{"meta":{"query_hash":"aca698f2aa0c","filters":{"venue":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing"},"cohort_total":6,"direct_labels_cover":0,"predictions_cover":6,"exported":6,"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/aca698f2aa0c","api":"https://metacan.xera.ac/api/v1/cohort?venue=Proceedings+of+the+37th+ACM%2FSIGAPP+Symposium+on+Applied+Computing"},"results":[{"id":"W4229000353","doi":"10.1145/3477314.3507053","title":"Fighting evil is not enough when refactoring metamodels","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Code refactoring; Correctness; Computer science; Context (archaeology); Quality (philosophy); Task (project management); Process (computing); Set (abstract data type); Domain (mathematical analysis); Metamodeling; Software engineering; Heuristic; Artificial intelligence; Programming language; Systems engineering; Engineering; Software","score_opus":0.027770503131642404,"score_gpt":0.24767176647928307,"score_spread":0.21990126334764068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229000353","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":[0.5467642,0.0011049358,0.43133128,0.0020309046,0.00013197973,0.00039261565,0.00031213052,0.012852052,0.0050800345],"genre_scores_gemma":[0.627458,0.00027212102,0.36820573,0.0004563924,0.000029217314,0.000115402094,0.00055566017,0.0013325324,0.0015749321],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9909739,0.004329413,0.00051295164,0.00110677,0.0027168288,0.00036015152],"domain_scores_gemma":[0.9571003,0.029184401,0.0031037366,0.007524389,0.002380739,0.0007064846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008394767,0.0015304498,0.0012079962,0.0010468639,0.00096471026,0.0028624758,0.0015576696,0.0021852176,0.001645466],"category_scores_gemma":[0.03904507,0.0008801713,0.0011219494,0.00070072315,0.0009059536,0.003965625,0.001975512,0.0020385794,0.0008615646],"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":[0.0030535366,0.0018514296,0.02784678,0.0017227163,0.0006466602,0.0012300097,0.004263402,0.09477236,0.20272507,0.0074713975,0.008791816,0.6456249],"study_design_scores_gemma":[0.00053167366,0.0030249958,0.022406483,0.00053163257,0.00060414436,0.0020682812,0.0030730865,0.74733406,0.15616627,0.03465644,0.029273758,0.00032926435],"about_ca_topic_score_codex":0.0014174923,"about_ca_topic_score_gemma":0.0030955062,"teacher_disagreement_score":0.008394767,"about_ca_system_score_codex":0.00067500956,"about_ca_system_score_gemma":0.0012865303,"threshold_uncertainty_score":0.04439628},"labels":[],"label_agreement":null},{"id":"W4229030011","doi":"10.1145/3477314.3507101","title":"OpenFlow rule placement in carrier networks for augmented reality applications","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","topic":"Software-Defined Networks and 5G","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":"Université du Québec à Montréal","funders":"","keywords":"OpenFlow; Computer science; Computer network; Software-defined networking; Multicast; Unicast; Cache; Distributed computing","score_opus":0.018006310645035044,"score_gpt":0.2503325000365304,"score_spread":0.23232618939149535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229030011","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":[0.11395417,0.00063160405,0.87880814,0.0002962542,0.00017790304,0.00021590992,0.000100882935,0.0016483349,0.004166733],"genre_scores_gemma":[0.75437754,0.0003714585,0.24264155,0.00014003564,0.00006924662,0.00014742788,0.00023803246,0.00014816097,0.0018665314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875796,0.00030446958,0.00008166532,0.00021540788,0.0003941629,0.0002462375],"domain_scores_gemma":[0.99871683,0.00046331208,0.0001770986,0.00022120992,0.0002691127,0.00015246033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015038127,0.00066625484,0.00059269497,0.00088254415,0.0011200625,0.0018441519,0.0015811686,0.00060876017,0.0018260089],"category_scores_gemma":[0.0038915996,0.0003811591,0.0003370529,0.0007530036,0.000573331,0.0021283198,0.0013939356,0.0006794724,0.00036499574],"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":[0.00038055424,0.00036589967,0.0026517743,0.00012988034,0.00004839556,0.0004236665,0.0003899529,0.62728226,0.02074639,0.02840314,0.006352571,0.3128255],"study_design_scores_gemma":[0.00002804283,0.00009365179,0.00026239405,0.000016009408,0.000015555908,0.0000764153,0.00007501745,0.9793682,0.0066520628,0.0090324,0.00436357,0.000016643342],"about_ca_topic_score_codex":0.005479571,"about_ca_topic_score_gemma":0.0068973387,"teacher_disagreement_score":0.005479571,"about_ca_system_score_codex":0.0012253247,"about_ca_system_score_gemma":0.0018246989,"threshold_uncertainty_score":0.010895371},"labels":[],"label_agreement":null},{"id":"W4229030702","doi":"10.1145/3477314.3507096","title":"Optimization