{"id":"W2227783245","doi":"","title":"Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Quebec City, ... (Lecture Notes in Computer Science)","year":2006,"lang":"en","type":"book","venue":"Springer eBooks","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Library science; Artificial intelligence; Data science; Operations research; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001155994,0.0003725467,0.0005998557,0.0008231966,0.0004035088,0.0001736542,0.001703682,0.0002933293,0.000004951346],"category_scores_gemma":[0.00008896529,0.000324911,0.0002268854,0.0004759639,0.00124855,0.0002020699,0.0002600279,0.0006799999,0.000001389216],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001226578,"about_ca_system_score_gemma":0.01595074,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5659246,"about_ca_topic_score_gemma":0.9925099,"domain_scores_codex":[0.9970782,0.00005443638,0.0008628832,0.0006983245,0.0005814758,0.0007246226],"domain_scores_gemma":[0.9976767,0.0005254134,0.0004141866,0.0004646758,0.0007425877,0.0001764886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001309615,0.00003634494,0.003719907,0.000554371,0.00006922847,0.00001372872,0.009917996,0.4188345,0.00001108473,0.2132727,0.0006596041,0.3528975],"study_design_scores_gemma":[0.0001561044,0.0001466599,0.0009820103,0.003032461,0.00003611458,0.000006420235,0.00007578759,0.3100872,0.002225623,0.6747106,0.007513805,0.00102728],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007486564,0.001618902,0.977648,0.00105049,0.00129518,0.001454746,0.0002113588,0.00004134024,0.01593135],"genre_scores_gemma":[0.939091,0.00001904362,0.05682077,0.0006629585,0.0002141917,0.00004969315,0.00003328207,0.00003511242,0.003073908],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9383424,"threshold_uncertainty_score":0.9999203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04095342543751496,"score_gpt":0.2863684457578436,"score_spread":0.2454150203203287,"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."}}