{"id":"W4226196214","doi":"10.14778/3503585.3503597","title":"COMET","year":2021,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Lossy compression; Computer science; Speedup; Overhead (engineering); Convolutional neural network; Compression ratio; Bandwidth (computing); Process (computing); Computer engineering; Artificial neural network; Compression (physics); Bounded function; Data compression; Algorithm; Parallel computing; Artificial intelligence; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001239538,0.001819553,0.001239107,0.001757282,0.001332278,0.004211514,0.004163404,0.003135997,0.3234817],"category_scores_gemma":[0.004526038,0.0008546013,0.001366848,0.001612414,0.0006345922,0.005290518,0.004841652,0.002997305,0.286431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096597,"about_ca_system_score_gemma":0.001703871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002647086,"about_ca_topic_score_gemma":0.003455692,"domain_scores_codex":[0.9983406,0.0001592756,0.00009035534,0.0003887293,0.0007991939,0.0002218784],"domain_scores_gemma":[0.9982542,0.0002367854,0.00009380072,0.000590498,0.0005389367,0.0002857648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003976877,0.0001217316,0.0007198048,0.0005918425,0.00005320018,0.0002554295,0.0001144416,0.002238872,0.004943126,0.0187351,0.7689211,0.2029076],"study_design_scores_gemma":[0.00008839219,0.00006676419,0.000452059,0.00007475837,0.00001889769,0.0002841798,0.00003744332,0.007645812,0.004237392,0.01019092,0.9768593,0.00004407132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.005397003,0.00416828,0.1590229,0.005397584,0.004679915,0.0008417901,0.05080109,0.278705,0.4909863],"genre_scores_gemma":[0.05803754,0.005041895,0.1384894,0.007345228,0.001671814,0.001511867,0.2097307,0.06855021,0.5096213],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3234817,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0152569447225827,"score_gpt":0.2409137465096306,"score_spread":0.2256568017870479,"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."}}