{"id":"W4388571636","doi":"10.1002/mrm.29877","title":"Diffusion‐weighted <scp>MR</scp> spectroscopy: Consensus, recommendations, and resources from acquisition to modeling","year":2023,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Lorentz Center; Agence Nationale de la Recherche; Wellcome Trust","keywords":"Diffusion; Spectroscopy; Nuclear magnetic resonance; Diffusion MRI; Nuclear magnetic resonance spectroscopy; Chemistry; Computer science; Physics; Medicine; Magnetic resonance imaging; Thermodynamics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002963752,0.0001961487,0.0004002613,0.0003282981,0.0001223938,0.00001324571,0.0001084842,0.0001027165,0.0001164705],"category_scores_gemma":[0.0003414752,0.0001673059,0.00002566689,0.0009366754,0.000124882,0.0000312026,0.00009439867,0.0002496581,0.0000419811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000671353,"about_ca_system_score_gemma":0.00002395037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003792612,"about_ca_topic_score_gemma":0.00005196254,"domain_scores_codex":[0.9983343,0.00004462763,0.000477426,0.0005012082,0.0002952416,0.0003471611],"domain_scores_gemma":[0.9988318,0.0004067878,0.00006844947,0.0004118811,0.00008025657,0.0002008217],"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":[0.0004311206,0.0004584205,0.03219212,0.0001759787,0.0000183859,0.0003519871,0.009996739,0.0005401257,0.2641643,0.003236446,0.2483626,0.4400718],"study_design_scores_gemma":[0.005918117,0.001671442,0.1206892,0.004069383,0.0001364057,0.00007180008,0.005960752,0.3011785,0.00361151,0.03338512,0.5229775,0.000330165],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9521871,0.006620048,0.00895037,0.02918818,0.00007812605,0.0009068948,0.0000397531,0.0003130153,0.00171647],"genre_scores_gemma":[0.7230334,0.02486,0.2347289,0.007920911,0.001153907,0.0008885098,0.0006966324,0.0001406835,0.006576997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4397416,"threshold_uncertainty_score":0.6822536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02284675747545379,"score_gpt":0.3187565266807643,"score_spread":0.2959097692053105,"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."}}