computer science; Embedding; Categorical variable; Ontology; Open Biomedical Ontologies; Biomedicine; Analytics; Categorization; Data science; Data integration; Information retrieval; Artificial intelligence; Machine learning; Data mining; Domain knowledge; Upper ontology; Bioinformatics; Ontology alignment","score_opus":0.10108495582195558,"score_gpt":0.36236254242282745,"score_spread":0.26127758660087186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387055551","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":[0.082434416,0.00093574094,0.91244525,0.00073291606,0.00008314065,0.00011707136,0.00036713263,0.0013575406,0.0015268117],"genre_scores_gemma":[0.70125943,0.00082248653,0.29288894,0.00043068914,0.0001161271,0.00013993034,0.0020635615,0.00010970069,0.0021690526],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986885,0.00048264934,0.00007921248,0.00037869258,0.00027629052,0.00009464304],"domain_scores_gemma":[0.997644,0.0012474349,0.00025941795,0.00041277468,0.00034024584,0.00009617479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015481352,0.000988603,0.0005649024,0.0021503565,0.00037844628,0.001058975,0.0013366217,0.0011632129,0.0011078877],"category_scores_gemma":[0.006902799,0.000261619,0.001270009,0.002310383,0.0006274921,0.0034705172,0.002681168,0.002080135,0.0003914062],"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":[0.00025121978,0.0006126907,0.0111552775,0.0002792331,0.00029788824,0.00026634624,0.00054269563,0.18273707,0.013842413,0.0131673515,0.0046702866,0.77217764],"study_design_scores_gemma":[0.000010474254,0.00009595455,0.0016878059,0.000022997552,0.000051334522,0.000115023635,0.00012435313,0.97630674,0.004228514,0.015011127,0.0023230475,0.000022593707],"about_ca_topic_score_codex":0.00579686,"about_ca_topic_score_gemma":0.008363713,"teacher_disagreement_score":0.00579686,"about_ca_system_score_codex":0.000892357,"about_ca_system_score_gemma":0.0008396755,"threshold_uncertainty_score":0.011526227},"labels":[],"label_agreement":null},{"id":"W4389098833","doi":"10.1145/3633518","title":"A Survey on Graph Representation Learning Methods","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":189,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Graph; Representation (politics); Data science; Theoretical computer science; Artificial intelligence; Information retrieval; Machine learning","score_opus":0.064175238834068,"score_gpt":0.3603229334070372,"score_spread":0.2961476945729692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389098833","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032755856,0.08418038,0.8957777,0.0024257845,0.0008332113,0.0001693339,0.0012870473,0.001822998,0.010227896],"genre_scores_gemma":[0.11238751,0.23401406,0.62089586,0.002509974,0.0031968504,0.000781935,0.00983137,0.0013613993,0.015020965],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998406,0.00048296468,0.0001348148,0.00037543496,0.00052066456,0.00008012002],"domain_scores_gemma":[0.9967879,0.0019933872,0.00013502444,0.00043509505,0.00057525653,0.00007345234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019276087,0.0017121995,0.0017749158,0.004134404,0.0005638717,0.002141113,0.002736048,0.0015180042,0.00663893],"category_scores_gemma":[0.008808111,0.0006349827,0.0017158973,0.007882593,0.0006742702,0.005419906,0.0014765714,0.0023638974,0.0037921402],"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":[0.000052323667,0.000110634915,0.0011120396,0.0019243115,0.00014273297,0.00006794285,0.00012434914,0.037293967,0.00070484605,0.057986442,0.044404607,0.8560758],"study_design_scores_gemma":[0.000034325065,0.00013044768,0.0017038858,0.0010408486,0.00015769363,0.0005360871,0.00021602082,0.48252997,0.0018117914,0.27814937,0.23360218,0.000087401204],"about_ca_topic_score_codex":0.004138348,"about_ca_topic_score_gemma":0.00365303,"teacher_disagreement_score":0.00663893,"about_ca_system_score_codex":0.0012583355,"about_ca_system_score_gemma":0.0013408924,"threshold_uncertainty_score":0.022209466},"labels":[],"label_agreement":null},{"id":"W4393191404","doi":"10.1145/3653980","title":"Explainable