{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000724316,0.00007008555,0.00009179431,0.00002031038,0.00008794058,0.00003873004,0.00084604,0.00001647306,0.000007668394],"category_scores_gemma":[0.00003884985,0.00005039876,0.00006079575,0.0005947909,0.0000340255,0.0001806629,0.0006610377,0.0000781549,0.00001101039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002987925,"about_ca_system_score_gemma":0.00001892826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001306282,"about_ca_topic_score_gemma":4.682423e-7,"domain_scores_codex":[0.9992681,0.000003001875,0.0001489892,0.0002127432,0.0002187436,0.0001484516],"domain_scores_gemma":[0.9994342,0.00003085559,0.0001135147,0.0002216342,0.0001614546,0.00003830885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001988579,0.0001113737,0.001673364,0.00002620958,0.00001977483,6.793973e-7,0.0002209517,0.00009895884,0.1649886,0.8121338,0.008713459,0.01201081],"study_design_scores_gemma":[0.0002399767,0.00002190494,0.003180599,0.00003290081,0.000009205817,0.00003697409,0.00004584853,0.001889412,0.8876387,0.07006523,0.03671422,0.0001249891],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4719045,0.002780229,0.1756043,0.137431,0.00344284,0.002952132,0.00002023575,0.001246352,0.2046185],"genre_scores_gemma":[0.9385456,0.00004757461,0.05972239,0.0006102766,0.00005749932,0.00005191736,2.908566e-7,0.000006250638,0.0009582337],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7420686,"threshold_uncertainty_score":0.2055202,"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."}}