{"id":"W2531138400","doi":"10.1088/2057-1976/1/4/047001","title":"Aspects of spinal bone marrow fat to water quantification with magnetic resonance spectroscopy at 3 T","year":2015,"lang":"en","type":"article","venue":"Biomedical Physics & Engineering Express","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society","keywords":"Vertebra; Chemistry; Nuclear magnetic resonance; Volunteer; Magnetic resonance imaging; Spectroscopy; Relaxation (psychology); Nuclear medicine; Anatomy; Medicine; Internal medicine; Physics; Radiology; Biology","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":[0.00006414167,0.0001452881,0.0002397222,0.00005811778,0.00002255752,0.000006223352,0.0001040715,0.00005554198,0.00001257649],"category_scores_gemma":[0.00001842751,0.0001061546,0.00003571984,0.0002293687,0.00007827467,0.00004510804,0.00006312611,0.0001127002,0.00002723799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009754511,"about_ca_system_score_gemma":0.0000244652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009568626,"about_ca_topic_score_gemma":1.493092e-7,"domain_scores_codex":[0.9989167,0.00000467679,0.0001995069,0.0002602954,0.0003630783,0.0002556746],"domain_scores_gemma":[0.9991801,0.000009306081,0.00003436106,0.0003958526,0.00009348257,0.0002868755],"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":[0.0001514631,0.0001319973,0.00002107976,0.00005863839,0.000003182467,0.000009368901,0.00007739825,0.0002670527,0.9934,0.00313225,0.001525857,0.001221704],"study_design_scores_gemma":[0.0007214929,0.001020862,0.0006397665,0.000293896,0.00003178542,0.00002222464,0.0000134893,0.001678095,0.7135855,0.0007040056,0.2810856,0.0002033299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1233528,0.0003287776,0.8746091,0.000883874,0.00005817477,0.0004348154,0.00001571677,0.0001766477,0.0001401527],"genre_scores_gemma":[0.717123,0.00001083432,0.2821624,0.00004964736,0.000239278,0.0001164274,0.00004986811,0.00003505635,0.0002134733],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5937702,"threshold_uncertainty_score":0.4328861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01786201439698667,"score_gpt":0.2757768735302066,"score_spread":0.25791485913322,"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."}}