{"id":"W3082798245","doi":"","title":"A Fixpoint Logic for Labeled Markov Processes","year":2003,"lang":"en","type":"preprint","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Markov chain; Fixed point; Markov process; Theoretical computer science; Algorithm; Mathematics; Statistics; Machine learning","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":[0.007889734,0.001605536,0.001833858,0.004171926,0.002874899,0.008083187,0.003610492,0.0038455,0.0133398],"category_scores_gemma":[0.01498878,0.001662854,0.003884528,0.005057272,0.005911848,0.01585854,0.004391865,0.008493231,0.001867249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005362363,"about_ca_system_score_gemma":0.002002663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004971077,"about_ca_topic_score_gemma":0.002468995,"domain_scores_codex":[0.995339,0.001728681,0.0003592522,0.001322884,0.0009507898,0.0002993537],"domain_scores_gemma":[0.9872959,0.009629799,0.0005200062,0.0009247384,0.001231438,0.0003981279],"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":[0.00001526214,0.000009528893,0.00008816741,0.00004518612,0.00001480615,0.00004503357,0.0001374293,0.00141329,0.0001694175,0.9919002,0.0009650927,0.005196483],"study_design_scores_gemma":[0.000007097884,0.000004610887,0.00001919042,0.00001521672,0.00001326062,0.00001795324,0.0000181823,0.007256805,0.0001417029,0.9906248,0.001872624,0.000008554418],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006441635,0.0009038745,0.9786912,0.002555061,0.0001298092,0.00004510081,0.0003636814,0.0005509704,0.01031878],"genre_scores_gemma":[0.3910746,0.003151485,0.581493,0.002562785,0.001395059,0.0004084308,0.00178063,0.0007347248,0.01739936],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0133398,"threshold_uncertainty_score":0.04462606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05741078396881019,"score_gpt":0.298262547841796,"score_spread":0.2408517638729858,"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."}}