finite mixture of mixtures of bounded asymmetric generalized Gaussian and Uniform distributions learning for energy demand management","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Energy Load and Power Forecasting","field":"Engineering","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":"Concordia University","funders":"","keywords":"Computer science; Bounded function; Gaussian; Mixture model; Energy (signal processing); Gaussian process; Mathematical optimization; Applied mathematics; Artificial intelligence; Mathematics; Statistics; Physics; Mathematical analysis","score_opus":0.00954383941726799,"score_gpt":0.22166796202049796,"score_spread":0.21212412260322996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393191404","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":[0.010388838,0.00020868816,0.98837984,0.0002599136,0.000017343848,0.00001962521,0.000063734544,0.00020332831,0.00045877913],"genre_scores_gemma":[0.70650285,0.0004875003,0.28898665,0.00028038595,0.00013853287,0.00022029995,0.00055294845,0.000110752946,0.0027200275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991666,0.00040484424,0.000035776662,0.00016829262,0.00014431641,0.000080132595],"domain_scores_gemma":[0.9976406,0.0016744162,0.00020965165,0.00019779465,0.00020653539,0.00007096142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018601578,0.0007303927,0.000961971,0.0008111127,0.00046750312,0.0011059075,0.0018005215,0.0012071012,0.0015466971],"category_scores_gemma":[0.0070106937,0.00052980304,0.001077112,0.0008971355,0.00097099494,0.0018940802,0.0014426433,0.0021470003,0.00040007735],"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":[0.0001160189,0.000060728915,0.0019417731,0.000063150495,0.000093842405,0.0000618321,0.00012497503,0.85934824,0.0010656957,0.08344184,0.0013408011,0.05234101],"study_design_scores_gemma":[0.000002626573,0.0000066881935,0.00010924071,0.0000028505362,0.0000037203752,0.000005001282,0.000003925488,0.98544717,0.00010797687,0.014120521,0.0001865842,0.0000037556777],"about_ca_topic_score_codex":0.005389169,"about_ca_topic_score_gemma":0.005259969,"teacher_disagreement_score":0.005389169,"about_ca_system_score_codex":0.0012632092,"about_ca_system_score_gemma":0.0009505443,"threshold_uncertainty_score":0.010715544},"labels":[],"label_agreement":null},{"id":"W4394877201","doi":"10.1145/3658673","title":"CGKPN: Cross-Graph Knowledge Propagation Network with Adaptive Connection for Reasoning-Based Machine Reading Comprehension","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Topic Modeling","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Central China Normal University; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Theoretical computer science; Graph; Comprehension; Semantic memory; Natural language processing; Cognition; Programming language","score_opus":0.025823321524726585,"score_gpt":0.27844403678114726,"score_spread":0.25262071525642066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394877201","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":[0.03425945,0.0016406422,0.9495586,0.001099987,0.00023354423,0.00024160341,0.0014135574,0.0074452558,0.0041072164],"genre_scores_gemma":[0.66465914,0.0014844444,0.31702533,0.0011198102,0.00019500087,0.0005999805,0.0051270314,0.0004806328,0.0093086865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944836,0.00011760519,0.000026226378,0.000249347,0.00010553256,0.000052883435],"domain_scores_gemma":[0.998995,0.0005229631,0.00007980283,0.000139445,0.00020828148,0.00005453458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009164861,0.0016164444,0.00084370933,0.0014291737,0.0006356845,0.001049706,0.003587299,0.0019791534,0.0033249552],"category_scores_gemma":[0.005005181,0.0007322053,0.0011631348,0.0014824175,0.00082172756,0.002946901,0.001610421,0.0026598384,0.001014935],"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":[0.00030755828,0.0002813931,0.0025428