{"id":"W2240712895","doi":"10.5006/c2011-11303","title":"Learning from Multiple Corrosion Growth Rate (Run-Comparison) Studies","year":2011,"lang":"en","type":"article","venue":"","topic":"Corrosion Behavior and Inhibition","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada)","funders":"","keywords":"Corrosion; Growth rate; Materials science; Metallurgy; Computer science; 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":[0.02383789,0.001076616,0.001248887,0.001596042,0.0003406923,0.001255581,0.001724096,0.001463223,0.00157908],"category_scores_gemma":[0.06429891,0.0003481796,0.001344858,0.001064472,0.0006942536,0.0019699,0.001490352,0.001729688,0.0004497365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006796123,"about_ca_system_score_gemma":0.0005394102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001723414,"about_ca_topic_score_gemma":0.001816665,"domain_scores_codex":[0.9927198,0.003813785,0.0005415012,0.001365189,0.001326956,0.0002328087],"domain_scores_gemma":[0.8825054,0.09758561,0.006052043,0.00635668,0.006810292,0.0006899807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002330101,0.00089429,0.1274257,0.0006635897,0.001415717,0.0005123312,0.000650612,0.6108136,0.004460132,0.00486716,0.002636745,0.2433301],"study_design_scores_gemma":[0.00004893779,0.001291944,0.01686161,0.00008010347,0.0001872724,0.0001946209,0.0001532082,0.964762,0.006693962,0.007994984,0.001657898,0.00007352579],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5710948,0.001703827,0.4208903,0.0003914995,0.0001559339,0.0003621929,0.00071013,0.0006725681,0.004018889],"genre_scores_gemma":[0.9491594,0.0002428163,0.04781636,0.00008364542,0.00005969312,0.0001440358,0.0008621605,0.00007273391,0.001559032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02383789,"threshold_uncertainty_score":0.1260682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1004238281273471,"score_gpt":0.2944080779650692,"score_spread":0.1939842498377221,"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."}}