of IoT slices in wifi enterprise networks","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","topic":"Software-Defined Networks and 5G","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Slicing; Virtualization; Distributed computing; Wireless network; Throughput; Reinforcement learning; Key (lock); Computer network; Wireless; Matching (statistics); Feature (linguistics); Artificial intelligence; Computer security","score_opus":0.008472110974163604,"score_gpt":0.206430082185353,"score_spread":0.19795797121118938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229030702","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":[0.19986346,0.00069918245,0.7915591,0.00033269147,0.000056267138,0.00009765802,0.00011879738,0.0002575983,0.0070151784],"genre_scores_gemma":[0.9736028,0.00011140883,0.02526608,0.000045521607,0.0000066665384,0.000033376687,0.000036729907,0.000023713115,0.0008738702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960333,0.000115811774,0.00001474326,0.00008158819,0.000055883986,0.00012867746],"domain_scores_gemma":[0.99925894,0.00041677576,0.00010656071,0.000044737244,0.00008636242,0.00008660036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010702478,0.0008820452,0.00071632484,0.00026338093,0.00035938955,0.00071253936,0.0007234015,0.0006857001,0.0015681142],"category_scores_gemma":[0.0027068097,0.00039753036,0.00033702332,0.00038154845,0.00060482667,0.0011483239,0.0010303083,0.0006422327,0.00011149073],"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":[0.00006660873,0.000022855998,0.0003017874,0.000016869422,0.000010224487,0.000039303086,0.000016645514,0.9837865,0.0013705881,0.0047756126,0.00027388736,0.009319016],"study_design_scores_gemma":[0.000004727777,0.000031197822,0.00011392693,0.000003585292,0.0000037775674,0.000011336351,0.000011991595,0.99714404,0.00039606664,0.0021409565,0.00013559745,0.0000027225253],"about_ca_topic_score_codex":0.0040041995,"about_ca_topic_score_gemma":0.0026263515,"teacher_disagreement_score":0.0040041995,"about_ca_system_score_codex":0.0011612816,"about_ca_system_score_gemma":0.00094370963,"threshold_uncertainty_score":0.008425713},"labels":[],"label_agreement":null},{"id":"W4229045681","doi":"10.1145/3477314.3507054","title":"On hybrid front-haul for 5G NR mmWave indoor coverage","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","topic":"Power Line Communications and Noise","field":"Engineering","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 Waterloo","funders":"","keywords":"Computer science; Channel (broadcasting); Spectral efficiency; Cascade; Electronic engineering; User equipment; Computer network; Real-time computing; Base station; Engineering","score_opus":0.01034377621777209,"score_gpt":0.21226371552939996,"score_spread":0.20191993931162788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229045681","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":[0.053830493,0.0010448879,0.93973494,0.00006234918,0.0000510732,0.000028040678,0.00003022468,0.0002633064,0.0049546477],"genre_scores_gemma":[0.82879806,0.00075131544,0.16688538,0.00015624426,0.00012581883,0.00003175918,0.00006166996,0.000020048514,0.0031698602],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997795,0.000046883924,0.000006189348,0.000034962,0.00008618957,0.000046246812],"domain_scores_gemma":[0.9998716,0.000040218896,0.000014618241,0.000029802217,0.00003432289,0.000009311008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017426562,0.00041822877,0.00020494359,0.00022909569,0.00031594496,0.00033800068,0.0004668069,0.00029650246,0.0008116757],"category_scores_gemma":[0.00026236285,0.0001198758,0.0001932303,0.0002685512,0.00025327588,0.0005342545,0.0004654324,0.0003099981,0.000335634],"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":[0.000436245,0.00012290757,0.0013512127,0.00028140185,0.000081136066,0.000512716,0.0001902746,0.20164384,0.27286047,0.028877633,0.0022665435,0.49137565],"study_design_scores_gemma":[0.000016590591,0.0004894489,0.00092404074,0.000026972371,0.000033360568,0.000619193,0.000051611547,0.9176657,0.06530626,0.0055498523,0.009292074,0.000024904552],"about_ca_topic_score_codex":0.0008345999,"about_ca_topic_score_gemma":0.0016940275,"teacher_disagreement_score":0.0008345999,"about_ca_system_score_codex":0.00027307158,"about_ca_system_score_gemma":0.00020321371,"threshold_uncertainty_score":0.0027152896},"labels":[],"label_agreement":null},{"id":"W4229056530","doi":"10.1145/3477314.3507050","title":"FL-MAB","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","topic":"Privacy-Preserving