{"meta":{"query_hash":"0d8cf5e2c2c9","filters":{"venue":"2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)"},"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/0d8cf5e2c2c9","api":"https://metacan.xera.ac/api/v1/cohort?venue=2018+55th+ACM%2FESDA%2FIEEE+Design+Automation+Conference+%28DAC%29"},"results":[{"id":"W2952857977","doi":"10.1109/dac.2018.8465915","title":"Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Convolutional neural network; Computer science; LOOM; Granularity; Inference; Energy (signal processing); Parallel computing; Layer (electronics); Deep neural networks; Artificial neural network; Algorithm; Artificial intelligence; Mathematics; Statistics; Operating system","score_opus":0.0962652447524089,"score_gpt":0.3100672727499506,"score_spread":0.21380202799754172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952857977","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":[0.09672406,0.002989376,0.80981445,0.00096211996,0.0006793858,0.00018665603,0.0015032337,0.062297918,0.024842745],"genre_scores_gemma":[0.53665245,0.00092837145,0.42345396,0.00091099436,0.00019033563,0.00037466642,0.0033917765,0.0023155937,0.031781897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967694,0.000030439085,0.000021026226,0.000066896195,0.00013383725,0.00007091069],"domain_scores_gemma":[0.9995479,0.00014030834,0.000052692358,0.00011452573,0.0001099708,0.00003454842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040853934,0.0011443998,0.00034762308,0.0006031676,0.0003367585,0.00090063544,0.0025256444,0.0005080282,0.01357895],"category_scores_gemma":[0.0016743548,0.0004687334,0.00048161112,0.00067193486,0.00033690303,0.0018964343,0.0012858008,0.001401471,0.0034240722],"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":[0.0015769331,0.0002998664,0.0040521747,0.0007887414,0.00021406218,0.00034298337,0.00022057813,0.08024512,0.10142417,0.026383357,0.09042165,0.6940304],"study_design_scores_gemma":[0.0002961462,0.00068308687,0.001766061,0.00011498172,0.00012794248,0.0002732505,0.000065941036,0.76368386,0.14720716,0.015805427,0.06987673,0.00009939248],"about_ca_topic_score_codex":0.004804214,"about_ca_topic_score_gemma":0.013297491,"teacher_disagreement_score":0.01357895,"about_ca_system_score_codex":0.0010224973,"about_ca_system_score_gemma":0.0015445909,"threshold_uncertainty_score":0.04542613},"labels":[],"label_agreement":null},{"id":"W4238447347","doi":"10.1109/dac.2018.8465835","title":"A Machine Learning Framework to Identify Detailed Routing Short Violations from a Placed Netlist","year":2018,"lang":"en","type":"article","venue":"2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)","topic":"Integrated Circuits and Semiconductor Failure Analysis","field":"Engineering","cited_by":36,"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; Microsemi (Canada); University of Calgary","funders":"","keywords":"Netlist; Computer science; Routing (electronic design automation); Router; Network routing; Metrics; Routing domain; Static routing; Machine learning; Distance-vector routing protocol; Artificial neural network; Routing protocol; Distributed computing; Artificial intelligence; Computer network; Embedded system","score_opus":0.05266024591243023,"score_gpt":0.29483534799917,"score_spread":0.24217510208673976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238447347","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":[0.030296586,0.00023699262,0.9671182,0.000080371276,0.000029632994,0.000049634113,0.00016406078,0.0013402438,0.0006843214],"genre_scores_gemma":[0.6540918,0.0002424355,0.34033135,0.00014568593,0.00011451914,0.00022306874,0.0008280413,0.00009262966,0.0039305426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971324,0.000049554747,0.000017530449,0.00008036429,0.000106559346,0.000032807333],"domain_scores_gemma":[0.99914384,0.0003320568,0.00017437043,0.00007377305,0.00023894357,0.00003697872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006122362,0.0011144234,0.00067555596,0.0012085376,0.00029644367,0.0005714638,0.0013309936,0.000838247,0.0012060106],"category_scores_gemma":[0.0015126099,0.0003153392,0.0004660657,0.0006640843,0.00032499124,0.0006949863,0.00042060128,0.0008428608,0.00040246625],"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":[0.00008513274,0.00014531844,0.002168306,0.000056873607,0.00005527756,0.00010720444,0.00002291931,0.84802645,0.0075749704,0.0015622034,0.0012452233,0.13895018],"study_design_scores_gemma":[0.0000018417986,0.000027680187,0.00020234821,0.0000020724535,0.000004270801,0.000013671229,0.0000018958108,0.99817,0.00074748776,0.00071011094,0.000116224284,0.0000024345943],"about_ca_topic_score_codex":0.0031860815,"about_ca_topic_score_gemma":0.005526439,"teacher_disagreement_score":0.0031860815,"about_ca_system_score_codex":0.0005728176,"about_ca_system_score_gemma":0.00081075775,"threshold_uncertainty_score":0.0063350797},"labels":[],"label_agreement":null},{"id":"W4243315213","doi":"10.1109/dac.2018.8465799","title":"An Architecture-Agnostic Integer Linear Programming Approach to CGRA Mapping","year":2018,"lang":"en","type":"article","venue":"2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)","topic":"Embedded Systems Design 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":"University of Toronto","funders":"","keywords":"Computer science; Architecture; Computer architecture; Integer programming; Parallel computing; Integer (computer science); Set (abstract data type); Linear programming; Programming language; Algorithm","score_opus":0.09188084650747681,"score_gpt":0.3160060538151676,"score_spread":0.2241252073076908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243315213","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":[0.009800764,0.00010682986,0.9826849,0.00015695141,0.000033555647,0.000053061227,0.00004409843,0.0012162662,0.005903631],"genre_scores_gemma":[0.26530144,0.00013757187,0.7299131,0.00021393553,0.00004322942,0.00018085031,0.00019455659,0.0004577019,0.0035576818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993703,0.00021700062,0.000025381012,0.00009450483,0.00020459213,0.000088274035],"domain_scores_gemma":[0.99932396,0.00037699772,0.00005952105,0.00009658124,0.00011009946,0.000032820964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072544254,0.000868491,0.00039558875,0.00045198528,0.00035890323,0.000946879,0.0011067062,0.0005770084,0.0039302926],"category_scores_gemma":[0.0020413226,0.00032069336,0.0006292792,0.00045111493,0.0004834141,0.0009250093,0.0010415526,0.0016470695,0.0006669784],"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":[0.00013581193,0.00017227214,0.00060190394,0.00016530557,0.000035498226,0.00015222418,0.000112195776,0.7692646,0.018202351,0.040311575,0.0036182355,0.16722801],"study_design_scores_gemma":[0.000013051012,0.000038946884,0.000042459644,0.0000065556796,0.0000063818334,0.00002890414,0.000015126767,0.9848684,0.003007141,0.010129606,0.0018386035,0.0000047496],"about_ca_topic_score_codex":0.0010945591,"about_ca_topic_score_gemma":0.0023349458,"teacher_disagreement_score":0.0039302926,"about_ca_system_score_codex":0.00050858245,"about_ca_system_score_gemma":0.00096440566,"threshold_uncertainty_score":0.013148189},"labels":[],"label_agreement":null}]}