245,0.00032015698,0.00023321589,0.00037004016,0.0002623187,0.6132885,0.0049975007,0.011671661,0.017129669,0.34859508],"study_design_scores_gemma":[0.000011613423,0.000024380033,0.00021444868,0.000009938736,0.000025604133,0.00003622326,0.000012704906,0.9901451,0.00061140896,0.007773961,0.001125675,0.000008962654],"about_ca_topic_score_codex":0.023227792,"about_ca_topic_score_gemma":0.024952449,"teacher_disagreement_score":0.023227792,"about_ca_system_score_codex":0.0018780553,"about_ca_system_score_gemma":0.0012774561,"threshold_uncertainty_score":0.046185136},"labels":[],"label_agreement":null},{"id":"W4400146946","doi":"10.1145/3675405","title":"Libby-Novick Beta-Liouville Distribution for Enhanced Anomaly Detection in Proportional Data","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Anomaly Detection Techniques and Applications","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":"Concordia University","funders":"","keywords":"Computer science; Anomaly (physics); Anomaly detection; Distribution (mathematics); BETA (programming language); Artificial intelligence; Mathematics; Physics; Condensed matter physics; Mathematical analysis","score_opus":0.029525830544751917,"score_gpt":0.2919575922318553,"score_spread":0.26243176168710336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400146946","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":[0.03788752,0.0001912073,0.958854,0.00028263594,0.00003622321,0.00004485297,0.000137464,0.0016036608,0.00096236257],"genre_scores_gemma":[0.77621883,0.00029384493,0.21734396,0.0005236314,0.00013579027,0.00018883181,0.0011769108,0.0004892421,0.0036290141],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978236,0.0007150043,0.00012917376,0.00054324575,0.0005475785,0.00024149963],"domain_scores_gemma":[0.9919905,0.004460037,0.0007061281,0.0013828349,0.001117624,0.0003429177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046244278,0.0009021531,0.0011595921,0.0022830071,0.00077995117,0.0022578784,0.0029189999,0.0016255071,0.00233179],"category_scores_gemma":[0.02012568,0.0003848798,0.0009288494,0.001913244,0.0019387337,0.004000048,0.002859245,0.00317631,0.0011859433],"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":[0.0010270231,0.00044761796,0.03877272,0.00023753231,0.000175832,0.0006421359,0.0007327851,0.26138008,0.018009242,0.116350435,0.00931733,0.55290717],"study_design_scores_gemma":[0.000009086158,0.000031958854,0.0012283007,0.000011883642,0.000005923126,0.0001749299,0.00004032241,0.9631019,0.0035042444,0.03062913,0.0012416821,0.000020701971],"about_ca_topic_score_codex":0.003902869,"about_ca_topic_score_gemma":0.003056212,"teacher_disagreement_score":0.0046244278,"about_ca_system_score_codex":0.0015321792,"about_ca_system_score_gemma":0.0015418255,"threshold_uncertainty_score":0.02445662},"labels":[],"label_agreement":null},{"id":"W4404573142","doi":"10.1145/3704922","title":"Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review","year":2024,"lang":"en","type":"review","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":10,"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; Université du Québec à Montréal","funders":"","keywords":"Computer science; Adaptation (eye); Concept drift; Systematic review; Data science; Information retrieval; Data mining; Data stream mining; MEDLINE","score_opus":0.04197330668910702,"score_gpt":0.32485600243052026,"score_spread":0.2828826957414132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404573142","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039264286,0.9964,0.0019070245,0.00046923626,0.00017206855,0.00009529245,0.00009775471,0.000028362585,0.00043763474],"genre_scores_gemma":[0.0032616025,0.9926777,0.0030439044,0.00033419515,0.00018764053,0.00013798337,0.00016739288,0.000012832236,0.0001767143],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.997428,0.0008134649,0.00065940764,0.0003970982,0.0006213969,0.00008060925],"domain_scores_gemma":[0.9667196,0.02686065,0.0021125437,0.00058218435,0.0034297572,0.00029536875