{"id":"W7062244978","doi":"","title":"In-Situ Ultrasonic Process Monitoring Sensors: Optimum Settings and Configuration","year":2001,"lang":"en","type":"report","venue":"NPARC","topic":"Advanced Power Generation Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Process (computing); Ultrasonic sensor; Work (physics); Process control","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001740394,0.001514787,0.001242128,0.0009716109,0.0006296479,0.00120491,0.001789036,0.001890504,0.005763552],"category_scores_gemma":[0.00357002,0.001283251,0.0004075177,0.0007259066,0.0003960812,0.002342915,0.0007519177,0.0008652345,0.001996642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005757841,"about_ca_system_score_gemma":0.0004812108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002203904,"about_ca_topic_score_gemma":0.0007685078,"domain_scores_codex":[0.998242,0.0005187186,0.0001107884,0.0004123668,0.0006163268,0.00009980847],"domain_scores_gemma":[0.9981223,0.0006139173,0.0002656192,0.0002202024,0.0007087131,0.00006931482],"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":[0.001429514,0.0003358542,0.003811912,0.0005009911,0.00005358375,0.00007162589,0.0001659938,0.00116188,0.8998941,0.0009730502,0.005475671,0.08612575],"study_design_scores_gemma":[0.00006163227,0.0004374192,0.002101861,0.00002448221,0.0001190197,0.0004265185,0.00005441343,0.004357697,0.9863399,0.0002116466,0.005839808,0.00002564031],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2432261,0.004297284,0.7218701,0.002208939,0.0008348104,0.0008161613,0.001680615,0.006490034,0.01857604],"genre_scores_gemma":[0.6552551,0.001828807,0.3305389,0.0004664784,0.0002288337,0.0003546463,0.00103615,0.0005381875,0.009752756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005763552,"threshold_uncertainty_score":0.01928103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621377231279176,"score_gpt":0.2767395780110941,"score_spread":0.2605258056983023,"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."}}