{"id":"W2039428410","doi":"10.1002/mrm.20590","title":"Fast spin‐echo for multiple mouse magnetic resonance phenotyping","year":2005,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Canada Research Chairs","keywords":"Nuclear magnetic resonance; Magnetic resonance imaging; Pulse sequence; Fast spin echo; Spin echo; Sequence (biology); Isotropy; Physics; Optics; Biology; Medicine; Genetics","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":[0.00140933,0.0006004614,0.000460818,0.0004111388,0.0002243139,0.0002989814,0.0005482804,0.0005300354,0.002003948],"category_scores_gemma":[0.001187538,0.0003409303,0.000199341,0.000343818,0.0003078731,0.0003381928,0.000440575,0.0006803309,0.0005914312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002085418,"about_ca_system_score_gemma":0.0002767259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003037789,"about_ca_topic_score_gemma":0.0009451431,"domain_scores_codex":[0.999629,0.000170678,0.00002136133,0.00005366377,0.0001021518,0.00002317269],"domain_scores_gemma":[0.9992472,0.0002588781,0.0001019768,0.0002055651,0.0001219546,0.00006439213],"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":[0.0002261657,0.00003298337,0.0002086885,0.00004971481,0.00001470489,0.00003881834,0.00001976274,0.0005013677,0.98231,0.0008652603,0.0003092574,0.01542328],"study_design_scores_gemma":[0.00008575809,0.0008889389,0.002479219,0.00001777385,0.00004759143,0.0008621152,0.00001272899,0.01340251,0.9655303,0.0007884596,0.01584854,0.00003614659],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1353059,0.001377058,0.8589212,0.0001620034,0.00008701603,0.0004368601,0.0002464886,0.001996926,0.001466548],"genre_scores_gemma":[0.1626822,0.000866051,0.8330508,0.00008248103,0.00001946934,0.0008223217,0.0003572248,0.0002375235,0.00188189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002003948,"threshold_uncertainty_score":0.007453382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02362760690365955,"score_gpt":0.325115904132026,"score_spread":0.3014882972283664,"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."}}