{"id":"W2136054697","doi":"10.4137/mri.s23555","title":"The Interface Between Iron Metabolism and Gene-Based Iron Contrast for MRI","year":2015,"lang":"en","type":"article","venue":"Magnetic Resonance Insights","topic":"Iron Metabolism and Disorders","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Cancer Care Ontario","keywords":"Magnetic resonance imaging; Context (archaeology); Gene expression; Molecular imaging; Cell biology; Cell; Nuclear magnetic resonance; Regulation of gene expression; Gene; Chemistry; Computational biology; Biophysics; Biology; Biochemistry; Genetics; Physics; In vivo; Medicine","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":[0.0008539199,0.0003395179,0.0004758861,0.0004941461,0.0002953463,0.001490702,0.0005844566,0.00116188,0.001074299],"category_scores_gemma":[0.001088209,0.0002733143,0.0001676265,0.0003673148,0.002108381,0.00164192,0.0008080701,0.001299591,0.0004597681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006758058,"about_ca_system_score_gemma":0.0003715712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002436007,"about_ca_topic_score_gemma":0.0003166578,"domain_scores_codex":[0.9996922,0.0001176096,0.0000134457,0.00004866918,0.00009600164,0.00003208383],"domain_scores_gemma":[0.9995448,0.0003148546,0.00004077706,0.00003077626,0.00003314146,0.00003571226],"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":[0.00008963385,0.00004682527,0.000422786,0.0004869106,0.00001110502,0.0006059481,0.0003002119,0.001454326,0.7550225,0.1862856,0.001242275,0.05403196],"study_design_scores_gemma":[0.00003167681,0.0005466578,0.002805717,0.0003147474,0.00004230377,0.005220433,0.0004310847,0.01674964,0.6533943,0.1700321,0.15034,0.00009137973],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1473016,0.125626,0.649337,0.02043579,0.0009380645,0.0001257216,0.0001639363,0.0007673678,0.05530452],"genre_scores_gemma":[0.6968322,0.04860592,0.240968,0.002822976,0.001013221,0.0001588485,0.0001036954,0.0003006222,0.009194488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001490702,"threshold_uncertainty_score":0.004903376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02016059271915511,"score_gpt":0.2735300296314293,"score_spread":0.2533694369122742,"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."}}