{"id":"W4320710283","doi":"10.1007/s00249-023-01631-6","title":"Systematic noise removal from analytical ultracentrifugation data with UltraScan","year":2023,"lang":"en","type":"article","venue":"European Biophysics Journal","topic":"Coagulation and Flocculation Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"Analytical Ultracentrifugation; Absorbance; Sedimentation; Noise (video); Sedimentation equilibrium; Chemistry; Subtraction; Optics; Analytical Chemistry (journal); Computational physics; Physics; Ultracentrifuge; Mathematics; Chromatography; Computer science","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":[0.003046476,0.00138193,0.0009943604,0.001999173,0.0016825,0.001533134,0.001246294,0.001229652,0.003047222],"category_scores_gemma":[0.0126815,0.0008220087,0.000573196,0.002615309,0.0007601699,0.001070499,0.001251828,0.001851158,0.0012309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007760348,"about_ca_system_score_gemma":0.002038499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003633419,"about_ca_topic_score_gemma":0.009328546,"domain_scores_codex":[0.9979526,0.0004700461,0.0002252116,0.0003868695,0.0007671656,0.0001980477],"domain_scores_gemma":[0.9933396,0.002504313,0.0004420582,0.001612377,0.001946735,0.0001548934],"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":[0.001424722,0.0004310657,0.01449817,0.0009988007,0.000527445,0.0006511172,0.001972536,0.007282084,0.7506454,0.006703665,0.009934946,0.20493],"study_design_scores_gemma":[0.00006024574,0.000197543,0.04560618,0.0001165062,0.0002694527,0.0005404569,0.0002677615,0.1368565,0.7868767,0.004600846,0.02439171,0.0002160565],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2415483,0.001475828,0.737774,0.0006987217,0.0006297014,0.0002858219,0.001515771,0.01251893,0.003552943],"genre_scores_gemma":[0.5826986,0.0009526939,0.399546,0.0008501657,0.0001133627,0.000605641,0.00386302,0.006560036,0.004810526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003633419,"threshold_uncertainty_score":0.01611155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03649067551207812,"score_gpt":0.2578006410805435,"score_spread":0.2213099655684653,"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."}}