{"id":"W2890121425","doi":"10.1145/3241653.3241659","title":"κDOT: scaling DOT with mutation and constructors","year":2018,"lang":"en","type":"article","venue":"","topic":"Logic, programming, and type systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scala; Computer science; Core (optical fiber); Object (grammar); Field (mathematics); Programming language; Artificial intelligence; Mathematics; Pure mathematics; Java","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001089745,0.00005357394,0.00005936845,0.00003071674,0.00009098929,0.0001464748,0.0001149341,0.00002264883,0.000011307],"category_scores_gemma":[0.000006637862,0.00003420651,0.000007645237,0.0001162694,0.0001065688,0.0001884866,0.00004072924,0.00002459178,0.00003315101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000607323,"about_ca_system_score_gemma":0.00001915153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001002282,"about_ca_topic_score_gemma":0.0000490434,"domain_scores_codex":[0.9995305,0.00001622937,0.00007376733,0.0001691545,0.00009701955,0.0001133498],"domain_scores_gemma":[0.9997092,0.00001865694,0.00003242549,0.0001335158,0.00006080903,0.00004541524],"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":[0.000003162739,0.000008972572,0.009976847,0.000009516076,0.00001402931,0.00001292378,0.001645207,8.911794e-7,0.0001371568,0.884303,0.0001092976,0.103779],"study_design_scores_gemma":[0.01029631,0.007137451,0.09918686,0.00009864014,0.0001336656,0.006787444,0.007792736,0.1908364,0.1359521,0.4077257,0.1295913,0.004461493],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1470457,0.00002983859,0.8311651,0.00008976857,0.0002507391,0.00009728146,5.635669e-8,0.0001544055,0.02116714],"genre_scores_gemma":[0.9781339,6.26655e-7,0.02146806,0.00007703651,0.00007242728,0.000002203613,3.237814e-7,0.000002442733,0.0002429765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8310882,"threshold_uncertainty_score":0.141246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0141806614211313,"score_gpt":0.2317803093414703,"score_spread":0.217599647920339,"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."}}