{"id":"W4400873005","doi":"10.2139/ssrn.4901981","title":"Bayesian Input-State-Parameter Inference of Hydrodynamic Bearings: From Partial Displacement Measurements to Force Reconstruction","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Hydraulic and Pneumatic Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Displacement (psychology); Inference; Bayesian inference; State (computer science); Bayesian probability; Computer science; Mathematics; Mechanics; Physics; Algorithm; Artificial intelligence","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":[0.002242972,0.001012281,0.001630053,0.0006557957,0.0004398886,0.001351844,0.001705556,0.001953214,0.001938501],"category_scores_gemma":[0.0178895,0.001748882,0.0008131787,0.000879222,0.001417279,0.002132423,0.001877548,0.002101796,0.0007190661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006900566,"about_ca_system_score_gemma":0.001951991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006280735,"about_ca_topic_score_gemma":0.005593412,"domain_scores_codex":[0.9993653,0.0002316202,0.00003865461,0.0001643008,0.0001289846,0.00007116915],"domain_scores_gemma":[0.9932287,0.005376475,0.0004168745,0.0004575601,0.0003649577,0.0001553984],"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":[0.0004404577,0.00009141004,0.002301576,0.0001902253,0.00009919514,0.00008393121,0.0001174749,0.8851219,0.004265829,0.01897758,0.001329324,0.08698105],"study_design_scores_gemma":[0.00001314991,0.00001092352,0.0004092836,0.000008573873,0.000005723148,0.00001058054,0.000004037397,0.9883874,0.000812779,0.01019651,0.0001317721,0.000009234556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03605871,0.0003002885,0.9618424,0.0004038211,0.0000399829,0.00001940092,0.000190361,0.0004212013,0.0007239858],"genre_scores_gemma":[0.8376898,0.0005118353,0.1568138,0.0001896594,0.0001791144,0.00009022505,0.001025034,0.0002423818,0.003258148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006280735,"threshold_uncertainty_score":0.01248831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01325918579994436,"score_gpt":0.243290626944319,"score_spread":0.2300314411443746,"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."}}