{"id":"W2139128755","doi":"10.1007/978-3-642-14052-5_17","title":"Reasoning with Higher-Order Abstract Syntax and Contexts: A Comparison","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Logic, programming, and type systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; University of Ottawa","funders":"","keywords":"Variety (cybernetics); Computer science; Axiom; Syntax; Set (abstract data type); Inference; Context (archaeology); Formal system; Artificial intelligence; Abstract syntax; Theoretical computer science; Programming language; Mathematics","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":[0.01435754,0.001384389,0.002473884,0.004102397,0.001517846,0.01861597,0.006354589,0.004693342,0.01176887],"category_scores_gemma":[0.0297532,0.001611531,0.002903954,0.005579968,0.009041774,0.0525213,0.007830464,0.004652797,0.001738149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002959513,"about_ca_system_score_gemma":0.002348327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002155788,"about_ca_topic_score_gemma":0.00205636,"domain_scores_codex":[0.9840242,0.00776315,0.001299465,0.001583201,0.004301407,0.001028604],"domain_scores_gemma":[0.9668783,0.02215232,0.001228259,0.006725666,0.00208995,0.0009256134],"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":[0.000174687,0.00004147195,0.0007851975,0.0005432761,0.00006384448,0.0000614319,0.001672888,0.001970532,0.0002678475,0.9374648,0.001766439,0.0551876],"study_design_scores_gemma":[0.00003247007,0.0000405462,0.0004750185,0.0001913238,0.00004817801,0.0001852368,0.0006107473,0.006222541,0.0005884777,0.9789345,0.01263878,0.00003230393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04557438,0.05496172,0.7772915,0.007273797,0.0005922806,0.0001337137,0.0006304036,0.001689018,0.1118532],"genre_scores_gemma":[0.6926514,0.02372555,0.2672139,0.001775076,0.001153724,0.0002067666,0.001075501,0.0009312814,0.01126693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01861597,"threshold_uncertainty_score":0.07593083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01652136302269471,"score_gpt":0.249804418101616,"score_spread":0.2332830550789213,"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."}}