{"id":"W3091193824","doi":"10.1145/3428225","title":"Automatic and efficient variability-aware lifting of functional programs","year":2020,"lang":"en","type":"preprint","venue":"Proceedings of the ACM on Programming Languages","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; General Motors of Canada","keywords":"Correctness; Computer science; Rewriting; Software; Programming language; Product (mathematics); Modulo; Program analysis; Software product line; Static analysis; Theoretical computer science; Software development; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001860883,0.001079478,0.0007688408,0.001139354,0.0007530553,0.001077967,0.00148684,0.0008839597,0.002642462],"category_scores_gemma":[0.007732973,0.0007808814,0.002297793,0.0005913191,0.001431292,0.001717367,0.002632729,0.002076556,0.001067683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000670447,"about_ca_system_score_gemma":0.001705477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00183017,"about_ca_topic_score_gemma":0.001835972,"domain_scores_codex":[0.9969505,0.0005340539,0.0001636482,0.0004329679,0.001421652,0.0004971719],"domain_scores_gemma":[0.9947792,0.002672166,0.0003589851,0.001205075,0.0008705291,0.0001139903],"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":[0.0005487971,0.0003650738,0.005411319,0.0008757742,0.000121309,0.001335326,0.001388329,0.1514961,0.2338612,0.05866053,0.00455305,0.5413831],"study_design_scores_gemma":[0.0001260079,0.000395828,0.002193367,0.0001471915,0.0001499182,0.000812032,0.0002688596,0.7193164,0.1584468,0.1048739,0.01314812,0.0001216981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06590188,0.0001007246,0.9238766,0.0001575667,0.00003495987,0.0002074959,0.0001356646,0.007059339,0.002525717],"genre_scores_gemma":[0.5223171,0.0001937159,0.4713939,0.0001965349,0.00006112587,0.0002529693,0.0007460302,0.001891248,0.002947358],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002642462,"threshold_uncertainty_score":0.009841442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04321467819769632,"score_gpt":0.2867136564902178,"score_spread":0.2434989782925215,"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."}}