{"id":"W2803299525","doi":"","title":"Inductive Two-layer Modeling with Parametric Bregman Transfer.","year":2018,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Parametric statistics; Layer (electronics); Computer science; Transfer (computing); Mathematics; Materials science; Statistics; Parallel computing","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":[0.0001322258,0.0001535415,0.0001177172,0.0001796563,0.0002184498,0.0002476563,0.0007606925,0.00003520118,0.0001317866],"category_scores_gemma":[0.00002065013,0.0001227118,0.00003884967,0.0004107231,0.0000568937,0.0003392248,0.00008546776,0.0004510329,0.0001138191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003610526,"about_ca_system_score_gemma":0.00004204828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001557649,"about_ca_topic_score_gemma":0.00005865074,"domain_scores_codex":[0.9987806,0.00005679856,0.0001702309,0.0004265534,0.000360446,0.0002053464],"domain_scores_gemma":[0.9992969,0.00005873569,0.00005206474,0.0002244499,0.0002924209,0.00007544731],"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":[0.00004633668,0.00007241132,0.001462215,0.000001633966,0.00004592706,0.000008383819,0.0003571491,0.05875922,0.00164493,0.8877893,0.00002138207,0.04979112],"study_design_scores_gemma":[0.0003528956,0.0002408951,0.0002548019,0.00003201273,0.000003967113,0.00001548018,0.00002419059,0.9922039,0.0004598637,0.005382763,0.0008622775,0.000166978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1719032,0.000008447658,0.8057922,0.002633511,0.0001501076,0.0001015715,0.000002008206,0.0001397635,0.01926925],"genre_scores_gemma":[0.9913773,0.00001279499,0.007556196,0.000385191,0.0002404608,0.00002407019,0.00000918832,0.00001221287,0.0003826099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9334447,"threshold_uncertainty_score":0.5004042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06569738614582407,"score_gpt":0.3198822740862566,"score_spread":0.2541848879404326,"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."}}