{"id":"W4409310616","doi":"10.1145/3720436","title":"IncIDFA: An Efficient and Generic Algorithm for Incremental Iterative Dataflow Analysis","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Programming Languages","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Engineering Research Board; IBM Canada","keywords":"Dataflow; Algorithm; Computer science; Parallel computing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003596549,0.0001383921,0.000220891,0.0001777486,0.0002065599,0.0001271106,0.0009865068,0.0000320872,6.199916e-7],"category_scores_gemma":[0.0002742939,0.00009281208,0.00007538712,0.000821853,0.00008127855,0.0003218983,0.001041856,0.00006906342,1.888459e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002534649,"about_ca_system_score_gemma":0.00001735233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004829814,"about_ca_topic_score_gemma":0.000009226089,"domain_scores_codex":[0.9990157,0.00001009223,0.0002048583,0.0003888185,0.0001843823,0.0001961693],"domain_scores_gemma":[0.999097,0.00006575468,0.0001664428,0.0004847569,0.0001460851,0.00003996025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000288213,0.0002493103,0.003766113,0.0002157862,0.0005360168,0.000001320719,0.003705775,0.00008946862,0.009266408,0.08731785,0.0003137998,0.8945093],"study_design_scores_gemma":[0.003564474,0.001903612,0.01499014,0.0009512914,0.001696959,0.00003379433,0.02123973,0.4399436,0.4678665,0.003919438,0.04212074,0.001769706],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5002168,0.0009809571,0.4963217,0.0007011283,0.0001798721,0.001114751,0.000129939,0.0001770606,0.0001777239],"genre_scores_gemma":[0.2681926,0.000005490125,0.7314109,0.0001312136,0.00003497109,0.0001093165,0.00001703485,0.000006471336,0.00009208749],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8927396,"threshold_uncertainty_score":0.3784766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01314837035727775,"score_gpt":0.2926624817676283,"score_spread":0.2795141114103505,"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."}}