{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005575703,0.0001883438,0.0004314991,0.00005052752,0.0002333745,0.00006402464,0.0003539055,0.00001457408,0.001470574],"category_scores_gemma":[0.00003183308,0.000145362,0.00006772677,0.000527991,0.0000959262,0.0000914611,0.0002139702,0.0001991099,0.0003460524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057682,"about_ca_system_score_gemma":0.0001040404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001985722,"about_ca_topic_score_gemma":0.00000129455,"domain_scores_codex":[0.9980353,0.000150405,0.0002317074,0.0004366658,0.0007845724,0.0003613605],"domain_scores_gemma":[0.9992421,0.00008939684,0.0001304846,0.0003656949,0.00003933372,0.0001329543],"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":[0.0005071169,0.001047827,0.00001257538,0.002102882,0.00003589053,0.00003946194,0.0002020145,0.01004511,0.8494729,0.06976211,0.003484892,0.0632872],"study_design_scores_gemma":[0.006031026,0.00510483,0.001209853,0.00391528,0.0008767,0.00007989076,0.001141797,0.07779128,0.8317724,0.038068,0.02922197,0.004786995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8994491,0.005004432,0.02992994,0.001274517,0.0003710327,0.004090776,0.0002476218,0.0005417087,0.05909093],"genre_scores_gemma":[0.9957921,0.0001440707,0.002364988,0.0005097713,0.0001053751,0.0009429097,0.00004031156,0.00003208764,0.00006837964],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09634306,"threshold_uncertainty_score":0.9994422,"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."}}