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007919109,0.0014289485,0.0027214317,0.0076053157,0.00054093526,0.002351195,0.002237824,0.0016867283,0.0030749459],"category_scores_gemma":[0.038321484,0.0008544384,0.002802543,0.0078134965,0.00070611754,0.0039597983,0.001156906,0.0013868713,0.0010234324],"study_design_candidate":"systematic_review","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":[0.00017102748,0.00011341469,0.0011510632,0.15098979,0.0009820359,0.00013265338,0.00029305305,0.0009903943,0.00039957126,0.002687228,0.013338306,0.82875144],"study_design_scores_gemma":[0.00020716366,0.0010320703,0.007869389,0.30165783,0.0101319235,0.002067895,0.0010136649,0.003841191,0.0019375933,0.010830645,0.6591775,0.00023319674],"about_ca_topic_score_codex":0.0027022413,"about_ca_topic_score_gemma":0.0047195065,"teacher_disagreement_score":0.007919109,"about_ca_system_score_codex":0.0011737071,"about_ca_system_score_gemma":0.0069003706,"threshold_uncertainty_score":0.041880727},"labels":[],"label_agreement":null},{"id":"W4405073635","doi":"10.1145/3706115","title":"Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Topological graph theory; Network topology; Graph; Robustness (evolution); Machine learning; Theoretical computer science; Data mining; Pathwidth; Line graph","score_opus":0.01694117523900615,"score_gpt":0.25195624868701516,"score_spread":0.235015073448009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405073635","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":[0.031512607,0.00014147449,0.96631384,0.00014625004,0.000023295177,0.00006779564,0.000058986814,0.0005567124,0.001179025],"genre_scores_gemma":[0.70121276,0.00015462714,0.29568315,0.00028885753,0.000056531317,0.00017925978,0.00044633698,0.00014993595,0.0018285633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989743,0.00031237435,0.000041432297,0.00038247945,0.00021521817,0.000074193995],"domain_scores_gemma":[0.9979438,0.00095878495,0.0003003195,0.00029674507,0.0004163618,0.00008397759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017133337,0.0011460416,0.0010510085,0.0014798357,0.00046369733,0.0010448786,0.0022237063,0.0013498663,0.00093575974],"category_scores_gemma":[0.005646458,0.00050643896,0.001058262,0.0010913387,0.0011539123,0.0023150437,0.001603857,0.0018345795,0.00027562396],"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":[0.00016799192,0.00021063855,0.0026338245,0.00009227804,0.00014968195,0.00013579811,0.00017928153,0.76319826,0.0075962557,0.015300891,0.0016505733,0.20868452],"study_design_scores_gemma":[0.0000035883397,0.000013953475,0.00008212213,0.0000018382743,0.000004987968,0.0000069760354,0.0000047709455,0.9964947,0.0005625444,0.0027247374,0.00009725626,0.0000025700112],"about_ca_topic_score_codex":0.0029797165,"about_ca_topic_score_gemma":0.0040082787,"teacher_disagreement_score":0.0029797165,"about_ca_system_score_codex":0.0011942942,"about_ca_system_score_gemma":0.0006254164,"threshold_uncertainty_score":0.009061098},"labels":[],"label_agreement":null},{"id":"W4409539127","doi":"10.1145/3729242","title":"Cascade Transformer for Hierarchical Semantic Reasoning in Text-Based Visual Question Answering","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Multimodal Machine Learning Applications","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":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Question answering; Transformer; Cascade; Natural language processing; Artificial intelligence; Information