{"id":"W2762605937","doi":"10.1145/3133879","title":"The VM already knew that: leveraging compile-time knowledge to optimize gradual typing","year":2017,"lang":"en","type":"article","venue":"Proceedings of the ACM on Programming Languages","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Programming language; Compile time; Programmer; Soundness; JavaScript; Code (set theory); Compiler; Object (grammar); Parallel computing; Artificial intelligence","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":[0.003438702,0.001127431,0.0006297379,0.0005861432,0.000875993,0.003157335,0.002698492,0.001136932,0.003665046],"category_scores_gemma":[0.02077775,0.001282181,0.001331091,0.0005465632,0.00269269,0.008073229,0.003669295,0.004555932,0.002901336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001112523,"about_ca_system_score_gemma":0.003573649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003180766,"about_ca_topic_score_gemma":0.003800867,"domain_scores_codex":[0.9967498,0.0006906699,0.0002645496,0.0006624193,0.001127994,0.0005047301],"domain_scores_gemma":[0.9869479,0.00266415,0.0006994627,0.008094024,0.001240396,0.0003540161],"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":[0.002126983,0.000533346,0.03567949,0.0008842522,0.0002640977,0.000944652,0.0044618,0.06324046,0.206141,0.1765829,0.02509194,0.4840491],"study_design_scores_gemma":[0.0002151223,0.0006991882,0.005776071,0.00055593,0.0003983016,0.0009186423,0.000604837,0.3990157,0.281628,0.1505784,0.1592393,0.0003704216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1117963,0.0006295528,0.8256047,0.00163544,0.0004253822,0.0001834715,0.0002433488,0.04656991,0.01291184],"genre_scores_gemma":[0.5063838,0.0004946512,0.4613895,0.001475292,0.0001379489,0.0001813336,0.0006483229,0.01829975,0.01098941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003665046,"threshold_uncertainty_score":0.01818579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03571118779894979,"score_gpt":0.3085080125902429,"score_spread":0.2727968247912931,"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."}}