{"id":"W1662824547","doi":"10.48550/arxiv.1010.1697","title":"Certifying cost annotations in compilers","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Logic, programming, and type systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Prevention of Organ Failure","funders":"","keywords":"Computer science; Compiler; Programming language; Object code; Principle of compositionality; Compiler correctness; Scalability; Mathematical proof; Code (set theory); Code generation; Artificial intelligence; Database; Operating system; Set (abstract data type); Key (lock)","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":[0.008013719,0.0008460407,0.001033486,0.001803603,0.00171478,0.004895683,0.002882461,0.002330689,0.003542551],"category_scores_gemma":[0.05082047,0.001458018,0.00179994,0.001265112,0.005536587,0.01045308,0.005248281,0.004558299,0.0009332467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002695438,"about_ca_system_score_gemma":0.003853815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002422052,"about_ca_topic_score_gemma":0.002337639,"domain_scores_codex":[0.9901663,0.002831919,0.0007635138,0.00126129,0.003769201,0.001207843],"domain_scores_gemma":[0.9552244,0.02525185,0.00296891,0.01111494,0.004812422,0.0006275285],"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":[0.000231959,0.0001036355,0.002692257,0.0005119226,0.00005894651,0.0003939916,0.000972491,0.05537689,0.01484946,0.8667562,0.003366397,0.05468591],"study_design_scores_gemma":[0.00006693295,0.00008358141,0.0005117804,0.0001566857,0.00008780986,0.0002704025,0.0001920305,0.1560331,0.0445301,0.7799968,0.01796781,0.0001029743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05982041,0.000206729,0.9264252,0.001178267,0.00020326,0.0001112928,0.0001902094,0.006695856,0.005168789],"genre_scores_gemma":[0.5380654,0.0003785807,0.4536987,0.000580754,0.0001902006,0.0002233543,0.0003728452,0.002383196,0.004106981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008013719,"threshold_uncertainty_score":0.04238111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1271858993241024,"score_gpt":0.2171337679932642,"score_spread":0.0899478686691618,"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."}}