Technologies in Data","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Monetization; Crowdsourcing; Quality (philosophy); Cryptocurrency; Convergence (economics); Set (abstract data type); Distributed computing; Computer security; Data mining; World Wide Web","score_opus":0.018474968846317684,"score_gpt":0.23734812255240761,"score_spread":0.21887315370608992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229056530","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":[0.030764101,0.0016241086,0.8644143,0.0029524753,0.00054623897,0.000727511,0.0018738972,0.006460298,0.09063714],"genre_scores_gemma":[0.63420445,0.00094942626,0.29515198,0.0024439192,0.00031683978,0.00091077323,0.0034408264,0.00075376756,0.061828006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640036,0.0009908897,0.0001611362,0.000876461,0.000990027,0.00058107014],"domain_scores_gemma":[0.9955591,0.001478649,0.00027300007,0.0014713506,0.00091345294,0.00030448736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024683843,0.0010601731,0.0013513493,0.0008727887,0.0013837499,0.0028781851,0.0027960488,0.00225423,0.024830513],"category_scores_gemma":[0.0089112865,0.00040856042,0.00093820586,0.00096505415,0.00117164,0.0035253896,0.0041457377,0.0022012491,0.0068864147],"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":[0.0008263163,0.000589761,0.0035335042,0.000619809,0.00018558743,0.000384562,0.0002613355,0.26248035,0.0061367946,0.2171207,0.067647025,0.4402142],"study_design_scores_gemma":[0.00011798485,0.00025606196,0.0005131075,0.000097105476,0.000040735104,0.00032983997,0.000106312866,0.7710625,0.0037675248,0.16578308,0.05788138,0.000044305838],"about_ca_topic_score_codex":0.0036354305,"about_ca_topic_score_gemma":0.004619187,"teacher_disagreement_score":0.024830513,"about_ca_system_score_codex":0.0019213887,"about_ca_system_score_gemma":0.0035723248,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4229076026","doi":"10.1145/3477314.3507301","title":"Generalized graph pattern discovery in linked data with data properties and a domain ontology","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing","topic":"Data Mining Algorithms and Applications","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":"Ste. Anne's Hospital; Université du Québec à Montréal","funders":"Genome Canada","keywords":"Computer science; Ontology; Data mining; Graph; Graph database; Linked data; Property (philosophy); Theoretical computer science; Abstraction; Knowledge extraction; Information retrieval; Semantic Web","score_opus":0.04711496985454908,"score_gpt":0.24792155250616518,"score_spread":0.2008065826516161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229076026","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":[0.038270425,0.00035683374,0.95584285,0.00075413496,0.000035687437,0.00025629514,0.0020877633,0.0013994527,0.0009966512],"genre_scores_gemma":[0.15819873,0.00035864554,0.8353234,0.00032421696,0.000033925236,0.00033909775,0.004388339,0.00016205569,0.00087170023],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945629,0.0016483244,0.0005794766,0.001427547,0.0015130881,0.0002685993],"domain_scores_gemma":[0.9884063,0.0066821077,0.0012975594,0.0023997286,0.00092159497,0.0002927196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003929178,0.0005574583,0.0008132581,0.0059028817,0.0011589046,0.0032478832,0.0014596274,0.0012249664,0.00083739386],"category_scores_gemma":[0.01922483,0.00074057793,0.00252281,0.007204854,0.0015589694,0.0056505664,0.0033590088,0.0015067705,0.00030617506],"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":[0.00040863972,0.00048755953,0.047885966,0.0022634207,0.000724429,0.006173216,0.0057284646,0.13334204,0.014063844,0.3504925,0.011795218,0.42663473],"study_design_scores_gemma":[0.000047577836,0.00007330708,0.004811141,0.00021652438,0.00014693281,0.0014122529,0.0016795328,0.52201897,0.007384428,0.4340878,0.028073357,0.00004811577],"about_ca_topic_score_codex":0.00709531,"about_ca_topic_score_gemma":0.011956636,"teacher_disagreement_score":0.00709531,"about_ca_system_score_codex":0.0015321661,"about_ca_system_score_gemma":0.0016035883,"threshold_uncertainty_score":0.020779729},"labels":[],"label_agreement":null}]}