{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002646695,0.0001771368,0.0001704071,0.00009993565,0.0001832102,0.0002628672,0.0009239293,0.00008758344,0.0005043238],"category_scores_gemma":[0.0001010394,0.0001525708,0.0001081829,0.0004200467,0.00008074506,0.0006337555,0.0002652154,0.0001622532,0.01274671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005389372,"about_ca_system_score_gemma":0.0001289009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008705854,"about_ca_topic_score_gemma":0.00003619649,"domain_scores_codex":[0.9984027,0.00004557287,0.0003028187,0.0005008382,0.0003242192,0.0004238488],"domain_scores_gemma":[0.9989548,0.0001445997,0.0001028709,0.0004957409,0.0001775764,0.0001243686],"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":[0.00001045903,0.00009146686,0.0003674202,0.00001007494,0.00001063767,0.00001476859,0.0008760701,0.0001029174,0.003179582,0.9117585,0.0001553242,0.08342279],"study_design_scores_gemma":[0.0000416511,0.000371837,0.0003449084,0.00006161131,0.000007554127,0.00006654445,0.0001820458,0.07766782,0.3278626,0.5864189,0.006510448,0.0004641623],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2747655,0.0007738794,0.6108801,0.001747281,0.002654283,0.0007711118,0.000004087874,0.0005899871,0.1078138],"genre_scores_gemma":[0.9915106,0.00001991598,0.00601584,0.0003599251,0.0001885354,0.00001883146,0.000004693336,0.00001095835,0.001870713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7167451,"threshold_uncertainty_score":0.988022,"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."}}