{"id":"W2794627393","doi":"10.48550/arxiv.1803.11034","title":"Automatic Generation of Optimal Reductions of Distributions","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Reduction (mathematics); Backtracking; Algorithm; Distribution (mathematics); Mathematics; Computer science; Property (philosophy); Mathematical optimization; Production (economics); Substitution (logic); Programming language","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":[0.002495152,0.0006948039,0.0006885073,0.00128981,0.0007253679,0.001326692,0.001767477,0.0007725356,0.005195309],"category_scores_gemma":[0.01283946,0.0006622135,0.001672624,0.0005621057,0.001704674,0.002186631,0.002534312,0.001582885,0.001184574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009341825,"about_ca_system_score_gemma":0.001885133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006854802,"about_ca_topic_score_gemma":0.00123433,"domain_scores_codex":[0.9964082,0.0009921485,0.0001916016,0.000827665,0.001253525,0.0003269365],"domain_scores_gemma":[0.9901725,0.006493483,0.0003171111,0.001911931,0.0009874993,0.0001173488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007550613,0.0003792695,0.003509262,0.0009527417,0.0001502539,0.001638287,0.001487684,0.1270953,0.1395571,0.3746538,0.01208082,0.3377404],"study_design_scores_gemma":[0.0001725677,0.0001846029,0.0008014388,0.0001070913,0.0001386011,0.000607081,0.0003002256,0.5486648,0.1612007,0.2629019,0.02484558,0.00007533535],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03955245,0.00009214458,0.9512419,0.0003313065,0.00004918302,0.0002716698,0.0003488601,0.0036255,0.004487068],"genre_scores_gemma":[0.3997901,0.0001604439,0.5924683,0.0001889696,0.00003880154,0.000464141,0.001175177,0.001915589,0.003798515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005195309,"threshold_uncertainty_score":0.01738006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07251052579286775,"score_gpt":0.2212001052544673,"score_spread":0.1486895794615995,"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."}}