{"id":"W4287751941","doi":"10.48550/arxiv.2007.13483","title":"Post-Workshop Report on Science meets Engineering in Deep Learning,\\n NeurIPS 2019, Vancouver","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Deep learning; Data science; Computer science; Event (particle physics); Robustness (evolution); Architecture; Artificial intelligence; Engineering ethics; Engineering; History","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004967623,0.001269233,0.0008167751,0.001488032,0.003555716,0.006872433,0.001868634,0.003006153,0.1785394],"category_scores_gemma":[0.004578824,0.0004537433,0.000789246,0.001095759,0.0009639297,0.002383091,0.0042788,0.004298809,0.07973067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00497137,"about_ca_system_score_gemma":0.01257049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06093382,"about_ca_topic_score_gemma":0.1634103,"domain_scores_codex":[0.9971216,0.0003591532,0.00008674931,0.0004707522,0.001323412,0.0006381623],"domain_scores_gemma":[0.9919981,0.000502119,0.00009099508,0.0002954554,0.003293841,0.003819415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008648368,0.00004815213,0.0002358523,0.00007085652,0.000009165366,0.00008970669,0.00005758309,0.0002235852,0.0005574275,0.001371556,0.9781979,0.01905167],"study_design_scores_gemma":[0.00002698545,0.00005753191,0.001384494,0.0000885035,0.000007720365,0.00004671106,0.0002386725,0.0005864202,0.0008803462,0.001193582,0.9954685,0.00002050343],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.01592397,0.01732396,0.02859721,0.1712929,0.2391607,0.001908137,0.02980761,0.002996973,0.4929884],"genre_scores_gemma":[0.01253422,0.00380024,0.003522038,0.003680101,0.005842033,0.000267697,0.00833543,0.0008154517,0.9612028],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.1785394,"threshold_uncertainty_score":0.5972739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03858935646820156,"score_gpt":0.2008012819524995,"score_spread":0.1622119254842979,"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."}}