{"id":"W2725233680","doi":"10.1016/j.artint.2018.10.007","title":"Gelfond–Zhang aggregates as propositional formulas","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Centre International de Mathématiques et Informatique de Toulouse; Ministerio de Economía y Competitividad; Xunta de Galicia; Simon Fraser University; Agence Nationale de la Recherche; Deutsche Forschungsgemeinschaft","keywords":"Answer set programming; Propositional calculus; Aggregate (composite); Syntax; Equivalence (formal languages); Semantics (computer science); Stable model semantics; Representation (politics); Propositional formula; Class (philosophy); Logical equivalence; Mathematics; Propositional variable; Computer science; Set (abstract data type); Discrete mathematics; Programming language; Artificial intelligence; Operational semantics; Intermediate logic; Description logic","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003029265,0.0007069221,0.001049408,0.004191881,0.001486578,0.005161285,0.002005693,0.001788236,0.004599517],"category_scores_gemma":[0.01029233,0.0005546684,0.001289512,0.003707512,0.002054875,0.01140292,0.002900738,0.002425424,0.0007763178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00302307,"about_ca_system_score_gemma":0.0007738987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002083308,"about_ca_topic_score_gemma":0.001923988,"domain_scores_codex":[0.9972879,0.0007558201,0.0002160857,0.000451281,0.0009979371,0.0002910362],"domain_scores_gemma":[0.9966844,0.0016493,0.0003645243,0.0005853212,0.0005473859,0.0001690952],"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":[0.00004647676,0.00001427192,0.0003038824,0.00004057492,0.00002260235,0.00008198142,0.000167466,0.002506688,0.0004235123,0.9845769,0.001721169,0.01009445],"study_design_scores_gemma":[0.00001350455,0.000008191803,0.0001235541,0.00001624014,0.00002866655,0.0000449855,0.00004970009,0.02564957,0.0008385984,0.9693388,0.003879154,0.000009049315],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1698311,0.003085213,0.7303653,0.005829447,0.000425696,0.00009858261,0.0006124278,0.0009541296,0.08879809],"genre_scores_gemma":[0.7962037,0.00116447,0.1824683,0.0006840065,0.0004685447,0.0001370336,0.0006719521,0.0002527089,0.01794939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005161285,"threshold_uncertainty_score":0.02193403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02289904309344189,"score_gpt":0.270212057011926,"score_spread":0.2473130139184841,"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."}}