{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004536808,0.0009212958,0.0002916766,0.0004819834,0.0002621288,0.000624517,0.001259141,0.001023915,0.01194674],"category_scores_gemma":[0.001370761,0.000281361,0.0004665293,0.0003909988,0.0003814937,0.001704024,0.001014086,0.001725498,0.004457529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006383276,"about_ca_system_score_gemma":0.0006106796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002634474,"about_ca_topic_score_gemma":0.003165415,"domain_scores_codex":[0.9997877,0.00002783468,0.0000100579,0.00006734736,0.00007484457,0.00003223144],"domain_scores_gemma":[0.9997593,0.00004177309,0.00001408418,0.00009331737,0.00007311269,0.00001855518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004486116,0.000269655,0.002297743,0.0003740409,0.0001691584,0.0004204553,0.0001364765,0.1451641,0.08269508,0.05508631,0.06495535,0.647983],"study_design_scores_gemma":[0.00005573629,0.0002678331,0.0008717318,0.00004377466,0.00003631606,0.0003748739,0.00003166114,0.8625522,0.04907102,0.04320444,0.04345896,0.00003145595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03547237,0.001575006,0.919785,0.001605634,0.0005777868,0.0001583414,0.0007240659,0.01336027,0.02674151],"genre_scores_gemma":[0.4594022,0.001503044,0.4834165,0.001471598,0.0002508493,0.0003109791,0.003513524,0.001362346,0.0487689],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01194674,"threshold_uncertainty_score":0.03996581,"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."}}