{"id":"W3101540591","doi":"10.1145/3434300","title":"Data flow refinement type inference","year":2021,"lang":"en","type":"preprint","venue":"Proceedings of the ACM on Programming Languages","topic":"Logic, programming, and type systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Science Foundation","keywords":"Type inference; Abstract interpretation; Computer science; Inference; Predicate abstraction; Semantics (computer science); Theoretical computer science; Soundness; Abstraction; Programming language; Data type; Algorithm; Artificial intelligence; Model checking","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":[0.007663372,0.00127079,0.001383339,0.003136349,0.001328983,0.00365046,0.004017417,0.002261709,0.0100528],"category_scores_gemma":[0.02954868,0.001570583,0.00423794,0.0018918,0.003352524,0.0082087,0.004602928,0.00487227,0.002892087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002348396,"about_ca_system_score_gemma":0.004218185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004496898,"about_ca_topic_score_gemma":0.004288971,"domain_scores_codex":[0.9914312,0.001753057,0.0008248821,0.001517748,0.003739651,0.0007335632],"domain_scores_gemma":[0.9870436,0.005138839,0.0008607795,0.004337715,0.002464086,0.0001548799],"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":[0.0002903136,0.0001205433,0.004161705,0.0007399578,0.0001556781,0.0002929821,0.0007657588,0.0482848,0.01467855,0.5998791,0.01223689,0.3183937],"study_design_scores_gemma":[0.00008282068,0.00007577943,0.0007438896,0.000328794,0.0001412791,0.0003688625,0.0001362931,0.3448527,0.04931065,0.5343356,0.06949696,0.0001264625],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001465237,0.00005780859,0.9940223,0.00009393368,0.00005053636,0.00008488484,0.0001920688,0.002725946,0.001307291],"genre_scores_gemma":[0.09050003,0.0002803625,0.9013017,0.000375432,0.0001178496,0.0003307289,0.001221521,0.001886418,0.003985938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0100528,"threshold_uncertainty_score":0.04052824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06334551667465678,"score_gpt":0.318797632600387,"score_spread":0.2554521159257302,"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."}}