{"id":"W4412523034","doi":"10.1016/j.jmr.2025.107923","title":"Rapid flow characterization measurements using a modified CPMG measurement with incremented echo times, phase cycling and filtering","year":2025,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Echo (communications protocol); Characterization (materials science); Phase (matter); Nuclear magnetic resonance; Cycling; Analytical Chemistry (journal); Flow (mathematics); Chemistry; Materials science; Physics; Nanotechnology; Chromatography; Computer science; Mechanics","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":[0.001872291,0.0007687496,0.0003834029,0.0009882117,0.0003461526,0.0003463447,0.0007711561,0.0007000463,0.0007581717],"category_scores_gemma":[0.00148057,0.0002503135,0.0002171704,0.0005270671,0.0008046926,0.0006991023,0.0005454991,0.001121477,0.0002768401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003492589,"about_ca_system_score_gemma":0.0005254979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001020147,"about_ca_topic_score_gemma":0.0009646592,"domain_scores_codex":[0.999414,0.000138091,0.00003450346,0.0001460553,0.0001980661,0.00006934656],"domain_scores_gemma":[0.9992524,0.0002704648,0.00009466722,0.0001169968,0.0002116967,0.00005379586],"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":[0.00007816105,0.00003948077,0.0001935361,0.00008283986,0.000007957689,0.00005096578,0.0000435586,0.0003922432,0.9849297,0.0005678268,0.0001184355,0.01349516],"study_design_scores_gemma":[0.00002036308,0.00030671,0.001617452,0.000007797063,0.00001578307,0.000137968,0.00001507448,0.008940901,0.9865654,0.0004057512,0.001922811,0.00004407087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2993656,0.001368816,0.695464,0.0003454461,0.0001770101,0.0003877968,0.0003054051,0.001310319,0.00127553],"genre_scores_gemma":[0.5269982,0.0008394004,0.46928,0.0002405677,0.00007971752,0.0004363769,0.0002423037,0.0001006529,0.001782825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001872291,"threshold_uncertainty_score":0.009901702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03476049500489044,"score_gpt":0.2361835985939256,"score_spread":0.2014231035890351,"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."}}