{"id":"W3165295415","doi":"10.48550/arxiv.2105.11205","title":"Deconvolution density estimation with penalised MLE","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deconvolution; Smoothness; Sample size determination; Transformation (genetics); Statistics; Sample (material); Mathematics; Fourier transform; Blind deconvolution; Noise (video); Maximum likelihood; SIGNAL (programming language); Computer science; Applied mathematics; Algorithm; Artificial intelligence; Mathematical analysis; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001276222,0.0002592012,0.0002677171,0.0001424637,0.0001797044,0.0003473502,0.001040946,0.0002055822,0.00005855393],"category_scores_gemma":[0.00002426458,0.0002692308,0.00009992254,0.0005831537,0.00008025791,0.000753459,0.0009469981,0.000389116,0.00004145419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001757893,"about_ca_system_score_gemma":0.0005460152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001679111,"about_ca_topic_score_gemma":0.000159407,"domain_scores_codex":[0.9983798,0.00008110084,0.0001504422,0.0009987045,0.0001114572,0.0002784465],"domain_scores_gemma":[0.9982883,0.00003853101,0.0002490018,0.0009651367,0.0003149765,0.0001440818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001382146,0.0003874136,0.01459564,0.0007640594,0.0002839164,0.002399485,0.001113549,0.4149994,0.0001250699,0.5551805,0.0003248943,0.009687878],"study_design_scores_gemma":[0.0004313475,0.00005413951,0.008844219,0.0002105212,0.00006526605,0.00003095241,0.00006099299,0.957107,0.0003936266,0.03231784,0.00003144913,0.0004526329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2500142,0.00002789812,0.7482837,0.0001011801,0.0001627289,0.0001284812,0.000002343366,0.0001563901,0.001123093],"genre_scores_gemma":[0.9686114,0.00004206372,0.03075845,0.00007630743,0.00002570595,9.308843e-7,0.00002895676,0.00001014051,0.0004460134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7185973,"threshold_uncertainty_score":0.999976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04043738399059722,"score_gpt":0.1703050415324563,"score_spread":0.129867657541859,"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."}}