{"id":"W2086250731","doi":"10.1002/nav.20068","title":"Shock model in Markovian environment","year":2005,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Shock (circulatory); Markov process; Reliability (semiconductor); Exponential function; Applied mathematics; Exponential distribution; Statistical physics; Markov chain; Mathematics; Magnitude (astronomy); Phase-type distribution; Markovian arrival process; Statistics; Physics; Mathematical analysis; Thermodynamics","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":[0.00105241,0.000114882,0.0001342702,0.0001648501,0.00005997052,0.00004074633,0.0002287519,0.0001350245,0.0001209318],"category_scores_gemma":[0.0005022382,0.0001144721,0.00003263101,0.0001876953,0.0001513532,0.0001078919,0.00008886297,0.0005610459,0.0002403145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006274702,"about_ca_system_score_gemma":0.00004691764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003524595,"about_ca_topic_score_gemma":0.00009099217,"domain_scores_codex":[0.9984064,0.00006168297,0.0002405739,0.0002272223,0.0004968478,0.0005673222],"domain_scores_gemma":[0.9993359,0.0001470597,0.00001046216,0.0003278198,0.00005593976,0.00012285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001950672,0.00005514012,0.0001423864,0.00003445226,0.000003965968,0.000006976081,0.0001052115,0.9832857,0.0005958684,0.003493286,0.002416992,0.009840484],"study_design_scores_gemma":[0.0002374143,0.00003563857,0.0003464223,0.00001836589,0.000001764112,9.690064e-7,0.00003392913,0.9854395,0.0004549217,0.004864601,0.008446929,0.000119484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04253618,0.0007310379,0.8260594,0.001630686,0.0001915405,0.0009990816,0.00005697276,0.0002710228,0.1275241],"genre_scores_gemma":[0.9781643,0.001421877,0.01876634,0.000027999,0.00009185739,0.00004539291,0.0000171931,0.00002847139,0.001436628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9356281,"threshold_uncertainty_score":0.4668036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06579873709004619,"score_gpt":0.3170136294650935,"score_spread":0.2512148923750473,"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."}}