{"id":"W2580401938","doi":"10.29007/vz48","title":"Proving uniformity and independence by self-composition and coupling","year":2018,"lang":"en","type":"article","venue":"EPiC series in computing","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Horizon 2020 Framework Programme; European Commission; Simons Foundation; National Science Foundation","keywords":"Mathematical proof; Probabilistic logic; Computer science; Theoretical computer science; Probabilistic CTL; Probabilistic argumentation; Independence (probability theory); Markov chain; Abstraction; Probabilistic relevance model; Proof assistant; Programming language; Mathematics; Probabilistic analysis of algorithms; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001067361,0.0001075013,0.0001229046,0.00007293219,0.0002756711,0.000171515,0.0002813967,0.00007035015,0.000001230979],"category_scores_gemma":[0.00008727968,0.0001146344,0.000008252819,0.0002617538,0.0001306504,0.0009174157,0.0004076131,0.0001795063,0.000001817701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005464529,"about_ca_system_score_gemma":0.00002671928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003531545,"about_ca_topic_score_gemma":0.000006027617,"domain_scores_codex":[0.9989763,0.000060265,0.0002360795,0.0003461456,0.0001587514,0.0002224971],"domain_scores_gemma":[0.9994298,0.0001116519,0.0001141088,0.0002353469,0.00006233204,0.00004682083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007123966,0.0001975574,0.2165262,0.0005109223,0.00004329173,0.00002999456,0.03114483,0.0006179074,0.02191416,0.2687121,0.0001324603,0.4600993],"study_design_scores_gemma":[0.0002422912,0.0001341141,0.03038272,0.000108193,0.000002855627,0.0001248774,0.0001825855,0.9491022,0.01631648,0.00296657,0.0002026909,0.000234484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5969828,0.0000934932,0.4020767,0.00008351073,0.0002136189,0.0001001354,2.717383e-7,0.0001227894,0.000326623],"genre_scores_gemma":[0.6143087,0.00001067649,0.3855728,0.00005135009,0.00004656872,0.000001571819,5.14717e-7,0.000003758702,0.000004043215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9484842,"threshold_uncertainty_score":0.4674655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01170210460895693,"score_gpt":0.2700041866828466,"score_spread":0.2583020820738897,"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."}}