retrieval","score_opus":0.010491793156558554,"score_gpt":0.308018523082175,"score_spread":0.2975267299256164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409539127","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":[0.020684157,0.00089852,0.9647592,0.00038607678,0.00010702123,0.00030727359,0.00080508715,0.008257406,0.003795258],"genre_scores_gemma":[0.5885856,0.0008149458,0.39742932,0.00082338677,0.000117670235,0.00038869842,0.0036946372,0.00031215852,0.007833611],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993399,0.000108739136,0.000042268926,0.00028931277,0.00014027073,0.00007959183],"domain_scores_gemma":[0.99944,0.0002552972,0.000042311192,0.00008778939,0.00013341969,0.000041188065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010450756,0.0012066017,0.0007105925,0.0012329193,0.00042228037,0.001006461,0.0024381552,0.0013204966,0.0066262186],"category_scores_gemma":[0.002895039,0.00044569594,0.0018628688,0.0007225037,0.0007091211,0.00354756,0.0014494902,0.0017096538,0.0020159734],"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":[0.00073647866,0.0005204321,0.0018497078,0.0007295528,0.00018507798,0.00065697776,0.0010064262,0.087279074,0.059120964,0.029570043,0.02054867,0.7977966],"study_design_scores_gemma":[0.000040821436,0.00011728951,0.0006012848,0.0000394933,0.00010272467,0.00019683647,0.00012977357,0.92489654,0.018189339,0.04944407,0.0062063555,0.000035475445],"about_ca_topic_score_codex":0.010348385,"about_ca_topic_score_gemma":0.013198014,"teacher_disagreement_score":0.010348385,"about_ca_system_score_codex":0.001466869,"about_ca_system_score_gemma":0.0010958988,"threshold_uncertainty_score":0.022166908},"labels":[],"label_agreement":null},{"id":"W4410398814","doi":"10.1145/3735650","title":"Robust Neural Model for Searching over Incomplete Graphs","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Graph Theory and Algorithms","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":"Toronto Metropolitan University; York University; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.03846906754591372,"score_gpt":0.2751686339396371,"score_spread":0.23669956639372336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410398814","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":[0.029519998,0.0005246323,0.9662483,0.00048034985,0.000037956208,0.00005229132,0.00049204315,0.0010790441,0.0015653003],"genre_scores_gemma":[0.79225856,0.0005949375,0.19820045,0.00042330977,0.00009323083,0.00024248402,0.0018142872,0.0002117617,0.006161025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999215,0.00021527597,0.000041911353,0.0002939156,0.00014977105,0.00008409274],"domain_scores_gemma":[0.9975904,0.0014091998,0.00030112526,0.0002743513,0.00035063038,0.0000743695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011803748,0.0009009633,0.0014714313,0.0012702171,0.00032159808,0.0010400787,0.00299005,0.002099799,0.002185855],"category_scores_gemma":[0.0072772414,0.00053336535,0.0007877789,0.0017755893,0.0009845213,0.003812438,0.0011675117,0.0018501335,0.0006289449],"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":[0.00009127357,0.00003512355,0.0003690043,0.00008144405,0.000029963621,0.000061313076,0.00004373098,0.94794005,0.0009542897,0.010483111,0.0014722114,0.038438432],"study_design_scores_gemma":[0.0000026495961,0.0000059831555,0.000029262772,0.0000020001612,0.0000023628331,0.0000060775096,0.0000033260458,0.99289316,0.00012216927,0.0068430705,0.00008792124,0.0000018750198],"about_ca_topic_score_codex":0.010244955,"about_ca_topic_score_gemma":0.008425625,"teacher_disagreement_score":0.010244955,"about_ca_system_score_codex":0.0017951388,"about_ca_system_score_gemma":0.0010713526,"threshold_uncertainty_score":0.020370662},"labels":[],"label_agreement":null},{"id":"W4411403346","doi":"10.1145/3744746","title":"A Comprehensive Overview of Large Language Models","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Topic Modeling","field":"Computer Science","cited_by":488,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Context (archaeology); Benchmarking; Frontier; Data science; Engineering ethics; Management science; Political