{"id":"W4254363650","doi":"10.1145/2858965.2814305","title":"Incremental computation with names","year":2015,"lang":"en","type":"article","venue":"ACM SIGPLAN Notices","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Defense Advanced Research Projects Agency; Center for Selective C-H Functionalization, National Science Foundation","keywords":"Computation; Computer science; Class (philosophy); Scratch; Probabilistic logic; Theoretical computer science; Functional programming; State (computer science); Programming language; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001483568,0.0000914287,0.00009005263,0.0000464214,0.00007628724,0.0001851341,0.0007658963,0.00002531701,0.00000541108],"category_scores_gemma":[0.00004143984,0.00006481502,0.00001100365,0.0001411552,0.00002820262,0.0008603332,0.0004006996,0.000061862,0.00008026227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002034574,"about_ca_system_score_gemma":0.00004890122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000139471,"about_ca_topic_score_gemma":0.00002350348,"domain_scores_codex":[0.9991679,0.00003611545,0.0001022568,0.0002289925,0.0003181659,0.0001465689],"domain_scores_gemma":[0.9992757,0.00008964507,0.00007492122,0.000389955,0.00006199308,0.0001078028],"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":[0.0003490469,0.0008114463,0.07640538,0.0001151716,0.0002169407,0.0005309982,0.01401883,0.0172059,0.001776103,0.03150702,0.1232575,0.7338057],"study_design_scores_gemma":[0.005195804,0.002303591,0.05451621,0.0002498535,0.00005687108,0.0001831761,0.001741477,0.8696501,0.006533491,0.0138257,0.04422921,0.001514475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3677931,0.0002505992,0.625621,0.001221037,0.0005241249,0.0001915687,0.00001294051,0.0003501755,0.004035445],"genre_scores_gemma":[0.7970473,0.000001261293,0.2026271,0.0001969617,0.00006645338,0.000003041337,0.0000241419,0.000004504393,0.0000292181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8524442,"threshold_uncertainty_score":0.264308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0406810661610968,"score_gpt":0.2739843194412407,"score_spread":0.2333032532801439,"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."}}