{"id":"W2397205586","doi":"","title":"One Vote for Type Families in Haskell","year":2008,"lang":"en","type":"article","venue":"Trends in Functional Programming","topic":"Logic, programming, and type systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Haskell; Computer science; Programming language; Functional programming; Data type; Type (biology); Sketch; Type inference; Type safety; Compiler; Abstract data type; Data structure; Context (archaeology); Theoretical computer science; Class (philosophy); Modularity (biology); Type theory; Artificial intelligence; Algorithm; Inference","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004094314,0.0001582946,0.0002282554,0.0004440952,0.0001427386,0.00007074209,0.0003272274,0.0001040056,0.00001038161],"category_scores_gemma":[0.00004381686,0.0001579354,0.00008729073,0.001182052,0.00007754192,0.000317738,0.00008902408,0.0001735444,0.00002992431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009098683,"about_ca_system_score_gemma":0.0000650613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002869453,"about_ca_topic_score_gemma":0.000495335,"domain_scores_codex":[0.9984095,0.00004555159,0.0003476065,0.0004552431,0.0002899219,0.0004521977],"domain_scores_gemma":[0.9993745,0.0001084725,0.00008439484,0.0002797231,0.00008816532,0.00006472005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006785062,0.0003870215,0.02281485,0.00003604639,0.00002316611,0.00002980567,0.0008761064,0.0003822399,0.00005957177,0.1002219,0.0007409024,0.8743606],"study_design_scores_gemma":[0.005765064,0.001675237,0.156457,0.00004728518,0.00002188922,0.0001679627,0.00029098,0.03096371,0.0004282321,0.03170387,0.7710686,0.001410126],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05556826,0.002048222,0.9194362,0.001398703,0.004567828,0.001175142,0.000002582768,0.000942243,0.01486081],"genre_scores_gemma":[0.9736444,0.00001131169,0.0240364,0.00006189563,0.0002492485,0.0001362207,0.00002884806,0.00001395029,0.001817776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9180761,"threshold_uncertainty_score":0.644042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1070054815079704,"score_gpt":0.2929933450937063,"score_spread":0.1859878635857359,"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."}}