{"id":"W4206286386","doi":"10.1145/3498700","title":"Mœbius: metaprogramming using contextual types: the stage where system f can pattern match on itself","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ACM on Programming Languages","topic":"Logic, programming, and type systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Espace pour la vie; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Metaprogramming; Computer science; Programming language; Exploit; Code generation; Code (set theory); Source code; Type inference; Theoretical computer science; Artificial intelligence; Key (lock); Inference","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.001883732,0.0004203241,0.0004871396,0.0001643396,0.001131715,0.0006525914,0.005865013,0.00009523257,0.00001476961],"category_scores_gemma":[0.0003746556,0.000253813,0.0003066075,0.0009769591,0.0001581152,0.0002278598,0.002488053,0.0006653782,0.00001082959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002842321,"about_ca_system_score_gemma":0.00008269915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001098637,"about_ca_topic_score_gemma":0.0000575562,"domain_scores_codex":[0.9965258,0.0001406716,0.0005767026,0.0007444151,0.001238104,0.0007743361],"domain_scores_gemma":[0.9972796,0.0002279605,0.0008205506,0.001320737,0.0002385744,0.0001125329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001378844,0.0009440774,0.01712179,0.002120846,0.000808282,0.00008663974,0.0402611,0.000470152,0.003291829,0.399334,0.00201877,0.5334046],"study_design_scores_gemma":[0.006949598,0.008748965,0.001711271,0.001582299,0.001154421,0.00155048,0.4493504,0.02294805,0.05318924,0.007062047,0.4399874,0.005765828],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959252,0.005177611,0.005530931,0.009167733,0.004065495,0.00778284,0.00005863358,0.002812745,0.006152031],"genre_scores_gemma":[0.9953213,0.000004329633,0.003013978,0.0003241642,0.0001884383,0.0003049038,0.000002656225,0.00005744954,0.0007828309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5276387,"threshold_uncertainty_score":0.9999914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02638656077483974,"score_gpt":0.2626384689665354,"score_spread":0.2362519081916957,"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."}}