{"id":"W3004484424","doi":"10.48550/arxiv.2002.03184","title":"Time-aware Large Kernel Convolutions","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Kernel (algebra); Computer science; Mathematics; Pure mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016597,0.0003571317,0.0004046215,0.0001280678,0.0002943705,0.0001615286,0.001902504,0.0002664553,0.0001981774],"category_scores_gemma":[0.00004649105,0.0004144558,0.0003190094,0.0005739536,0.0001203171,0.0004030706,0.003049563,0.0005744353,0.001031895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001458851,"about_ca_system_score_gemma":0.0002590615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005931628,"about_ca_topic_score_gemma":0.00001949941,"domain_scores_codex":[0.9977576,0.0002229696,0.0001973239,0.001263291,0.0001002866,0.0004585162],"domain_scores_gemma":[0.998124,0.00009665068,0.000205745,0.001107306,0.0001935661,0.0002727378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003468673,0.0002643018,0.0006312691,0.00008214852,0.0004514528,0.0007507651,0.0006278764,0.8097564,0.0002341615,0.1500265,0.03652948,0.0006108884],"study_design_scores_gemma":[0.0003365595,0.00003191579,0.0003165515,0.0000413063,0.00006948682,0.000001877296,0.00004946783,0.9799037,0.0001096632,0.01274762,0.005939759,0.0004520847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0028891,0.0000788054,0.9915736,0.0007144341,0.000640213,0.0002641557,0.00009170978,0.0003653284,0.003382656],"genre_scores_gemma":[0.9931341,0.00009248991,0.002706421,0.0003729242,0.0002455464,0.000001192014,0.00004576359,0.00002151959,0.003380007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.990245,"threshold_uncertainty_score":0.9998307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05933492449385821,"score_gpt":0.1800063729435879,"score_spread":0.1206714484497297,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}