{"id":"W2989561359","doi":"10.48550/arxiv.1911.08769","title":"Inspect Transfer Learning Architecture with Dilated Convolution","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Waterloo","funders":"","keywords":"Architecture; Convolution (computer science); Transfer of learning; Computer science; Medicine; Artificial intelligence; Geography","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"],"consensus_categories":[],"category_scores_codex":[0.0001968323,0.0003409924,0.0003363533,0.0002729688,0.0001943264,0.0001235882,0.001144948,0.0002859553,0.0000343251],"category_scores_gemma":[0.00001714143,0.0003245007,0.0001516828,0.0005438741,0.00008096413,0.0001867501,0.000537787,0.001461328,0.000139113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001226397,"about_ca_system_score_gemma":0.0001842823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002833357,"about_ca_topic_score_gemma":0.00005136894,"domain_scores_codex":[0.9980279,0.0002667751,0.0001341025,0.001075209,0.0001195341,0.000376471],"domain_scores_gemma":[0.9987416,0.00007840467,0.0001026218,0.0008508634,0.00009867007,0.0001279018],"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":[0.00005902545,0.00003883522,0.007758983,0.00005922823,0.0000856914,0.0001328682,0.0005403936,0.9656705,0.00002851549,0.02437761,0.00003215255,0.00121618],"study_design_scores_gemma":[0.001034756,0.0002738815,0.007564547,0.0002047888,0.00007906093,0.0000212616,0.0000342297,0.9825124,0.00006635076,0.004192282,0.003322673,0.0006938343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3801633,0.00003059961,0.6139565,0.0001199493,0.0001873837,0.0001663276,0.00000203973,0.0004402127,0.004933661],"genre_scores_gemma":[0.9944847,0.00003970036,0.0006870934,0.00006167367,0.00005234938,5.214395e-7,0.00002681863,0.00002470835,0.004622395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6143214,"threshold_uncertainty_score":0.9999207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.026154002066767,"score_gpt":0.1594527030100485,"score_spread":0.1332987009432815,"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."}}