{"id":"W4282975211","doi":"10.1103/physrevapplied.17.064031","title":"Robust Spin Relaxometry with Fast Adaptive Bayesian Estimation","year":2022,"lang":"en","type":"article","venue":"Physical Review Applied","topic":"Diamond and Carbon-based Materials Research","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Relaxometry; Computer science; Sensitivity (control systems); Physics; Spin (aerodynamics); Computational physics; Materials science; Nuclear magnetic resonance; Algorithm; Electronic engineering; Spin echo; Magnetic resonance imaging","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":[0.002854382,0.00119,0.001047617,0.0007741514,0.0005145947,0.001008505,0.001435532,0.001436448,0.001536346],"category_scores_gemma":[0.009260302,0.0009984,0.0007381932,0.0008273338,0.001130407,0.001528268,0.001649454,0.002454198,0.0006803248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009900315,"about_ca_system_score_gemma":0.002204164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004846295,"about_ca_topic_score_gemma":0.008102229,"domain_scores_codex":[0.9988315,0.0005650708,0.00005223656,0.0002330606,0.0002527495,0.00006533232],"domain_scores_gemma":[0.9968321,0.002135931,0.0003085393,0.0003164549,0.0003300917,0.00007693846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005335332,0.0001613478,0.001424704,0.0002919599,0.0002915188,0.0001254688,0.0001293348,0.7021365,0.04073822,0.04705396,0.002884867,0.2042287],"study_design_scores_gemma":[0.00002274641,0.00002977022,0.0002066293,0.00000814924,0.000009031628,0.00002286067,0.000004661497,0.9866796,0.004023192,0.00808619,0.0008799551,0.00002727058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005119944,0.0001822313,0.9935243,0.0001389723,0.00001664787,0.00003949336,0.00004932252,0.0004384972,0.0004906334],"genre_scores_gemma":[0.1330346,0.0004322306,0.8632265,0.0001587086,0.00006725916,0.0003039652,0.0003877771,0.0002099849,0.00217905],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004846295,"threshold_uncertainty_score":0.01509559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02259137518791688,"score_gpt":0.2829750563171691,"score_spread":0.2603836811292523,"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."}}