{"id":"W2742491330","doi":"10.3390/met7080305","title":"Inspection of Prebaked Carbon Anodes Using Multi-Spectral Acousto-Ultrasonic Excitation","year":2017,"lang":"en","type":"article","venue":"Metals","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alcoa (Canada); Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Université Laval","keywords":"Anode; Ultrasonic sensor; Materials science; Excitation; Characterization (materials science); Raw material; Acoustics; Partial least squares regression; Computer science; Electrode; Physics; Engineering; Electrical engineering; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002748713,0.0003479347,0.0002422421,0.0005378687,0.0002125492,0.0003392943,0.0004264446,0.0005450192,0.0007578228],"category_scores_gemma":[0.0005348155,0.0002156838,0.0001830888,0.0003374705,0.0003528087,0.000330283,0.0003445515,0.000331063,0.0002365651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003194962,"about_ca_system_score_gemma":0.0002661057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007969455,"about_ca_topic_score_gemma":0.003088074,"domain_scores_codex":[0.9996654,0.00002141363,0.00001273981,0.00007160758,0.0002054882,0.00002343807],"domain_scores_gemma":[0.9995263,0.000166415,0.00007323339,0.00005199606,0.0001631287,0.00001897396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006606102,0.00001449714,0.0009803049,0.00006475037,0.000004250053,0.00006280647,0.00005375914,0.0005622836,0.9865536,0.00006630908,0.00004165928,0.01152971],"study_design_scores_gemma":[0.000003916075,0.0001733109,0.00903322,0.000006841341,0.00001100704,0.0001900306,0.00007805679,0.0076016,0.982165,0.00004748361,0.000676594,0.00001291309],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.915405,0.0006767662,0.08213448,0.00007190017,0.00003828796,0.00005586501,0.0001213015,0.0003802855,0.001116183],"genre_scores_gemma":[0.9326643,0.0004369806,0.06506456,0.0000359855,0.000009117103,0.00003617368,0.0001232024,0.00003342119,0.001596291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007969455,"threshold_uncertainty_score":0.002535164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03604631627842309,"score_gpt":0.2713633537283005,"score_spread":0.2353170374498774,"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."}}