science; Engineering; Management","score_opus":0.041916858637249886,"score_gpt":0.307768274351576,"score_spread":0.26585141571432613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411403346","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027032692,0.17498478,0.7742202,0.008656428,0.0011088821,0.00032368448,0.00836263,0.005980137,0.023659931],"genre_scores_gemma":[0.116883256,0.27176562,0.5455529,0.0042337817,0.0060451096,0.0019747405,0.032051522,0.0025580348,0.018934932],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99738544,0.0010457587,0.00029512288,0.0004219268,0.0007491369,0.00010264032],"domain_scores_gemma":[0.9939644,0.0043456266,0.00027877107,0.0006233522,0.00067247957,0.00011542134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035038546,0.0017370526,0.0014591195,0.0044655725,0.0007505256,0.0044085854,0.002607852,0.0017404908,0.010180098],"category_scores_gemma":[0.012138018,0.00097230775,0.002192413,0.0054227468,0.0008275392,0.0065181716,0.0020880667,0.0029543764,0.007107844],"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":[0.00010972584,0.00011838186,0.0021259016,0.004219814,0.00039591047,0.0003827566,0.00048092206,0.051077504,0.002023827,0.1914044,0.090913765,0.65674704],"study_design_scores_gemma":[0.000023253862,0.000084636056,0.0014110126,0.0014233928,0.00018286383,0.0006307563,0.0001858462,0.19381233,0.001348266,0.32290113,0.47788337,0.000113182025],"about_ca_topic_score_codex":0.005581778,"about_ca_topic_score_gemma":0.0061090426,"teacher_disagreement_score":0.010180098,"about_ca_system_score_codex":0.0017571669,"about_ca_system_score_gemma":0.0034312129,"threshold_uncertainty_score":0.03405577},"labels":[],"label_agreement":null},{"id":"W4414596481","doi":"10.1145/3762197","title":"Query Performance Prediction Using Neural Query Space Proximity","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Advanced Image and Video Retrieval 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":"Toronto Metropolitan University; University of Guelph; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Query expansion; Embedding; Subspace topology; Query optimization; Query language; Sargable; Property (philosophy); Quality (philosophy); Ranking (information retrieval)","score_opus":0.022920297931868006,"score_gpt":0.28153209318374967,"score_spread":0.25861179525188166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414596481","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":[0.33152544,0.0025090296,0.6573443,0.00049476494,0.00007488814,0.00019380708,0.000926938,0.0033532418,0.003577562],"genre_scores_gemma":[0.9529884,0.0004189279,0.04369959,0.00011485884,0.00006211215,0.00009141927,0.0014014908,0.00012074298,0.001102383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804187,0.00036620698,0.00015544437,0.00050214847,0.0007784496,0.00015574574],"domain_scores_gemma":[0.99484456,0.0029014905,0.0005599505,0.0005125771,0.001061883,0.00011944874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018539161,0.0009183602,0.0010179528,0.0018195309,0.00030155125,0.0014278119,0.0012392788,0.00093587744,0.0010147812],"category_scores_gemma":[0.013034309,0.00032893653,0.000571978,0.0013741559,0.0006121563,0.002977639,0.0011661124,0.00113634,0.00074689504],"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":[0.00062423764,0.0003469803,0.02261616,0.00033062897,0.00017241764,0.00019549033,0.00025423235,0.67710984,0.020694403,0.0043690857,0.00408536,0.26920116],"study_design_scores_gemma":[0.000007215256,0.000055961624,0.0012141684,0.0000035363473,0.000010687014,0.00003428047,0.000019112847,0.9942912,0.0028263421,0.0012743339,0.0002519209,0.000011131034],"about_ca_topic_score_codex":0.0075368863,"about_ca_topic_score_gemma":0.005020128,"teacher_disagreement_score":0.0075368863,"about_ca_system_score_codex":0.0011361992,"about_ca_system_score_gemma":0.0007800478,"threshold_uncertainty_score":0.014986038},"labels":[],"label_agreement":null},{"id":"W4417043386","doi":"10.1145/3779428","title":"Learning Context-aware Term Importance for Query Performance Prediction","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Information Retrieval and Search Behavior","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 Toronto; University of Waterloo; Toronto Metropolitan University","funders":"","keywords":"Query expansion; Relevance (law); Task (project management); Term (time); Query optimization; Term