{"id":"W1997928252","doi":"10.1115/ipc2012-90491","title":"Bayesian Model for Calibration of ILI Tools","year":2012,"lang":"en","type":"article","venue":"","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada); Western University","funders":"","keywords":"Calibration; Field (mathematics); Observational error; Standard deviation; Probabilistic logic; Bayesian probability; Function (biology); Computer science; Algorithm; Statistics; Mathematics","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":[0.007110571,0.001574217,0.002338269,0.002384614,0.0008071113,0.002301897,0.004279462,0.003069218,0.003949541],"category_scores_gemma":[0.02061377,0.001709961,0.001554139,0.002460041,0.002163346,0.003109979,0.001861812,0.002742351,0.00154728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002229084,"about_ca_system_score_gemma":0.001732677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01158059,"about_ca_topic_score_gemma":0.007731784,"domain_scores_codex":[0.9966267,0.001211187,0.0001649962,0.0008656839,0.0008190536,0.0003123678],"domain_scores_gemma":[0.9911118,0.005238819,0.00121345,0.00066641,0.001619352,0.0001501933],"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":[0.0001348971,0.00003622557,0.001762892,0.0001095513,0.0000887974,0.000118745,0.0001122487,0.9291435,0.001364746,0.04251825,0.00087196,0.02373803],"study_design_scores_gemma":[0.00001710932,0.00002424731,0.0005628708,0.00002150033,0.00002060788,0.00004198859,0.00001062405,0.9837738,0.0004278021,0.01434465,0.0007235648,0.00003132889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008386366,0.0002557354,0.9888729,0.0001706186,0.00002667517,0.00006803666,0.0002152543,0.0002870519,0.001717313],"genre_scores_gemma":[0.731962,0.001257385,0.2521209,0.0003478656,0.0001764286,0.0009449017,0.001978245,0.0002622741,0.01094999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01158059,"threshold_uncertainty_score":0.03760469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04345371063279742,"score_gpt":0.2672116094826495,"score_spread":0.2237578988498521,"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."}}