{"id":"W2007686354","doi":"10.1007/s10334-014-0453-4","title":"Tracking metabolite dynamics in plants via indirect 13C chemical shift imaging with an interleaved variable density acquisition weighted sampling pattern","year":2014,"lang":"en","type":"article","venue":"Magnetic Resonance Materials in Physics Biology and Medicine","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"","keywords":"Sampling (signal processing); Imaging phantom; Tracking (education); Compressed sensing; Computer science; Artificial intelligence; Computer vision; Noise (video); Data acquisition; Pattern recognition (psychology); Temporal resolution; Image resolution; Biological system; Image (mathematics); Physics; Optics; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002510245,0.0002449184,0.0001654476,0.0002219315,0.0002437336,0.0003337672,0.0003682902,0.0002789446,0.0005530289],"category_scores_gemma":[0.0003161589,0.0002340381,0.0001226747,0.0002939095,0.0004075646,0.0003541167,0.0004323258,0.000618952,0.0001065603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003763732,"about_ca_system_score_gemma":0.0004390711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001790452,"about_ca_topic_score_gemma":0.004710871,"domain_scores_codex":[0.9999484,0.000008285178,0.000001945903,0.00001545364,0.00001770459,0.000008189356],"domain_scores_gemma":[0.9998282,0.00004447093,0.00004611275,0.00002660451,0.00003074207,0.00002381735],"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":[0.0001379671,0.00001480941,0.0006403524,0.00003658923,0.00000785793,0.00002879713,0.00002653903,0.0009978487,0.9903923,0.0004466519,0.0000741942,0.007196],"study_design_scores_gemma":[0.00002383533,0.0001590546,0.005710306,0.000009503997,0.00003452767,0.000153968,0.00002730158,0.04839924,0.9433569,0.0004296993,0.001673803,0.00002187042],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8311737,0.0003824507,0.1656087,0.0002027867,0.00002334052,0.00003832559,0.0002335931,0.0003671231,0.001969941],"genre_scores_gemma":[0.8332836,0.0006414724,0.1634444,0.00006185071,0.0000144531,0.00007064907,0.0001924548,0.0001059625,0.002185171],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001790452,"threshold_uncertainty_score":0.003560066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006445772498657418,"score_gpt":0.2670160810298432,"score_spread":0.2605703085311858,"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."}}