{"id":"W2086967111","doi":"10.1109/inm.2007.374804","title":"A Hybrid Approach to Operating System Discovery using Answer Set Programming","year":2007,"lang":"en","type":"article","venue":"","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Set (abstract data type); Answer set programming; Simple (philosophy); Representation (politics); Logic programming; Artificial intelligence; Programming language","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":[0.01226757,0.001117472,0.001370149,0.004136393,0.002425819,0.007595855,0.005966649,0.003042686,0.005555164],"category_scores_gemma":[0.01768055,0.00106907,0.004327872,0.003138793,0.006372411,0.01603231,0.006706366,0.00566718,0.001009567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002624851,"about_ca_system_score_gemma":0.003077771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003118171,"about_ca_topic_score_gemma":0.003171575,"domain_scores_codex":[0.9878576,0.004863703,0.0007713487,0.001688045,0.004150162,0.0006691407],"domain_scores_gemma":[0.983267,0.01272601,0.0006267486,0.001690349,0.001358945,0.0003308669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001334781,0.0001850863,0.0005600836,0.0003222639,0.0001261768,0.0002653235,0.0009565398,0.02528046,0.002120164,0.8801204,0.003178948,0.08675111],"study_design_scores_gemma":[0.00006077785,0.00005152795,0.00008215029,0.00006412892,0.00008661381,0.0002156051,0.0002229008,0.1876167,0.003366344,0.7970516,0.01113322,0.00004852773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002291598,0.0001181101,0.9929756,0.001051682,0.0000262095,0.00008717899,0.00005656778,0.000420149,0.002972975],"genre_scores_gemma":[0.08703475,0.0003087838,0.9084316,0.0006353954,0.0001219557,0.0002911203,0.000270161,0.0001260302,0.002780133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01226757,"threshold_uncertainty_score":0.06487781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03235120864900761,"score_gpt":0.2684411039609387,"score_spread":0.2360898953119311,"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."}}