{"id":"W2726753079","doi":"10.2168/lmcs-4(2:2)2008","title":"Approximating a Behavioural Pseudometric without Discount for Probabilistic Systems","year":2008,"lang":"en","type":"article","venue":"Logical Methods in Computer Science","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); York University","funders":"","keywords":"Probabilistic logic; Duality (order theory); Interval (graph theory); Dual (grammatical number); Discounting; Order (exchange); Zero (linguistics); Decision theory; Degree (music)","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":[0.01064617,0.0002944622,0.0005399215,0.0007122493,0.0005667075,0.0004302807,0.003259646,0.0001231685,0.000001465932],"category_scores_gemma":[0.002840805,0.0002305246,0.0001090222,0.004674687,0.0009250112,0.001382286,0.0008906121,0.0003414289,0.00000601889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003281684,"about_ca_system_score_gemma":0.0002188492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003134847,"about_ca_topic_score_gemma":5.523906e-7,"domain_scores_codex":[0.9952751,0.0007549061,0.0009358759,0.001316441,0.0008331485,0.0008845264],"domain_scores_gemma":[0.9966384,0.001310347,0.0003638686,0.001131347,0.0003227341,0.000233263],"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":[0.00003088266,0.0006068644,0.01326064,0.0002026836,0.0000090708,0.00003530017,0.002194059,0.02075972,0.003579323,0.510959,0.00002479777,0.4483376],"study_design_scores_gemma":[0.0003449439,0.0003046771,0.01674887,0.00004797432,0.000003836648,0.000243719,0.00001061841,0.9704652,0.0008032387,0.01064235,0.00004921591,0.0003353372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02974012,0.00009827098,0.9664374,0.00007444112,0.002024019,0.001197817,0.000001806377,0.000246263,0.0001798751],"genre_scores_gemma":[0.2113417,0.000004048805,0.7881348,0.00009475528,0.00009940795,0.0003007131,6.072744e-7,0.000009917406,0.00001413936],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9497055,"threshold_uncertainty_score":0.9400521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1758177199586406,"score_gpt":0.4064235178591127,"score_spread":0.2306057979004721,"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."}}