{"id":"W2905151321","doi":"10.1016/j.jmr.2018.12.003","title":"Ultra-short echo time imaging with multiple echo refocusing for porous media T2 mapping","year":2018,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Saudi Aramco; ConocoPhillips","keywords":"Echo (communications protocol); Relaxation (psychology); Spin echo; Weighting; SIGNAL (programming language); Nuclear magnetic resonance; Biological system; Porous medium; Voxel; T2 relaxation; Temporal resolution; Chemistry; Physics; Optics; Magnetic resonance imaging; Computer science; Porosity; Acoustics; Computer vision","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":[],"consensus_categories":[],"category_scores_codex":[0.0002412966,0.0001469081,0.0002378118,0.0000686386,0.0001919646,0.00006598874,0.0002398377,0.00002296275,0.0001946669],"category_scores_gemma":[0.0000208706,0.0001172002,0.00008598116,0.0001675785,0.000115157,0.0001759834,0.00001568957,0.0001852656,0.00001963107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003593536,"about_ca_system_score_gemma":0.00008871335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002153722,"about_ca_topic_score_gemma":0.0000043482,"domain_scores_codex":[0.9989175,0.00002047364,0.0003839029,0.0001807638,0.0002066144,0.0002907754],"domain_scores_gemma":[0.9990436,0.0001440089,0.0002486334,0.000211782,0.0002551291,0.00009685853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004724628,0.000410718,0.09520102,0.00003408018,0.00007908257,0.00002362014,0.002261719,0.00004422844,0.2973793,0.001600799,0.01033659,0.5921564],"study_design_scores_gemma":[0.00838488,0.002230983,0.1096309,0.001698539,0.0003809933,0.0003693431,0.002547135,0.01025944,0.3043509,0.021642,0.5367293,0.001775534],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8417234,0.003551208,0.1235124,0.001479646,0.0003688792,0.0008227833,0.00009184446,0.00004867021,0.02840118],"genre_scores_gemma":[0.9563503,0.00001656982,0.04161434,0.00007242224,0.001418663,0.0000176271,0.00000398792,0.000026512,0.0004796206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5903808,"threshold_uncertainty_score":0.4779287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008863676513103817,"score_gpt":0.2753996425523577,"score_spread":0.2665359660392538,"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."}}