Discrimination; Web query classification; Performance prediction; Rank (graph theory)","score_opus":0.01809151885803668,"score_gpt":0.26475431540830374,"score_spread":0.24666279655026707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417043386","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":[0.2664198,0.01262904,0.70544815,0.0009263397,0.00036876253,0.0003816734,0.002693609,0.0071331724,0.0039994987],"genre_scores_gemma":[0.8757796,0.0016137533,0.11406713,0.00033510898,0.0006083975,0.00020096752,0.004553973,0.00032562995,0.002515353],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986387,0.00026612333,0.000092954,0.00041130974,0.0004483189,0.00014250982],"domain_scores_gemma":[0.9960821,0.0022369085,0.00043123192,0.00040667393,0.0006917503,0.0001512573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015870527,0.0013480136,0.0014635378,0.0025605615,0.00041192072,0.0009669445,0.0013340197,0.0010859071,0.0008664077],"category_scores_gemma":[0.010789702,0.00039207653,0.00066991494,0.0024321584,0.00057934545,0.0021190483,0.00092095794,0.0018752589,0.00089848274],"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":[0.00130393,0.00057669583,0.020730129,0.0006738641,0.00021111712,0.00027346457,0.000285401,0.27264953,0.059942696,0.004389219,0.019248484,0.6197154],"study_design_scores_gemma":[0.00003574579,0.00016218596,0.002509513,0.000014387246,0.00005137223,0.00011004884,0.00002549963,0.9857362,0.006748291,0.0034110323,0.0011692004,0.00002647118],"about_ca_topic_score_codex":0.0060511087,"about_ca_topic_score_gemma":0.008812937,"teacher_disagreement_score":0.0060511087,"about_ca_system_score_codex":0.0010121096,"about_ca_system_score_gemma":0.0011034602,"threshold_uncertainty_score":0.012031794},"labels":[],"label_agreement":null},{"id":"W4417121803","doi":"10.1145/3779421","title":"Improved Image Classification using Lightweight Deep Neural Network Enhancements","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Advanced Neural Network Applications","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":"Advanced Micro Devices (Canada)","funders":"","keywords":"Convolutional neural network; Inference; Binary number; Pattern recognition (psychology); Artificial neural network; Contextual image classification; Deep learning; Binary classification","score_opus":0.022651556828293368,"score_gpt":0.2857864674172074,"score_spread":0.26313491058891403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417121803","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":[0.03946233,0.0007955198,0.9480956,0.00023854582,0.00019198612,0.00008033469,0.00028916064,0.005219321,0.005627172],"genre_scores_gemma":[0.46880996,0.00056857726,0.5186189,0.0003396124,0.00010755658,0.00009528949,0.0013510709,0.00029623145,0.009812818],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964845,0.00002816407,0.000019836394,0.00009042798,0.00015983018,0.00005337689],"domain_scores_gemma":[0.99956864,0.00008114252,0.00004357672,0.000105202686,0.00017715686,0.000024261315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004587869,0.00080692134,0.00057360023,0.0007235524,0.00023591709,0.00083895813,0.001507412,0.0005351423,0.003408894],"category_scores_gemma":[0.0013191836,0.00031194417,0.00044070225,0.00072151504,0.00029356364,0.001914777,0.0010711546,0.0010116715,0.0016638538],"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":[0.0002658057,0.00020849067,0.0018593151,0.00023151746,0.00008253427,0.00013865193,0.0000585952,0.13376985,0.08504784,0.011752337,0.009786967,0.75679815],"study_design_scores_gemma":[0.000009076659,0.00004641113,0.0004731698,0.000010678927,0.000018733595,0.00005243965,0.000012013237,0.97092146,0.021434987,0.0030245765,0.0039871084,0.000009319932],"about_ca_topic_score_codex":0.005468631,"about_ca_topic_score_gemma":0.010754367,"teacher_disagreement_score":0.005468631,"about_ca_system_score_codex":0.0008438347,"about_ca_system_score_gemma":0.00071478204,"threshold_uncertainty_score":0.011403859},"labels":[],"label_agreement":null}]}