{"meta":{"query_hash":"2bc43763f887","filters":{"venue":"2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/2bc43763f887","api":"https://metacan.xera.ac/api/v1/cohort?venue=2022+IEEE+65th+International+Midwest+Symposium+on+Circuits+and+Systems+%28MWSCAS%29"},"results":[{"id":"W4292862258","doi":"10.1109/mwscas54063.2022.9859428","title":"FPGA-Based Architectures for Random Forest Acceleration","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)","topic":"Neural Networks 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":"University of Waterloo","funders":"","keywords":"Computer science; Random forest; Field-programmable gate array; Software; Acceleration; Implementation; Software implementation; Embedded system; Computer engineering; Parallel computing; Computer architecture; Real-time computing; Machine learning; Operating system; Software engineering","score_opus":0.025611232203403205,"score_gpt":0.2598958673849199,"score_spread":0.23428463518151668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292862258","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":[0.111581035,0.003946882,0.8028921,0.0006011259,0.0010205444,0.0003512045,0.0011236819,0.025906008,0.052577376],"genre_scores_gemma":[0.66040105,0.00092302996,0.31684095,0.00040566572,0.00014207259,0.0002561489,0.001499838,0.00030537823,0.019225862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978286,0.000031805692,0.0000120800705,0.00004132052,0.00007133829,0.0000606485],"domain_scores_gemma":[0.9998086,0.000050799426,0.000019162428,0.000033805263,0.000074517586,0.000013181741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024314196,0.0006046904,0.000253738,0.00067498034,0.00021873224,0.0004963249,0.0010290311,0.00030277963,0.020843673],"category_scores_gemma":[0.0005518042,0.00020612883,0.00025570006,0.00061808573,0.00010016062,0.0006037358,0.0002485536,0.0003684479,0.0054655233],"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":[0.00090364116,0.00021834431,0.0028707716,0.00061526115,0.00010195629,0.00040607832,0.00009624872,0.03822053,0.062102087,0.011574044,0.04106569,0.84182537],"study_design_scores_gemma":[0.0003957507,0.0018735828,0.0071936324,0.00025002484,0.00019057382,0.001816306,0.00013573989,0.64814454,0.15696141,0.00713615,0.17577884,0.00012351363],"about_ca_topic_score_codex":0.0021654582,"about_ca_topic_score_gemma":0.00362082,"teacher_disagreement_score":0.020843673,"about_ca_system_score_codex":0.00036672095,"about_ca_system_score_gemma":0.000444183,"threshold_uncertainty_score":0.06972909},"labels":[],"label_agreement":null},{"id":"W4292863087","doi":"10.1109/mwscas54063.2022.9859273","title":"An Efficient Method for Sequential Circuit Reliability Estimation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)","topic":"Radiation Effects in Electronics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Reliability (semiconductor); Computer science; Monte Carlo method; Combinational logic; Sequential logic; Convergence (economics); Process (computing); Algorithm; Circuit reliability; Reliability engineering; Electronic circuit; Logic gate; Mathematics; Statistics; Engineering","score_opus":0.014304016296926608,"score_gpt":0.2741858334009598,"score_spread":0.2598818171040332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292863087","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":[0.00056279637,0.000033454133,0.9989705,0.000008016941,0.0000060195475,0.000013329896,0.000014070572,0.00022587799,0.00016600016],"genre_scores_gemma":[0.05309935,0.00017543774,0.9442062,0.00003330111,0.000028805884,0.0001942315,0.00015939015,0.00017071032,0.0019326129],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994609,0.0001217946,0.000027686661,0.00008486617,0.0002784056,0.000026330285],"domain_scores_gemma":[0.9988757,0.0005498703,0.00008670455,0.00014476628,0.00032141153,0.000021584672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074993685,0.00083304336,0.0006572686,0.0011788113,0.0003673384,0.00044206256,0.00084728794,0.00057626393,0.004293988],"category_scores_gemma":[0.0033814718,0.00046330813,0.00064656086,0.0009579354,0.00032459086,0.0009563313,0.0005874705,0.0010103447,0.001543453],"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":[0.00010543835,0.00006590692,0.0010819016,0.00026865554,0.0001004948,0.000119102566,0.00010170006,0.40425023,0.03955025,0.037847612,0.004023743,0.51248497],"study_design_scores_gemma":[0.000008893655,0.000020343748,0.00017272687,0.000009806167,0.000009770257,0.00007375733,0.000004646831,0.9864225,0.004008561,0.0063673183,0.00289242,0.000009276337],"about_ca_topic_score_codex":0.0020476875,"about_ca_topic_score_gemma":0.0027457192,"teacher_disagreement_score":0.004293988,"about_ca_system_score_codex":0.0004274291,"about_ca_system_score_gemma":0.0010529471,"threshold_uncertainty_score":0.014364779},"labels":[],"label_agreement":null},{"id":"W4292873932","doi":"10.1109/mwscas54063.2022.9859341","title":"MorIRNet: A Deep Image Retrieval Network using Morphological Feature and Residual Block","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)","topic":"Advanced Image and Video Retrieval 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":"Concordia University","funders":"","keywords":"Computer science; Convolutional neural network; Residual; Artificial intelligence; Benchmark (surveying); Block (permutation group theory); Image retrieval; Pattern recognition (psychology); Feature (linguistics); Deep learning; Feature extraction; Contextual image classification; Image (mathematics); Algorithm; Mathematics","score_opus":0.02381683306590218,"score_gpt":0.2753605592275453,"score_spread":0.25154372616164317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292873932","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":[0.045418244,0.0031026562,0.9268619,0.00037039374,0.00033042268,0.00035004556,0.0015615321,0.014337066,0.0076677427],"genre_scores_gemma":[0.36665243,0.0025020726,0.57625467,0.0010389492,0.0002322049,0.00043291447,0.009757996,0.0007301355,0.042398673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998073,0.000016644328,0.000012760993,0.00005235675,0.000077514684,0.000033502183],"domain_scores_gemma":[0.99988616,0.000020046218,0.000015573005,0.000024831092,0.000040344792,0.000013098249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037057645,0.0008211515,0.00082093396,0.0011170193,0.00026957013,0.0007136896,0.0018377169,0.0009574381,0.0052707656],"category_scores_gemma":[0.00075409876,0.00037663538,0.00078903395,0.0008730611,0.0002794978,0.0014977582,0.00086546346,0.000799651,0.002541962],"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":[0.00064989185,0.000297033,0.0010467244,0.00033554438,0.0002197557,0.00025977052,0.000047511545,0.07099296,0.06417617,0.0047014104,0.02787028,0.82940286],"study_design_scores_gemma":[0.00007793028,0.00035419,0.00063142163,0.00003053039,0.000078549136,0.00032545882,0.000019994137,0.9530116,0.028224438,0.0024283568,0.014775474,0.000042088854],"about_ca_topic_score_codex":0.006967374,"about_ca_topic_score_gemma":0.008160501,"teacher_disagreement_score":0.006967374,"about_ca_system_score_codex":0.00068081415,"about_ca_system_score_gemma":0.00073257386,"threshold_uncertainty_score":0.017632484},"labels":[],"label_agreement":null}]}